diff --git a/Data/some_soccer_data.csv b/Data/some_soccer_data.csv new file mode 100644 index 0000000..c2cdab8 --- /dev/null +++ b/Data/some_soccer_data.csv @@ -0,0 +1,21 @@ +PLAYER,SALARY,GP,G,A,SOT,PPG,P +"Sergio Agüero + Forward — Manchester City",$19.2m,16,14,3,34,13.12,209.98 +"Eden Hazard + Midfield — Chelsea",$18.9m,21,8,4,17,13.05,274.04 +"Alexis Sánchez + Forward — Arsenal",$17.6m,,12,7,29,11.19,223.86 +"Yaya Touré + Midfield — Manchester City",$16.6m,18,7,1,19,10.99,197.91 +"Ángel Di María + Midfield — Manchester United",$15.0m,13,3,,13,10.17,132.23 +"Santiago Cazorla + Midfield — Arsenal",$14.8m,20,4,,20,9.97, +"David Silva + Midfield — Manchester City",$14.3m,15,6,2,11,10.35,155.26 +"Cesc Fàbregas + Midfield — Chelsea",$14.0m,20,2,14,10,10.47,209.49 +"Saido Berahino + Forward — West Brom",$13.8m,21,9,0,20,7.02,147.43 +"Steven Gerrard + Midfield — Liverpool",$13.8m,20,5,1,11,7.5,150.01 diff --git a/Images/ipython_links_remedy2.png b/Images/ipython_links_remedy2.png new file mode 100644 index 0000000..ae3abea Binary files /dev/null and b/Images/ipython_links_remedy2.png differ diff --git a/README.md b/README.md index 15dad33..05de8e6 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -### A collection of useful scripts, tutorials, and other Python-related things +

A collection of useful scripts, tutorials, and other Python-related things


@@ -9,9 +9,11 @@ - [// Python tips and tutorials](#-python-tips-and-tutorials) - [// Python and the web](#-python-and-the-web) - [// Algorithms](#-algorithms) +- [// Plotting and Visualization](#-plotting-and-visualization) - [// Benchmarks](#-benchmarks) -- [// Other](#-other) +- [// Python and "Data Science"](#-python-and-data-science) - [// Useful scripts and snippets](#-useful-scripts-and-snippets) +- [// Other](#-other) - [// Links](#-links) @@ -19,8 +21,7 @@
-###// Python tips and tutorials -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] +Python tips and tutorials [back to top] - A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1)] @@ -34,7 +35,6 @@ - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown](./tutorials/installing_scientific_packages.md)] - - Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)] - Using Cython with and without IPython magic [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)] @@ -47,129 +47,174 @@ - A collection of useful regular expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb)] +- Quick guide for dealing with missing numbers in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/numpy_nan_quickguide.ipynb)] + +- A random collection of useful Python snippets [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb)] + +- Things in pandas I wish I'd had known earlier [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/things_in_pandas.ipynb)] + + +
-###// Python and the web -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] +Python and the web [back to top] - Creating internal links in IPython Notebooks and Markdown docs [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)] -- Converting Markdown to HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)] +- Converting Markdown to HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)]
-###// Algorithms -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] - -*The algorithms category was moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)* +Algorithms and Data Structures [back to top] +*This category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)* -- Sorting Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/sorting/sorting_algorithms.ipynb?create=1)] +- Sorting Algorithms [[Collection of IPython Notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks/tree/master/ipython_nbs/sorting) - Linear regression via the least squares fit method [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb?create=1)] - Dixon's Q test to identify outliers for small sample sizes [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)] -- Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)] - - Counting points inside a hypercube [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/geometry/points_in_hybercube.ipynb)] +- Singly Linked List [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/data-structures/singly-linked-list.ipynb)]
-###// Benchmarks -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] +Plotting and Visualization [back to top] -*The benchmark category was moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)* +*The matplotlib-gallery in IPython notebooks has been moved to a separate GitHub repository [matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)* -- **1** - Reversing strings [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day1_string_reverse.ipynb)] -- **2** - Calculating sample means [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day2_mean_values.ipynb)] -- **3** - 6 different ways to count elements using a dict [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day3_dictionary_counting.ipynb)] -- **4** - Python vs. Cython vs. Numba [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.ipynb)] -- **4.2** - (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)] -- **5** - Comparing 9 ways for flattening lists of sublists [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day5_flattening_lists.ipynb)] -- **6** - Determining if a string is a number [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day6_string_is_number.ipynb)] -- **7** - Speeding up NumPy array expressions with Numexpr [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_numpy_numexpr.ipynb)] -- **7.2** - Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)] -- **8** - Calculating square roots and exponents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day8_sqrt_and_exp.ipynb)] -- **9** - The most Pythonic way to check if a string ends with a particular substring [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day9_string_endswith.ipynb)] -- **10** - Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)] -- **11** - The `deque` container data type [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day11_deque_container.ipynb)] -- **12** - Lightning fast insertion into sorted lists via the `bisect` module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day12_insert_into_sorted_list.ipynb)] -- **13** - Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)] -- **14** - Python's and NumPy's in-place operator functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day14_inplace_operators.ipynb)] -- **15** - Array indexing in NumPy: Extracting rows and columns [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day15_array_indexing_numpy.ipynb)] -- **16** - Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)] -- **17** - Stacking NumPy arrays [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day17_numpy_stacking.ipynb)] +**Featured articles**: +- Preparing Plots for Publication [[IPython nb](http://nbviewer.ipython.org/github/rasbt/matplotlib-gallery/blob/master/ipynb/publication.ipynb)] -###// Other -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] -- Happy Mother's [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)] -- Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)] -- Python Book Reviews [[Markdown](./other/python_book_reviews.md)]
+Benchmarks [back to top] -###// Useful scripts and snippets -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] -- [IPython magic function %watermark](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) - for printing date- and time-stamps and various system info +- Simple tricks to speed up the sum calculation in pandas [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/pandas_sum_tricks.ipynb)] + +
+*More benchmarks can be found in the separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)* + +**Featured articles**: -- [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files. -- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)] -- A random string generator [function](./useful_scripts/random_string_generator.py) +- (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)] +- Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)] +- Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)] + +- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)] + +- Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
-###// Links -[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)] +Python and "Data Science" [back to top] + +*The "data science"-related posts have been moved to a separate GitHub repository [pattern_classification](https://github.com/rasbt/pattern_classification)* + +**Featured articles**: + +- Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)] + +- About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb)] + +- Principal Component Analysis (PCA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/principal_component_analysis.ipynb)] + +- Linear Discriminant Analysis (LDA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/linear_discriminant_analysis.ipynb)] + +- Kernel density estimation via the Parzen-window technique [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/parameter_estimation_techniques/parzen_window_technique.ipynb)] + + +
+Useful scripts and snippets [back to top] +- [watermark](https://github.com/rasbt/watermark) - An IPython magic extension for printing date and time stamps, version numbers, and hardware information. -- [PyPI - the Python Package Index](https://pypi.python.org/pypi) - the official repository for all open source Python modules and packages +- [Shell script](./useful_scripts/prepend_python_shebang.sh) For prepending Python-shebangs to .py files. -- [PEP 8](http://legacy.python.org/dev/peps/pep-0008/) - The official style guide for Python code +- A random string generator [function](./useful_scripts/random_string_generator.py). +- [Converting large CSV files](https://github.com/rasbt/python_reference/blob/master/useful_scripts/large_csv_to_sqlite.py) to SQLite databases using pandas. +- [Sparsifying a matrix](https://github.com/rasbt/python_reference/blob/master/useful_scripts/sparsify_matrix.py) by zeroing out all elements but the top k elements in a row using NumPy. + +
+ +Other [back to top] + +- [Python book reviews](./other/python_book_reviews.md) +- [Happy Mother's Day Plot](./other/happy_mothers_day.ipynb) + +
+ +Links [back to top] + + + +- [PyPI - the Python Package Index](https://pypi.python.org/pypi) - The official repository for all open source Python modules and packages. + +- [PEP 8](https://www.python.org/dev/peps/pep-0008/) - The official style guide for Python code. + +- [PEP 257](https://www.python.org/dev/peps/pep-0257/) - Python's official docstring conventions; [pep257 - Python style guide checker](https://pypi.python.org/pypi/pep257) + + +
**// News** -- [Python subreddit](http://www.reddit.com/r/Python/) - my favorite resource to catch up with Python news and great Python-related articles +- [Python subreddit](http://www.reddit.com/r/Python/) - My favorite resource to catch up with Python news and great Python-related articles. -- [Python community on Google+](https://plus.google.com/communities/103393744324769547228) - a nice and friendly community to share and discuss everything about Python +- [Python community on Google+](https://plus.google.com/communities/103393744324769547228) - A nice and friendly community to share and discuss everything about Python. -- [Python Weekly](http://www.pythonweekly.com) - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python +- [Python Weekly](http://www.pythonweekly.com) - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python. +
**// Resources for learning Python** -- [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) - one of the most popular and recommended resources for learning Python +- [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - A free Python book for experienced programmers. + +- [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - A free best-practice handbook for both novices and experts. + +- [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - An introduction for beginners starting with basic concepts of programming. -- [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - a free Python book for experienced programmers +- [A Byte of Python](https://python.swaroopch.com/) - a free book on programming using the Python language. -- [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - a free best-practice handbook for both novices and experts +- [Python Patterns](http://matthiaseisen.com/pp/) - A directory of proven, reusable solutions to common programming problems. -- [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - an introduction for beginners starting with basic concepts of programming +- [Intro to Computer Science - Build a Search Engine & a Social Network](https://www.udacity.com/course/intro-to-computer-science--cs101) - A great, free course for learning Python if you haven't programmed before. + +
**// My favorite Python projects and packages** -- [The IPython Notebook](http://ipython.org/notebook.html) - an interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document. +- [The IPython Notebook](http://ipython.org/notebook.html) - An interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document. + +- [matplotlib](http://matplotlib.org) - Python's favorite plotting library. + +- [NumPy](http://www.numpy.org) - A library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. + +- [SciPy](http://www.scipy.org) - A library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ... + -- [SciPy Stack](http://www.scipy.org/index.html) - Python packages (NumPy, pandas, SciPy, IPython, Matplotlib) for scientific computing +- [pandas](http://pandas.pydata.org) - High-performance, easy-to-use data structures and data analysis tools build on top of NumPy. -- [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code +- [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code. -- [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators) +- [Numba](http://numba.pydata.org) - A just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators) -- [scikit-learn](http://scikit-learn.org/stable/) - a powerful machine learning library for Python and tools for efficient data mining and analysis +- [scikit-learn](http://scikit-learn.org/stable/) - A powerful machine learning library for Python and tools for efficient data mining and analysis. diff --git a/benchmarks/pandas_sum_tricks.ipynb b/benchmarks/pandas_sum_tricks.ipynb new file mode 100644 index 0000000..db58109 --- /dev/null +++ b/benchmarks/pandas_sum_tricks.ipynb @@ -0,0 +1,764 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:3de4720b58999a1f88844021c43acd1d6d6db6da3315538f9faac86a69424446" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%load_ext watermark \n", + "%watermark -d -v -a 'Sebastian Raschka' -p numpy,pandas" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "The watermark extension is already loaded. To reload it, use:\n", + " %reload_ext watermark\n", + "Sebastian Raschka 24/12/2014 \n", + "\n", + "CPython 3.4.2\n", + "IPython 2.3.1\n", + "\n", + "numpy 1.9.1\n", + "pandas 0.15.2\n" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + " a b c d\n", + "995 995 995 995 995\n", + "996 996 996 996 996\n", + "997 997 997 997 997\n", + "998 998 998 998 998\n", + "999 999 999 999 999" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's assume we are interested in calculating the sum of column `a`, `c`, and `d`, which would look like this:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "df.loc[:, ['a', 'c', 'd']].sum(axis=0)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "a 499500\n", + "c 499500\n", + "d 499500\n", + "dtype: int64" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the `.loc` method is probably the most \"costliest\" one for this kind of operation. Since we are only intersted in the resulting numbers (i.e., the column sums), there is no need to make a copy of the array. Anyway, let's use the method above as a reference for comparison:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# 1\n", + "%timeit -n 1000 -r 5 df.loc[:, ['a', 'c', 'd']].sum(axis=0)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1000 loops, best of 5: 1.37 ms per loop\n" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although this is a rather small DataFrame (1000 x 4), let's see by how much we can speed it up using a different slicing method:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# 2\n", + "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].sum(axis=0)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1000 loops, best of 5: 986 \u00b5s per loop\n" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, let us use the Numpy representation of the `NDFrame` via the `.values` attribue:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# 3\n", + "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].values.sum(axis=0)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1000 loops, best of 5: 687 \u00b5s per loop\n" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the speed improvements in #2 and #3 were not really a surprise, the next \"trick\" surprised me a little bit. Here, we are calculating the sum of each column separately rather than slicing the array." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "[df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "[499500, 499500, 499500]" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# 4\n", + "%timeit -n 1000 -r 5 [df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1000 loops, best of 5: 64.4 \u00b5s per loop\n" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, this is an almost 10x improvement!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One more thing: Let's try the Einstein summation convention [`einsum`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from numpy import einsum\n", + "[einsum('i->', df[col].values) for col in ('a', 'c', 'd')]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 10, + "text": [ + "[499500, 499500, 499500]" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# 5\n", + "%timeit -n 1000 -r 5 [einsum('i->', df[col].values) for col in ('a', 'c', 'd')]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "1000 loops, best of 5: 55.7 \u00b5s per loop\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Conclusion:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using some simple tricks, the column sum calculation improved from 1370 to 57.7 \u00b5s per loop (approx. 25x faster!)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "What about larger DataFrames?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, what does this trend look like for larger DataFrames?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import timeit\n", + "import random\n", + "from numpy import einsum\n", + "import pandas as pd\n", + "\n", + "def run_loc_sum(df):\n", + " return df.loc[:, ['a', 'c', 'd']].sum(axis=0)\n", + "\n", + "def run_einsum(df):\n", + " return [einsum('i->', df[col].values) for col in ('a', 'c', 'd')]\n", + "\n", + "orders = [10**i for i in range(4, 8)]\n", + "loc_res = []\n", + "einsum_res = []\n", + "\n", + "for n in orders:\n", + "\n", + " df = pd.DataFrame()\n", + " for col in ('a', 'b', 'c', 'd'):\n", + " df[col] = pd.Series(range(n), index=range(n))\n", + " \n", + " print('n=%s (%s of %s)' %(n, orders.index(n)+1, len(orders)))\n", + "\n", + " loc_res.append(min(timeit.Timer('run_loc_sum(df)' , \n", + " 'from __main__ import run_loc_sum, df').repeat(repeat=5, number=1)))\n", + "\n", + " einsum_res.append(min(timeit.Timer('run_einsum(df)' , \n", + " 'from __main__ import run_einsum, df').repeat(repeat=5, number=1)))\n", + "\n", + "print('finished')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "n=10000 (1 of 4)\n", + "n=100000 (2 of 4)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "n=1000000 (3 of 4)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "n=10000000 (4 of 4)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "finished" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 23 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from matplotlib import pyplot as plt\n", + "\n", + "def plot_1():\n", + " \n", + " fig = plt.figure(figsize=(12,6))\n", + " \n", + " plt.plot(orders, loc_res, \n", + " label=\"df.loc[:, ['a', 'c', 'd']].sum(axis=0)\", \n", + " lw=2, alpha=0.6)\n", + " plt.plot(orders,einsum_res, \n", + " label=\"[einsum('i->', df[col].values) for col in ('a', 'c', 'd')]\", \n", + " lw=2, alpha=0.6)\n", + "\n", + " plt.title('Pandas Column Sums', fontsize=20)\n", + " plt.xlim([min(orders), max(orders)])\n", + " plt.grid()\n", + "\n", + " #plt.xscale('log')\n", + " plt.ticklabel_format(style='plain', axis='x')\n", + " plt.legend(loc='upper left', fontsize=14)\n", + " plt.xlabel('Number of rows', fontsize=16)\n", + " plt.ylabel('time in seconds', fontsize=16)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "plot_1()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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1As+yncWegisREZEySHNgxJ/U3sSXs2dh9WoXVP30kysLCoKoKDefqlkzKKaJ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\nuZlvyoq5piyZb6oXFfk5fYzxl8Av243dUfD+D4E/zDou9Sz798OiRbB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+ "text": [ + "" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like that the benefit of calculating the sums separately for each column becomes even larger the more rows the DataFrame has." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another question to ask: How does this scale if we have a growing number of columns?" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import timeit\n", + "import random\n", + "from numpy import einsum\n", + "import pandas as pd\n", + "\n", + "def run_loc_sum(df, n):\n", + " return df.loc[:, 0:n-1].sum(axis=0)\n", + "\n", + "def run_einsum(df, n):\n", + " return [einsum('i->', df[col].values) for col in range(0,n-1)]\n", + "\n", + "orders = [10**i for i in range(2, 5)]\n", + "loc_res = []\n", + "einsum_res = []\n", + "\n", + "for n in orders:\n", + "\n", + " df = pd.DataFrame()\n", + " for col in range(n):\n", + " df[col] = pd.Series(range(1000), index=range(1000))\n", + " \n", + " print('n=%s (%s of %s)' %(n, orders.index(n)+1, len(orders)))\n", + "\n", + " loc_res.append(min(timeit.Timer('run_loc_sum(df, n)' , \n", + " 'from __main__ import run_loc_sum, df, n').repeat(repeat=5, number=1)))\n", + "\n", + " einsum_res.append(min(timeit.Timer('run_einsum(df, n)' , \n", + " 'from __main__ import run_einsum, df, n').repeat(repeat=5, number=1)))\n", + "\n", + "print('finished')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "n=100 (1 of 3)\n", + "n=1000 (2 of 3)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "n=10000 (3 of 3)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "finished" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "from matplotlib import pyplot as plt\n", + "\n", + "def plot_2():\n", + " \n", + " fig = plt.figure(figsize=(12,6))\n", + " \n", + " plt.plot(orders, loc_res, \n", + " label=\"df.loc[:, 0:n-1].sum(axis=0)\", \n", + " lw=2, alpha=0.6)\n", + " plt.plot(orders,einsum_res, \n", + " label=\"[einsum('i->', df[col].values) for col in range(0,n-1)]\", \n", + " lw=2, alpha=0.6)\n", + "\n", + " plt.title('Pandas Column Sums', fontsize=20)\n", + " plt.xlim([min(orders), max(orders)])\n", + " plt.grid()\n", + "\n", + " #plt.xscale('log')\n", + " plt.ticklabel_format(style='plain', axis='x')\n", + " plt.legend(loc='upper left', fontsize=14)\n", + " plt.xlabel('Number of columns', fontsize=16)\n", + " plt.ylabel('time in seconds', fontsize=16)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + "plot_2()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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SEggLC2P//v15cn1vfWFhYVxzzTV5Umd+MmrUKN/9/eMf/wh1czJ15swZGjZsyPz58/Os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H1+I1BUHDwJDKdxxoG30vgj7n6gyZjPEc83bScKYCwDnilp1858221bOAJcTwSLB4CXiP+P/z2T\nJiLSbcx9KZbci4iIiIiI9BZlrkRERERERCqg4EpERERERKQCCq5EREREREQqoOBKRERERESkAgqu\nREREREREKqDgSkREREREpAIKrkRERERERCqg4EpERERERKQCCq5EREREREQq8A/8v0nMEi/XJgAA\nAABJRU5ErkJggg==\n", + "text": [ + "" + ] + } + ], + "prompt_number": 37 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/howtos_as_py_files/closures.py b/howtos_as_py_files/closures.py deleted file mode 100755 index dc8dfea..0000000 --- a/howtos_as_py_files/closures.py +++ /dev/null @@ -1,17 +0,0 @@ -# Python 3.x -# sr 11/04/2013 -# closures -# - -def create_message(msg_txt): - def _priv_msg(message): # private, no access from outside - print("{}: {}".format(msg_txt, message)) - return _priv_msg # returns a function - -new_msg = create_message("My message") -# note, new_msg is a function - -new_msg("Hello, World") -# prints: "My message: Hello, World" - -# print(dir(create_message.__closure__)) diff --git a/howtos_as_py_files/cmd_line_args_1_sysarg.py b/howtos_as_py_files/cmd_line_args_1_sysarg.py deleted file mode 100644 index b8f8cbf..0000000 --- a/howtos_as_py_files/cmd_line_args_1_sysarg.py +++ /dev/null @@ -1,24 +0,0 @@ -# Getting command line arguments via sys.arg -# sr 11/30/2013 - -import sys - -def error(msg): - """Prints error message, sends it to stderr, and quites the program.""" - sys.exit(msg) - - -args = sys.argv[1:] # sys.argv[0] is the name of the python script itself - -try: - arg1 = int(args[0]) - arg2 = args[1] - arg3 = args[2] - print("Everything okay!") - -except ValueError: - error("First argument must be integer type!") - -except IndexError: - error("Requires 3 arguments!") - diff --git a/howtos_as_py_files/cpu_time.py b/howtos_as_py_files/cpu_time.py deleted file mode 100755 index 472cae7..0000000 --- a/howtos_as_py_files/cpu_time.py +++ /dev/null @@ -1,18 +0,0 @@ -# sr 10/29/13 -# Calculates elapsed CPU time in seconds as float. - -import time - -start_time = time.clock() - -i = 0 -while i < 10000000: - i += 1 - -elapsed_time = time.clock() - start_time -print "Time elapsed: {} seconds".format(elapsed_time) - -# prints "Time elapsed: 1.06 seconds" -# on 4 x 2.80 Ghz Intel Xeon, 6 Gb RAM - - diff --git a/howtos_as_py_files/date_time.py b/howtos_as_py_files/date_time.py deleted file mode 100644 index 28e7bcc..0000000 --- a/howtos_as_py_files/date_time.py +++ /dev/null @@ -1,13 +0,0 @@ -# Sebastian Raschka, 03/2014 -# Date and Time in Python - -import time - -# print time HOURS:MINUTES:SECONDS -# e.g., '10:50:58' -print(time.strftime("%H:%M:%S")) - - -# print current date DAY:MONTH:YEAR -# e.g., '06/03/2014' -print(time.strftime("%d/%m/%Y")) diff --git a/howtos_as_py_files/diff_files.py b/howtos_as_py_files/diff_files.py deleted file mode 100644 index 9399695..0000000 --- a/howtos_as_py_files/diff_files.py +++ /dev/null @@ -1,21 +0,0 @@ -# Sebastian Raschka, 2014 -# -# Print lines that are different between 2 files. Insensitive -# to the order of the file contents. - -id_set1 = set() -id_set2 = set() - -with open('id_file1.txt', 'r') as id_file: - for line in id_file: - id_set1.add(line.strip()) - -with open('id_file2.txt', 'r') as id_file: - for line in id_file: - id_set2.add(line.strip()) - -diffs = id_set2.difference(id_set1) - -for d in diffs: - print(d) -print("Total differences:",len(diffs)) diff --git a/howtos_as_py_files/doctest_example.py b/howtos_as_py_files/doctest_example.py deleted file mode 100644 index 246ecf8..0000000 --- a/howtos_as_py_files/doctest_example.py +++ /dev/null @@ -1,47 +0,0 @@ -# doctest example -# Sebastian Raschka 11/19/2013 - -def subtract(a, b): - """ - Subtracts second from first number and returns result. - >>> subtract(10, 5) - 5 - >>> subtract(11, 0.7) - 10.3 - """ - return a-b - -def hello_world(): - """ - Returns 'Hello, World' - >>> hello_world() - "Hello, World" - >>> hello_world() - 'Hello, World' - """ - return "Hello, World" - - -if __name__ == "__main__": # is 'false' if imported - import doctest - doctest.testmod() - - -""" RESULTS - -sebastian ~/Desktop> python3 doctest_example.py -********************************************************************** -File "doctest_example.py", line 17, in __main__.hello_world -Failed example: - hello_world() -Expected: - "Hello, World" -Got: - 'Hello, World' -********************************************************************** -1 items had failures: - 1 of 2 in __main__.hello_world -***Test Failed*** 1 failures. -sebastian ~/Desktop> - -""" diff --git a/howtos_as_py_files/file_browsing.py b/howtos_as_py_files/file_browsing.py deleted file mode 100644 index ce1cb5f..0000000 --- a/howtos_as_py_files/file_browsing.py +++ /dev/null @@ -1,80 +0,0 @@ -# File system operations using Python -# sr 11/30/2013 - - -import os -import shutil -import glob - -# working directory -c_dir = os.getcwd() # show current working directory -os.listdir(c_dir) # shows all files in the working directory -os.chdir('~/Data') # change working directory - - -# get all files in a directory -glob.glob('/Users/sebastian/Desktop/*') - -# e.g., ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt'] - - - -# walk -tree = os.walk(c_dir) -# moves through sub directories and creates a 'generator' object of tuples -# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]), -# (...), ... - - - -#check files: returns either True or False -os.exists('../rel_path') -os.exists('/home/abs_path') -os.isfile('./file.txt') -os.isdir('./subdir') - - - -# file permission (True or False -os.access('./some_file', os.F_OK) # File exists? Python 2.7 -os.access('./some_file', os.R_OK) # Ok to read? Python 2.7 -os.access('./some_file', os.W_OK) # Ok to write? Python 2.7 -os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7 -os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7 - - - -# join (creates operating system dependent paths) -os.path.join('a', 'b', 'c') -# 'a/b/c' on Unix/Linux -# 'a\\b\\c' on Windows -os.path.normpath('a/b/c') # converts file separators - - - -# os.path: direcory and file names -os.path.samefile('./some_file', '/home/some_file') # True if those are the same -os.path.dirname('./some_file') # returns '.' (everythin but last component) -os.path.basename('./some_file') # returns 'some_file' (only last component -os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file) -os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt') -os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt') -os.path.isabs('./some_file.txt') # returns False (not an absolute path) -os.path.abspath('./some_file.txt') - - - - -# create and delete files and directories -os.mkdir('./test') # create a new direcotory -os.rmdir('./test') # removes an empty direcotory -os.removedirs('./test') # removes nested empty directories -os.remove('file.txt') # removes an individual file -shutil.rmtree('./test') # removes directory (empty or not empty) - -os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist -shutil.move('./dir_before', './renamed') # renames directory always - -shutil.copytree('./orig', './copy') # copies a directory recursively -shutil.copyfile('file', 'copy') # copies a file - diff --git a/howtos_as_py_files/get_filename.py b/howtos_as_py_files/get_filename.py deleted file mode 100755 index a05f92a..0000000 --- a/howtos_as_py_files/get_filename.py +++ /dev/null @@ -1,63 +0,0 @@ -# Python 2.7 -# prompt user for file of specific type(s). -# 11/01/13 sebastian raschka - -import os.path - -def get_filename(file_type): - '''repeatedly prompts user for a file of specific type. - arguments: - file_type: list with accepted file types as strings. - returns: - (string): absolute path to the specified input file. - ''' - while True: - print "\n\nplease enter a file name, \nor type --help to get"\ - " a list of the accepted file formats" - file_name = raw_input(": ") - if file_name == "--help": - print "\naccepted file format(s): ", - for f in file_type: - print f, - continue - if not os.path.isfile(file_name): - print "\n\nsorry, this file doesn't exist. please try again.\n" - continue - if not (file_name.split(".")[-1] in file_type): - print "\nplease provide a file in correct format." - continue - break - return os.path.abspath(file_name) - -#get_filename(["txt", "doc"]) - - -# =========================== -# EXAMPLE -# =========================== - -''' -[bash]~/Desktop >python get_filename.py - - -please enter a file name, -or type --help to get a list of the accepted file formats -: --help - -accepted file format(s): txt doc - -please enter a file name, -or type --help to get a list of the accepted file formats -: test.tx - - -sorry, this file doesn't exist. please try again. - - - -please enter a file name, -or type --help to get a list of the accepted file formats -: test.txt -[bash]~/Desktop > -''' - diff --git a/howtos_as_py_files/get_minmax_indeces.py b/howtos_as_py_files/get_minmax_indeces.py deleted file mode 100644 index 1fe5b2a..0000000 --- a/howtos_as_py_files/get_minmax_indeces.py +++ /dev/null @@ -1,12 +0,0 @@ -# Sebastian Raschka, 03/2014 -# Getting the positions of min and max values in a list - -import operator - -values = [1, 2, 3, 4, 5] - -min_index, min_value = min(enumerate(values), key=operator.itemgetter(1)) -max_index, max_value = max(enumerate(values), key=operator.itemgetter(1)) - -print('min_index:', min_index, 'min_value:', min_value) -print('max_index:', max_index, 'max_value:', max_value) diff --git a/howtos_as_py_files/namedtuple_example.py b/howtos_as_py_files/namedtuple_example.py deleted file mode 100644 index 0546d85..0000000 --- a/howtos_as_py_files/namedtuple_example.py +++ /dev/null @@ -1,5 +0,0 @@ -from collections import namedtuple - -my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub']) -p = my_namedtuple(1, 2, 3, 4, 5) -print(p.x, p.y, p.z) diff --git a/howtos_as_py_files/normalize_data.py b/howtos_as_py_files/normalize_data.py deleted file mode 100644 index 117d2fb..0000000 --- a/howtos_as_py_files/normalize_data.py +++ /dev/null @@ -1,15 +0,0 @@ -# Sebastian Raschka, 03/2014 - -def normalize_val(x, data_list): - """ - Normalizes a value to a data list returning a float - between 0.0 and 1.0. - Returns the original object if value is not a integer or float. - - """ - if isinstance(x, float) or isinstance(x, int): - numerator = x - min(data_list) - denominator = max(data_list) - min(data_list) - return numerator/denominator - else: - return x diff --git a/howtos_as_py_files/numpy_matrix.py b/howtos_as_py_files/numpy_matrix.py deleted file mode 100644 index 06d5eb2..0000000 --- a/howtos_as_py_files/numpy_matrix.py +++ /dev/null @@ -1,36 +0,0 @@ -# numpy matrix operations -# sr 12/01/2013 - -import numpy - -ary1 = numpy.array([1,2,3,4,5]) # must be same type -ary2 = numpy.zeros((3,4)) # 3x4 matrix consisiting of 0s -ary3 = numpy.ones((3,4)) # 3x4 matrix consisiting of 1s -ary4 = numpy.identity(3) # 3x3 identity matrix -ary5 = ary1.copy() # make a copy of ary1 - -item1 = ary3[0, 0] # item in row1, column1 - -ary2.shape # tuple of dimensions. Here: (3,4) -ary2.size # number of elements. Here: 12 - - -ary2_t = ary2.transpose() # transposes matrix - -ary2.ravel() # makes an array linear (1-dimensional) - # by concatenating rows -ary2.reshape(2,6) # reshapes array (must have same dimensions) - -ary3[0:2, 0:3] # submatrix of first 2 rows and first 3 columns - -ary3 = ary3[[2,0,1]] # re-arrange rows - - -# element-wise operations - -ary1 + ary1 -ary1 * ary1 -numpy.dot(ary1, ary1) # matrix/vector (dot) product - -numpy.sum(ary1) # sums up all elements in the array -numpy.mean(ary1) # average of all elements in the array diff --git a/howtos_as_py_files/os_shutil_fileops.py b/howtos_as_py_files/os_shutil_fileops.py deleted file mode 100644 index d517f22..0000000 --- a/howtos_as_py_files/os_shutil_fileops.py +++ /dev/null @@ -1,22 +0,0 @@ -# sr 11/19/2013 -# common file operations in os and shutil modules - -import shutil -import os - -# Getting files of particular type from directory -files = [f for f in os.listdir(s_pdb_dir) if f.endswith(".txt")] - -# Copy and move -shutil.copyfile("/path/to/file", "/path/to/new/file") -shutil.copy("/path/to/file", "/path/to/directory") -shutil.move("/path/to/file","/path/to/directory") - -# Check if file or directory exists -os.path.exists("file or directory") -os.path.isfile("file") -os.path.isdir("directory") - -# Working directory and absolute path to files -os.getcwd() -os.path.abspath("file") diff --git a/howtos_as_py_files/pickle_module.py b/howtos_as_py_files/pickle_module.py deleted file mode 100755 index 81afd92..0000000 --- a/howtos_as_py_files/pickle_module.py +++ /dev/null @@ -1,23 +0,0 @@ -# sr 10/29/13 -# The pickle module converts Python objects into byte streams -# to save them as a file on your drive for re-use. -# -# module documentation http://docs.python.org/2/library/pickle.html - -import pickle - -#### Generate some object -my_dict = dict() -for i in range(1,1000): - my_dict[i] = "some text" - -#### Save object to file -pickle_out = open('my_file.pkl', 'wb') -pickle.dump(my_dict, pickle_out) -pickle_out.close() - -#### Load object from file -my_object_file = open('my_file.pkl', 'rb') -my_dict = pickle.load(my_object_file) -my_object_file.close() - diff --git a/howtos_as_py_files/pil_image_processing.py b/howtos_as_py_files/pil_image_processing.py deleted file mode 100644 index e69de29..0000000 diff --git a/howtos_as_py_files/python2_vs_3_version_info.py b/howtos_as_py_files/python2_vs_3_version_info.py deleted file mode 100644 index 19e7bb2..0000000 --- a/howtos_as_py_files/python2_vs_3_version_info.py +++ /dev/null @@ -1,24 +0,0 @@ -# Sebastian Raschka 04/10/2014 - -import sys - -def give_letter(word): - for letter in word: - yield letter - -if sys.version_info[0] == 3: - print('executed in Python 3.x') - test = give_letter('Hello') - print(next(test)) - print('in for-loop:') - for l in test: - print(l) - -# if Python 2.x -if sys.version_info[0] == 2: - print('executed in Python 2.x') - test = give_letter('Hello') - print(test.next()) - print('in for-loop:') - for l in test: - print(l) diff --git a/howtos_as_py_files/read_file.py b/howtos_as_py_files/read_file.py deleted file mode 100755 index 567ae0c..0000000 --- a/howtos_as_py_files/read_file.py +++ /dev/null @@ -1,44 +0,0 @@ -# Different methods to read from text files -# sr 11/18/2013 -# Python 3.x - -# Note: rb opens file in binary mode to avoid issues with Windows systems -# where '\r\n' is used instead of '\n' as newline character(s). - - -# A) Reading in Byte chunks -reader_a = open("file.txt", "rb") -chunks = [] -data = reader_a.read(64) # reads first 64 bytes -while data != "": - chunks.append(data) - data = reader_a.read(64) -if data: - chunks.append(data) -print (len(chunks)) -reader_a.close() - - -# B) Reading whole file at once into a list of lines -with open("file.txt", "rb") as reader_b: # recommended syntax, auto closes - data = reader_b.readlines() # data is assigned a list of lines -print (len(data)) - - -# C) Reading whole file at once into a string -with open("file.txt", "rb") as reader_c: - data = reader_c.read() # data is assigned a list of lines -print (len(data)) - - -# D) Reading line by line into a list -data = [] -with open("file.txt", "rb") as reader_d: - for line in reader_d: - data.append(line) -print (len(data)) - - - - - diff --git a/howtos_as_py_files/reg_expr_1_basics.py b/howtos_as_py_files/reg_expr_1_basics.py deleted file mode 100644 index 5fafab8..0000000 --- a/howtos_as_py_files/reg_expr_1_basics.py +++ /dev/null @@ -1,101 +0,0 @@ -# Examples for using Python's Regular expression module "re" -# sr 11/30/2013 - -import re - -'''OVERVIEW - '|' means 'or' - '.' matches any single character - '()' groups into substrings -''' - - - - - -# read in data -fileobj = '''abc mno -def pqr -ghi stu -jkl vwx''' - -data = fileobj.strip().split('\n') - - -# A >> if 's' in line -print (50*'-' + '\nA\n' + 50*'-') -for line in data: - if re.search('abc', line): - print(">>", line) - else: - print(" ", line) - -''' --------------------------------------------------- -A --------------------------------------------------- ->> abc mno - def pqr - ghi stu - jkl vwx''' - - - -# B >> if 's' in line or 'r' in line -print (50*'-' + '\nB\n' + 50*'-') -for line in data: - if re.search('abc|efg', line): - print(">>", line) - else: - print(" ", line) - -''' --------------------------------------------------- -B --------------------------------------------------- ->> abc mno - def pqr - ghi stu - jkl vwx ----------------''' - - -# C >> -# use () to remember which object was found and return a match object -print (50*'-' + '\nC\n' + 50*'-') -for line in data: - match = re.search('(abc|efg)', line) # note the parantheses - if match: - print(match.group(1)) # prints 'abc' if found, else None - # match.group(0) is the whole pattern that matched - -''' --------------------------------------------------- -C --------------------------------------------------- -abc''' - - - -# read in data -fileobj = '''2013-01-01 -2012-02-02 -ghi stu -2012-03-03''' - -data = fileobj.strip().split('\n') - - -# D >> use '.' to match 'any character' -print (50*'-' + '\nD\n' + 50*'-') -for line in data: - match = re.search('(2012)-(..)-(..)', line) # note the parantheses - if match: - print(match.group(1), match.group(2), match.group(3)) - -''' --------------------------------------------------- -D --------------------------------------------------- -2012 02 02 -2012 03 03''' diff --git a/howtos_as_py_files/reg_expr_2_operators.py b/howtos_as_py_files/reg_expr_2_operators.py deleted file mode 100644 index 4994159..0000000 --- a/howtos_as_py_files/reg_expr_2_operators.py +++ /dev/null @@ -1,127 +0,0 @@ -# Examples for using Python's Regular expression module "re" -# sr 11/30/2013 - -import re - -'''OVERVIEW - '*' matches all characters that follow (0 or more) - '+' matches all characters that follow (1 or more) - '?' makes the previous character optional - '{4}' previous character must match exactly 4 times - '{2-4}' previous character must match exactly 2-4 times - '[0-9]' matches all characters in the set of numbers 0 to 9 - '[A-Z]' matches all characters in the set of A to Z - '\d' matches all digits, e.g., '4', '9' ... - '\D' matches all NON-digit characters - '\s' matches all space characters: '', '\t', '\r', '\n' - '\S' matches all NON-space characters - '\w' matches all non-punctuation characters (i.e., letters and digits) - '\W' matches all NON-letter and NON-digit characters - '^bla' NOT-matches 'bla' - 'let$' matches 'let' but not 'letter' - '\b' matches transition between non-word characters and word characters - -''' - -data = '''2013-01-01 -2012-02-02 -aaaa-02-02 -aa-02-02 --04-04 -2000 02-02 -ghi stu -2012-03-03'''.strip().split('\n') - - -# A >> '*' matches all characters that follow (0 or more) -print (50*'-' + '\nA\n' + 50*'-') - -for line in data: - match = re.search('(.*)-(..)-(..)', line) # note the parantheses - if match: - print(match.group(1), match.group(2), match.group(3)) - -''' --------------------------------------------------- -A --------------------------------------------------- -2013 01 01 -2012 02 02 -aaaa 02 02 -aa 02 02 - 04 04 -2012 03 03 -''' - - -# B >> '+' matches all characters that follow (1 or more) -print (50*'-' + '\nB\n' + 50*'-') - -for line in data: - match = re.search('(.+)-(..)-(..)', line) # note the parantheses - if match: - print(match.group(1), match.group(2), match.group(3)) - -''' --------------------------------------------------- -B --------------------------------------------------- -2013 01 01 -2012 02 02 -aaaa 02 02 -aa 02 02 -2012 03 03 -''' - - -# C >> '?' makes the previous character optional -print (50*'-' + '\nC\n' + 50*'-') - -for line in data: - match = re.search('(.+)-?(..)-(..)', line) # note the parantheses - if match: - print(match.group(1), match.group(2), match.group(3)) - -''' --------------------------------------------------- -C --------------------------------------------------- -2013- 01 01 -2012- 02 02 -aaaa- 02 02 -aa- 02 02 -- 04 04 -2000 02 02 -2012- 03 03 -''' - -# D >> '{4}' previous character must match exactly 4 times -print (50*'-' + '\nD\n' + 50*'-') - -for line in data: - match = re.search('(a{4})-(..)-(..)', line) # note the parantheses - if match: - print(match.group(1), match.group(2), match.group(3)) - -''' --------------------------------------------------- -D --------------------------------------------------- -aaaa 02 02 -''' - -# E >>'{2-4}' previous character must match exactly 2-4 times -print (50*'-' + '\nE\n' + 50*'-') - -for line in data: - match = re.search('(a{2,4})-(..)-(..)', line) # note the parantheses - if match: - print(match.group(1), match.group(2), match.group(3)) - -''' --------------------------------------------------- -E --------------------------------------------------- -aaaa 02 02 -aa 02 02 -''' diff --git a/howtos_as_py_files/sorting_multiple_lists_by_col.py b/howtos_as_py_files/sorting_multiple_lists_by_col.py deleted file mode 100644 index 7c534cb..0000000 --- a/howtos_as_py_files/sorting_multiple_lists_by_col.py +++ /dev/null @@ -1,39 +0,0 @@ -# Sebastian Raschka 2014 - -""" -You have 3 lists that you want to sort "relative" to each other, -for example, picturing each list as a row in a 3x3 matrix: sort it by columns - -######################## -If the input lists are -######################## - - list1 = ['c','b','a'] - list2 = [6,5,4] - list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a'] - -######################## -the desired outcome is: -######################## - - ['a', 'b', 'c'] - [4, 5, 6] - ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c'] - -######################## -and NOT: -######################## - - ['a', 'b', 'c'] - [4, 5, 6] - ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a'] - - -""" - -list1 = ['c','b','a'] -list2 = [6,5,4] -list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a'] - - -list1, list2, list3 = zip(*sorted(zip(list1, list2, list3))) diff --git a/howtos_as_py_files/timeit_test.py b/howtos_as_py_files/timeit_test.py deleted file mode 100644 index 31bb93e..0000000 --- a/howtos_as_py_files/timeit_test.py +++ /dev/null @@ -1,24 +0,0 @@ -# Sebastian Raschka, 03/2014 -# comparing string formating: %s and .format() - -import timeit - -format_res = timeit.timeit("['{}'.format(i) for i in range(10000)]", number=1000) - -binaryop_res = timeit.timeit("['%s' %i for i in range(10000)]", number=1000) - -print('{}: {}\n{}: {}'.format('format()', format_res, '%s', binaryop_res)) - -################################ -# On my machine -################################ -# -# Python 3.4.0 -# MacOS X 10.9.2 -# 2.5 GHz Intel Core i5 -# 4 GB 1600 Mhz DDR3 -# -################################ -# format(): 2.815331667999999 -# %s: 1.630353775999538 -################################ diff --git a/howtos_as_py_files/zen_of_python.py b/howtos_as_py_files/zen_of_python.py deleted file mode 100644 index d82cacd..0000000 --- a/howtos_as_py_files/zen_of_python.py +++ /dev/null @@ -1,24 +0,0 @@ ->>> import this -""" -The Zen of Python, by Tim Peters - -Beautiful is better than ugly. -Explicit is better than implicit. -Simple is better than complex. -Complex is better than complicated. -Flat is better than nested. -Sparse is better than dense. -Readability counts. -Special cases aren't special enough to break the rules. -Although practicality beats purity. -Errors should never pass silently. -Unless explicitly silenced. -In the face of ambiguity, refuse the temptation to guess. -There should be one-- and preferably only one --obvious way to do it. -Although that way may not be obvious at first unless you're Dutch. -Now is better than never. -Although never is often better than *right* now. -If the implementation is hard to explain, it's a bad idea. -If the implementation is easy to explain, it may be a good idea. -Namespaces are one honking great idea -- let's do more of those! -""" diff --git a/ipython_magic/README.md b/ipython_magic/README.md new file mode 100644 index 0000000..fb167a8 --- /dev/null +++ b/ipython_magic/README.md @@ -0,0 +1,8 @@ +watermark +========= + +An IPython magic extension for printing date and time stamps, version numbers, and hardware information + +![](./images/watermark_ex1.png) + +**watermark is now located and maintained in a separate GitHub repository:** [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark) \ No newline at end of file diff --git a/ipython_magic/images/watermark_ex1.png b/ipython_magic/images/watermark_ex1.png new file mode 100644 index 0000000..6e95b03 Binary files /dev/null and b/ipython_magic/images/watermark_ex1.png differ diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb index 3c2433e..5f07900 100644 --- a/ipython_magic/watermark.ipynb +++ b/ipython_magic/watermark.ipynb @@ -1,429 +1,453 @@ { - "metadata": { - "name": "", - "signature": "sha256:ae521d31a2ed8eb2f249825915f64611628365cb0ced53ef18be691238c0018e" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Sebastian Raschka](http://sebastianraschka.com) \n", + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Sebastian Raschka](http://sebastianraschka.com) \n", + "\n", + "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference/)\n", + "\n", + "
\n", + "I would be happy to hear your comments and suggestions. \n", + "Please feel free to drop me a note via\n", + "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### watermark is now located and maintained in a separate GitHub repository: [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IPython magic function documentation - `%watermark`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I wrote this simple `watermark` IPython magic function to conveniently add date- and time-stamps to my IPython notebooks. Also, I often want to document various system information, e.g., for my [Python benchmarks](https://github.com/rasbt/One-Python-benchmark-per-day) series.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `watermark` line magic can be directly installed from my GitHub repository via" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Installed watermark.py. To use it, type:\n", + " %load_ext watermark\n" + ] + } + ], + "source": [ + "install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the `%watermark` magic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To load the `date` magic, execute the following line in your IPython notebook or current IPython shell" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext watermark" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to display the optional `watermark` arguments, type" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%watermark?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
  %watermark [-a AUTHOR] [-d] [-e] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME]\n",
+    "                 [-v] [-p PACKAGES] [-h] [-m] [-g] [-w]\n",
+    "\n",
+    " \n",
+    "IPython magic function to print date/time stamps \n",
+    "and various system information.\n",
+    "\n",
+    "watermark version 1.2.1\n",
+    "\n",
+    "optional arguments:\n",
+    "  -a AUTHOR, --author AUTHOR\n",
+    "                        prints author name\n",
+    "  -d, --date            prints current date as MM/DD/YYYY\n",
+    "  -e, --eurodate        prints current date as DD/MM/YYYY\n",
+    "  -n, --datename        prints date with abbrv. day and month names\n",
+    "  -t, --time            prints current time\n",
+    "  -z, --timezone        appends the local time zone\n",
+    "  -u, --updated         appends a string \"Last updated: \"\n",
+    "  -c CUSTOM_TIME, --custom_time CUSTOM_TIME\n",
+    "                        prints a valid strftime() string\n",
+    "  -v, --python          prints Python and IPython version\n",
+    "  -p PACKAGES, --packages PACKAGES\n",
+    "                        prints versions of specified Python modules and\n",
+    "                        packages\n",
+    "  -h, --hostname        prints the host name\n",
+    "  -m, --machine         prints system and machine info\n",
+    "  -g, --githash         prints current Git commit hash\n",
+    "  -w, --watermark       prints the current version of watermark\n",
+    "File:      ~/.ipython/extensions/watermark.py\n",
+    "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Examples" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "06/29/2015 15:34:42\n", "\n", - "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference/)\n", + "CPython 3.4.3\n", + "IPython 3.2.0\n", "\n", - "
\n", - "I would be happy to hear your comments and suggestions. \n", - "Please feel free to drop me a note via\n", - "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "IPython magic function documentation - `%watermark`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I wrote this simple `watermark` IPython magic function to conveniently add date- and time-stamps to my IPython notebooks. Also, I often want to document various system information, e.g., for my [Python benchmarks](https://github.com/rasbt/One-Python-benchmark-per-day) series.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Installation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `watermark` line magic can be directly installed from my GitHub repository via" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Installed watermark.py. To use it, type:\n", - " %load_ext watermark\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Loading the `%watermark` magic" + "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", + "system : Darwin\n", + "release : 14.3.0\n", + "machine : x86_64\n", + "processor : i386\n", + "CPU cores : 4\n", + "interpreter: 64bit\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To load the `date` magic, execute the following line in your IPython notebook or current IPython shell" + } + ], + "source": [ + "%watermark" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "06/29/2015 15:34:43 \n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%load_ext watermark" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "%watermark -d -t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Mon Jun 29 2015 15:34:44 EDT\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Usage" + } + ], + "source": [ + "%watermark -u -n -t -z" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPython 3.4.3\n", + "IPython 3.2.0\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to display the optional `watermark` arguments, type" + } + ], + "source": [ + "%watermark -v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", + "system : Darwin\n", + "release : 14.3.0\n", + "machine : x86_64\n", + "processor : i386\n", + "CPU cores : 4\n", + "interpreter: 64bit\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark?" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
  %watermark [-a AUTHOR] [-d] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME] [-v]\n",
-      "                 [-p PACKAGES] [-h] [-m] [-g]\n",
-      "\n",
-      " \n",
-      "IPython magic function to print date/time stamps \n",
-      "and various system information.\n",
+    }
+   ],
+   "source": [
+    "%watermark -m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPython 3.4.3\n", + "IPython 3.2.0\n", "\n", - "watermark version 1.1.0\n", + "numpy 1.9.2\n", + "scipy 0.15.1\n", "\n", - "optional arguments:\n", - " -a AUTHOR, --author AUTHOR\n", - " prints author name\n", - " -d, --date prints current date\n", - " -n, --datename prints date with abbrv. day and month names\n", - " -t, --time prints current time\n", - " -z, --timezone appends the local time zone\n", - " -u, --updated appends a string \"Last updated: \"\n", - " -c CUSTOM_TIME, --custom_time CUSTOM_TIME\n", - " prints a valid strftime() string\n", - " -v, --python prints Python and IPython version\n", - " -p PACKAGES, --packages PACKAGES\n", - " prints versions of specified Python modules and\n", - " packages\n", - " -h, --hostname prints the host name\n", - " -m, --machine prints system and machine info\n", - " -g, --githash prints current Git commit hash\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Examples" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "29/06/2014 01:19:10\n", - "\n", - "CPython 3.4.1\n", - "IPython 2.1.0\n", - "\n", - "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", - "system : Darwin\n", - "release : 13.2.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 2\n", - "interpreter: 64bit\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -d -t" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "29/06/2014 01:19:11 \n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", + "system : Darwin\n", + "release : 14.3.0\n", + "machine : x86_64\n", + "processor : i386\n", + "CPU cores : 4\n", + "interpreter: 64bit\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -u -n -t -z" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Last updated: Sun Jun 19 2014 01:19:12 EDT\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -v" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CPython 3.4.1\n", - "IPython 2.1.0\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -m" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", - "system : Darwin\n", - "release : 13.2.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 2\n", - "interpreter: 64bit\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -v -m -p numpy,scipy" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CPython 3.4.1\n", - "IPython 2.1.0\n", - "\n", - "numpy 1.8.1\n", - "scipy 0.14.0\n", - "\n", - "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", - "system : Darwin\n", - "release : 13.2.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 2\n", - "interpreter: 64bit\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -a \"John Doe\" -d -v -m -g" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "John Doe 29/06/2014 01:20:48 EDT\n", - "\n", - "CPython 3.4.1\n", - "IPython 2.1.0\n", - "\n", - "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", - "system : Darwin\n", - "release : 13.2.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU cores : 2\n", - "interpreter: 64bit\n", - "Git hash : fbe7759fd7e0298bf0bd05ea4aac01b87aa8ed25\n" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + } + ], + "source": [ + "%watermark -v -m -p numpy,scipy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "John Doe 06/29/2015 \n", + "\n", + "CPython 3.4.3\n", + "IPython 3.2.0\n", + "\n", + "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", + "system : Darwin\n", + "release : 14.3.0\n", + "machine : x86_64\n", + "processor : i386\n", + "CPU cores : 4\n", + "interpreter: 64bit\n", + "Git hash : 06830b939358f35f2daf50af42509e603c93d9b4\n" ] } ], - "metadata": {} + "source": [ + "%watermark -a \"John Doe\" -d -v -m -g" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/other/python_book_reviews.md b/other/python_book_reviews.md index bc1105f..4b762ea 100644 --- a/other/python_book_reviews.md +++ b/other/python_book_reviews.md @@ -3,14 +3,13 @@ # Python Book Reviews -- [Matplotlib Plotting Cookbook](#Matplotlib-Plotting-Cookbook) -- [Python High Performance Programming](#Python-High-Performance-Programming) -- [Learning Ipython for Interactive Computing and Data Visualization](#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization) -- [The Practice of Computing Using Python (2nd Edition)](#The-Practice-of-Computing-Using-Python-(2nd-Edition)) +- [Matplotlib Plotting Cookbook](#matplotlib-plotting-cookbook) +- [Python High Performance Programming](#python-high-performance-programming) +- [Learning IPython for Interactive Computing and Data Visualization](#learning-ipython-for-interactive-computing-and-data-visualization) +- [The Practice of Computing Using Python (2nd Edition)](#the-practice-of-computing-using-python-(2nd-Edition)) +- [How to Make Mistakes in Python](#how-to-make-mistakes-in-python) -
- **Where are the links?** I decided to **not** post any links to any online shop here - I don't want to advertise anything but merely want to leave my brief thoughts in hope that it might be helpful to one or the other. @@ -18,25 +17,20 @@ I decided to **not** post any links to any online shop here - I don't want to ad **About the rating scale/review scores** -Most popular review sites provide some sort of rating, e.g., 7/10, 90/100, 3 starts out of 5 etc. +Most popular review sites provide some sort of rating, e.g., 7/10, 90/100, 3 stars out of 5 etc. I have to admit that I am not a big fan of those review scores - and you won't find them here. Based on my experience, review scores are just kindling all sorts of arguments, destructive debates, and hate-mails. Let's be honest, every opinion is subjective, and I think that boiling it down to a final score is just an annoyance for everyone. -
- - -
+--- +### Matplotlib Plotting Cookbook - -### Matplotlib Plotting Cookbook -[[back to top](#table-of-contents)] ***by Alexandre Devert*** - -Paperback: 222 pages -Release Date: March 2014 -ISBN: 1849513260 -ISBN 13: 9781849513265 +- Paperback: 222 pages +- Release Date: March 2014 +- ISBN: 1849513260 +- ISBN 13: 9781849513265 +- Publisher: Packt **A good alternative to the official matplotlib documentation** @@ -56,42 +50,41 @@ But to it's defense, my hard copy of the "Gnuplot in Action" is also presented i Not a real point of criticism but more like a suggestion for future editions: as big fan of it, I was actually looking for this section that mentions how to use it in IPython notebooks (%pylab inline vs. matplotlib inline), and maybe also plotly for additional value :) -
+--- - ### Python High Performance Programming -[[back to top](#table-of-contents)] ***by Gabriele Lanaro*** - -Paperback: 108 pages -Release Date: December 2013 -ISBN: 1783288450 -ISBN 13: 9781783288458 + +- Paperback: 108 pages +- Release Date: December 2013 +- ISBN: 1783288450 +- ISBN 13: 9781783288458 +- Publisher: Packt **Really recommended book for Python beginners** A really nice read! It covered 4 important topics: how to profile & benchmark Python code, NumPy, C-extensions via Cython, and parallel programming. However, I found it a little bit too brief on all of the topics, a little bit more depth would have been nice. -Also, I missed a few parts, like general Python tricks for better performance (e.g., in-place operators for mutable types and many many others that I started to create benchmarks for here: https://github.com/rasbt/One-Python-benchmark-per-day) +Also, I missed a few parts, like general Python tricks for better performance (e.g., in-place operators for mutable types and many many others that I started to create benchmarks for here: https://github.com/rasbt/One-Python-benchmark-per-day) And another thing that I think would be worth adding in a future addition would be the JIT (just-in-time) compilers, such as parakeet or Numba, especially since Numexpr was briefly mentioned in the NumPy section. But overall I think it is a very recommended read for Python beginners! -
+--- + +### Learning Ipython for Interactive Computing and Data Visualization + - -###Learning Ipython for Interactive Computing and Data Visualization -[[back to top](#table-of-contents)] - ***by Cyrille Rossant*** - -Paperback: 138 pages -Release Date: April 2013 -ISBN: 1782169938 -ISBN 13: 9781782169932 + +- Paperback: 138 pages +- Release Date: April 2013 +- ISBN: 1782169938 +- ISBN 13: 9781782169932 +- Publisher: Packt @@ -100,23 +93,38 @@ ISBN 13: 9781782169932 It's a brief but good book that provides a good introduction to the IPython environment. I think the high-performance chapter that explained the usage of NumPy among others was a little bit redundant, since it is a general Python topic and is not necessarily specific to IPython. And on the other hand, the chapters on customizing IPython and especially writing own IPython magic extensions were way too brief - when I wrote my own extensions, I needed to look more closely at the IPython extension source code to be able to handle this task. But still, this is a nice book that I would recommend to people who are fairly new to Python and people who want to get a taste of IPython! -
+--- + +### The Practice of Computing Using Python (2nd Edition) - -###The Practice of Computing Using Python (2nd Edition) -[[back to top](#table-of-contents)] ***by William F. Punch and Richard Enbody*** - -Paperback: 792 pages -Release Date: February 25, 2012 -ISBN-10: 013280557X -ISBN-13: 978-0132805575 +- Paperback: 792 pages +- Release Date: February 25, 2012 +- ISBN-10: 013280557X +- ISBN-13: 978-0132805575 +- Publisher: Pearson **A great first Python book** This was actually my first Python book. It is not meant to be a thorough coverage of all the greatest Python features and capabilities, but it provides a great introduction to computing and programming in general by using the Python language. It is maybe a little bit to trivial for programmers who just want to pick up the syntax Python language, but I would really recommend this book as a first introduction to people who have never programmed before - I think that Python is a very nice language to pick up this valuable skill. -I am a big fan of books that contains self-assessments: from short exercises up to bigger project assignments, and this book comes with a huge abundance of valuable material, which is a big bonus point. \ No newline at end of file +I am a big fan of books that contains self-assessments: from short exercises up to bigger project assignments, and this book comes with a huge abundance of valuable material, which is a big bonus point. + + +--- + +### How to Make Mistakes in Python + + +***by Mike Pirnat*** + + +- e-Book: 154 pages +- Release Date: October, 2015 +- Publisher: O'Reilly + + +Although I already have many years of experience with coding in Python, I thought that it couldn't hurt to read through this book -- I got the free copy via O'Reilly, and it's relatively short. Sure, many topics throughout this book are trivial for an experienced Python programmer, but I believe that it's a great summary for someone who just got started with this programming language. Although the author doesn't go into technical depths regarding e.g., pylint, unit testing, etc., I think that his descriptions are sufficient, and a reader can always look at the online documentation of the respective tools. What's more important is that the author gives good reasons WHY we should use/do certain things, and I really like the use of paraphrased examples from real-world use cases. It's a solid book overall! diff --git a/python_patterns/README.md b/python_patterns/README.md new file mode 100644 index 0000000..37a1770 --- /dev/null +++ b/python_patterns/README.md @@ -0,0 +1,3 @@ +# A collection of useful Python snippets + +[View](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb) the IPython Notebook. \ No newline at end of file diff --git a/python_patterns/id_file1.txt b/python_patterns/id_file1.txt new file mode 100644 index 0000000..a600893 --- /dev/null +++ b/python_patterns/id_file1.txt @@ -0,0 +1,3 @@ +1234 +2342 +2341 \ No newline at end of file diff --git a/python_patterns/id_file2.txt b/python_patterns/id_file2.txt new file mode 100644 index 0000000..d05914a --- /dev/null +++ b/python_patterns/id_file2.txt @@ -0,0 +1,3 @@ +5234 +3344 +2341 \ No newline at end of file diff --git a/python_patterns/my_file.pkl b/python_patterns/my_file.pkl new file mode 100644 index 0000000..f24f898 Binary files /dev/null and b/python_patterns/my_file.pkl differ diff --git a/python_patterns/patterns.ipynb b/python_patterns/patterns.ipynb new file mode 100644 index 0000000..64c6fe1 --- /dev/null +++ b/python_patterns/patterns.ipynb @@ -0,0 +1,1600 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Go back](https://github.com/rasbt/python_reference) to the `python_reference` repository." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A random collection of useful Python snippets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I just cleaned my hard drive and found a couple of useful Python snippets that I had some use for in the past. I thought it would be worthwhile to collect them in a IPython notebook for personal reference and share it with people who might find them useful too. \n", + "Most of those snippets are hopefully self-explanatory, but I am planning to add more comments and descriptions in future." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [Bitstrings from positive and negative elements in a list](#Bitstrings-from-positive-and-negative-elements-in-a-list)\n", + "- [Command line arguments 1 - sys.argv](#Command-line-arguments-1---sys.argv)\n", + "- [Data and time basics](#Data-and-time-basics)\n", + "- [Differences between 2 files](#Differences-between-2-files)\n", + "- [Differences between successive elements in a list](#Differences-between-successive-elements-in-a-list)\n", + "- [Doctest example](#Doctest-example)\n", + "- [English language detection](#English-language-detection)\n", + "- [File browsing basics](#File-browsing-basics)\n", + "- [File reading basics](#File-reading-basics)\n", + "- [Indices of min and max elements from a list](#Indices-of-min-and-max-elements-from-a-list)\n", + "- [Lambda functions](#Lambda-functions)\n", + "- [Private functions](#Private-functions)\n", + "- [Namedtuples](#Namedtuples)\n", + "- [Normalizing data](#Normalizing-data)\n", + "- [NumPy essentials](#NumPy-essentials)\n", + "- [Pickling Python objects to bitstreams](#Pickling-Python-objects-to-bitstreams)\n", + "- [Python version check](#Python-version-check)\n", + "- [Runtime within a script](#Runtime-within-a-script)\n", + "- [Sorting lists of tuples by elements](#Sorting-lists-of-tuples-by-elements)\n", + "- [Sorting multiple lists relative to each other](#Sorting-multiple-lists-relative-to-each-other)\n", + "- [Using namedtuples](#Using-namedtuples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sebastian Raschka 26/09/2014 \n", + "\n", + "CPython 3.4.1\n", + "IPython 2.0.0\n" + ] + } + ], + "source": [ + "%watermark -d -a \"Sebastian Raschka\" -v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bitstrings from positive and negative elements in a list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input values [ 1. 2. 0.3 -1. -2. ]\n", + "bitstring [1 1 1 0 0]\n" + ] + } + ], + "source": [ + "# Generating a bitstring from a Python list or numpy array\n", + "# where all postive values -> 1\n", + "# all negative values -> 0\n", + "\n", + "import numpy as np\n", + "\n", + "def make_bitstring(ary):\n", + " return np.where(ary > 0, 1, 0)\n", + "\n", + "\n", + "def faster_bitstring(ary):\n", + " return np.where(ary > 0).astype('i1')\n", + "\n", + "### Example:\n", + "\n", + "ary1 = np.array([1, 2, 0.3, -1, -2])\n", + "print('input values %s' %ary1)\n", + "print('bitstring %s' %make_bitstring(ary1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Command line arguments 1 - sys.argv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting cmd_line_args_1_sysarg.py\n" + ] + } + ], + "source": [ + "%%file cmd_line_args_1_sysarg.py\n", + "import sys\n", + "\n", + "def error(msg):\n", + " \"\"\"Prints error message, sends it to stderr, and quites the program.\"\"\"\n", + " sys.exit(msg)\n", + "\n", + "args = sys.argv[1:] # sys.argv[0] is the name of the python script itself\n", + "\n", + "try:\n", + " arg1 = int(args[0])\n", + " arg2 = args[1]\n", + " arg3 = args[2]\n", + " print(\"Everything okay!\")\n", + "\n", + "except ValueError:\n", + " error(\"First argument must be integer type!\")\n", + "\n", + "except IndexError:\n", + " error(\"Requires 3 arguments!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Everything okay!\n" + ] + } + ], + "source": [ + "% run cmd_line_args_1_sysarg.py 1 2 3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SystemExit", + "evalue": "First argument must be integer type!", + "output_type": "error", + "traceback": [ + "An exception has occurred, use %tb to see the full traceback.\n", + "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m First argument must be integer type!\n" + ] + } + ], + "source": [ + "% run cmd_line_args_1_sysarg.py a 2 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data and time basics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13:28:05\n", + "26/09/2014\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "# print time HOURS:MINUTES:SECONDS\n", + "# e.g., '10:50:58'\n", + "print(time.strftime(\"%H:%M:%S\"))\n", + "\n", + "# print current date DAY:MONTH:YEAR\n", + "# e.g., '06/03/2014'\n", + "print(time.strftime(\"%d/%m/%Y\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Differences between 2 files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing id_file1.txt\n" + ] + } + ], + "source": [ + "%%file id_file1.txt\n", + "1234\n", + "2342\n", + "2341" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing id_file2.txt\n" + ] + } + ], + "source": [ + "%%file id_file2.txt\n", + "5234\n", + "3344\n", + "2341" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5234\n", + "3344\n", + "Total differences: 2\n" + ] + } + ], + "source": [ + "# Print lines that are different between 2 files. Insensitive\n", + "# to the order of the file contents.\n", + "\n", + "id_set1 = set()\n", + "id_set2 = set()\n", + "\n", + "with open('id_file1.txt', 'r') as id_file:\n", + " for line in id_file:\n", + " id_set1.add(line.strip())\n", + "\n", + "with open('id_file2.txt', 'r') as id_file:\n", + " for line in id_file:\n", + " id_set2.add(line.strip()) \n", + "\n", + "diffs = id_set2.difference(id_set1)\n", + "\n", + "for d in diffs:\n", + " print(d)\n", + "print(\"Total differences:\",len(diffs))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Differences between successive elements in a list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 2, 3]\n" + ] + } + ], + "source": [ + "from itertools import islice\n", + "\n", + "lst = [1,2,3,5,8]\n", + "diff = [j - i for i, j in zip(lst, islice(lst, 1, None))]\n", + "print(diff)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Doctest example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ok\n" + ] + } + ], + "source": [ + "def subtract(a, b):\n", + " \"\"\"\n", + " Subtracts second from first number and returns result.\n", + " >>> subtract(10, 5)\n", + " 5\n", + " >>> subtract(11, 0.7)\n", + " 10.3\n", + " \"\"\"\n", + " return a-b\n", + "\n", + "if __name__ == \"__main__\": # is 'false' if imported\n", + " import doctest\n", + " doctest.testmod()\n", + " print('ok')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "**********************************************************************\n", + "File \"__main__\", line 4, in __main__.hello_world\n", + "Failed example:\n", + " hello_world()\n", + "Expected:\n", + " 'Hello, World'\n", + "Got:\n", + " 'hello world'\n", + "**********************************************************************\n", + "1 items had failures:\n", + " 1 of 1 in __main__.hello_world\n", + "***Test Failed*** 1 failures.\n" + ] + } + ], + "source": [ + "def hello_world():\n", + " \"\"\"\n", + " Returns 'Hello, World'\n", + " >>> hello_world()\n", + " 'Hello, World'\n", + " \"\"\"\n", + " return 'hello world'\n", + "\n", + "if __name__ == \"__main__\": # is 'false' if imported\n", + " import doctest\n", + " doctest.testmod()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## English language detection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2\n" + ] + } + ], + "source": [ + "import nltk\n", + "\n", + "def eng_ratio(text):\n", + " ''' Returns the ratio of non-English to English words from a text '''\n", + "\n", + " english_vocab = set(w.lower() for w in nltk.corpus.words.words()) \n", + " text_vocab = set(w.lower() for w in text.split() if w.lower().isalpha()) \n", + " unusual = text_vocab.difference(english_vocab)\n", + " diff = len(unusual)/len(text_vocab)\n", + " return diff\n", + " \n", + "text = 'This is a test fahrrad'\n", + "\n", + "print(eng_ratio(text))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File browsing basics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "import glob\n", + "\n", + "# working directory\n", + "c_dir = os.getcwd() # show current working directory\n", + "os.listdir(c_dir) # shows all files in the working directory\n", + "os.chdir('~/Data') # change working directory\n", + "\n", + "\n", + "# get all files in a directory\n", + "glob.glob('/Users/sebastian/Desktop/*')\n", + "\n", + "# e.g., ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt']\n", + "\n", + "# walk\n", + "tree = os.walk(c_dir) \n", + "# moves through sub directories and creates a 'generator' object of tuples\n", + "# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]), \n", + "# (...), ...\n", + "\n", + "#check files: returns either True or False\n", + "os.exists('../rel_path')\n", + "os.exists('/home/abs_path')\n", + "os.isfile('./file.txt')\n", + "os.isdir('./subdir')\n", + "\n", + "\n", + "# file permission (True or False\n", + "os.access('./some_file', os.F_OK) # File exists? Python 2.7\n", + "os.access('./some_file', os.R_OK) # Ok to read? Python 2.7\n", + "os.access('./some_file', os.W_OK) # Ok to write? Python 2.7\n", + "os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7\n", + "os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7\n", + "\n", + "# join (creates operating system dependent paths)\n", + "os.path.join('a', 'b', 'c')\n", + "# 'a/b/c' on Unix/Linux\n", + "# 'a\\\\b\\\\c' on Windows\n", + "os.path.normpath('a/b/c') # converts file separators\n", + "\n", + "\n", + "# os.path: direcory and file names\n", + "os.path.samefile('./some_file', '/home/some_file') # True if those are the same\n", + "os.path.dirname('./some_file') # returns '.' (everythin but last component)\n", + "os.path.basename('./some_file') # returns 'some_file' (only last component\n", + "os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file)\n", + "os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt')\n", + "os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt')\n", + "os.path.isabs('./some_file.txt') # returns False (not an absolute path)\n", + "os.path.abspath('./some_file.txt')\n", + "\n", + "\n", + "# create and delete files and directories\n", + "os.mkdir('./test') # create a new direcotory\n", + "os.rmdir('./test') # removes an empty direcotory\n", + "os.removedirs('./test') # removes nested empty directories\n", + "os.remove('file.txt') # removes an individual file\n", + "shutil.rmtree('./test') # removes directory (empty or not empty)\n", + "\n", + "os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist\n", + "shutil.move('./dir_before', './renamed') # renames directory always\n", + "\n", + "shutil.copytree('./orig', './copy') # copies a directory recursively\n", + "shutil.copyfile('file', 'copy') # copies a file\n", + "\n", + " \n", + "# Getting files of particular type from directory\n", + "files = [f for f in os.listdir(s_pdb_dir) if f.endswith(\".txt\")]\n", + " \n", + "# Copy and move\n", + "shutil.copyfile(\"/path/to/file\", \"/path/to/new/file\") \n", + "shutil.copy(\"/path/to/file\", \"/path/to/directory\")\n", + "shutil.move(\"/path/to/file\",\"/path/to/directory\")\n", + " \n", + "# Check if file or directory exists\n", + "os.path.exists(\"file or directory\")\n", + "os.path.isfile(\"file\")\n", + "os.path.isdir(\"directory\")\n", + " \n", + "# Working directory and absolute path to files\n", + "os.getcwd()\n", + "os.path.abspath(\"file\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File reading basics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Note: rb opens file in binary mode to avoid issues with Windows systems\n", + "# where '\\r\\n' is used instead of '\\n' as newline character(s).\n", + "\n", + "\n", + "# A) Reading in Byte chunks\n", + "reader_a = open(\"file.txt\", \"rb\")\n", + "chunks = []\n", + "data = reader_a.read(64) # reads first 64 bytes\n", + "while data != \"\":\n", + " chunks.append(data)\n", + " data = reader_a.read(64)\n", + "if data:\n", + " chunks.append(data)\n", + "print(len(chunks))\n", + "reader_a.close()\n", + "\n", + "\n", + "# B) Reading whole file at once into a list of lines\n", + "with open(\"file.txt\", \"rb\") as reader_b: # recommended syntax, auto closes\n", + " data = reader_b.readlines() # data is assigned a list of lines\n", + "print(len(data))\n", + "\n", + "\n", + "# C) Reading whole file at once into a string\n", + "with open(\"file.txt\", \"rb\") as reader_c:\n", + " data = reader_c.read() # data is assigned a list of lines\n", + "print(len(data))\n", + "\n", + "\n", + "# D) Reading line by line into a list\n", + "data = []\n", + "with open(\"file.txt\", \"rb\") as reader_d:\n", + " for line in reader_d:\n", + " data.append(line)\n", + "print(len(data))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Indices of min and max elements from a list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min_index: 0 min_value: 1\n", + "max_index: 4 max_value: 5\n" + ] + } + ], + "source": [ + "import operator\n", + "\n", + "values = [1, 2, 3, 4, 5]\n", + "\n", + "min_index, min_value = min(enumerate(values), key=operator.itemgetter(1))\n", + "max_index, max_value = max(enumerate(values), key=operator.itemgetter(1))\n", + "\n", + "print('min_index:', min_index, 'min_value:', min_value)\n", + "print('max_index:', max_index, 'max_value:', max_value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lambda functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Lambda functions are just a short-hand way or writing\n", + "# short function definitions\n", + "\n", + "def square_root1(x):\n", + " return x**0.5\n", + " \n", + "square_root2 = lambda x: x**0.5\n", + "\n", + "assert(square_root1(9) == square_root2(9))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Private functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My message: Hello, World\n" + ] + } + ], + "source": [ + "def create_message(msg_txt):\n", + " def _priv_msg(message): # private, no access from outside\n", + " print(\"{}: {}\".format(msg_txt, message))\n", + " return _priv_msg # returns a function\n", + "\n", + "new_msg = create_message(\"My message\")\n", + "# note, new_msg is a function\n", + "\n", + "new_msg(\"Hello, World\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Namedtuples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 2 3\n" + ] + } + ], + "source": [ + "from collections import namedtuple\n", + "\n", + "my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub'])\n", + "p = my_namedtuple(1, 2, 3, 4, 5)\n", + "print(p.x, p.y, p.z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalizing data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def normalize(data, min_val=0, max_val=1):\n", + " \"\"\"\n", + " Normalizes values in a list of data points to a range, e.g.,\n", + " between 0.0 and 1.0. \n", + " Returns the original object if value is not a integer or float.\n", + " \n", + " \"\"\"\n", + " norm_data = []\n", + " data_min = min(data)\n", + " data_max = max(data)\n", + " for x in data:\n", + " numerator = x - data_min\n", + " denominator = data_max - data_min\n", + " x_norm = (max_val-min_val) * numerator/denominator + min_val\n", + " norm_data.append(x_norm)\n", + " return norm_data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[0.0, 0.25, 0.5, 0.75, 1.0]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "normalize([1,2,3,4,5])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[-10.0, -5.0, 0.0, 5.0, 10.0]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "normalize([1,2,3,4,5], min_val=-10, max_val=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NumPy essentials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "ary1 = np.array([1,2,3,4,5]) # must be same type\n", + "ary2 = np.zeros((3,4)) # 3x4 matrix consisiting of 0s \n", + "ary3 = np.ones((3,4)) # 3x4 matrix consisiting of 1s \n", + "ary4 = np.identity(3) # 3x3 identity matrix\n", + "ary5 = ary1.copy() # make a copy of ary1\n", + "\n", + "item1 = ary3[0, 0] # item in row1, column1\n", + "\n", + "ary2.shape # tuple of dimensions. Here: (3,4)\n", + "ary2.size # number of elements. Here: 12\n", + "\n", + "\n", + "ary2_t = ary2.transpose() # transposes matrix\n", + "\n", + "ary2.ravel() # makes an array linear (1-dimensional)\n", + " # by concatenating rows\n", + "ary2.reshape(2,6) # reshapes array (must have same dimensions)\n", + "\n", + "ary3[0:2, 0:3] # submatrix of first 2 rows and first 3 columns \n", + "\n", + "ary3 = ary3[[2,0,1]] # re-arrange rows\n", + "\n", + "\n", + "# element-wise operations\n", + "\n", + "ary1 + ary1\n", + "ary1 * ary1\n", + "numpy.dot(ary1, ary1) # matrix/vector (dot) product\n", + "\n", + "numpy.sum(ary1, axis=1) # sum of a 1D array, column sums of a 2D array\n", + "numpy.mean(ary1, axis=1) # mean of a 1D array, column means of a 2D array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pickling Python objects to bitstreams" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 'some text', 2: 'some text', 3: 'some text', 4: 'some text', 5: 'some text', 6: 'some text', 7: 'some text', 8: 'some text', 9: 'some text'}\n" + ] + } + ], + "source": [ + "import pickle\n", + "\n", + "#### Generate some object\n", + "my_dict = dict()\n", + "for i in range(1,10):\n", + " my_dict[i] = \"some text\"\n", + "\n", + "#### Save object to file\n", + "pickle_out = open('my_file.pkl', 'wb')\n", + "pickle.dump(my_dict, pickle_out)\n", + "pickle_out.close()\n", + "\n", + "#### Load object from file\n", + "my_object_file = open('my_file.pkl', 'rb')\n", + "my_dict = pickle.load(my_object_file)\n", + "my_object_file.close()\n", + "\n", + "print(my_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Python version check" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "executed in Python 3.x\n", + "H\n", + "in for-loop:\n", + "e\n", + "l\n", + "l\n", + "o\n" + ] + } + ], + "source": [ + "import sys\n", + "\n", + "def give_letter(word):\n", + " for letter in word:\n", + " yield letter\n", + "\n", + "if sys.version_info[0] == 3:\n", + " print('executed in Python 3.x')\n", + " test = give_letter('Hello')\n", + " print(next(test))\n", + " print('in for-loop:')\n", + " for l in test:\n", + " print(l)\n", + "\n", + "# if Python 2.x\n", + "if sys.version_info[0] == 2:\n", + " print('executed in Python 2.x')\n", + " test = give_letter('Hello')\n", + " print(test.next())\n", + " print('in for-loop:') \n", + " for l in test:\n", + " print(l)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Runtime within a script" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time elapsed: 0.49176900000000057 seconds\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "start_time = time.clock()\n", + "\n", + "for i in range(10000000):\n", + " pass\n", + "\n", + "elapsed_time = time.clock() - start_time\n", + "print(\"Time elapsed: {} seconds\".format(elapsed_time))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time elapsed: 0.3550995970144868 seconds\n" + ] + } + ], + "source": [ + "import timeit\n", + "elapsed_time = timeit.timeit('for i in range(10000000): pass', number=1)\n", + "print(\"Time elapsed: {} seconds\".format(elapsed_time))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sorting lists of tuples by elements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n" + ] + } + ], + "source": [ + "# Here, we make use of the \"key\" parameter of the in-built \"sorted()\" function \n", + "# (also available for the \".sort()\" method), which let's us define a function \n", + "# that is called on every element that is to be sorted. In this case, our \n", + "# \"key\"-function is a simple lambda function that returns the last item \n", + "# from every tuple.\n", + "\n", + "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n", + "\n", + "sorted_list = sorted(a_list, key=lambda e: e[::-1])\n", + "\n", + "print(sorted_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(2, 3, 'a'), (3, 2, 'b'), (2, 2, 'b'), (1, 3, 'c')]\n" + ] + } + ], + "source": [ + "# prints [(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n", + "\n", + "# If we are only interesting in sorting the list by the last element\n", + "# of the tuple and don't care about a \"tie\" situation, we can also use\n", + "# the index of the tuple item directly instead of reversing the tuple \n", + "# for efficiency.\n", + "\n", + "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n", + "\n", + "sorted_list = sorted(a_list, key=lambda e: e[-1])\n", + "\n", + "print(sorted_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sorting multiple lists relative to each other" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "input values:\n", + " ['c', 'b', 'a'] [6, 5, 4] ['some-val-associated-with-c', 'another_val-b', 'z_another_third_val-a']\n", + "\n", + "\n", + "sorted output:\n", + " ['a', 'b', 'c'] [4, 5, 6] ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "You have 3 lists that you want to sort \"relative\" to each other,\n", + "for example, picturing each list as a row in a 3x3 matrix: sort it by columns\n", + "\n", + "########################\n", + "If the input lists are\n", + "########################\n", + "\n", + " list1 = ['c','b','a']\n", + " list2 = [6,5,4]\n", + " list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n", + "\n", + "########################\n", + "the desired outcome is:\n", + "########################\n", + "\n", + " ['a', 'b', 'c'] \n", + " [4, 5, 6] \n", + " ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n", + "\n", + "########################\n", + "and NOT:\n", + "########################\n", + "\n", + " ['a', 'b', 'c'] \n", + " [4, 5, 6] \n", + " ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a']\n", + "\n", + "\n", + "\"\"\"\n", + "\n", + "list1 = ['c','b','a']\n", + "list2 = [6,5,4]\n", + "list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n", + "\n", + "print('input values:\\n', list1, list2, list3)\n", + "\n", + "list1, list2, list3 = [list(t) for t in zip(*sorted(zip(list1, list2, list3)))]\n", + "\n", + "print('\\n\\nsorted output:\\n', list1, list2, list3 )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using namedtuples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[back to top](#Table-of-Contents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`namedtuples` are high-performance container datatypes in the [`collection`](https://docs.python.org/2/library/collections.html) module (part of Python's stdlib since 2.6).\n", + "`namedtuple()` is factory function for creating tuple subclasses with named fields." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X-coordinate: 1\n" + ] + } + ], + "source": [ + "from collections import namedtuple\n", + "\n", + "Coordinates = namedtuple('Coordinates', ['x', 'y', 'z'])\n", + "point1 = Coordinates(1, 2, 3)\n", + "print('X-coordinate: %d' % point1.x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/templates/webapp_ex1/README.md b/templates/webapp_ex1/README.md new file mode 100644 index 0000000..d07794d --- /dev/null +++ b/templates/webapp_ex1/README.md @@ -0,0 +1,13 @@ +Sebastian Raschka, 2015 + +# Flask Example App 1 + +A simple Flask app that calculates the sum of two numbers entered in the respective input fields. + +A more detailed description is going to follow some time in future. + +You can run the app locally by executing `python app.py` within this directory. + +
+ +![](./img/img_1.png) diff --git a/templates/webapp_ex1/app.py b/templates/webapp_ex1/app.py new file mode 100644 index 0000000..5314e37 --- /dev/null +++ b/templates/webapp_ex1/app.py @@ -0,0 +1,31 @@ +from flask import Flask, render_template, request +from wtforms import Form, DecimalField, validators + +app = Flask(__name__) + + +class EntryForm(Form): + x_entry = DecimalField('x:', + places=10, + validators=[validators.NumberRange(-1e10, 1e10)]) + y_entry = DecimalField('y:', + places=10, + validators=[validators.NumberRange(-1e10, 1e10)]) + +@app.route('/') +def index(): + form = EntryForm(request.form) + return render_template('entry.html', form=form, z='') + +@app.route('/results', methods=['POST']) +def results(): + form = EntryForm(request.form) + z = '' + if request.method == 'POST' and form.validate(): + x = request.form['x_entry'] + y = request.form['y_entry'] + z = float(x) + float(y) + return render_template('entry.html', form=form, z=z) + +if __name__ == '__main__': + app.run(debug=True) \ No newline at end of file diff --git a/templates/webapp_ex1/img/img_1.png b/templates/webapp_ex1/img/img_1.png new file mode 100644 index 0000000..bfc3510 Binary files /dev/null and b/templates/webapp_ex1/img/img_1.png differ diff --git a/templates/webapp_ex1/static/style.css b/templates/webapp_ex1/static/style.css new file mode 100644 index 0000000..8abda7b --- /dev/null +++ b/templates/webapp_ex1/static/style.css @@ -0,0 +1,7 @@ +body{ + width:600px; +} + +#button{ + padding-top: 20px; +} \ No newline at end of file diff --git a/templates/webapp_ex1/templates/_formhelpers.html b/templates/webapp_ex1/templates/_formhelpers.html new file mode 100644 index 0000000..5790894 --- /dev/null +++ b/templates/webapp_ex1/templates/_formhelpers.html @@ -0,0 +1,12 @@ +{% macro render_field(field) %} +
{{ field.label }} +
{{ field(**kwargs)|safe }} + {% if field.errors %} +
    + {% for error in field.errors %} +
  • {{ error }}
  • + {% endfor %} +
+ {% endif %} +
+{% endmacro %} \ No newline at end of file diff --git a/templates/webapp_ex1/templates/entry.html b/templates/webapp_ex1/templates/entry.html new file mode 100644 index 0000000..c31100d --- /dev/null +++ b/templates/webapp_ex1/templates/entry.html @@ -0,0 +1,26 @@ + + + + Webapp Ex 1 + + + + +{% from "_formhelpers.html" import render_field %} + +
+
+ {{ render_field(form.x_entry, cols='1', rows='1') }} + {{ render_field(form.y_entry, cols='1', rows='1') }} +
+
+ +
+ +
+ x + y = {{ z }} +
+
+ + + \ No newline at end of file diff --git a/tutorials/classifiers.pkl b/tutorials/classifiers.pkl deleted file mode 100644 index 1272af5..0000000 Binary files a/tutorials/classifiers.pkl and /dev/null differ diff --git a/tutorials/cython_bubblesort_nomagic.c b/tutorials/cython_bubblesort_nomagic.c deleted file mode 100644 index 028e020..0000000 --- a/tutorials/cython_bubblesort_nomagic.c +++ /dev/null @@ -1,19147 +0,0 @@ -/* Generated by Cython 0.20.2 on Sun Jul 6 22:33:02 2014 */ - -#define PY_SSIZE_T_CLEAN -#ifndef CYTHON_USE_PYLONG_INTERNALS -#ifdef PYLONG_BITS_IN_DIGIT -#define CYTHON_USE_PYLONG_INTERNALS 0 -#else -#include "pyconfig.h" -#ifdef PYLONG_BITS_IN_DIGIT -#define CYTHON_USE_PYLONG_INTERNALS 1 -#else -#define CYTHON_USE_PYLONG_INTERNALS 0 -#endif -#endif -#endif -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02040000 - #error Cython requires Python 2.4+. -#else -#define CYTHON_ABI "0_20_2" -#include /* For offsetof */ -#ifndef offsetof -#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef Py_HUGE_VAL - #define Py_HUGE_VAL HUGE_VAL -#endif -#ifdef PYPY_VERSION -#define CYTHON_COMPILING_IN_PYPY 1 -#define CYTHON_COMPILING_IN_CPYTHON 0 -#else -#define CYTHON_COMPILING_IN_PYPY 0 -#define CYTHON_COMPILING_IN_CPYTHON 1 -#endif -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 -#define Py_OptimizeFlag 0 -#endif -#if PY_VERSION_HEX < 0x02050000 - typedef int Py_ssize_t; - #define PY_SSIZE_T_MAX INT_MAX - #define PY_SSIZE_T_MIN INT_MIN - #define PY_FORMAT_SIZE_T "" - #define CYTHON_FORMAT_SSIZE_T "" - #define PyInt_FromSsize_t(z) PyInt_FromLong(z) - #define PyInt_AsSsize_t(o) __Pyx_PyInt_As_int(o) - #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? 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(Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_COMPILING_IN_CPYTHON -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? 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- } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; 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-static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsigned long value); - -static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *); - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - #define __Pyx_CREAL(z) ((z).real()) - #define __Pyx_CIMAG(z) ((z).imag()) - #else - #define __Pyx_CREAL(z) (__real__(z)) - #define __Pyx_CIMAG(z) (__imag__(z)) - #endif -#else - #define __Pyx_CREAL(z) ((z).real) - #define __Pyx_CIMAG(z) ((z).imag) -#endif -#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX - #define __Pyx_SET_CREAL(z,x) ((z).real(x)) - #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) -#else - #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) - #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) -#endif - -static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); - -#if CYTHON_CCOMPLEX - #define __Pyx_c_eqf(a, b) ((a)==(b)) - #define __Pyx_c_sumf(a, b) ((a)+(b)) - #define __Pyx_c_difff(a, b) ((a)-(b)) - #define __Pyx_c_prodf(a, b) ((a)*(b)) - #define __Pyx_c_quotf(a, b) ((a)/(b)) - #define __Pyx_c_negf(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zerof(z) ((z)==(float)0) - #define __Pyx_c_conjf(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_absf(z) (::std::abs(z)) - #define __Pyx_c_powf(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zerof(z) ((z)==0) - #define __Pyx_c_conjf(z) (conjf(z)) - #if 1 - #define __Pyx_c_absf(z) (cabsf(z)) - #define __Pyx_c_powf(a, b) (cpowf(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); - static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); - #if 1 - static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); - #endif -#endif - -static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); - -#if CYTHON_CCOMPLEX - #define __Pyx_c_eq(a, b) ((a)==(b)) - #define __Pyx_c_sum(a, b) ((a)+(b)) - #define __Pyx_c_diff(a, b) ((a)-(b)) - #define __Pyx_c_prod(a, b) ((a)*(b)) - #define __Pyx_c_quot(a, b) ((a)/(b)) - #define __Pyx_c_neg(a) (-(a)) - #ifdef __cplusplus - #define __Pyx_c_is_zero(z) ((z)==(double)0) - #define __Pyx_c_conj(z) (::std::conj(z)) - #if 1 - #define __Pyx_c_abs(z) (::std::abs(z)) - #define __Pyx_c_pow(a, b) (::std::pow(a, b)) - #endif - #else - #define __Pyx_c_is_zero(z) ((z)==0) - #define __Pyx_c_conj(z) (conj(z)) - #if 1 - #define __Pyx_c_abs(z) (cabs(z)) - #define __Pyx_c_pow(a, b) (cpow(a, b)) - #endif - #endif -#else - static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); - static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); - #if 1 - static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); - #endif -#endif - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs, - char order, int ndim); - -static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize); - -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object); - -static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig); - -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/ - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); - -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); - -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj); - -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *); - -static int __Pyx_check_binary_version(void); - -#if !defined(__Pyx_PyIdentifier_FromString) -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) -#else - #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) -#endif -#endif - -static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ - -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ - -typedef struct { - int code_line; - PyCodeObject* code_object; -} __Pyx_CodeObjectCacheEntry; -struct __Pyx_CodeObjectCache { - int count; - int max_count; - __Pyx_CodeObjectCacheEntry* entries; -}; -static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); -static PyCodeObject *__pyx_find_code_object(int code_line); -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); - -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename); /*proto*/ - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ - - -/* Module declarations from 'cpython.buffer' */ - -/* Module declarations from 'cpython.ref' */ - -/* Module declarations from 'libc.string' */ - -/* Module declarations from 'libc.stdio' */ - -/* Module declarations from 'cpython.object' */ - -/* Module declarations from '__builtin__' */ - -/* Module declarations from 'cpython.type' */ -static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; - -/* Module declarations from 'libc.stdlib' */ - -/* Module declarations from 'numpy' */ - -/* Module declarations from 'numpy' */ -static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; -static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; -static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; -static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; -static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; -static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ - -/* Module declarations from 'cython.view' */ - -/* Module declarations from 'cython' */ - -/* Module declarations from 'cython_bubblesort_nomagic' */ -static PyTypeObject *__pyx_array_type = 0; -static PyTypeObject *__pyx_MemviewEnum_type = 0; -static PyTypeObject *__pyx_memoryview_type = 0; -static PyTypeObject *__pyx_memoryviewslice_type = 0; -static PyObject *generic = 0; -static PyObject *strided = 0; -static PyObject *indirect = 0; -static PyObject *contiguous = 0; -static PyObject *indirect_contiguous = 0; -static PyObject *__pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(PyArrayObject *, int __pyx_skip_dispatch); /*proto*/ -static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ -static void *__pyx_align_pointer(void *, size_t); /*proto*/ -static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ -static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ -static PyObject *_unellipsify(PyObject *, int); /*proto*/ -static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ -static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ -static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ -static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ -static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ -static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ -static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ -static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/ -static int __pyx_memoryview_err(PyObject *, char *); /*proto*/ -static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ -static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ -static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 }; -#define __Pyx_MODULE_NAME "cython_bubblesort_nomagic" -int __pyx_module_is_main_cython_bubblesort_nomagic = 0; - -/* Implementation of 'cython_bubblesort_nomagic' */ -static PyObject *__pyx_builtin_xrange; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin_RuntimeError; -static PyObject *__pyx_builtin_MemoryError; -static PyObject *__pyx_builtin_enumerate; -static PyObject *__pyx_builtin_Ellipsis; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_id; -static PyObject *__pyx_builtin_IndexError; -static PyObject *__pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_inp_ary); /* proto */ -static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ -static int __pyx_array_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ -static int __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_array_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *get_memview_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_array_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ -static PyObject *__pyx_array_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ -static int __pyx_array_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ -static int __pyx_MemviewEnum_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ -static PyObject *__pyx_MemviewEnum_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static int __pyx_memoryview_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ -static void __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ -static int __pyx_memoryview_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ -static int __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static PyObject *__pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static Py_ssize_t __pyx_memoryview_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); 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* cdef bint negative_step - * - * if not is_slice: # <<<<<<<<<<<<<< - * - * if start < 0: - */ - __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":786 - * if not is_slice: - * - * if start < 0: # <<<<<<<<<<<<<< - * start += shape - * if not 0 <= start < shape: - */ - __pyx_t_1 = ((__pyx_v_start < 0) != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":787 - * - * if start < 0: - * start += shape # <<<<<<<<<<<<<< - * if not 0 <= start < shape: - * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) - */ - __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - goto __pyx_L4; - } - __pyx_L4:; - - /* "View.MemoryView":788 - * if start < 0: - * start += shape - * if not 0 <= start < shape: # <<<<<<<<<<<<<< - * _err_dim(IndexError, "Index out of bounds (axis %d)", dim) - * else: - */ - __pyx_t_1 = (0 <= __pyx_v_start); - if (__pyx_t_1) { - __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); - } - __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0); - if (__pyx_t_2) { - 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* if have_start: - * if start < 0: - * start += shape # <<<<<<<<<<<<<< - * if start < 0: - * start = 0 - */ - __pyx_v_start = (__pyx_v_start + __pyx_v_shape); - - /* "View.MemoryView":801 - * if start < 0: - * start += shape - * if start < 0: # <<<<<<<<<<<<<< - * start = 0 - * elif start >= shape: - */ - __pyx_t_2 = ((__pyx_v_start < 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":802 - * start += shape - * if start < 0: - * start = 0 # <<<<<<<<<<<<<< - * elif start >= shape: - * if negative_step: - */ - __pyx_v_start = 0; - goto __pyx_L9; - } - __pyx_L9:; - goto __pyx_L8; - } - - /* "View.MemoryView":803 - * if start < 0: - * start = 0 - * elif start >= shape: # <<<<<<<<<<<<<< - * if negative_step: - * start = shape - 1 - */ - __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":804 - * start = 0 - * elif start >= shape: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - __pyx_t_2 = (__pyx_v_negative_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":805 - * elif start >= shape: - * if negative_step: - * start = shape - 1 # <<<<<<<<<<<<<< - * else: - * start = shape - */ - __pyx_v_start = (__pyx_v_shape - 1); - goto __pyx_L10; - } - /*else*/ { - - /* "View.MemoryView":807 - * start = shape - 1 - * else: - * start = shape # <<<<<<<<<<<<<< - * else: - * if negative_step: - */ - __pyx_v_start = __pyx_v_shape; - } - __pyx_L10:; - goto __pyx_L8; - } - __pyx_L8:; - goto __pyx_L7; - } - /*else*/ { - - /* "View.MemoryView":809 - * start = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * start = shape - 1 - * else: - */ - __pyx_t_2 = (__pyx_v_negative_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":810 - * else: - * if negative_step: - * start = shape - 1 # <<<<<<<<<<<<<< - * else: - * start = 0 - */ - __pyx_v_start = (__pyx_v_shape - 1); - goto __pyx_L11; - } - /*else*/ { - - /* "View.MemoryView":812 - * start = shape - 1 - * else: - * start = 0 # <<<<<<<<<<<<<< - * - * if have_stop: - 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goto __pyx_L13; - } - - /* "View.MemoryView":819 - * if stop < 0: - * stop = 0 - * elif stop > shape: # <<<<<<<<<<<<<< - * stop = shape - * else: - */ - __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":820 - * stop = 0 - * elif stop > shape: - * stop = shape # <<<<<<<<<<<<<< - * else: - * if negative_step: - */ - __pyx_v_stop = __pyx_v_shape; - goto __pyx_L13; - } - __pyx_L13:; - goto __pyx_L12; - } - /*else*/ { - - /* "View.MemoryView":822 - * stop = shape - * else: - * if negative_step: # <<<<<<<<<<<<<< - * stop = -1 - * else: - */ - __pyx_t_2 = (__pyx_v_negative_step != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":823 - * else: - * if negative_step: - * stop = -1 # <<<<<<<<<<<<<< - * else: - * stop = shape - */ - __pyx_v_stop = -1; - goto __pyx_L15; - } - /*else*/ { - - /* "View.MemoryView":825 - * stop = -1 - * else: - * stop = shape # <<<<<<<<<<<<<< - * - * if not have_step: - */ - __pyx_v_stop = __pyx_v_shape; - } - __pyx_L15:; - } - 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* break - * - */ - __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); - - /* "View.MemoryView":1080 - * if mslice.shape[i] > 1: - * c_stride = mslice.strides[i] - * break # <<<<<<<<<<<<<< - * - * for i in range(ndim): - */ - goto __pyx_L4_break; - } - } - __pyx_L4_break:; - - /* "View.MemoryView":1082 - * break - * - * for i in range(ndim): # <<<<<<<<<<<<<< - * if mslice.shape[i] > 1: - * f_stride = mslice.strides[i] - */ - __pyx_t_1 = __pyx_v_ndim; - for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) { - __pyx_v_i = __pyx_t_3; - - /* "View.MemoryView":1083 - * - * for i in range(ndim): - * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< - * f_stride = mslice.strides[i] - * break - */ - __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1084 - * for i in range(ndim): - * if mslice.shape[i] > 1: - * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< - * break - * - */ - __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); - - /* "View.MemoryView":1085 - * if mslice.shape[i] > 1: - * f_stride = mslice.strides[i] - * break # <<<<<<<<<<<<<< - * - * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): - */ - goto __pyx_L7_break; - } - } - __pyx_L7_break:; - - /* "View.MemoryView":1087 - * break - * - * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< - * return 'C' - * else: - */ - __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1088 - * - * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): - * return 'C' # <<<<<<<<<<<<<< - * else: - * return 'F' - */ - __pyx_r = 'C'; - goto __pyx_L0; - } - /*else*/ { - - /* "View.MemoryView":1090 - * return 'C' - * else: - * return 'F' # <<<<<<<<<<<<<< - * - * @cython.cdivision(True) - */ - __pyx_r = 'F'; - goto __pyx_L0; - } - - /* "View.MemoryView":1069 - * - * @cname('__pyx_get_best_slice_order') - * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil: # <<<<<<<<<<<<<< - * """ - * Figure out the best memory access order for a given slice. - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1093 - * - * @cython.cdivision(True) - * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< - * char *dst_data, Py_ssize_t *dst_strides, - * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, - */ - -static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - CYTHON_UNUSED Py_ssize_t __pyx_v_i; - CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; - Py_ssize_t __pyx_v_dst_extent; - Py_ssize_t __pyx_v_src_stride; - Py_ssize_t __pyx_v_dst_stride; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - int __pyx_t_4; - Py_ssize_t __pyx_t_5; - Py_ssize_t __pyx_t_6; - - /* "View.MemoryView":1100 - * - * cdef Py_ssize_t i - * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] - */ - __pyx_v_src_extent = (__pyx_v_src_shape[0]); - - /* "View.MemoryView":1101 - * cdef Py_ssize_t i - * cdef Py_ssize_t src_extent = src_shape[0] - * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t src_stride = src_strides[0] - * cdef Py_ssize_t dst_stride = dst_strides[0] - */ - __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); - - /* "View.MemoryView":1102 - * cdef Py_ssize_t src_extent = src_shape[0] - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - */ - __pyx_v_src_stride = (__pyx_v_src_strides[0]); - - /* "View.MemoryView":1103 - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] - * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< - * - * if ndim == 1: - */ - __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); - - /* "View.MemoryView":1105 - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - * if ndim == 1: # <<<<<<<<<<<<<< - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - */ - __pyx_t_1 = ((__pyx_v_ndim == 1) != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":1106 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - __pyx_t_1 = ((__pyx_v_src_stride > 0) != 0); - if (__pyx_t_1) { - __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1107 - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - */ - __pyx_t_3 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); - if (__pyx_t_3) { - __pyx_t_3 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); - } - __pyx_t_4 = (__pyx_t_3 != 0); - } else { - __pyx_t_4 = __pyx_t_2; - } - __pyx_t_2 = __pyx_t_4; - } else { - __pyx_t_2 = __pyx_t_1; - } - if (__pyx_t_2) { - - /* "View.MemoryView":1108 - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)); - goto __pyx_L4; - } - /*else*/ { - - /* "View.MemoryView":1110 - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - */ - __pyx_t_5 = __pyx_v_dst_extent; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1111 - * else: - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< - * src_data += src_stride - * dst_data += dst_stride - */ - memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize); - - /* "View.MemoryView":1112 - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * else: - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1113 - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L4:; - goto __pyx_L3; - } - /*else*/ { - - /* "View.MemoryView":1115 - * dst_data += dst_stride - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * _copy_strided_to_strided(src_data, src_strides + 1, - * dst_data, dst_strides + 1, - */ - __pyx_t_5 = __pyx_v_dst_extent; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1116 - * else: - * for i in range(dst_extent): - * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< - * dst_data, dst_strides + 1, - * src_shape + 1, dst_shape + 1, - */ - _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); - - /* "View.MemoryView":1120 - * src_shape + 1, dst_shape + 1, - * ndim - 1, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1121 - * ndim - 1, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L3:; - - /* "View.MemoryView":1093 - * - * @cython.cdivision(True) - * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< - * char *dst_data, Py_ssize_t *dst_strides, - * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, - */ - - /* function exit code */ -} - -/* "View.MemoryView":1123 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - */ - -static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - - /* "View.MemoryView":1126 - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< - * src.shape, dst.shape, ndim, itemsize) - * - */ - _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); - - /* "View.MemoryView":1123 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) nogil: - */ - - /* function exit code */ -} - -/* "View.MemoryView":1130 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef int i - */ - -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { - int __pyx_v_i; - Py_ssize_t __pyx_v_size; - Py_ssize_t __pyx_r; - Py_ssize_t __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - - /* "View.MemoryView":1133 - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef int i - * cdef Py_ssize_t size = src.memview.view.itemsize # <<<<<<<<<<<<<< - * - * for i in range(ndim): - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_size = __pyx_t_1; - - /* "View.MemoryView":1135 - * cdef Py_ssize_t size = src.memview.view.itemsize - * - * for i in range(ndim): # <<<<<<<<<<<<<< - * size *= src.shape[i] - * - */ - __pyx_t_2 = __pyx_v_ndim; - for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { - __pyx_v_i = __pyx_t_3; - - /* "View.MemoryView":1136 - * - * for i in range(ndim): - * size *= src.shape[i] # <<<<<<<<<<<<<< - * - * return size - */ - __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i])); - } - - /* "View.MemoryView":1138 - * size *= src.shape[i] - * - * return size # <<<<<<<<<<<<<< - * - * @cname('__pyx_fill_contig_strides_array') - */ - __pyx_r = __pyx_v_size; - goto __pyx_L0; - - /* "View.MemoryView":1130 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef int i - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1141 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) nogil: - */ - -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { - int __pyx_v_idx; - Py_ssize_t __pyx_r; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - - /* "View.MemoryView":1150 - * cdef int idx - * - * if order == 'F': # <<<<<<<<<<<<<< - * for idx in range(ndim): - * strides[idx] = stride - */ - __pyx_t_1 = ((__pyx_v_order == 'F') != 0); - if (__pyx_t_1) { - - /* "View.MemoryView":1151 - * - * if order == 'F': - * for idx in range(ndim): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride = stride * shape[idx] - */ - __pyx_t_2 = __pyx_v_ndim; - for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { - __pyx_v_idx = __pyx_t_3; - - /* "View.MemoryView":1152 - * if order == 'F': - * for idx in range(ndim): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride = stride * shape[idx] - * else: - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1153 - * for idx in range(ndim): - * strides[idx] = stride - * stride = stride * shape[idx] # <<<<<<<<<<<<<< - * else: - * for idx in range(ndim - 1, -1, -1): - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - goto __pyx_L3; - } - /*else*/ { - - /* "View.MemoryView":1155 - * stride = stride * shape[idx] - * else: - * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride = stride * shape[idx] - */ - for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { - __pyx_v_idx = __pyx_t_2; - - /* "View.MemoryView":1156 - * else: - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride = stride * shape[idx] - * - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1157 - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride - * stride = stride * shape[idx] # <<<<<<<<<<<<<< - * - * return stride - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - } - __pyx_L3:; - - /* "View.MemoryView":1159 - * stride = stride * shape[idx] - * - * return stride # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_copy_data_to_temp') - */ - __pyx_r = __pyx_v_stride; - goto __pyx_L0; - - /* "View.MemoryView":1141 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) nogil: - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1162 - * - * @cname('__pyx_memoryview_copy_data_to_temp') - * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *tmpslice, - * char order, - */ - -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { - 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- /* "View.MemoryView":1234 - * cdef int i - * cdef char order = get_best_order(&src, src_ndim) - * cdef bint broadcasting = False # <<<<<<<<<<<<<< - * cdef bint direct_copy = False - * cdef __Pyx_memviewslice tmp - */ - __pyx_v_broadcasting = 0; - - /* "View.MemoryView":1235 - * cdef char order = get_best_order(&src, src_ndim) - * cdef bint broadcasting = False - * cdef bint direct_copy = False # <<<<<<<<<<<<<< - * cdef __Pyx_memviewslice tmp - * - */ - __pyx_v_direct_copy = 0; - - /* "View.MemoryView":1238 - * cdef __Pyx_memviewslice tmp - * - * if src_ndim < dst_ndim: # <<<<<<<<<<<<<< - * broadcast_leading(&src, src_ndim, dst_ndim) - * elif dst_ndim < src_ndim: - */ - __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1239 - * - * if src_ndim < dst_ndim: - * broadcast_leading(&src, src_ndim, dst_ndim) # <<<<<<<<<<<<<< - * elif dst_ndim < src_ndim: - * broadcast_leading(&dst, dst_ndim, src_ndim) - */ - __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim); - goto __pyx_L3; - } - - /* "View.MemoryView":1240 - * if src_ndim < dst_ndim: - * broadcast_leading(&src, src_ndim, dst_ndim) - * elif dst_ndim < src_ndim: # <<<<<<<<<<<<<< - * broadcast_leading(&dst, dst_ndim, src_ndim) - * - */ - __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1241 - * broadcast_leading(&src, src_ndim, dst_ndim) - * elif dst_ndim < src_ndim: - * broadcast_leading(&dst, dst_ndim, src_ndim) # <<<<<<<<<<<<<< - * - * cdef int ndim = max(src_ndim, dst_ndim) - */ - __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim); - goto __pyx_L3; - } - __pyx_L3:; - - /* "View.MemoryView":1243 - * broadcast_leading(&dst, dst_ndim, src_ndim) - * - * cdef int ndim = max(src_ndim, dst_ndim) # <<<<<<<<<<<<<< - * - * for i in range(ndim): - */ - __pyx_t_3 = __pyx_v_dst_ndim; - __pyx_t_4 = __pyx_v_src_ndim; - if (((__pyx_t_3 > __pyx_t_4) != 0)) { - __pyx_t_5 = __pyx_t_3; - } else { - __pyx_t_5 = __pyx_t_4; - } - __pyx_v_ndim = __pyx_t_5; - - /* "View.MemoryView":1245 - * cdef int ndim = max(src_ndim, dst_ndim) - * - * for i in range(ndim): # <<<<<<<<<<<<<< - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: - */ - __pyx_t_5 = __pyx_v_ndim; - for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_5; __pyx_t_3+=1) { - __pyx_v_i = __pyx_t_3; - - /* "View.MemoryView":1246 - * - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: # <<<<<<<<<<<<<< - * if src.shape[i] == 1: - * broadcasting = True - */ - __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1247 - * for i in range(ndim): - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: # <<<<<<<<<<<<<< - * broadcasting = True - * src.strides[i] = 0 - */ - __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1248 - * if src.shape[i] != dst.shape[i]: - * if src.shape[i] == 1: - * broadcasting = True # <<<<<<<<<<<<<< - * src.strides[i] = 0 - * else: - */ - __pyx_v_broadcasting = 1; - - /* "View.MemoryView":1249 - * if src.shape[i] == 1: - * broadcasting = True - * src.strides[i] = 0 # <<<<<<<<<<<<<< - * else: - * _err_extents(i, dst.shape[i], src.shape[i]) - */ - (__pyx_v_src.strides[__pyx_v_i]) = 0; - goto __pyx_L7; - } - /*else*/ { - - /* "View.MemoryView":1251 - * src.strides[i] = 0 - * else: - * _err_extents(i, dst.shape[i], src.shape[i]) # <<<<<<<<<<<<<< - * - * if src.suboffsets[i] >= 0: - */ - __pyx_t_4 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1251; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - } - __pyx_L7:; - goto __pyx_L6; - } - __pyx_L6:; - - /* "View.MemoryView":1253 - * _err_extents(i, dst.shape[i], src.shape[i]) - * - * if src.suboffsets[i] >= 0: # <<<<<<<<<<<<<< - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - */ - __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1254 - * - * if src.suboffsets[i] >= 0: - * _err_dim(ValueError, "Dimension %d is not direct", i) # <<<<<<<<<<<<<< - * - * if slices_overlap(&src, &dst, ndim, itemsize): - */ - __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1254; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L8; - } - __pyx_L8:; - } - - /* "View.MemoryView":1256 - * _err_dim(ValueError, "Dimension %d is not direct", i) - * - * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< - * - * if not slice_is_contig(&src, order, ndim): - */ - __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1258 - * if slices_overlap(&src, &dst, ndim, itemsize): - * - * if not slice_is_contig(&src, order, ndim): # <<<<<<<<<<<<<< - * order = get_best_order(&dst, ndim) - * - */ - __pyx_t_2 = ((!(__pyx_memviewslice_is_contig((&__pyx_v_src), __pyx_v_order, __pyx_v_ndim) != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1259 - * - * if not slice_is_contig(&src, order, ndim): - * order = get_best_order(&dst, ndim) # <<<<<<<<<<<<<< - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) - */ - __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim); - goto __pyx_L10; - } - __pyx_L10:; - - /* "View.MemoryView":1261 - * order = get_best_order(&dst, ndim) - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) # <<<<<<<<<<<<<< - * src = tmp - * - */ - __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1261; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_v_tmpdata = __pyx_t_6; - - /* "View.MemoryView":1262 - * - * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) - * src = tmp # <<<<<<<<<<<<<< - * - * if not broadcasting: - */ - __pyx_v_src = __pyx_v_tmp; - goto __pyx_L9; - } - __pyx_L9:; - - /* "View.MemoryView":1264 - * src = tmp - * - * if not broadcasting: # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1267 - * - * - * if slice_is_contig(&src, 'C', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(&dst, 'C', ndim) - * elif slice_is_contig(&src, 'F', ndim): - */ - __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'C', __pyx_v_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1268 - * - * if slice_is_contig(&src, 'C', ndim): - * direct_copy = slice_is_contig(&dst, 'C', ndim) # <<<<<<<<<<<<<< - * elif slice_is_contig(&src, 'F', ndim): - * direct_copy = slice_is_contig(&dst, 'F', ndim) - */ - __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'C', __pyx_v_ndim); - goto __pyx_L12; - } - - /* "View.MemoryView":1269 - * if slice_is_contig(&src, 'C', ndim): - * direct_copy = slice_is_contig(&dst, 'C', ndim) - * elif slice_is_contig(&src, 'F', ndim): # <<<<<<<<<<<<<< - * direct_copy = slice_is_contig(&dst, 'F', ndim) - * - */ - __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'F', __pyx_v_ndim) != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1270 - * direct_copy = slice_is_contig(&dst, 'C', ndim) - * elif slice_is_contig(&src, 'F', ndim): - * direct_copy = slice_is_contig(&dst, 'F', ndim) # <<<<<<<<<<<<<< - * - * if direct_copy: - */ - __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'F', __pyx_v_ndim); - goto __pyx_L12; - } - __pyx_L12:; - - /* "View.MemoryView":1272 - * direct_copy = slice_is_contig(&dst, 'F', ndim) - * - * if direct_copy: # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - __pyx_t_2 = (__pyx_v_direct_copy != 0); - if (__pyx_t_2) { - - /* "View.MemoryView":1274 - * if direct_copy: - * - * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - - /* "View.MemoryView":1275 - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) # <<<<<<<<<<<<<< - * refcount_copying(&dst, dtype_is_object, ndim, True) - * free(tmpdata) - */ - memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)); - - /* "View.MemoryView":1276 - * refcount_copying(&dst, dtype_is_object, ndim, False) - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< - * free(tmpdata) - * return 0 - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - - /* "View.MemoryView":1277 - * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) - * refcount_copying(&dst, dtype_is_object, ndim, True) - * free(tmpdata) # <<<<<<<<<<<<<< - * return 0 - * - */ - free(__pyx_v_tmpdata); - - /* "View.MemoryView":1278 - * refcount_copying(&dst, dtype_is_object, ndim, True) - * free(tmpdata) - * return 0 # <<<<<<<<<<<<<< - * - * if order == 'F' == get_best_order(&dst, ndim): - */ - __pyx_r = 0; - goto __pyx_L0; - } - goto __pyx_L11; - } - __pyx_L11:; - - /* "View.MemoryView":1280 - * return 0 - * - * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_2 = (__pyx_v_order == 'F'); - if (__pyx_t_2) { - __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); - } - __pyx_t_7 = (__pyx_t_2 != 0); - if (__pyx_t_7) { - - /* "View.MemoryView":1283 - * - * - * transpose_memslice(&src) # <<<<<<<<<<<<<< - * transpose_memslice(&dst) - * - */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1283; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - - /* "View.MemoryView":1284 - * - * transpose_memslice(&src) - * transpose_memslice(&dst) # <<<<<<<<<<<<<< - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - */ - __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1284; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L14; - } - __pyx_L14:; - - /* "View.MemoryView":1286 - * transpose_memslice(&dst) - * - * refcount_copying(&dst, dtype_is_object, ndim, False) # <<<<<<<<<<<<<< - * copy_strided_to_strided(&src, &dst, ndim, itemsize) - * refcount_copying(&dst, dtype_is_object, ndim, True) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); - - /* "View.MemoryView":1287 - * - * refcount_copying(&dst, dtype_is_object, ndim, False) - * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< - * refcount_copying(&dst, dtype_is_object, ndim, True) - * - */ - copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); - - /* "View.MemoryView":1288 - * refcount_copying(&dst, dtype_is_object, ndim, False) - * copy_strided_to_strided(&src, &dst, ndim, itemsize) - * refcount_copying(&dst, dtype_is_object, ndim, True) # <<<<<<<<<<<<<< - * - * free(tmpdata) - */ - __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); - - /* "View.MemoryView":1290 - * refcount_copying(&dst, dtype_is_object, ndim, True) - * - * free(tmpdata) # <<<<<<<<<<<<<< - * return 0 - * - */ - free(__pyx_v_tmpdata); - - /* "View.MemoryView":1291 - * - * free(tmpdata) - * return 0 # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_broadcast_leading') - */ - __pyx_r = 0; - goto __pyx_L0; - - /* "View.MemoryView":1222 - * - * @cname('__pyx_memoryview_copy_contents') - * cdef int memoryview_copy_contents(__Pyx_memviewslice src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice dst, - * int src_ndim, int dst_ndim, - */ - - /* function exit code */ - __pyx_L1_error:; - { - #ifdef WITH_THREAD - PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); - #endif - __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); - #ifdef WITH_THREAD - PyGILState_Release(__pyx_gilstate_save); - #endif - } - __pyx_r = -1; - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1294 - * - * @cname('__pyx_memoryview_broadcast_leading') - * cdef void broadcast_leading(__Pyx_memviewslice *slice, # <<<<<<<<<<<<<< - * int ndim, - * int ndim_other) nogil: - */ - -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_slice, int __pyx_v_ndim, int __pyx_v_ndim_other) { - int __pyx_v_i; - int __pyx_v_offset; - int __pyx_t_1; - int __pyx_t_2; - - /* "View.MemoryView":1298 - * int ndim_other) nogil: - * cdef int i - * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< - * - * for i in range(ndim - 1, -1, -1): - */ - __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); - - /* "View.MemoryView":1300 - * cdef int offset = ndim_other - ndim - * - * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * slice.shape[i + offset] = slice.shape[i] - * slice.strides[i + offset] = slice.strides[i] - */ - for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { - __pyx_v_i = __pyx_t_1; - - /* "View.MemoryView":1301 - * - * for i in range(ndim - 1, -1, -1): - * slice.shape[i + offset] = slice.shape[i] # <<<<<<<<<<<<<< - * slice.strides[i + offset] = slice.strides[i] - * slice.suboffsets[i + offset] = slice.suboffsets[i] - */ - (__pyx_v_slice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->shape[__pyx_v_i]); - - /* "View.MemoryView":1302 - * for i in range(ndim - 1, -1, -1): - * slice.shape[i + offset] = slice.shape[i] - * slice.strides[i + offset] = slice.strides[i] # <<<<<<<<<<<<<< - * slice.suboffsets[i + offset] = slice.suboffsets[i] - * - */ - (__pyx_v_slice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->strides[__pyx_v_i]); - - /* "View.MemoryView":1303 - * slice.shape[i + offset] = slice.shape[i] - * slice.strides[i + offset] = slice.strides[i] - * slice.suboffsets[i + offset] = slice.suboffsets[i] # <<<<<<<<<<<<<< - 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- size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; /* Consume from buffer string */ - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; /* breaks both loops as ctx->enc_count == 0 */ - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; /* empty struct */ - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; /* not a 'break' in the loop */ - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': /* substruct */ - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; /* Erase processed last struct element */ - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': /* end of substruct; either repeat or move on */ - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; /* Erase processed last struct element */ - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -static int -__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference) -{ - __Pyx_RefNannyDeclarations - int i, retval=-1; - Py_buffer *buf = &memview->view; - __Pyx_RefNannySetupContext("init_memviewslice", 0); - if (!buf) { - PyErr_SetString(PyExc_ValueError, - "buf is NULL."); - goto fail; - } else if (memviewslice->memview || memviewslice->data) { - PyErr_SetString(PyExc_ValueError, - "memviewslice is already initialized!"); - goto fail; - } - if (buf->strides) { - for (i = 0; i < ndim; i++) { - memviewslice->strides[i] = buf->strides[i]; - } - } else { - Py_ssize_t stride = buf->itemsize; - for (i = ndim - 1; i >= 0; i--) { - memviewslice->strides[i] = stride; - stride *= buf->shape[i]; - } - } - for (i = 0; i < ndim; i++) { - memviewslice->shape[i] = buf->shape[i]; - if (buf->suboffsets) { - memviewslice->suboffsets[i] = buf->suboffsets[i]; - } else { - memviewslice->suboffsets[i] = -1; - } - } - memviewslice->memview = memview; - memviewslice->data = (char *)buf->buf; - if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { - Py_INCREF(memview); - } - retval = 0; - goto no_fail; -fail: - memviewslice->memview = 0; - memviewslice->data = 0; - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} -static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) { - va_list vargs; - char msg[200]; - va_start(vargs, fmt); -#ifdef HAVE_STDARG_PROTOTYPES - va_start(vargs, fmt); -#else - va_start(vargs); -#endif - vsnprintf(msg, 200, fmt, vargs); - Py_FatalError(msg); - va_end(vargs); -} -static CYTHON_INLINE int -__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)++; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE int -__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)--; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE void -__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) -{ - int first_time; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (!memview || (PyObject *) memview == Py_None) - return; /* allow uninitialized memoryview assignment */ - if (__pyx_get_slice_count(memview) < 0) - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - first_time = __pyx_add_acquisition_count(memview) == 0; - if (first_time) { - if (have_gil) { - Py_INCREF((PyObject *) memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_INCREF((PyObject *) memview); - PyGILState_Release(_gilstate); - } - } -} -static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice, - int have_gil, int lineno) { - int last_time; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (!memview ) { - return; - } else if ((PyObject *) memview == Py_None) { - memslice->memview = NULL; - return; - } - if (__pyx_get_slice_count(memview) <= 0) - __pyx_fatalerror("Acquisition count is %d (line %d)", - __pyx_get_slice_count(memview), lineno); - last_time = __pyx_sub_acquisition_count(memview) == 1; - memslice->data = NULL; - if (last_time) { - if (have_gil) { - Py_CLEAR(memslice->memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_CLEAR(memslice->memview); - PyGILState_Release(_gilstate); - } - } else { - memslice->memview = NULL; - } -} - -static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_Restore(type, value, tb); -#endif -} -static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(type, value, tb); -#endif -} - -static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", - name, type->tp_name, Py_TYPE(obj)->tp_name); -} -static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, - const char *name, int exact) -{ - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (none_allowed && obj == Py_None) return 1; - else if (exact) { - if (likely(Py_TYPE(obj) == type)) return 1; - #if PY_MAJOR_VERSION == 2 - else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(PyObject_TypeCheck(obj, type))) return 1; - } - __Pyx_RaiseArgumentTypeInvalid(name, obj, type); - return 0; -} - -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); -#if PY_VERSION_HEX >= 0x02060000 - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; -#endif - result = (*call)(func, arg, kw); -#if PY_VERSION_HEX >= 0x02060000 - Py_LeaveRecursiveCall(); -#endif - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - #if PY_VERSION_HEX < 0x02050000 - if (PyClass_Check(type)) { - #else - if (PyType_Check(type)) { - #endif -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - #if PY_VERSION_HEX < 0x02050000 - if (PyInstance_Check(type)) { - type = (PyObject*) ((PyInstanceObject*)type)->in_class; - Py_INCREF(type); - } else { - type = 0; - PyErr_SetString(PyExc_TypeError, - "raise: exception must be an old-style class or instance"); - goto raise_error; - } - #else - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - #endif - } - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else /* Python 3+ */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - if (PyObject_IsSubclass(instance_class, type)) { - type = instance_class; - } else { - instance_class = NULL; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -static void __Pyx_RaiseArgtupleInvalid( - const char* func_name, - int exact, - Py_ssize_t num_min, - Py_ssize_t num_max, - Py_ssize_t num_found) -{ - Py_ssize_t num_expected; - const char *more_or_less; - if (num_found < num_min) { - num_expected = num_min; - more_or_less = "at least"; - } else { - num_expected = num_max; - more_or_less = "at most"; - } - if (exact) { - more_or_less = "exactly"; - } - PyErr_Format(PyExc_TypeError, - "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", - func_name, more_or_less, num_expected, - (num_expected == 1) ? "" : "s", num_found); -} - -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - while (PyDict_Next(kwds, &pos, &key, &value)) { - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = (**name == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else - if (s1 == s2) { - return (equals == Py_EQ); - } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { - const char *ps1, *ps2; - Py_ssize_t length = PyBytes_GET_SIZE(s1); - if (length != PyBytes_GET_SIZE(s2)) - return (equals == Py_NE); - ps1 = PyBytes_AS_STRING(s1); - ps2 = PyBytes_AS_STRING(s2); - if (ps1[0] != ps2[0]) { - return (equals == Py_NE); - } else if (length == 1) { - return (equals == Py_EQ); - } else { - int result = memcmp(ps1, ps2, (size_t)length); - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -#endif -} - -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { -#if CYTHON_COMPILING_IN_PYPY - return PyObject_RichCompareBool(s1, s2, equals); -#else -#if PY_MAJOR_VERSION < 3 - PyObject* owned_ref = NULL; -#endif - int s1_is_unicode, s2_is_unicode; - if (s1 == s2) { - goto return_eq; - } - s1_is_unicode = PyUnicode_CheckExact(s1); - s2_is_unicode = PyUnicode_CheckExact(s2); -#if PY_MAJOR_VERSION < 3 - if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) { - owned_ref = PyUnicode_FromObject(s2); - if (unlikely(!owned_ref)) - return -1; - s2 = owned_ref; - s2_is_unicode = 1; - } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) { - owned_ref = PyUnicode_FromObject(s1); - if (unlikely(!owned_ref)) - return -1; - s1 = owned_ref; - s1_is_unicode = 1; - } else if (((!s2_is_unicode) & (!s1_is_unicode))) { - return __Pyx_PyBytes_Equals(s1, s2, equals); - } -#endif - if (s1_is_unicode & s2_is_unicode) { - Py_ssize_t length; - int kind; - void *data1, *data2; - #if CYTHON_PEP393_ENABLED - if (unlikely(PyUnicode_READY(s1) < 0) || unlikely(PyUnicode_READY(s2) < 0)) - return -1; - #endif - length = __Pyx_PyUnicode_GET_LENGTH(s1); - if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) { - goto return_ne; - } - kind = __Pyx_PyUnicode_KIND(s1); - if (kind != __Pyx_PyUnicode_KIND(s2)) { - goto return_ne; - } - data1 = __Pyx_PyUnicode_DATA(s1); - data2 = __Pyx_PyUnicode_DATA(s2); - if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { - goto return_ne; - } else if (length == 1) { - goto return_eq; - } else { - int result = memcmp(data1, data2, (size_t)(length * kind)); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & s2_is_unicode) { - goto return_ne; - } else if ((s2 == Py_None) & s1_is_unicode) { - goto return_ne; - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -return_eq: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_EQ); -return_ne: - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(owned_ref); - #endif - return (equals == Py_NE); -#endif -} - -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_COMPILING_IN_CPYTHON -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -static CYTHON_INLINE PyObject* __Pyx_decode_c_string( - const char* cstring, Py_ssize_t start, Py_ssize_t stop, - const char* encoding, const char* errors, - PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) { - Py_ssize_t length; - if (unlikely((start < 0) | (stop < 0))) { - length = strlen(cstring); - if (start < 0) { - start += length; - if (start < 0) - start = 0; - } - if (stop < 0) - stop += length; - } - length = stop - start; - if (unlikely(length <= 0)) - return PyUnicode_FromUnicode(NULL, 0); - cstring += start; - if (decode_func) { - return decode_func(cstring, length, errors); - } else { - return PyUnicode_Decode(cstring, length, encoding, errors); - } -} - -static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -#else - PyErr_GetExcInfo(type, value, tb); -#endif -} -static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(type, value, tb); -#endif -} - -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_COMPILING_IN_CPYTHON - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; -#else - PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); - PyErr_SetExcInfo(*type, *value, *tb); -#endif - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} - -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (PyErr_ExceptionMatches(PyExc_OverflowError)) - PyErr_Clear(); - else - return NULL; - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { - PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); -} - -static CYTHON_INLINE long __Pyx_div_long(long a, long b) { - long q = a / b; - long r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno, - CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename, - int full_traceback) { - PyObject *old_exc, *old_val, *old_tb; - PyObject *ctx; - __Pyx_ErrFetch(&old_exc, &old_val, &old_tb); - if (full_traceback) { - Py_XINCREF(old_exc); - Py_XINCREF(old_val); - Py_XINCREF(old_tb); - __Pyx_ErrRestore(old_exc, old_val, old_tb); - PyErr_PrintEx(1); - } - #if PY_MAJOR_VERSION < 3 - ctx = PyString_FromString(name); - #else - ctx = PyUnicode_FromString(name); - #endif - __Pyx_ErrRestore(old_exc, old_val, old_tb); - if (!ctx) { - PyErr_WriteUnraisable(Py_None); - } else { - PyErr_WriteUnraisable(ctx); - Py_DECREF(ctx); - } -} - -static int __Pyx_SetVtable(PyObject *dict, void *vtable) { -#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0) - PyObject *ob = PyCapsule_New(vtable, 0, 0); -#else - PyObject *ob = PyCObject_FromVoidPtr(vtable, 0); -#endif - if (!ob) - goto bad; - if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0) - goto bad; - Py_DECREF(ob); - return 0; -bad: - Py_XDECREF(ob); - return -1; -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - #if PY_VERSION_HEX >= 0x02060000 - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - #endif - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags); - #if PY_VERSION_HEX < 0x02060000 - if (obj->ob_type->tp_dict) { - PyObject *getbuffer_cobj = PyObject_GetItem( - obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer); - if (getbuffer_cobj) { - getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj); - Py_DECREF(getbuffer_cobj); - if (!func) - goto fail; - return func(obj, view, flags); - } else { - PyErr_Clear(); - } - } - #endif - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); -#if PY_VERSION_HEX < 0x02060000 -fail: -#endif - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - #if PY_VERSION_HEX >= 0x02060000 - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - #endif - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - #if PY_VERSION_HEX < 0x02060000 - if (obj->ob_type->tp_dict) { - PyObject *releasebuffer_cobj = PyObject_GetItem( - obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer); - if (releasebuffer_cobj) { - releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj); - Py_DECREF(releasebuffer_cobj); - if (!func) - goto fail; - func(obj, view); - return; - } else { - PyErr_Clear(); - } - } - #endif - goto nofail; -#if PY_VERSION_HEX < 0x02060000 -fail: -#endif - PyErr_WriteUnraisable(obj); -nofail: - Py_DECREF(obj); - view->obj = NULL; -} -#endif /* PY_MAJOR_VERSION < 3 */ - - - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsigned long value) { - const unsigned long neg_one = (unsigned long) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(unsigned long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(unsigned long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) { - return PyLong_FromUnsignedLongLong((unsigned long long) value); - } - } else { - if (sizeof(unsigned long) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(unsigned long) <= sizeof(long long)) { - return PyLong_FromLongLong((long long) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(unsigned long), - little, !is_unsigned); - } -} - -#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func) \ - { \ - func_type value = func(x); \ - if (sizeof(target_type) < sizeof(func_type)) { \ - if (unlikely(value != (func_type) (target_type) value)) { \ - func_type zero = 0; \ - PyErr_SetString(PyExc_OverflowError, \ - (is_unsigned && unlikely(value < zero)) ? \ - "can't convert negative value to " #target_type : \ - "value too large to convert to " #target_type); \ - return (target_type) -1; \ - } \ - } \ - return (target_type) value; \ - } - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #endif -#endif -static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) { - const unsigned long neg_one = (unsigned long) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(unsigned long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(unsigned long, long, PyInt_AS_LONG) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned long"); - return (unsigned long) -1; - } - return (unsigned long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned long"); - return (unsigned long) -1; - } - if (sizeof(unsigned long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, PyLong_AsUnsignedLong) - } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) { - __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long long, PyLong_AsUnsignedLongLong) - } - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (sizeof(unsigned long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT(unsigned long, long, PyLong_AsLong) - } else if (sizeof(unsigned long) <= sizeof(long long)) { - __PYX_VERIFY_RETURN_INT(unsigned long, long long, PyLong_AsLongLong) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - unsigned long val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (unsigned long) -1; - } - } else { - unsigned long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (unsigned long) -1; - val = __Pyx_PyInt_As_unsigned_long(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -#if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(a, a); - case 3: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, a); - case 4: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } - r = a.real; - theta = 0; - } else { - r = __Pyx_c_absf(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -#if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(a, a); - case 3: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, a); - case 4: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } - r = a.real; - theta = 0; - } else { - r = __Pyx_c_abs(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(int) <= sizeof(unsigned long long)) { - return PyLong_FromUnsignedLongLong((unsigned long long) value); - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(long long)) { - return PyLong_FromLongLong((long long) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #endif -#endif -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(int)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (int) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; - } - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong) - } else if (sizeof(int) <= sizeof(unsigned long long)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong) - } - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(int)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(int) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(int) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong) - } else if (sizeof(int) <= sizeof(long long)) { - __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -} - -static int -__pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs, - char order, int ndim) -{ - int i, index, step, start; - Py_ssize_t itemsize = mvs->memview->view.itemsize; - if (order == 'F') { - step = 1; - start = 0; - } else { - step = -1; - start = ndim - 1; - } - for (i = 0; i < ndim; i++) { - index = start + step * i; - if (mvs->suboffsets[index] >= 0 || mvs->strides[index] != itemsize) - return 0; - itemsize *= mvs->shape[index]; - } - return 1; -} - -static void -__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, - void **out_start, void **out_end, - int ndim, size_t itemsize) -{ - char *start, *end; - int i; - start = end = slice->data; - for (i = 0; i < ndim; i++) { - Py_ssize_t stride = slice->strides[i]; - Py_ssize_t extent = slice->shape[i]; - if (extent == 0) { - *out_start = *out_end = start; - return; - } else { - if (stride > 0) - end += stride * (extent - 1); - else - start += stride * (extent - 1); - } - } - *out_start = start; - *out_end = end + itemsize; -} -static int -__pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize) -{ - void *start1, *end1, *start2, *end2; - __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); - __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); - return (start1 < end2) && (start2 < end1); -} - -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object) -{ - __Pyx_RefNannyDeclarations - int i; - __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; - struct __pyx_memoryview_obj *from_memview = from_mvs->memview; - Py_buffer *buf = &from_memview->view; - PyObject *shape_tuple = NULL; - PyObject *temp_int = NULL; - struct __pyx_array_obj *array_obj = NULL; - struct __pyx_memoryview_obj *memview_obj = NULL; - __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); - for (i = 0; i < ndim; i++) { - if (from_mvs->suboffsets[i] >= 0) { - PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " - "indirect dimensions (axis %d)", i); - goto fail; - } - } - shape_tuple = PyTuple_New(ndim); - if (unlikely(!shape_tuple)) { - goto fail; - } - __Pyx_GOTREF(shape_tuple); - for(i = 0; i < ndim; i++) { - temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); - if(unlikely(!temp_int)) { - goto fail; - } else { - PyTuple_SET_ITEM(shape_tuple, i, temp_int); - temp_int = NULL; - } - } - array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); - if (unlikely(!array_obj)) { - goto fail; - } - __Pyx_GOTREF(array_obj); - memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - (PyObject *) array_obj, contig_flag, - dtype_is_object, - from_mvs->memview->typeinfo); - if (unlikely(!memview_obj)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) - goto fail; - if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, - dtype_is_object) < 0)) - goto fail; - goto no_fail; -fail: - __Pyx_XDECREF(new_mvs.memview); - new_mvs.memview = NULL; - new_mvs.data = NULL; -no_fail: - __Pyx_XDECREF(shape_tuple); - __Pyx_XDECREF(temp_int); - __Pyx_XDECREF(array_obj); - __Pyx_RefNannyFinishContext(); - return new_mvs; -} - -static CYTHON_INLINE PyObject * -__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig) -{ - PyObject *cobj; -#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0) - cobj = PyCapsule_New(p, sig, NULL); -#else - cobj = PyCObject_FromVoidPtr(p, NULL); -#endif - return cobj; -} - -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - #if PY_VERSION_HEX >= 0x02050000 - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; /* try absolute import on failure */ - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } - #else - if (level>0) { - PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); - goto bad; - } - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, NULL); - #endif -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(long) <= sizeof(unsigned long long)) { - return PyLong_FromUnsignedLongLong((unsigned long long) value); - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(long long)) { - return PyLong_FromLongLong((long long) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #endif -#endif -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { - const char neg_one = (char) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(char) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; - } - return (char) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(char)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (char) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; - } - if (sizeof(char) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, PyLong_AsUnsignedLong) - } else if (sizeof(char) <= sizeof(unsigned long long)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long long, PyLong_AsUnsignedLongLong) - } - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(char)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(char) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(char) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (sizeof(char) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT(char, long, PyLong_AsLong) - } else if (sizeof(char) <= sizeof(long long)) { - __PYX_VERIFY_RETURN_INT(char, long long, PyLong_AsLongLong) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - char val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (char) -1; - } - } else { - char val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (char) -1; - val = __Pyx_PyInt_As_char(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #endif -#endif -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; - } - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong) - } else if (sizeof(long) <= sizeof(unsigned long long)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong) - } - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; - } - } - #endif -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong) - } else if (sizeof(long) <= sizeof(long long)) { - __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -} - -static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (buf->strides[dim] != sizeof(void *)) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (buf->strides[dim] != buf->itemsize) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (stride < buf->itemsize) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - } else { - if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (spec & (__Pyx_MEMVIEW_PTR)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (buf->suboffsets) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec) -{ - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (buf->suboffsets && buf->suboffsets[dim] >= 0) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessible " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (stride * buf->itemsize != buf->strides[i] && - buf->shape[i] > 1) - { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (stride * buf->itemsize != buf->strides[i] && - buf->shape[i] > 1) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; - } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (buf->ndim != ndim) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned) buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (!__pyx_check_strides(buf, i, ndim, spec)) - goto fail; - if (!__pyx_check_suboffsets(buf, i, ndim, spec)) - goto fail; - } - if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0, - PyBUF_RECORDS, 1, - &__Pyx_TypeInfo_long, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - #if PY_VERSION_HEX < 0x02050000 - return PyErr_Warn(NULL, message); - #else - return PyErr_WarnEx(NULL, message, 1); - #endif - } - return 0; -} - -#ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -#ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility", - module_name, class_name); - #if PY_VERSION_HEX < 0x02050000 - if (PyErr_Warn(NULL, warning) < 0) goto bad; - #else - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - #endif - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling", - module_name, class_name); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = (start + end) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, /*int argcount,*/ - 0, /*int kwonlyargcount,*/ - 0, /*int nlocals,*/ - 0, /*int stacksize,*/ - 0, /*int flags,*/ - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, /*int firstlineno,*/ - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_globals = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_globals = PyModule_GetDict(__pyx_m); - if (!py_globals) goto bad; - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - py_globals, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - py_frame->f_lineno = py_line; - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else /* Python 3+ has unicode identifiers */ - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -#else /* PY_VERSION_HEX < 0x03030000 */ - if (PyUnicode_READY(o) == -1) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (PyUnicode_IS_ASCII(o)) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ - return PyUnicode_AsUTF8AndSize(o, length); -#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ -#endif /* PY_VERSION_HEX < 0x03030000 */ - } else -#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ -#if !CYTHON_COMPILING_IN_PYPY -#if PY_VERSION_HEX >= 0x02060000 - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { - PyNumberMethods *m; - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (PyInt_Check(x) || PyLong_Check(x)) -#else - if (PyLong_Check(x)) -#endif - return Py_INCREF(x), x; - m = Py_TYPE(x)->tp_as_number; -#if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = PyNumber_Long(x); - } -#else - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Long(x); - } -#endif - if (res) { -#if PY_MAJOR_VERSION < 3 - if (!PyInt_Check(res) && !PyLong_Check(res)) { -#else - if (!PyLong_Check(res)) { -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - name, name, Py_TYPE(res)->tp_name); - Py_DECREF(res); - return NULL; - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #endif -#endif -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) - return PyInt_AS_LONG(b); -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 - #if CYTHON_USE_PYLONG_INTERNALS - switch (Py_SIZE(b)) { - case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0]; - case 0: return 0; - case 1: return ((PyLongObject*)b)->ob_digit[0]; - } - #endif - #endif - #if PY_VERSION_HEX < 0x02060000 - return PyInt_AsSsize_t(b); - #else - return PyLong_AsSsize_t(b); - #endif - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { -#if PY_VERSION_HEX < 0x02050000 - if (ival <= LONG_MAX) - return PyInt_FromLong((long)ival); - else { - unsigned char *bytes = (unsigned char *) &ival; - int one = 1; int little = (int)*(unsigned char*)&one; - return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); - } -#else - return PyInt_FromSize_t(ival); -#endif -} - - -#endif /* Py_PYTHON_H */ diff --git a/tutorials/cython_bubblesort_nomagic.pyx b/tutorials/cython_bubblesort_nomagic.pyx deleted file mode 100644 index 0d35766..0000000 --- a/tutorials/cython_bubblesort_nomagic.pyx +++ /dev/null @@ -1,21 +0,0 @@ - -cimport numpy as np -cimport cython -@cython.boundscheck(False) -@cython.wraparound(False) -cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary): - """ The Cython implementation of Bubblesort with NumPy memoryview.""" - cdef unsigned long length, i, swapped, ele, temp - cdef long[:] np_ary = inp_ary - length = np_ary.shape[0] - swapped = 1 - for i in xrange(0, length): - if swapped: - swapped = 0 - for ele in xrange(0, length-i-1): - if np_ary[ele] > np_ary[ele + 1]: - temp = np_ary[ele + 1] - np_ary[ele + 1] = np_ary[ele] - np_ary[ele] = temp - swapped = 1 - return inp_ary \ No newline at end of file diff --git a/tutorials/hello_world.py b/tutorials/hello_world.py deleted file mode 100644 index 498b2df..0000000 --- a/tutorials/hello_world.py +++ /dev/null @@ -1,3 +0,0 @@ -def hello_world(): - """This is a hello world example function.""" - print('Hello, World!') \ No newline at end of file diff --git a/tutorials/installing_scientific_packages.md b/tutorials/installing_scientific_packages.md index 0439c71..918d293 100644 --- a/tutorials/installing_scientific_packages.md +++ b/tutorials/installing_scientific_packages.md @@ -278,7 +278,7 @@ print its path: Finally, we can set an `alias` in our `.bash_profile` or `.bash_rc` file to -conviniently run IPython from the console. E.g., +conveniently run IPython from the console. E.g., diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html deleted file mode 100644 index 2a5a0c5..0000000 --- a/tutorials/key_differences_between_python_2_and_3.html +++ /dev/null @@ -1,4242 +0,0 @@ - - - - - -Notebook - - - - - - - - - - - - - - - - - - - - - - -
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-I would be happy to hear your comments and suggestions.
Please feel free to drop me a note via twitter, , or google+. -
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Key differences between Python 2.7.x and Python 3.x

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Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines "just go with the version your favorite tutorial was written in, and check out the differences later on."

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But what if you are starting a new project and have the choice to pick? I would say there is currently no "right" or "wrong" as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project.

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The __future__ module

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Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built __future__ module in Python 2. It is recommended to use __future__ imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via

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from __future__ import division
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More features that can be imported from the __future__ module are listed in the table below:

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-nested_scopes - -2.1.0b1 - -2.2 - -PEP 227: Statically Nested Scopes -
-generators - -2.2.0a1 - -2.3 - -PEP 255: Simple Generators -
-division - -2.2.0a2 - -3.0 - -PEP 238: Changing the Division Operator -
-absolute_import - -2.5.0a1 - -3.0 - -PEP 328: Imports: Multi-Line and Absolute/Relative -
-with_statement - -2.5.0a1 - -2.6 - -PEP 343: The “with” Statement -
-print_function - -2.6.0a2 - -3.0 - -PEP 3105: Make print a function -
-unicode_literals - -2.6.0a2 - -3.0 - -PEP 3112: Bytes literals in Python 3000 -
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The print function

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Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the print() function, meaning that we have to wrap the object that we want to print in parantheses.

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Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a SyntaxError if we called the print function the Python 2-way without the parentheses.

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Python 2

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Python 3

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Note:

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Printing "Hello, World" above via Python 2 looked quite "normal". However, if we have multiple objects inside the parantheses, we will create a tuple, since print is a "statement" in Python 2, not a function call.

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Integer division

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This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a SyntaxError).
So, I still tend to use a float(3)/2 or 3/2.0 instead of a 3/2 in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a from __future__ import division in your Python 2 scripts).

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Python 2 has ASCII str() types, separate unicode(), but no byte type.

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Now, in Python 3, we finally have Unicode (utf-8) strings, and 2 byte classes: byte and bytearrays.

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The usage of xrange() is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension.
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Thanks to its "lazy-evaluation", the advantage of the regular range() is that xrange() is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch!

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Note about the speed differences in Python 2 and 3

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Some people pointed out the speed difference between Python 3's range() and Python2's xrange(). Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2.

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Raising exceptions

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print('Python', python_version())
-raise IOError("file error")
-
- -
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- -
-
- - -
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-Python 3.4.1
-
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- -
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----------------------------------------------------------------------------
-OSError                                   Traceback (most recent call last)
-<ipython-input-11-c350544d15da> in <module>()
-      1 print('Python', python_version())
-----> 2 raise IOError("file error")
-
-OSError: file error
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Handling exceptions

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Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the "as" keyword now

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Python 2

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-In [10]: -
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print 'Python', python_version()
-try:
-    let_us_cause_a_NameError
-except NameError, err:
-    print err, '--> our error message'
-
- -
-
-
- -
-
- - -
-
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-Python 2.7.6
-name 'let_us_cause_a_NameError' is not defined --> our error message
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Python 3

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-In [12]: -
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print('Python', python_version())
-try:
-    let_us_cause_a_NameError
-except NameError as err:
-    print(err, '--> our error message')
-
- -
-
-
- -
-
- - -
-
-
-Python 3.4.1
-name 'let_us_cause_a_NameError' is not defined --> our error message
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The next() function and .next() method

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Since next() (.next()) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the next() function is all that remains in Python 3 (calling the .next() method raises an AttributeError).

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Python 2

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-In [11]: -
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print 'Python', python_version()
-
-my_generator = (letter for letter in 'abcdefg')
-
-next(my_generator)
-my_generator.next()
-
- -
-
-
- -
-
- - -
-
-
-Python 2.7.6
-
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- Out[11]:
- - -
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-'b'
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Python 3

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-In [13]: -
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print('Python', python_version())
-
-my_generator = (letter for letter in 'abcdefg')
-
-next(my_generator)
-
- -
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- -
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- - -
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-Python 3.4.1
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- Out[13]:
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-'a'
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-In [14]: -
-
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-
my_generator.next()
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- -
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- - -
-
-
----------------------------------------------------------------------------
-AttributeError                            Traceback (most recent call last)
-<ipython-input-14-125f388bb61b> in <module>()
-----> 1 my_generator.next()
-
-AttributeError: 'generator' object has no attribute 'next'
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For-loop variables and the global namespace leak

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Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!

-

This goes back to a change that was made in Python 3.x and is described in What’s New In Python 3.0 as follows:

-

"List comprehensions no longer support the syntactic form [... for var in item1, item2, ...]. Use [... for var in (item1, item2, ...)] instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a list() constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."

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Python 2

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-In [12]: -
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print 'Python', python_version()
-
-i = 1
-print 'before: i =', i
-
-print 'comprehension: ', [i for i in range(5)]
-
-print 'after: i =', i
-
- -
-
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- -
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- - -
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-Python 2.7.6
-before: i = 1
-comprehension:  [0, 1, 2, 3, 4]
-after: i = 4
-
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- -
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Python 3

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-In [15]: -
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-
print('Python', python_version())
-
-i = 1
-print('before: i =', i)
-
-print('comprehension:', [i for i in range(5)])
-
-print('after: i =', i)
-
- -
-
-
- -
-
- - -
-
-
-Python 3.4.1
-before: i = 1
-comprehension: [0, 1, 2, 3, 4]
-after: i = 1
-
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Comparing unorderable types

-
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Another nice change in Python 3 is that a TypeError is raised as warning if we try to compare unorderable types.

-
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Python 2

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-In [2]: -
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print 'Python', python_version()
-print "[1, 2] > 'foo' = ", [1, 2] > 'foo'
-print "(1, 2) > 'foo' = ", (1, 2) > 'foo'
-print "[1, 2] > (1, 2) = ", [1, 2] > (1, 2)
-
- -
-
-
- -
-
- - -
-
-
-Python 2.7.6
-[1, 2] > 'foo' =  False
-(1, 2) > 'foo' =  True
-[1, 2] > (1, 2) =  False
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Python 3

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-In [16]: -
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-
print('Python', python_version())
-print("[1, 2] > 'foo' = ", [1, 2] > 'foo')
-print("(1, 2) > 'foo' = ", (1, 2) > 'foo')
-print("[1, 2] > (1, 2) = ", [1, 2] > (1, 2))
-
- -
-
-
- -
-
- - -
-
-
-Python 3.4.1
-
-
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-
- -
-
-
----------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-<ipython-input-16-a9031729f4a0> in <module>()
-      1 print('Python', python_version())
-----> 2 print("[1, 2] > 'foo' = ", [1, 2] > 'foo')
-      3 print("(1, 2) > 'foo' = ", (1, 2) > 'foo')
-      4 print("[1, 2] > (1, 2) = ", [1, 2] > (1, 2))
-
-TypeError: unorderable types: list() > str()
-
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Parsing user inputs via input()

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-

Fortunately, the input() function was fixed in Python 3 so that it always stores the user inputs as str objects. In order to avoid the dangerous behavior in Python 2 to read in other types than strings, we have to use raw_input() instead.

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-

Python 2

-
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-
Python 2.7.6 
-[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
-Type "help", "copyright", "credits" or "license" for more information.
-
->>> my_input = input('enter a number: ')
-
-enter a number: 123
-
->>> type(my_input)
-<type 'int'>
-
->>> my_input = raw_input('enter a number: ')
-
-enter a number: 123
-
->>> type(my_input)
-<type 'str'>
-
-
-
-
-
-
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-
-
-


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Python 3

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-
Python 3.4.1 
-[GCC 4.2.1 (Apple Inc. build 5577)] on darwin
-Type "help", "copyright", "credits" or "license" for more information.
-
->>> my_input = input('enter a number: ')
-
-enter a number: 123
-
->>> type(my_input)
-<class 'str'>
-
-
-
-
-
-
-
-
-
-



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-
-

Returning iterable objects instead of lists

-
-
-
- - -
-
-
-
-
-

As we have already seen in the xrange section, some functions and methods return iterable objects in Python 3 now - instead of lists in Python 2.

-

Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.

-

And for those cases where we really need the list-objects, we can simply convert the iterable object into a list via the list() function.

-
-
-
-
-
-
-
-
-

Python 2

-
-
-
-
-
-
-In [2]: -
-
-
-
print 'Python', python_version() 
-
-print range(3) 
-print type(range(3))
-
- -
-
-
- -
-
- - -
-
-
-Python 2.7.6
-[0, 1, 2]
-<type 'list'>
-
-
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- -
-
- -
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-
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-
-

Python 3

-
-
-
-
-
-
-In [7]: -
-
-
-
print('Python', python_version())
-
-print(range(3))
-print(type(range(3)))
-print(list(range(3)))
-
- -
-
-
- -
-
- - -
-
-
-Python 3.4.1
-range(0, 3)
-<class 'range'>
-[0, 1, 2]
-
-
-
-
- -
-
- -
-
-
-
-
-
-


-
-
-
-
-
-
-
-
-

Some more commonly used functions and methods that don't return lists anymore in Python 3:

-
    -
  • zip()

  • -
  • map()

  • -
  • filter()

  • -
  • dictionary's .keys() method

  • -
  • dictionary's .values() method

  • -
  • dictionary's .items() method

  • -
-
-
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-
-
-
-
-



-
-
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-
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-
-
-
- -
-
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- - - -
-
-
-In []: -
-
-
-
 
-
- -
-
-
- -
-
-
- - diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb index f3e2067..68feac3 100644 --- a/tutorials/key_differences_between_python_2_and_3.ipynb +++ b/tutorials/key_differences_between_python_2_and_3.ipynb @@ -1,2194 +1,2292 @@ { - "metadata": { - "name": "", - "signature": "sha256:1a71ccc70829239143d02cebcb97bec031b45e676ebad340fc04c9bd4a5760bf" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Sebastian Raschka](http://sebastianraschka.com) \n", - "\n", - "last updated 05/27/2014\n", - "\n", - "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n", - "\n", - "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb) \n", - "\n", - "- [Link to the GitHub repository python_reference](https://github.com/rasbt/python_reference)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "I would be happy to hear your comments and suggestions. \n", - "Please feel free to drop me a note via\n", - "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Key differences between Python 2.7.x and Python 3.x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines \"just go with the version your favorite tutorial was written in, and check out the differences later on.\"\n", - "\n", - "But what if you are starting a new project and have the choice to pick? I would say there is currently no \"right\" or \"wrong\" as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Sections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "- [Using the `__future__` module](#future_module)\n", - "\n", - "- [The print function](#The-print-function)\n", - "\n", - "- [Integer division](#Integer-division)\n", - "\n", - "- [Unicode](#Unicode)\n", - "\n", - "- [xrange](#xrange)\n", - "\n", - "- [Raising exceptions](#Raising-exceptions)\n", - "\n", - "- [Handling exceptions](#Handling-exceptions)\n", - "\n", - "- [The next() function and .next() method](#The-next-function-and-next-method)\n", - "\n", - "- [For-loop variables and the global namespace leak](#For-loop-variables-and-the-global-namespace-leak)\n", - "\n", - "- [Comparing unorderable types](#Comparing-unorderable-types)\n", - "\n", - "- [Parsing user inputs via input()](#Parsing-user-inputs-via-input)\n", - "\n", - "- [Returning iterable objects instead of lists](#Returning-iterable-objects-instead-of-lists)\n", - "\n", - "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The `__future__` module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built `__future__` module in Python 2. It is recommended to use `__future__` imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via\n", - "\n", - " from __future__ import division\n", - " \n", - "More features that can be imported from the `__future__` module are listed in the table below:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
featureoptional inmandatory ineffect
nested_scopes2.1.0b12.2PEP 227:\n", - "Statically Nested Scopes
generators2.2.0a12.3PEP 255:\n", - "Simple Generators
division2.2.0a23.0PEP 238:\n", - "Changing the Division Operator
absolute_import2.5.0a13.0PEP 328:\n", - "Imports: Multi-Line and Absolute/Relative
with_statement2.5.0a12.6PEP 343:\n", - "The “with” Statement
print_function2.6.0a23.0PEP 3105:\n", - "Make print a function
unicode_literals2.6.0a23.0PEP 3112:\n", - "Bytes literals in Python 3000
\n", - "
\n", - "
(Source: [https://docs.python.org/2/library/__future__.html](https://docs.python.org/2/library/__future__.html#module-__future__))
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from platform import python_version" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The print function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the `print()` function, meaning that we have to wrap the object that we want to print in parantheses. \n", - "\n", - "Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "print 'Hello, World!'\n", - "print('Hello, World!')\n", - "print \"text\", ; print 'print more text on the same line'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "Hello, World!\n", - "Hello, World!\n", - "text print more text on the same line\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "print('Hello, World!')\n", - "\n", - "print(\"some text,\", end=\"\") \n", - "print(' print more text on the same line')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "Hello, World!\n", - "some text, print more text on the same line\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Hello, World!'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print 'Hello, World!'\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:**\n", - "\n", - "Printing \"Hello, World\" above via Python 2 looked quite \"normal\". However, if we have multiple objects inside the parantheses, we will create a tuple, since `print` is a \"statement\" in Python 2, not a function call." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "print('a', 'b')\n", - "print 'a', 'b'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.7\n", - "('a', 'b')\n", - "a b\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Integer division" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a `SyntaxError`). \n", - "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a `from __future__ import division` in your Python 2 scripts)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "print '3 / 2 =', 3 / 2\n", - "print '3 // 2 =', 3 // 2\n", - "print '3 / 2.0 =', 3 / 2.0\n", - "print '3 // 2.0 =', 3 // 2.0" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "3 / 2 = 1\n", - "3 // 2 = 1\n", - "3 / 2.0 = 1.5\n", - "3 // 2.0 = 1.0\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "print('3 / 2 =', 3 / 2)\n", - "print('3 // 2 =', 3 // 2)\n", - "print('3 / 2.0 =', 3 / 2.0)\n", - "print('3 // 2.0 =', 3 // 2.0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "3 / 2 = 1.5\n", - "3 // 2 = 1\n", - "3 / 2.0 = 1.5\n", - "3 // 2.0 = 1.0\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Unicode" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python 2 has ASCII `str()` types, separate `unicode()`, but no `byte` type. \n", - "\n", - "Now, in Python 3, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print type(unicode('this is like a python3 str type'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print type(b'byte type does not exist')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'they are really' + b' the same'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "they are really the same\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print type(bytearray(b'bytearray oddly does exist though'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "strings are now utf-8 \u03bcnico\u0394\u00e9!\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version(), end=\"\")\n", - "print(' has', type(b' bytes for storing data'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1 has \n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('and Python', python_version(), end=\"\")\n", - "print(' also has', type(bytearray(b'bytearrays')))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "and Python 3.4.1 also has \n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "'note that we cannot add a string' + b'bytes for data'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "Can't convert 'bytes' object to str implicitly", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'note that we cannot add a string'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mb'bytes for data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: Can't convert 'bytes' object to str implicitly" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "xrange" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "The usage of `xrange()` is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension. \n", - "The behavior was quite similar to a generator (i.e., \"lazy evaluation\"), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely. \n", - "\n", - "\n", - "Thanks to its \"lazy-evaluation\", the advantage of the regular `range()` is that `xrange()` is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch! \n", - "\n", - "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore (`xrange()` raises a `NameError` in Python 3)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import timeit\n", - "\n", - "n = 10000\n", - "def test_range(n):\n", - " return for i in range(n):\n", - " pass\n", - " \n", - "def test_xrange(n):\n", - " for i in xrange(n):\n", - " pass " - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "\n", - "print '\\ntiming range()'\n", - "%timeit test_range(n)\n", - "\n", - "print '\\n\\ntiming xrange()'\n", - "%timeit test_xrange(n)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "\n", - "timing range()\n", - "1000 loops, best of 3: 433 \u00b5s per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - "\n", - "timing xrange()\n", - "1000 loops, best of 3: 350 \u00b5s per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "\n", - "print('\\ntiming range()')\n", - "%timeit test_range(n)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "\n", - "timing range()\n", - "1000 loops, best of 3: 520 \u00b5s per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(xrange(10))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'xrange' is not defined", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "The `__contains__` method for `range` objects in Python 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out). The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and Boolean types.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = 10000000" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def val_in_range(x, val):\n", - " return val in range(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def val_in_xrange(x, val):\n", - " return val in xrange(x)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "assert(val_in_range(x, x/2) == True)\n", - "assert(val_in_range(x, x//2) == True)\n", - "%timeit val_in_range(x, x/2)\n", - "%timeit val_in_range(x, x//2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "1 loops, best of 3: 742 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "1000000 loops, best of 3: 1.19 \u00b5s per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the `timeit` results above, you see that the execution for the \"look up\" was about 60,000 faster when it was of an integer type rather than a float. However, since Python 2.x's `range` or `xrange` doesn't have a `__contains__` method, the \"look-up speed\" wouldn't be that much different for integers or floats:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "assert(val_in_xrange(x, x/2.0) == True)\n", - "assert(val_in_xrange(x, x/2) == True)\n", - "assert(val_in_range(x, x/2) == True)\n", - "assert(val_in_range(x, x//2) == True)\n", - "%timeit val_in_xrange(x, x/2.0)\n", - "%timeit val_in_xrange(x, x/2)\n", - "%timeit val_in_range(x, x/2.0)\n", - "%timeit val_in_range(x, x/2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.7\n", - "1 loops, best of 3: 285 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "1 loops, best of 3: 179 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "1 loops, best of 3: 658 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "1 loops, best of 3: 556 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below the \"proofs\" that the `__contain__` method wasn't added to Python 2.x yet:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "range.__contains__" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 8, - "text": [ - "" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "range.__contains__" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.7\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'builtin_function_or_method' object has no attribute '__contains__'", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute '__contains__'" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "xrange.__contains__" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.7\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "type object 'xrange' has no attribute '__contains__'", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: type object 'xrange' has no attribute '__contains__'" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "Note about the speed differences in Python 2 and 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some people pointed out the speed difference between Python 3's `range()` and Python2's `xrange()`. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def test_while():\n", - " i = 0\n", - " while i < 20000:\n", - " i += 1\n", - " return" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "%timeit test_while()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "100 loops, best of 3: 2.68 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "%timeit test_while()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "1000 loops, best of 3: 1.72 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Raising exceptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "raise IOError, \"file error\"" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "IOError", - "evalue": "file error", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIOError\u001b[0m: file error" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "raise IOError(\"file error\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "IOError", - "evalue": "file error", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIOError\u001b[0m: file error" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "raise IOError, \"file error\"" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m raise IOError, \"file error\"\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The proper way to raise an exception in Python 3:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "raise IOError(\"file error\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n" - ] - }, - { - "ename": "OSError", - "evalue": "file error", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mOSError\u001b[0m: file error" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Handling exceptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the \"`as`\" keyword now" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "try:\n", - " let_us_cause_a_NameError\n", - "except NameError, err:\n", - " print err, '--> our error message'" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "name 'let_us_cause_a_NameError' is not defined --> our error message\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Sebastian Raschka](http://sebastianraschka.com) \n", + "\n", + "last updated 07/02/2016\n", + "\n", + "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n", + "\n", + "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb) \n", + "\n", + "- [Link to the GitHub repository python_reference](https://github.com/rasbt/python_reference)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "I would be happy to hear your comments and suggestions. \n", + "Please feel free to drop me a note via\n", + "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Key differences between Python 2.7.x and Python 3.x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines \"just go with the version your favorite tutorial was written in, and check out the differences later on.\"\n", + "\n", + "But what if you are starting a new project and have the choice to pick? I would say there is currently no \"right\" or \"wrong\" as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "- [Using the `__future__` module](#future_module)\n", + "\n", + "- [The print function](#The-print-function)\n", + "\n", + "- [Integer division](#Integer-division)\n", + "\n", + "- [Unicode](#Unicode)\n", + "\n", + "- [xrange](#xrange)\n", + "\n", + "- [Raising exceptions](#Raising-exceptions)\n", + "\n", + "- [Handling exceptions](#Handling-exceptions)\n", + "\n", + "- [The next() function and .next() method](#The-next-function-and-next-method)\n", + "\n", + "- [For-loop variables and the global namespace leak](#For-loop-variables-and-the-global-namespace-leak)\n", + "\n", + "- [Comparing unorderable types](#Comparing-unorderable-types)\n", + "\n", + "- [Parsing user inputs via input()](#Parsing-user-inputs-via-input)\n", + "\n", + "- [Returning iterable objects instead of lists](#Returning-iterable-objects-instead-of-lists)\n", + "\n", + "- [Banker's Rounding](#Banker's-Rounding)\n", + "\n", + "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `__future__` module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built `__future__` module in Python 2. It is recommended to use `__future__` imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via\n", + "\n", + " from __future__ import division\n", + " \n", + "More features that can be imported from the `__future__` module are listed in the table below:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
featureoptional inmandatory ineffect
nested_scopes2.1.0b12.2PEP 227:\n", + "Statically Nested Scopes
generators2.2.0a12.3PEP 255:\n", + "Simple Generators
division2.2.0a23.0PEP 238:\n", + "Changing the Division Operator
absolute_import2.5.0a13.0PEP 328:\n", + "Imports: Multi-Line and Absolute/Relative
with_statement2.5.0a12.6PEP 343:\n", + "The “with” Statement
print_function2.6.0a23.0PEP 3105:\n", + "Make print a function
unicode_literals2.6.0a23.0PEP 3112:\n", + "Bytes literals in Python 3000
\n", + "
\n", + "
(Source: [https://docs.python.org/2/library/__future__.html](https://docs.python.org/2/library/__future__.html#module-__future__))
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from platform import python_version" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The print function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the `print()` function, meaning that we have to wrap the object that we want to print in parantheses. \n", + "\n", + "Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "Hello, World!\n", + "Hello, World!\n", + "text print more text on the same line\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" + } + ], + "source": [ + "print 'Python', python_version()\n", + "print 'Hello, World!'\n", + "print('Hello, World!')\n", + "print \"text\", ; print 'print more text on the same line'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "Hello, World!\n", + "some text, print more text on the same line\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "try:\n", - " let_us_cause_a_NameError\n", - "except NameError as err:\n", - " print(err, '--> our error message')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "name 'let_us_cause_a_NameError' is not defined --> our error message\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "print('Python', python_version())\n", + "print('Hello, World!')\n", + "\n", + "print(\"some text,\", end=\"\") \n", + "print(' print more text on the same line')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print 'Hello, World!'\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" + } + ], + "source": [ + "print 'Hello, World!'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:**\n", + "\n", + "Printing \"Hello, World\" above via Python 2 looked quite \"normal\". However, if we have multiple objects inside the parantheses, we will create a tuple, since `print` is a \"statement\" in Python 2, not a function call." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.7\n", + "('a', 'b')\n", + "a b\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "The next() function and .next() method" + } + ], + "source": [ + "print 'Python', python_version()\n", + "print('a', 'b')\n", + "print 'a', 'b'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Integer division" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a `SyntaxError`). \n", + "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a `from __future__ import division` in your Python 2 scripts)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "3 / 2 = 1\n", + "3 // 2 = 1\n", + "3 / 2.0 = 1.5\n", + "3 // 2.0 = 1.0\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" + } + ], + "source": [ + "print 'Python', python_version()\n", + "print '3 / 2 =', 3 / 2\n", + "print '3 // 2 =', 3 // 2\n", + "print '3 / 2.0 =', 3 / 2.0\n", + "print '3 // 2.0 =', 3 // 2.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "3 / 2 = 1.5\n", + "3 // 2 = 1\n", + "3 / 2.0 = 1.5\n", + "3 // 2.0 = 1.0\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since `next()` (`.next()`) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the `next()` function is all that remains in Python 3 (calling the `.next()` method raises an `AttributeError`)." + } + ], + "source": [ + "print('Python', python_version())\n", + "print('3 / 2 =', 3 / 2)\n", + "print('3 // 2 =', 3 // 2)\n", + "print('3 / 2.0 =', 3 / 2.0)\n", + "print('3 // 2.0 =', 3 // 2.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Unicode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python 2 has ASCII `str()` types, separate `unicode()`, but no `byte` type. \n", + "\n", + "Now, in Python 3, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" + } + ], + "source": [ + "print 'Python', python_version()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "\n", - "my_generator = (letter for letter in 'abcdefg')\n", - "\n", - "next(my_generator)\n", - "my_generator.next()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "'b'" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + } + ], + "source": [ + "print type(unicode('this is like a python3 str type'))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" + } + ], + "source": [ + "print type(b'byte type does not exist')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "they are really the same\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "\n", - "my_generator = (letter for letter in 'abcdefg')\n", - "\n", - "next(my_generator)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n" - ] - }, - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 13, - "text": [ - "'a'" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_generator.next()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'generator' object has no attribute 'next'", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'next'" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "print 'they are really' + b' the same'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "For-loop variables and the global namespace leak" + } + ], + "source": [ + "print type(bytearray(b'bytearray oddly does exist though'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "strings are now utf-8 μnicoΔé!\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" + } + ], + "source": [ + "print('Python', python_version())\n", + "print('strings are now utf-8 \\u03BCnico\\u0394é!')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1 has \n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!\n", - "\n", - "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n", - "\n", - "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\"" + } + ], + "source": [ + "print('Python', python_version(), end=\"\")\n", + "print(' has', type(b' bytes for storing data'))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "and Python 3.4.1 also has \n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" + } + ], + "source": [ + "print('and Python', python_version(), end=\"\")\n", + "print(' also has', type(bytearray(b'bytearrays')))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Can't convert 'bytes' object to str implicitly", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'note that we cannot add a string'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mb'bytes for data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: Can't convert 'bytes' object to str implicitly" ] - }, + } + ], + "source": [ + "'note that we cannot add a string' + b'bytes for data'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## xrange" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "The usage of `xrange()` is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension. \n", + "The behavior was quite similar to a generator (i.e., \"lazy evaluation\"), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely. \n", + "\n", + "\n", + "Thanks to its \"lazy-evaluation\", the advantage of the regular `range()` is that `xrange()` is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch! \n", + "\n", + "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore (`xrange()` raises a `NameError` in Python 3)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import timeit\n", + "\n", + "n = 10000\n", + "def test_range(n):\n", + " return for i in range(n):\n", + " pass\n", + " \n", + "def test_xrange(n):\n", + " for i in xrange(n):\n", + " pass " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", "\n", - "i = 1\n", - "print 'before: i =', i\n", + "timing range()\n", + "1000 loops, best of 3: 433 µs per loop\n", "\n", - "print 'comprehension: ', [i for i in range(5)]\n", "\n", - "print 'after: i =', i" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "before: i = 1\n", - "comprehension: [0, 1, 2, 3, 4]\n", - "after: i = 4\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" + "timing xrange()\n", + "1000 loops, best of 3: 350 µs per loop\n" ] - }, + } + ], + "source": [ + "print 'Python', python_version()\n", + "\n", + "print '\\ntiming range()'\n", + "%timeit test_range(n)\n", + "\n", + "print '\\n\\ntiming xrange()'\n", + "%timeit test_xrange(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", "\n", - "i = 1\n", - "print('before: i =', i)\n", - "\n", - "print('comprehension:', [i for i in range(5)])\n", - "\n", - "print('after: i =', i)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "before: i = 1\n", - "comprehension: [0, 1, 2, 3, 4]\n", - "after: i = 1\n" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Comparing unorderable types" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" + "timing range()\n", + "1000 loops, best of 3: 520 µs per loop\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another nice change in Python 3 is that a `TypeError` is raised as warning if we try to compare unorderable types." + } + ], + "source": [ + "print('Python', python_version())\n", + "\n", + "print('\\ntiming range()')\n", + "%timeit test_range(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'xrange' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" + } + ], + "source": [ + "print(xrange(10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `__contains__` method for `range` objects in Python 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out). The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and Boolean types.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x = 10000000" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def val_in_range(x, val):\n", + " return val in range(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def val_in_xrange(x, val):\n", + " return val in xrange(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "1 loops, best of 3: 742 ms per loop\n", + "1000000 loops, best of 3: 1.19 µs per loop\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version()\n", - "print \"[1, 2] > 'foo' = \", [1, 2] > 'foo'\n", - "print \"(1, 2) > 'foo' = \", (1, 2) > 'foo'\n", - "print \"[1, 2] > (1, 2) = \", [1, 2] > (1, 2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "[1, 2] > 'foo' = False\n", - "(1, 2) > 'foo' = True\n", - "[1, 2] > (1, 2) = False\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + } + ], + "source": [ + "print('Python', python_version())\n", + "assert(val_in_range(x, x/2) == True)\n", + "assert(val_in_range(x, x//2) == True)\n", + "%timeit val_in_range(x, x/2)\n", + "%timeit val_in_range(x, x//2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the `timeit` results above, you see that the execution for the \"look up\" was about 60,000 faster when it was of an integer type rather than a float. However, since Python 2.x's `range` or `xrange` doesn't have a `__contains__` method, the \"look-up speed\" wouldn't be that much different for integers or floats:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.7\n", + "1 loops, best of 3: 285 ms per loop\n", + "1 loops, best of 3: 179 ms per loop\n", + "1 loops, best of 3: 658 ms per loop\n", + "1 loops, best of 3: 556 ms per loop\n" ] - }, + } + ], + "source": [ + "print 'Python', python_version()\n", + "assert(val_in_xrange(x, x/2.0) == True)\n", + "assert(val_in_xrange(x, x/2) == True)\n", + "assert(val_in_range(x, x/2) == True)\n", + "assert(val_in_range(x, x//2) == True)\n", + "%timeit val_in_xrange(x, x/2.0)\n", + "%timeit val_in_xrange(x, x/2)\n", + "%timeit val_in_range(x, x/2.0)\n", + "%timeit val_in_range(x, x/2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below the \"proofs\" that the `__contain__` method wasn't added to Python 2.x yet:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "print(\"[1, 2] > 'foo' = \", [1, 2] > 'foo')\n", - "print(\"(1, 2) > 'foo' = \", (1, 2) > 'foo')\n", - "print(\"[1, 2] > (1, 2) = \", [1, 2] > (1, 2))" - ], - "language": "python", + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unorderable types: list() > str()", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"(1, 2) > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > (1, 2) = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()" - ] - } - ], - "prompt_number": 16 - }, + "output_type": "execute_result" + } + ], + "source": [ + "print('Python', python_version())\n", + "range.__contains__" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.7\n" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" + "ename": "AttributeError", + "evalue": "'builtin_function_or_method' object has no attribute '__contains__'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute '__contains__'" ] - }, + } + ], + "source": [ + "print 'Python', python_version()\n", + "range.__contains__" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Parsing user inputs via input()" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.7\n" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" + "ename": "AttributeError", + "evalue": "type object 'xrange' has no attribute '__contains__'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: type object 'xrange' has no attribute '__contains__'" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fortunately, the `input()` function was fixed in Python 3 so that it always stores the user inputs as `str` objects. In order to avoid the dangerous behavior in Python 2 to read in other types than `strings`, we have to use `raw_input()` instead." + } + ], + "source": [ + "print 'Python', python_version()\n", + "xrange.__contains__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Note about the speed differences in Python 2 and 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some people pointed out the speed difference between Python 3's `range()` and Python2's `xrange()`. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def test_while():\n", + " i = 0\n", + " while i < 20000:\n", + " i += 1\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "100 loops, best of 3: 2.68 ms per loop\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" + } + ], + "source": [ + "print('Python', python_version())\n", + "%timeit test_while()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "1000 loops, best of 3: 1.72 ms per loop\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Python 2.7.6 \n",
-      "[GCC 4.0.1 (Apple Inc. build 5493)] on darwin\n",
-      "Type "help", "copyright", "credits" or "license" for more information.\n",
-      "\n",
-      ">>> my_input = input('enter a number: ')\n",
-      "\n",
-      "enter a number: 123\n",
-      "\n",
-      ">>> type(my_input)\n",
-      "<type 'int'>\n",
-      "\n",
-      ">>> my_input = raw_input('enter a number: ')\n",
-      "\n",
-      "enter a number: 123\n",
-      "\n",
-      ">>> type(my_input)\n",
-      "<type 'str'>\n",
-      "
\n" + } + ], + "source": [ + "print 'Python', python_version()\n", + "%timeit test_while()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Raising exceptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + } + ], + "source": [ + "print 'Python', python_version()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IOError", + "evalue": "file error", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIOError\u001b[0m: file error" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" + } + ], + "source": [ + "raise IOError, \"file error\"" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "IOError", + "evalue": "file error", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIOError\u001b[0m: file error" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
Python 3.4.1 \n",
-      "[GCC 4.2.1 (Apple Inc. build 5577)] on darwin\n",
-      "Type "help", "copyright", "credits" or "license" for more information.\n",
-      "\n",
-      ">>> my_input = input('enter a number: ')\n",
-      "\n",
-      "enter a number: 123\n",
-      "\n",
-      ">>> type(my_input)\n",
-      "<class 'str'>\n",
-      "
\n" + } + ], + "source": [ + "raise IOError(\"file error\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "print('Python', python_version())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m raise IOError, \"file error\"\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] - }, + } + ], + "source": [ + "raise IOError, \"file error\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The proper way to raise an exception in Python 3:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Returning iterable objects instead of lists" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" + "ename": "OSError", + "evalue": "file error", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mOSError\u001b[0m: file error" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we have already seen in the [`xrange`](#xrange) section, some functions and methods return iterable objects in Python 3 now - instead of lists in Python 2. \n", - "\n", - "Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.\n", - "\n", - "And for those cases where we really need the `list`-objects, we can simply convert the iterable object into a `list` via the `list()` function." + } + ], + "source": [ + "print('Python', python_version())\n", + "raise IOError(\"file error\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Handling exceptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the \"`as`\" keyword now" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "name 'let_us_cause_a_NameError' is not defined --> our error message\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 2" + } + ], + "source": [ + "print 'Python', python_version()\n", + "try:\n", + " let_us_cause_a_NameError\n", + "except NameError, err:\n", + " print err, '--> our error message'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "name 'let_us_cause_a_NameError' is not defined --> our error message\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print 'Python', python_version() \n", - "\n", - "print range(3) \n", - "print type(range(3))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 2.7.6\n", - "[0, 1, 2]\n", - "\n" - ] - } - ], - "prompt_number": 2 - }, + } + ], + "source": [ + "print('Python', python_version())\n", + "try:\n", + " let_us_cause_a_NameError\n", + "except NameError as err:\n", + " print(err, '--> our error message')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The next() function and .next() method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since `next()` (`.next()`) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the `next()` function is all that remains in Python 3 (calling the `.next()` method raises an `AttributeError`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Python', python_version())\n", - "\n", - "print(range(3))\n", - "print(type(range(3)))\n", - "print(list(range(3)))" - ], - "language": "python", + "data": { + "text/plain": [ + "'b'" + ] + }, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Python 3.4.1\n", - "range(0, 3)\n", - "\n", - "[0, 1, 2]\n" - ] - } - ], - "prompt_number": 7 - }, + "output_type": "execute_result" + } + ], + "source": [ + "print 'Python', python_version()\n", + "\n", + "my_generator = (letter for letter in 'abcdefg')\n", + "\n", + "next(my_generator)\n", + "my_generator.next()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n" ] }, { - "cell_type": "markdown", + "data": { + "text/plain": [ + "'a'" + ] + }, + "execution_count": 13, "metadata": {}, - "source": [ - "**Some more commonly used functions and methods that don't return lists anymore in Python 3:**\n", - "\n", - "- `zip()`\n", - "\n", - "- `map()`\n", - "\n", - "- `filter()`\n", - "\n", - "- dictionary's `.keys()` method\n", - "\n", - "- dictionary's `.values()` method\n", - "\n", - "- dictionary's `.items()` method\n" + "output_type": "execute_result" + } + ], + "source": [ + "print('Python', python_version())\n", + "\n", + "my_generator = (letter for letter in 'abcdefg')\n", + "\n", + "next(my_generator)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'generator' object has no attribute 'next'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'next'" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "my_generator.next()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## For-loop variables and the global namespace leak" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!\n", + "\n", + "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n", + "\n", + "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "before: i = 1\n", + "comprehension: [0, 1, 2, 3, 4]\n", + "after: i = 4\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "More articles about Python 2 and Python 3" + } + ], + "source": [ + "print 'Python', python_version()\n", + "\n", + "i = 1\n", + "print 'before: i =', i\n", + "\n", + "print 'comprehension: ', [i for i in range(5)]\n", + "\n", + "print 'after: i =', i" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "before: i = 1\n", + "comprehension: [0, 1, 2, 3, 4]\n", + "after: i = 1\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to the section-overview](#Sections)]" + } + ], + "source": [ + "print('Python', python_version())\n", + "\n", + "i = 1\n", + "print('before: i =', i)\n", + "\n", + "print('comprehension:', [i for i in range(5)])\n", + "\n", + "print('after: i =', i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing unorderable types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another nice change in Python 3 is that a `TypeError` is raised as warning if we try to compare unorderable types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "[1, 2] > 'foo' = False\n", + "(1, 2) > 'foo' = True\n", + "[1, 2] > (1, 2) = False\n" ] - }, + } + ], + "source": [ + "print 'Python', python_version()\n", + "print \"[1, 2] > 'foo' = \", [1, 2] > 'foo'\n", + "print \"(1, 2) > 'foo' = \", (1, 2) > 'foo'\n", + "print \"[1, 2] > (1, 2) = \", [1, 2] > (1, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.\n", - "\n", - "\n", - "**// Porting to Python 3** \n", - "\n", - "- [Should I use Python 2 or Python 3 for my development activity?](https://wiki.python.org/moin/Python2orPython3)\n", - "\n", - "- [What\u2019s New In Python 3.0](https://docs.python.org/3.0/whatsnew/3.0.html)\n", - "\n", - "- [Porting to Python 3](http://python3porting.com/differences.html)\n", - "\n", - "- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html) \n", - "\n", - "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n", - "\n", - "**// Pro and anti Python 3**\n", - "\n", - "- [10 awesome features of Python that you can't use because you refuse to upgrade to Python 3](http://asmeurer.github.io/python3-presentation/slides.html#1)\n", - "\n", - "- [Everything you did not want to know about Unicode in Python 3](http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/)\n", - "\n", - "- [Python 3 is killing Python](https://medium.com/@deliciousrobots/5d2ad703365d/)\n", - "\n", - "- [Python 3 can revive Python](https://medium.com/p/2a7af4788b10)\n", - "\n", - "- [Python 3 is fine](http://sealedabstract.com/rants/python-3-is-fine/)\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] + "ename": "TypeError", + "evalue": "unorderable types: list() > str()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"(1, 2) > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > (1, 2) = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()" + ] + } + ], + "source": [ + "print('Python', python_version())\n", + "print(\"[1, 2] > 'foo' = \", [1, 2] > 'foo')\n", + "print(\"(1, 2) > 'foo' = \", (1, 2) > 'foo')\n", + "print(\"[1, 2] > (1, 2) = \", [1, 2] > (1, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parsing user inputs via input()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fortunately, the `input()` function was fixed in Python 3 so that it always stores the user inputs as `str` objects. In order to avoid the dangerous behavior in Python 2 to read in other types than `strings`, we have to use `raw_input()` instead." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Python 2.7.6 \n",
+    "[GCC 4.0.1 (Apple Inc. build 5493)] on darwin\n",
+    "Type "help", "copyright", "credits" or "license" for more information.\n",
+    "\n",
+    ">>> my_input = input('enter a number: ')\n",
+    "\n",
+    "enter a number: 123\n",
+    "\n",
+    ">>> type(my_input)\n",
+    "<type 'int'>\n",
+    "\n",
+    ">>> my_input = raw_input('enter a number: ')\n",
+    "\n",
+    "enter a number: 123\n",
+    "\n",
+    ">>> type(my_input)\n",
+    "<type 'str'>\n",
+    "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
Python 3.4.1 \n",
+    "[GCC 4.2.1 (Apple Inc. build 5577)] on darwin\n",
+    "Type "help", "copyright", "credits" or "license" for more information.\n",
+    "\n",
+    ">>> my_input = input('enter a number: ')\n",
+    "\n",
+    "enter a number: 123\n",
+    "\n",
+    ">>> type(my_input)\n",
+    "<class 'str'>\n",
+    "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Returning iterable objects instead of lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we have already seen in the [`xrange`](#xrange) section, some functions and methods return iterable objects in Python 3 now - instead of lists in Python 2. \n", + "\n", + "Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.\n", + "\n", + "And for those cases where we really need the `list`-objects, we can simply convert the iterable object into a `list` via the `list()` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.6\n", + "[0, 1, 2]\n", + "\n" + ] + } + ], + "source": [ + "print 'Python', python_version() \n", + "\n", + "print range(3) \n", + "print type(range(3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.4.1\n", + "range(0, 3)\n", + "\n", + "[0, 1, 2]\n" + ] + } + ], + "source": [ + "print('Python', python_version())\n", + "\n", + "print(range(3))\n", + "print(type(range(3)))\n", + "print(list(range(3)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Some more commonly used functions and methods that don't return lists anymore in Python 3:**\n", + "\n", + "- `zip()`\n", + "\n", + "- `map()`\n", + "\n", + "- `filter()`\n", + "\n", + "- dictionary's `.keys()` method\n", + "\n", + "- dictionary's `.values()` method\n", + "\n", + "- dictionary's `.items()` method\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Banker's Rounding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python 3 adopted the now standard way of rounding decimals when it results in a tie (.5) at the last significant digits. Now, in Python 3, decimals are rounded to the nearest even number. Although it's an inconvenience for code portability, it's supposedly a better way of rounding compared to rounding up as it avoids the bias towards large numbers. For more information, see the excellent Wikipedia articles and paragraphs:\n", + "- [https://en.wikipedia.org/wiki/Rounding#Round_half_to_even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)\n", + "- [https://en.wikipedia.org/wiki/IEEE_floating_point#Roundings_to_nearest](https://en.wikipedia.org/wiki/IEEE_floating_point#Roundings_to_nearest)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 2.7.12\n" + ] + } + ], + "source": [ + "print 'Python', python_version()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16.0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(15.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(16.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Python 3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.5.1\n" + ] + } + ], + "source": [ + "print('Python', python_version())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(15.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "round(16.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More articles about Python 2 and Python 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to the section-overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.\n", + "\n", + "\n", + "**// Porting to Python 3** \n", + "\n", + "- [Should I use Python 2 or Python 3 for my development activity?](https://wiki.python.org/moin/Python2orPython3)\n", + "\n", + "- [What’s New In Python 3.0](https://docs.python.org/3.0/whatsnew/3.0.html)\n", + "\n", + "- [Porting to Python 3](http://python3porting.com/differences.html)\n", + "\n", + "- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html) \n", + "\n", + "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n", + "\n", + "**// Pro and anti Python 3**\n", + "\n", + "- [10 awesome features of Python that you can't use because you refuse to upgrade to Python 3](http://asmeurer.github.io/python3-presentation/slides.html#1)\n", + "\n", + "- [Everything you did not want to know about Unicode in Python 3](http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/)\n", + "\n", + "- [Python 3 is killing Python](https://medium.com/@deliciousrobots/5d2ad703365d/)\n", + "\n", + "- [Python 3 can revive Python](https://medium.com/p/2a7af4788b10)\n", + "\n", + "- [Python 3 is fine](http://sealedabstract.com/rants/python-3-is-fine/)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/tutorials/key_differences_between_python_2_and_3.md b/tutorials/key_differences_between_python_2_and_3.md deleted file mode 100644 index 972e667..0000000 --- a/tutorials/key_differences_between_python_2_and_3.md +++ /dev/null @@ -1,416 +0,0 @@ -[Sebastian Raschka](http://sebastianraschka.com) -last updated: 05/24/2014 - -
- -**This is a subsection of ["A collection of not-so-obvious Python stuff you should know!"](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)** - - - -
- -## Key differences between Python 2 and 3 -
- -There are some good articles already that are summarizing the differences between Python 2 and 3, e.g., - -- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3) - -- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html) - -- [http://python3porting.com/differences.html](http://python3porting.com/differences.html) - -- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html) - -etc. - -But it might be still worthwhile, especially for Python newcomers, to take a look at some of those! -(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.) - - - -
- -### Overview - Key differences between Python 2 and 3 - - - - -- [Unicode](#unicode) -- [The print statement](#print) -- [Integer division](#integer_div) -- [xrange()](#xrange) -- [Raising exceptions](#raising_exceptions) -- [Handling exceptions](#handling_exceptions) -- [next() function and .next() method](#next_next) -- [Loop variables and leaking into the global scope](#loop_leak) -- [Comparing unorderable types](#compare_unorder) - -
-
- - - -
-
- -### Unicode... - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - - -####- Python 2: -We have ASCII `str()` types, separate `unicode()`, but no `byte` type -####- Python 3: -Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s - -
- -
#############
-# Python 2
-#############
-
->>> type(unicode('is like a python3 str()'))
-<type 'unicode'>
-
->>> type(b'byte type does not exist')
-<type 'str'>
-
->>> 'they are really' + b' the same'
-'they are really the same'
-
->>> type(bytearray(b'bytearray oddly does exist though'))
-<type 'bytearray'>
-
-#############
-# Python 3
-#############
-
->>> print('strings are now utf-8 \u03BCnico\u0394é!')
-strings are now utf-8 μnicoΔé!
-
-
->>> type(b' and we have byte types for storing data')
-<class 'bytes'>
-
->>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))
-<class 'bytearray'>
-
->>> 'string' + b'bytes for data'
-Traceback (most recent call last):s
-  File "<stdin>", line 1, in <module>
-TypeError: Can't convert 'bytes' object to str implicitly
-
- - - -
-
- -### The print statement - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - -Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ... - -
-
# Python 2
->>> print 'Hello, World!'
-Hello, World!
->>> print('Hello, World!')
-Hello, World!
-
-# Python 3
->>> print('Hello, World!')
-Hello, World!
->>> print 'Hello, World!'
-  File "<stdin>", line 1
-    print 'Hello, World!'
-                        ^
-SyntaxError: invalid syntax
-
- -
- -And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=""` in Python 3: - -
- -
# Python 2
->>> print "line 1", ; print 'same line'
-line 1 same line
-
-# Python 3
->>> print("line 1", end="") ; print (" same line")
-line 1 same line
-
- - - -
-
- -### Integer division - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - - -This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed. -So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts). - -
-
# Python 2
->>> 3 / 2
-1
->>> 3 // 2
-1
->>> 3 / 2.0
-1.5
->>> 3 // 2.0
-1.0
-
-# Python 3
->>> 3 / 2
-1.5
->>> 3 // 2
-1
->>> 3 / 2.0
-1.5
->>> 3 // 2.0
-1.0
-
- - - -
-
- -###`xrange()` - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - - -`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch! -In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore. - - -
# Python 2
-> python -m timeit 'for i in range(1000000):' ' pass'
-10 loops, best of 3: 66 msec per loop
-
-    > python -m timeit 'for i in xrange(1000000):' ' pass'
-10 loops, best of 3: 27.8 msec per loop
-
-# Python 3
-> python3 -m timeit 'for i in range(1000000):' ' pass'
-10 loops, best of 3: 51.1 msec per loop
-
-> python3 -m timeit 'for i in xrange(1000000):' ' pass'
-Traceback (most recent call last):
-  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py", line 292, in main
-    x = t.timeit(number)
-  File "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py", line 178, in timeit
-    timing = self.inner(it, self.timer)
-  File "<timeit-src>", line 6, in inner
-    for i in xrange(1000000):
-NameError: name 'xrange' is not defined
-
- - - -
-
- -### Raising exceptions - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - - - -Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses: - -
-
# Python 2
->>> raise IOError, "file error"
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-IOError: file error
->>> raise IOError("file error")
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-IOError: file error
-
-    
-# Python 3    
->>> raise IOError, "file error"
-  File "<stdin>", line 1
-    raise IOError, "file error"
-                 ^
-SyntaxError: invalid syntax
->>> raise IOError("file error")
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-OSError: file error
-
- - - -
-
- -### Handling exceptions - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - - - -Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword! - -
# Python 2
->>> try:
-...     blabla
-... except NameError, err:
-...     print err, '--> our error msg'
-... 
-name 'blabla' is not defined --> our error msg
-
-# Python 3
->>> try:
-...     blabla
-... except NameError as err:
-...     print(err, '--> our error msg')
-... 
-name 'blabla' is not defined --> our error msg
-
- - - -
-
- -### The `next()` function and `.next()` method - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - -Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3! - -
# Python 2
->>> my_generator = (letter for letter in 'abcdefg')
->>> my_generator.next()
-'a'
->>> next(my_generator)
-'b'
-
-# Python 3
->>> my_generator = (letter for letter in 'abcdefg')
->>> next(my_generator)
-'a'
->>> my_generator.next()
-Traceback (most recent call last):
-  File "<stdin>", line 1, in <module>
-AttributeError: 'generator' object has no attribute 'next'
-
- - - -
-
- -### In Python 3.x for-loop variables don't leak into the global namespace anymore - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - -This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows: - -"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope." - -
-`[In:]` -
from platform import python_version
-print('This code cell was executed in Python', python_version())
-
-i = 1
-print([i for i in range(5)])
-print(i, '-> i in global')
-
- -
-`[Out:]` -
This code cell was executed in Python 3.3.5
-[0, 1, 2, 3, 4]
-1 -> i in global
-
- - -
-
-
-`[In:]` -
from platform import python_version
-print 'This code cell was executed in Python', python_version()
-
-i = 1
-print [i for i in range(5)]
-print i, '-> i in global' 
-
- -
-`[Out:]` -
This code cell was executed in Python 2.7.6
-[0, 1, 2, 3, 4]
-4 -> i in global
-
- - - -
-
- -#### Python 3.x prevents us from comparing unorderable types - -[[back to Python 2.x vs 3.x overview](#py23_overview)] - -
-`[In:]` -
from platform import python_version
-print 'This code cell was executed in Python', python_version()
-
-print [1, 2] > 'foo'
-print (1, 2) > 'foo'
-print [1, 2] > (1, 2)
-
- -
-`[Out:]` -
This code cell was executed in Python 2.7.6
-False
-True
-False
-
- -
-
-
- -`[In:]` -
from platform import python_version
-print('This code cell was executed in Python', python_version())
-
-print([1, 2] > 'foo')
-print((1, 2) > 'foo')
-print([1, 2] > (1, 2))
-
- -`[Out:]` -
This code cell was executed in Python 3.3.5
----------------------------------------------------------------------------
-TypeError                                 Traceback (most recent call last)
-<ipython-input-3-1d774c677f73> in <module>()
-      2 print('This code cell was executed in Python', python_version())
-      3 
-----> 4 [1, 2] > 'foo'
-      5 (1, 2) > 'foo'
-      6 [1, 2] > (1, 2)
-
-TypeError: unorderable types: list() > str()
-
diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html index 7624ea4..8d9762c 100644 --- a/tutorials/matrix_cheatsheet_only.html +++ b/tutorials/matrix_cheatsheet_only.html @@ -14,6 +14,13 @@ + + diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb index a3b0916..b3566c9 100644 --- a/tutorials/multiprocessing_intro.ipynb +++ b/tutorials/multiprocessing_intro.ipynb @@ -1,1151 +1,1105 @@ { - "metadata": { - "name": "", - "signature": "sha256:a96ed2f762cf56d93a4e5345428c7db5ec576916158ce54446dfdf837ec7e505" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Sebastian Raschka](http://sebastianraschka.com) \n", - "\n", - "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1) \n", - "\n", - "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb) \n", - "\n", - "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference)\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import time\n", - "print('Last updated: %s' %time.strftime('%d/%m/%Y'))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Last updated: 20/06/2014\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "I would be happy to hear your comments and suggestions. \n", - "Please feel free to drop me a note via\n", - "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Parallel processing via the `multiprocessing` module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-core processor in 2013.\n", - "\n", - "However, the default Python interpreter was designed with simplicity in mind and has a thread-safe mechanism, the so-called \"GIL\" (Global Interpreter Lock). In order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing, or single-threading).\n", - "\n", - "In this introduction to Python's `multiprocessing` module, we will see how we can spawn multiple subprocesses to avoid some of the GIL's disadvantages." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Sections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- [An introduction to parallel programming using Python's `multiprocessing` module](#An-introduction-to-parallel-programming-using-Python's-`multiprocessing`-module)\n", - " - [Multi-Threading vs. Multi-Processing](#Multi-Threading-vs.-Multi-Processing)\n", - "- [Introduction to the `multiprocessing` module](#Introduction-to-the-multiprocessing-module)\n", - " - [The `Process` class](#The-Process-class)\n", - " - [How to retrieve results in a particular order](#How-to-retrieve-results-in-a-particular-order)\n", - " - [The `Pool` class](#The-Pool-class)\n", - "- [Kernel density estimation as benchmarking function](#Kernel-density-estimation-as-benchmarking-function)\n", - " - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n", - " - [Sample data and `timeit` benchmarks](#Sample-data-and-timeit-benchmarks)\n", - " - [Benchmarking functions](#Benchmarking-functions)\n", - " - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n", - "- [Results](#Results)\n", - "- [Conclusion](#Conclusion)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "\n", - "Multi-Threading vs. Multi-Processing\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Depending on the application, two common approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-tasks\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper synchronization, for example, if processes are writing to the same memory location at the same time. \n", - "\n", - "A safer approach (although it comes with an additional overhead due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n", - "\n", - "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Introduction to the `multiprocessing` module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Library has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point. \n", - "\n", - "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for parallel programming." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "The `Process` class" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The most basic approach is probably to use the `Process` class from the `multiprocessing` module. \n", - "Here, we will use a simple queue function to compute the cubes for the 6 numbers 1, 2, 3, 4, 5, and 6 in 6 parallel processes." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import multiprocessing as mp\n", - "import random\n", - "import string\n", - "\n", - "random.seed(123)\n", - "\n", - "# Define an output queue\n", - "output = mp.Queue()\n", - "\n", - "# define a example function\n", - "def rand_string(length, output):\n", - " \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n", - " rand_str = ''.join(random.choice(\n", - " string.ascii_lowercase \n", - " + string.ascii_uppercase \n", - " + string.digits)\n", - " for i in range(length))\n", - " output.put(rand_str)\n", - "\n", - "# Setup a list of processes that we want to run\n", - "processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(4)]\n", - "\n", - "# Run processes\n", - "for p in processes:\n", - " p.start()\n", - "\n", - "# Exit the completed processes\n", - "for p in processes:\n", - " p.join()\n", - "\n", - "# Get process results from the output queue\n", - "results = [output.get() for p in processes]\n", - "\n", - "print(results)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['BJWNs', 'GOK0H', '7CTRJ', 'THDF3']\n" - ] - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "How to retrieve results in a particular order " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The order of the obtained results does not necessarily have to match the order of the processes (in the `processes` list). Since we eventually use the `.get()` method to retrieve the results from the `Queue` sequentially, the order in which the processes finished determines the order of our results. \n", - "E.g., if the second process has finished just before the first process, the order of the strings in the `results` list could have also been\n", - "`['PQpqM', 'yzQfA', 'SHZYV', 'PSNkD']` instead of `['yzQfA', 'PQpqM', 'SHZYV', 'PSNkD']`\n", - "\n", - "If our application required us to retrieve results in a particular order, one possibility would be to refer to the processes' `._identity` attribute. In this case, we could also simply use the values from our `range` object as position argument. The modified code would be:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Define an output queue\n", - "output = mp.Queue()\n", - "\n", - "# define a example function\n", - "def rand_string(length, pos, output):\n", - " \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n", - " rand_str = ''.join(random.choice(\n", - " string.ascii_lowercase \n", - " + string.ascii_uppercase \n", - " + string.digits)\n", - " for i in range(length))\n", - " output.put((pos, rand_str))\n", - "\n", - "# Setup a list of processes that we want to run\n", - "processes = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)]\n", - "\n", - "# Run processes\n", - "for p in processes:\n", - " p.start()\n", - "\n", - "# Exit the completed processes\n", - "for p in processes:\n", - " p.join()\n", - "\n", - "# Get process results from the output queue\n", - "results = [output.get() for p in processes]\n", - "\n", - "print(results)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[(0, 'h5hoV'), (1, 'fvdmN'), (2, 'rxGX4'), (3, '8hDJj')]\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And the retrieved results would be tuples, for example, `[(0, 'KAQo6'), (1, '5lUya'), (2, 'nj6Q0'), (3, 'QQvLr')]` \n", - "or `[(1, '5lUya'), (3, 'QQvLr'), (0, 'KAQo6'), (2, 'nj6Q0')]`\n", - "\n", - "To make sure that we retrieved the results in order, we could simply sort the results and optionally get rid of the position argument:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "results.sort()\n", - "results = [r[1] for r in results]\n", - "print(results)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['h5hoV', 'fvdmN', 'rxGX4', '8hDJj']\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**A simpler way to to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "The `Pool` class" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class. \n", - "\n", - "There are four methods that are particularly interesing:\n", - "\n", - " - Pool.apply\n", - " \n", - " - Pool.map\n", - " \n", - " - Pool.apply_async\n", - " \n", - " - Pool.map_async\n", - " \n", - "The `Pool.apply` and `Pool.map` methods are basically equivalents to Python's in-built [`apply`](https://docs.python.org/2/library/functions.html#apply) and [`map`](https://docs.python.org/2/library/functions.html#map) functions." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we come to the `async` variants of the `Pool` methods, let us take a look at a simple example using `Pool.apply` and `Pool.map`. Here, we will set the number of processes to 4, which means that the `Pool` class will only allow 4 processes running at the same time." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def cube(x):\n", - " return x**3" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pool = mp.Pool(processes=4)\n", - "results = [pool.apply(cube, args=(x,)) for x in range(1,7)]\n", - "print(results)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 8, 27, 64, 125, 216]\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pool = mp.Pool(processes=4)\n", - "results = pool.map(cube, range(1,7))\n", - "print(results)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 8, 27, 64, 125, 216]\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `Pool.map` and `Pool.apply` will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications. \n", - "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n", - "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pool = mp.Pool(processes=4)\n", - "results = [pool.apply_async(cube, args=(x,)) for x in range(1,7)]\n", - "output = [p.get() for p in results]\n", - "print(output)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 8, 27, 64, 125, 216]\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Kernel density estimation as benchmarking function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the following approach, I want to do a simple comparison of a serial vs. multiprocessing approach where I will use a slightly more complex function than the `cube` example, which he have been using above. \n", - "\n", - "Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique. \n", - "I don't want to go into much detail about the theory of this technique, since we are mostly interested to see how `multiprocessing` can be used for performance improvements, but you are welcome to read my more detailed article about the [Parzen-window method here](http://sebastianraschka.com/Articles/2014_parzen_density_est.html). " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "\n", - "def parzen_estimation(x_samples, point_x, h):\n", - " \"\"\"\n", - " Implementation of a hypercube kernel for Parzen-window estimation.\n", - "\n", - " Keyword arguments:\n", - " x_sample:training sample, 'd x 1'-dimensional numpy array\n", - " x: point x for density estimation, 'd x 1'-dimensional numpy array\n", - " h: window width\n", - " \n", - " Returns the predicted pdf as float.\n", - "\n", - " \"\"\"\n", - " k_n = 0\n", - " for row in x_samples:\n", - " x_i = (point_x - row[:,np.newaxis]) / (h)\n", - " for row in x_i:\n", - " if np.abs(row) > (1/2):\n", - " break\n", - " else: # \"completion-else\"*\n", - " k_n += 1\n", - " return (k_n / len(x_samples)) / (h**point_x.shape[1])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "**A quick note about the \"completion else**\n", - "\n", - "Sometimes I receive comments about whether I used this for-else combination intentionally or if it happened by mistake. That is a legitimate question, since this \"completion-else\" is rarely used (that's what I call it, I am not aware if there is an \"official\" name for this, if so, please let me know). \n", - "I have a more detailed explanation [here](http://sebastianraschka.com/Articles/2014_deep_python.html#else_clauses) in one of my blog-posts, but in a nutshell: In contrast to a conditional else (in combination with if-statements), the \"completion else\" is only executed if the preceding code block (here the `for`-loop) has finished.\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "The Parzen-window method in a nutshell" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So what this function does in a nutshell: It counts points in a defined region (the so-called window), and divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region.\n", - "\n", - "Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%matplotlib inline" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from itertools import product, combinations\n", - "fig = plt.figure(figsize=(7,7))\n", - "ax = fig.gca(projection='3d')\n", - "ax.set_aspect(\"equal\")\n", - "\n", - "# Plot Points\n", - "\n", - "# samples within the cube\n", - "X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])\n", - "\n", - "X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],\n", - " [0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],\n", - " [0.7,-0.6,-0.8]])\n", - "\n", - "for row in X_inside:\n", - " ax.scatter(row[0], row[1], row[2], color=\"r\", s=50, marker='^')\n", - "\n", - "for row in X_outside: \n", - " ax.scatter(row[0], row[1], row[2], color=\"k\", s=50)\n", - "\n", - "# Plot Cube\n", - "h = [-0.5, 0.5]\n", - "for s, e in combinations(np.array(list(product(h,h,h))), 2):\n", - " if np.sum(np.abs(s-e)) == h[1]-h[0]:\n", - " ax.plot3D(*zip(s,e), color=\"g\")\n", - " \n", - "ax.set_xlim(-1.5, 1.5)\n", - "ax.set_ylim(-1.5, 1.5)\n", - "ax.set_zlim(-1.5, 1.5)\n", - "\n", - "plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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1D1/LxUKsFze9R60d9eFAL/5iR984M+fJOwEVFB3UdRuxWMw17hcCSQnmeR6B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- "text": [ - "" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "point_x = np.array([[0],[0],[0]])\n", - "X_all = np.vstack((X_inside,X_outside))\n", - "\n", - "print('p(x) =', parzen_estimation(X_all, point_x, h=1))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "p(x) = 0.3\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Sample data and `timeit` benchmarks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "\n", - "np.random.seed(123)\n", - "\n", - "# Generate random 2D-patterns\n", - "mu_vec = np.array([0,0])\n", - "cov_mat = np.array([[1,0],[0,1]])\n", - "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The expected probability of a point at the center of the distribution is ~ 0.15915 as we can see below. \n", - "And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above. \n", - "\n", - "\n", - "In order to make a \"good\" prediction via the Parzen-window technique, it is - among other things - crucial to select an appropriate window with. Here, we will use multiple processes to predict the density at the center of the bivariate Gaussian distribution using different window widths." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from scipy.stats import multivariate_normal\n", - "var = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n", - "print('actual probability density:', var.pdf([0,0]))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "actual probability density: 0.159154943092\n" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Sebastian Raschka](http://sebastianraschka.com) \n", + "\n", + "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1) \n", + "\n", + "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb) \n", + "\n", + "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: 20/06/2014\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Benchmarking functions" + } + ], + "source": [ + "import time\n", + "print('Last updated: %s' %time.strftime('%d/%m/%Y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "I would be happy to hear your comments and suggestions. \n", + "Please feel free to drop me a note via\n", + "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parallel processing via the `multiprocessing` module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-core processor in 2013.\n", + "\n", + "However, the default Python interpreter was designed with simplicity in mind and has a thread-safe mechanism, the so-called \"GIL\" (Global Interpreter Lock). In order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing, or single-threading).\n", + "\n", + "In this introduction to Python's `multiprocessing` module, we will see how we can spawn multiple subprocesses to avoid some of the GIL's disadvantages." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [An introduction to parallel programming using Python's `multiprocessing` module](#An-introduction-to-parallel-programming-using-Python's-`multiprocessing`-module)\n", + " - [Multi-Threading vs. Multi-Processing](#Multi-Threading-vs.-Multi-Processing)\n", + "- [Introduction to the `multiprocessing` module](#Introduction-to-the-multiprocessing-module)\n", + " - [The `Process` class](#The-Process-class)\n", + " - [How to retrieve results in a particular order](#How-to-retrieve-results-in-a-particular-order)\n", + " - [The `Pool` class](#The-Pool-class)\n", + "- [Kernel density estimation as benchmarking function](#Kernel-density-estimation-as-benchmarking-function)\n", + " - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n", + " - [Sample data and `timeit` benchmarks](#Sample-data-and-timeit-benchmarks)\n", + " - [Benchmarking functions](#Benchmarking-functions)\n", + " - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n", + "- [Results](#Results)\n", + "- [Conclusion](#Conclusion)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-Threading vs. Multi-Processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Depending on the application, two common approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-tasks\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper synchronization, for example, if processes are writing to the same memory location at the same time. \n", + "\n", + "A safer approach (although it comes with an additional overhead due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n", + "\n", + "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Introduction to the `multiprocessing` module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Library has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point. \n", + "\n", + "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for parallel programming." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `Process` class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The most basic approach is probably to use the `Process` class from the `multiprocessing` module. \n", + "Here, we will use a simple queue function to generate four random strings in parallel." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['BJWNs', 'GOK0H', '7CTRJ', 'THDF3']\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" + } + ], + "source": [ + "import multiprocessing as mp\n", + "import random\n", + "import string\n", + "\n", + "random.seed(123)\n", + "\n", + "# Define an output queue\n", + "output = mp.Queue()\n", + "\n", + "# define a example function\n", + "def rand_string(length, output):\n", + " \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n", + " rand_str = ''.join(random.choice(\n", + " string.ascii_lowercase \n", + " + string.ascii_uppercase \n", + " + string.digits)\n", + " for i in range(length))\n", + " output.put(rand_str)\n", + "\n", + "# Setup a list of processes that we want to run\n", + "processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(4)]\n", + "\n", + "# Run processes\n", + "for p in processes:\n", + " p.start()\n", + "\n", + "# Exit the completed processes\n", + "for p in processes:\n", + " p.join()\n", + "\n", + "# Get process results from the output queue\n", + "results = [output.get() for p in processes]\n", + "\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How to retrieve results in a particular order " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The order of the obtained results does not necessarily have to match the order of the processes (in the `processes` list). Since we eventually use the `.get()` method to retrieve the results from the `Queue` sequentially, the order in which the processes finished determines the order of our results. \n", + "E.g., if the second process has finished just before the first process, the order of the strings in the `results` list could have also been\n", + "`['PQpqM', 'yzQfA', 'SHZYV', 'PSNkD']` instead of `['yzQfA', 'PQpqM', 'SHZYV', 'PSNkD']`\n", + "\n", + "If our application required us to retrieve results in a particular order, one possibility would be to refer to the processes' `._identity` attribute. In this case, we could also simply use the values from our `range` object as position argument. The modified code would be:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0, 'h5hoV'), (1, 'fvdmN'), (2, 'rxGX4'), (3, '8hDJj')]\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function. \n", - "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width. \n", - "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to to sort our list of results later." + } + ], + "source": [ + "# Define an output queue\n", + "output = mp.Queue()\n", + "\n", + "# define a example function\n", + "def rand_string(length, pos, output):\n", + " \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n", + " rand_str = ''.join(random.choice(\n", + " string.ascii_lowercase \n", + " + string.ascii_uppercase \n", + " + string.digits)\n", + " for i in range(length))\n", + " output.put((pos, rand_str))\n", + "\n", + "# Setup a list of processes that we want to run\n", + "processes = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)]\n", + "\n", + "# Run processes\n", + "for p in processes:\n", + " p.start()\n", + "\n", + "# Exit the completed processes\n", + "for p in processes:\n", + " p.join()\n", + "\n", + "# Get process results from the output queue\n", + "results = [output.get() for p in processes]\n", + "\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the retrieved results would be tuples, for example, `[(0, 'KAQo6'), (1, '5lUya'), (2, 'nj6Q0'), (3, 'QQvLr')]` \n", + "or `[(1, '5lUya'), (3, 'QQvLr'), (0, 'KAQo6'), (2, 'nj6Q0')]`\n", + "\n", + "To make sure that we retrieved the results in order, we could simply sort the results and optionally get rid of the position argument:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['h5hoV', 'fvdmN', 'rxGX4', '8hDJj']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def parzen_estimation(x_samples, point_x, h):\n", - " k_n = 0\n", - " for row in x_samples:\n", - " x_i = (point_x - row[:,np.newaxis]) / (h)\n", - " for row in x_i:\n", - " if np.abs(row) > (1/2):\n", - " break\n", - " else: # \"completion-else\"*\n", - " k_n += 1\n", - " return (h, (k_n / len(x_samples)) / (h**point_x.shape[1]))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 15 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def serial(samples, x, widths):\n", - " return [parzen_estimation(samples, x, w) for w in widths]\n", - "\n", - "def multiprocess(processes, samples, x, widths):\n", - " pool = mp.Pool(processes=processes)\n", - " results = [pool.apply_async(parzen_estimation, args=(samples, x, w)) for w in widths]\n", - " results = [p.get() for p in results]\n", - " results.sort() # to sort the results by input window width\n", - " return results" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just to get an idea what the results would look like (i.e., the predicted densities for different window widths):" + } + ], + "source": [ + "results.sort()\n", + "results = [r[1] for r in results]\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**A simpler way to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `Pool` class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class. \n", + "\n", + "There are four methods that are particularly interesting:\n", + "\n", + " - Pool.apply\n", + " \n", + " - Pool.map\n", + " \n", + " - Pool.apply_async\n", + " \n", + " - Pool.map_async\n", + " \n", + "The `Pool.apply` and `Pool.map` methods are basically equivalents to Python's in-built [`apply`](https://docs.python.org/2/library/functions.html#apply) and [`map`](https://docs.python.org/2/library/functions.html#map) functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we come to the `async` variants of the `Pool` methods, let us take a look at a simple example using `Pool.apply` and `Pool.map`. Here, we will set the number of processes to 4, which means that the `Pool` class will only allow 4 processes running at the same time." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def cube(x):\n", + " return x**3" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 8, 27, 64, 125, 216]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "widths = np.arange(0.1, 1.3, 0.1)\n", - "point_x = np.array([[0],[0]])\n", - "results = []\n", - "\n", - "results = multiprocess(4, x_2Dgauss, point_x, widths)\n", - "\n", - "for r in results:\n", - " print('h = %s, p(x) = %s' %(r[0], r[1]))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "h = 0.1, p(x) = 0.016\n", - "h = 0.2, p(x) = 0.0305\n", - "h = 0.3, p(x) = 0.045\n", - "h = 0.4, p(x) = 0.06175\n", - "h = 0.5, p(x) = 0.078\n", - "h = 0.6, p(x) = 0.0911666666667\n", - "h = 0.7, p(x) = 0.106\n", - "h = 0.8, p(x) = 0.117375\n", - "h = 0.9, p(x) = 0.132666666667\n", - "h = 1.0, p(x) = 0.1445\n", - "h = 1.1, p(x) = 0.157090909091\n", - "h = 1.2, p(x) = 0.1685\n" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915. \n", - "Thus, for the benchmark, let us create 100 evenly spaced window width in the range of 1.0 to 1.2." + } + ], + "source": [ + "pool = mp.Pool(processes=4)\n", + "results = [pool.apply(cube, args=(x,)) for x in range(1,7)]\n", + "print(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 8, 27, 64, 125, 216]\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "pool = mp.Pool(processes=4)\n", + "results = pool.map(cube, range(1,7))\n", + "print(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished, which is quite useful if we want to obtain results in a particular order for certain applications. \n", + "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n", + "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 8, 27, 64, 125, 216]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "widths = np.linspace(1.0, 1.2, 100)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import timeit\n", - "\n", - "mu_vec = np.array([0,0])\n", - "cov_mat = np.array([[1,0],[0,1]])\n", - "n = 10000\n", - "\n", - "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n", - "\n", - "benchmarks = []\n", - "\n", - "benchmarks.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n", - " 'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n", - "\n", - "benchmarks.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n", - " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n", - "\n", - "benchmarks.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n", - " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n", - "\n", - "benchmarks.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n", - " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n", - "\n", - "benchmarks.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n", - " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" + } + ], + "source": [ + "pool = mp.Pool(processes=4)\n", + "results = [pool.apply_async(cube, args=(x,)) for x in range(1,7)]\n", + "output = [p.get() for p in results]\n", + "print(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Kernel density estimation as benchmarking function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the following approach, I want to do a simple comparison of a serial vs. multiprocessing approach where I will use a slightly more complex function than the `cube` example, which he have been using above. \n", + "\n", + "Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique. \n", + "I don't want to go into much detail about the theory of this technique, since we are mostly interested to see how `multiprocessing` can be used for performance improvements, but you are welcome to read my more detailed article about the [Parzen-window method here](http://sebastianraschka.com/Articles/2014_parzen_density_est.html). " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def parzen_estimation(x_samples, point_x, h):\n", + " \"\"\"\n", + " Implementation of a hypercube kernel for Parzen-window estimation.\n", + "\n", + " Keyword arguments:\n", + " x_sample:training sample, 'd x 1'-dimensional numpy array\n", + " x: point x for density estimation, 'd x 1'-dimensional numpy array\n", + " h: window width\n", + " \n", + " Returns the predicted pdf as float.\n", + "\n", + " \"\"\"\n", + " k_n = 0\n", + " for row in x_samples:\n", + " x_i = (point_x - row[:,np.newaxis]) / (h)\n", + " for row in x_i:\n", + " if np.abs(row) > (1/2):\n", + " break\n", + " else: # \"completion-else\"*\n", + " k_n += 1\n", + " return (k_n / len(x_samples)) / (h**point_x.shape[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "**A quick note about the \"completion else**\n", + "\n", + "Sometimes I receive comments about whether I used this for-else combination intentionally or if it happened by mistake. That is a legitimate question, since this \"completion-else\" is rarely used (that's what I call it, I am not aware if there is an \"official\" name for this, if so, please let me know). \n", + "I have a more detailed explanation [here](http://sebastianraschka.com/Articles/2014_deep_python.html#else_clauses) in one of my blog-posts, but in a nutshell: In contrast to a conditional else (in combination with if-statements), the \"completion else\" is only executed if the preceding code block (here the `for`-loop) has finished.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The Parzen-window method in a nutshell" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So what this function does in a nutshell: It counts points in a defined region (the so-called window), and divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region.\n", + "\n", + "Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1D1/LxUKsFze9R60d9eFAL/5iR984M+fJOwEVFB3UdRuxWMw17hcCSQnmeR6B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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from itertools import product, combinations\n", + "fig = plt.figure(figsize=(7,7))\n", + "ax = fig.gca(projection='3d')\n", + "ax.set_aspect(\"equal\")\n", + "\n", + "# Plot Points\n", + "\n", + "# samples within the cube\n", + "X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])\n", + "\n", + "X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],\n", + " [0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],\n", + " [0.7,-0.6,-0.8]])\n", + "\n", + "for row in X_inside:\n", + " ax.scatter(row[0], row[1], row[2], color=\"r\", s=50, marker='^')\n", + "\n", + "for row in X_outside: \n", + " ax.scatter(row[0], row[1], row[2], color=\"k\", s=50)\n", + "\n", + "# Plot Cube\n", + "h = [-0.5, 0.5]\n", + "for s, e in combinations(np.array(list(product(h,h,h))), 2):\n", + " if np.sum(np.abs(s-e)) == h[1]-h[0]:\n", + " ax.plot3D(*zip(s,e), color=\"g\")\n", + " \n", + "ax.set_xlim(-1.5, 1.5)\n", + "ax.set_ylim(-1.5, 1.5)\n", + "ax.set_zlim(-1.5, 1.5)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "p(x) = 0.3\n" ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Preparing the plotting of the results" + } + ], + "source": [ + "point_x = np.array([[0],[0],[0]])\n", + "X_all = np.vstack((X_inside,X_outside))\n", + "\n", + "print('p(x) =', parzen_estimation(X_all, point_x, h=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sample data and `timeit` benchmarks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "np.random.seed(123)\n", + "\n", + "# Generate random 2D-patterns\n", + "mu_vec = np.array([0,0])\n", + "cov_mat = np.array([[1,0],[0,1]])\n", + "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The expected probability of a point at the center of the distribution is ~ 0.15915 as we can see below. \n", + "And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above. \n", + "\n", + "\n", + "In order to make a \"good\" prediction via the Parzen-window technique, it is - among other things - crucial to select an appropriate window with. Here, we will use multiple processes to predict the density at the center of the bivariate Gaussian distribution using different window widths." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "actual probability density: 0.159154943092\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" + } + ], + "source": [ + "from scipy.stats import multivariate_normal\n", + "var = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n", + "print('actual probability density:', var.pdf([0,0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmarking functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function. \n", + "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width. \n", + "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to sort our list of results later." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def parzen_estimation(x_samples, point_x, h):\n", + " k_n = 0\n", + " for row in x_samples:\n", + " x_i = (point_x - row[:,np.newaxis]) / (h)\n", + " for row in x_i:\n", + " if np.abs(row) > (1/2):\n", + " break\n", + " else: # \"completion-else\"*\n", + " k_n += 1\n", + " return (h, (k_n / len(x_samples)) / (h**point_x.shape[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def serial(samples, x, widths):\n", + " return [parzen_estimation(samples, x, w) for w in widths]\n", + "\n", + "def multiprocess(processes, samples, x, widths):\n", + " pool = mp.Pool(processes=processes)\n", + " results = [pool.apply_async(parzen_estimation, args=(samples, x, w)) for w in widths]\n", + " results = [p.get() for p in results]\n", + " results.sort() # to sort the results by input window width\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just to get an idea what the results would look like (i.e., the predicted densities for different window widths):" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h = 0.1, p(x) = 0.016\n", + "h = 0.2, p(x) = 0.0305\n", + "h = 0.3, p(x) = 0.045\n", + "h = 0.4, p(x) = 0.06175\n", + "h = 0.5, p(x) = 0.078\n", + "h = 0.6, p(x) = 0.0911666666667\n", + "h = 0.7, p(x) = 0.106\n", + "h = 0.8, p(x) = 0.117375\n", + "h = 0.9, p(x) = 0.132666666667\n", + "h = 1.0, p(x) = 0.1445\n", + "h = 1.1, p(x) = 0.157090909091\n", + "h = 1.2, p(x) = 0.1685\n" ] - }, + } + ], + "source": [ + "widths = np.arange(0.1, 1.3, 0.1)\n", + "point_x = np.array([[0],[0]])\n", + "results = []\n", + "\n", + "results = multiprocess(4, x_2Dgauss, point_x, widths)\n", + "\n", + "for r in results:\n", + " print('h = %s, p(x) = %s' %(r[0], r[1]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915. \n", + "Thus, for the benchmark, let us create 100 evenly spaced window width in the range of 1.0 to 1.2." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "widths = np.linspace(1.0, 1.2, 100)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import timeit\n", + "\n", + "mu_vec = np.array([0,0])\n", + "cov_mat = np.array([[1,0],[0,1]])\n", + "n = 10000\n", + "\n", + "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n", + "\n", + "benchmarks = []\n", + "\n", + "benchmarks.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n", + " 'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n", + "\n", + "benchmarks.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n", + " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n", + "\n", + "benchmarks.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n", + " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n", + "\n", + "benchmarks.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n", + " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n", + "\n", + "benchmarks.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n", + " 'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing the plotting of the results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import platform\n", + "\n", + "def print_sysinfo():\n", + " \n", + " print('\\nPython version :', platform.python_version())\n", + " print('compiler :', platform.python_compiler())\n", + " \n", + " print('\\nsystem :', platform.system())\n", + " print('release :', platform.release())\n", + " print('machine :', platform.machine())\n", + " print('processor :', platform.processor())\n", + " print('CPU count :', mp.cpu_count())\n", + " print('interpreter:', platform.architecture()[0])\n", + " print('\\n\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "\n", + "def plot_results():\n", + " bar_labels = ['serial', '2', '3', '4', '6']\n", + "\n", + " fig = plt.figure(figsize=(10,8))\n", + "\n", + " # plot bars\n", + " y_pos = np.arange(len(benchmarks))\n", + " plt.yticks(y_pos, bar_labels, fontsize=16)\n", + " bars = plt.barh(y_pos, benchmarks,\n", + " align='center', alpha=0.4, color='g')\n", + "\n", + " # annotation and labels\n", + " \n", + " for ba,be in zip(bars, benchmarks):\n", + " plt.text(ba.get_width() + 2, ba.get_y() + ba.get_height()/2,\n", + " '{0:.2%}'.format(benchmarks[0]/be), \n", + " ha='center', va='bottom', fontsize=12)\n", + " \n", + " plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n", + " plt.ylabel('number of processes', fontsize=14)\n", + " t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n", + " plt.ylim([-1,len(benchmarks)+0.5])\n", + " plt.xlim([0,max(benchmarks)*1.1])\n", + " plt.vlines(benchmarks[0], -1, len(benchmarks)+0.5, linestyles='dashed')\n", + " plt.grid()\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "import platform\n", - "\n", - "def print_sysinfo():\n", - " \n", - " print('\\nPython version :', platform.python_version())\n", - " print('compiler :', platform.python_compiler())\n", - " \n", - " print('\\nsystem :', platform.system())\n", - " print('release :', platform.release())\n", - " print('machine :', platform.machine())\n", - " print('processor :', platform.processor())\n", - " print('CPU count :', mp.cpu_count())\n", - " print('interpreter:', platform.architecture()[0])\n", - " print('\\n\\n')" - ], - "language": "python", + "data": { + "image/png": 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Jy8wHH3yA1NRU9O/fH9nZ2YiJicHIkSNRv359+Pr6Ii0tTVp37dq1KCgowMCBA5GVlaXy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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [], - "prompt_number": 20 + "output_type": "display_data" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "\n", - "def plot_results():\n", - " bar_labels = ['serial', '2', '3', '4', '6']\n", + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - " fig = plt.figure(figsize=(10,8))\n", + "Python version : 3.4.1\n", + "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", "\n", - " # plot bars\n", - " y_pos = np.arange(len(benchmarks))\n", - " plt.yticks(y_pos, bar_labels, fontsize=16)\n", - " bars = plt.barh(y_pos, benchmarks,\n", - " align='center', alpha=0.4, color='g')\n", + "system : Darwin\n", + "release : 13.2.0\n", + "machine : x86_64\n", + "processor : i386\n", + "CPU count : 4\n", + "interpreter: 64bit\n", "\n", - " # annotation and labels\n", - " \n", - " for ba,be in zip(bars, benchmarks):\n", - " plt.text(ba.get_width() + 2, ba.get_y() + ba.get_height()/2,\n", - " '{0:.2%}'.format(benchmarks[0]/be), \n", - " ha='center', va='bottom', fontsize=12)\n", - " \n", - " plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n", - " plt.ylabel('number of processes', fontsize=14)\n", - " t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n", - " plt.ylim([-1,len(benchmarks)+0.5])\n", - " plt.xlim([0,max(benchmarks)*1.1])\n", - " plt.vlines(benchmarks[0], -1, len(benchmarks)+0.5, linestyles='dashed')\n", - " plt.grid()\n", - "\n", - " plt.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Results" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "plot_results()\n", - "print_sysinfo()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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5uTm8vb019nQDz34cGB8fj1GjRuHYsWN47733cPz4cURFReHdd99V+xHi6z6f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- "text": [ - "" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Python version : 3.4.1\n", - "compiler : GCC 4.2.1 (Apple Inc. build 5577)\n", - "\n", - "system : Darwin\n", - "release : 13.2.0\n", - "machine : x86_64\n", - "processor : i386\n", - "CPU count : 4\n", - "interpreter: 64bit\n", - "\n", - "\n", - "\n" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Conclusion" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#Sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine, the submission of 6 parallel 6 processes doesn't lead to a further performance improvement, which makes sense for a 4-core CPU. \n", - "We also notice that there was a significant performance increase when we were using 3 instead of only 2 processes in parallel. However, the performance increase was less significant when we moved up to 4 parallel processes, respectively. \n", - "This can be attributed to the fact that in this case, the CPU consists of only 4 cores, and system processes, such as the operating system, are also running in the background. Thus, the fourth core simply does not have enough capacity left to further increase the performance of the fourth process to a large extend. And we also have to keep in mind that every additional process comes with an additional overhead for inter-process communication. \n", "\n", - "Also, an improvement due to parallel processing only makes sense if our tasks are \"CPU-bound\" where the majority of the task is spent in the CPU in contrast to I/O bound tasks, i.e., tasks that are processing data from a disk. " + "\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] } ], - "metadata": {} + "source": [ + "plot_results()\n", + "print_sysinfo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine, the submission of 6 parallel 6 processes doesn't lead to a further performance improvement, which makes sense for a 4-core CPU. \n", + "We also notice that there was a significant performance increase when we were using 3 instead of only 2 processes in parallel. However, the performance increase was less significant when we moved up to 4 parallel processes, respectively. \n", + "This can be attributed to the fact that in this case, the CPU consists of only 4 cores, and system processes, such as the operating system, are also running in the background. Thus, the fourth core simply does not have enough capacity left to further increase the performance of the fourth process to a large extend. And we also have to keep in mind that every additional process comes with an additional overhead for inter-process communication. \n", + "\n", + "Also, an improvement due to parallel processing only makes sense if our tasks are \"CPU-bound\" where the majority of the task is spent in the CPU in contrast to I/O bound tasks, i.e., tasks that are processing data from a disk. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tutorials/not_so_obvious_python_stuff.ipynb b/tutorials/not_so_obvious_python_stuff.ipynb index c2e2948..cf683b0 100644 --- a/tutorials/not_so_obvious_python_stuff.ipynb +++ b/tutorials/not_so_obvious_python_stuff.ipynb @@ -1,4316 +1,4151 @@ { - "metadata": { - "name": "", - "signature": "sha256:d87105a74c8f25016f90bdec495a890a988277a3f51e0589febbbac87720b033" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Sebastian Raschka](http://sebastianraschka.com) \n", + "\n", + "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb) \n", + "- [Link to the GitHub repository](https://github.com/rasbt/python_reference) \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext watermark" + ] + }, { - "cells": [ + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Sebastian Raschka](http://sebastianraschka.com) \n", - "last updated: 05/24/2014 ([Changelog](#changelog))\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "last updated: 2018-06-09 \n", "\n", - "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb) \n", - "- [Link to the GitHub repository](https://github.com/rasbt/python_reference) \n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### All code was executed in Python 3.4 (unless stated otherwise)" + "CPython 3.6.4\n", + "IPython 6.2.1\n" ] - }, + } + ], + "source": [ + "%watermark -d -u -v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.\n", + "\n", + "([Changelog](#changelog))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# A collection of not-so-obvious Python stuff you should know!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "I am really looking forward to your comments and suggestions to improve and \n", + "extend this little collection! Just send me a quick note \n", + "via Twitter: [@rasbt](https://twitter.com/rasbt) \n", + "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sections\n", + "- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n", + "\n", + "- [Assignment operators and lists - simple-add vs. add-AND operators](#pm_in_lists)\n", + "\n", + "- [`True` and `False` in the datetime module](#datetime_module)\n", + "\n", + "- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n", + "\n", + "- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n", + "\n", + "- [Picking `True` values from logical `and`s and `or`s](#false_true_expressions)\n", + "\n", + "- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n", + "\n", + "- [Be aware of the consuming generator](#consuming_generator)\n", + "\n", + "- [`bool` is a subclass of `int`](#bool_int)\n", + "\n", + "- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n", + "\n", + "- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n", + "\n", + "- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n", + "\n", + "- [List comprehensions are fast, but generators are faster!?](#list_generator)\n", + "\n", + "- [Public vs. private class methods and name mangling](#private_class)\n", + "\n", + "- [The consequences of modifying a list when looping through it](#looping_pitfall)\n", + "\n", + "- [Dynamic binding and typos in variable names](#dynamic_binding)\n", + "\n", + "- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n", + "\n", + "- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n", + "\n", + "- [Creating copies of mutable objects](#copy_mutable)\n", + "\n", + "- [Key differences between Python 2 and 3](#python_differences)\n", + "\n", + "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n", + "\n", + "- [Abortive statements in `finally` blocks](#finally_blocks)\n", + "\n", + "- [Assigning types to variables as values](#variable_types)\n", + "\n", + "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n", + "\n", + "- [Keyword argument unpacking syntax - `*args` and `**kwargs`](#splat_op)\n", + "\n", + "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n", + "\n", + "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n", + "\n", + "- [Interning of compile-time constants vs. run-time expressions](#string_interning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The C3 class resolution algorithm for multiple class inheritance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we are dealing with multiple inheritance, according to the newer C3 class resolution algorithm, the following applies: \n", + "Assuming that child class C inherits from two parent classes A and B, \"class A should be checked before class B\".\n", + "\n", + "If you want to learn more, please read the [original blog](http://python-history.blogspot.ru/2010/06/method-resolution-order.html) post by Guido van Rossum.\n", + "\n", + "(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A collection of not-so-obvious Python stuff you should know!" + "name": "stdout", + "output_type": "stream", + "text": [ + "class A\n" ] - }, + } + ], + "source": [ + "class A(object):\n", + " def foo(self):\n", + " print(\"class A\")\n", + "\n", + "class B(object):\n", + " def foo(self):\n", + " print(\"class B\")\n", + "\n", + "class C(A, B):\n", + " pass\n", + "\n", + "C().foo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So what actually happened above was that class `C` looked in the scope of the parent class `A` for the method `.foo()` first (and found it)!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I received an email containing a suggestion which uses a more nested example to illustrate Guido van Rossum's point a little bit better:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "I am really looking forward to your comments and suggestions to improve and \n", - "extend this little collection! Just send me a quick note \n", - "via Twitter: [@rasbt](https://twitter.com/rasbt) \n", - "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n", - "
" + "name": "stdout", + "output_type": "stream", + "text": [ + "class C\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" + } + ], + "source": [ + "class A(object):\n", + " def foo(self):\n", + " print(\"class A\")\n", + "\n", + "class B(A):\n", + " pass\n", + "\n", + "class C(A):\n", + " def foo(self):\n", + " print(\"class C\")\n", + "\n", + "class D(B,C):\n", + " pass\n", + "\n", + "D().foo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, class `D` searches in `B` first, which in turn inherits from `A` (note that class `C` also inherits from `A`, but has its own `.foo()` method) so that we come up with the search order: `D, B, C, A`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assignment operators and lists - simple-add vs. add-AND operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object. \n", + "\n", + "However, if we use the assignment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ID: 4486856904\n", + "ID (+=): 4486856904\n", + "ID (list = list + ...): 4486959368\n" ] - }, + } + ], + "source": [ + "a_list = []\n", + "print('ID:', id(a_list))\n", + "\n", + "a_list += [1]\n", + "print('ID (+=):', id(a_list))\n", + "\n", + "a_list = a_list + [2]\n", + "print('ID (list = list + ...):', id(a_list))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just for reference, the `.append()` and `.extends()` methods are modifying the `list` object in place, just as expected." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sections\n", - "- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n", - "\n", - "- [Assignment operators and lists - simple-add vs. add-AND operators](#pm_in_lists)\n", - "\n", - "- [`True` and `False` in the datetime module](#datetime_module)\n", - "\n", - "- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n", - "\n", - "- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n", - "\n", - "- [Picking `True` values from logical `and`s and `or`s](#false_true_expressions)\n", - "\n", - "- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n", - "\n", - "- [Be aware of the consuming generator](#consuming_generator)\n", - "\n", - "- [`bool` is a subclass of `int`](#bool_int)\n", - "\n", - "- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n", - "\n", - "- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n", - "\n", - "- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n", - "\n", - "- [List comprehensions are fast, but generators are faster!?](#list_generator)\n", - "\n", - "- [Public vs. private class methods and name mangling](#private_class)\n", - "\n", - "- [The consequences of modifying a list when looping through it](#looping_pitfall)\n", - "\n", - "- [Dynamic binding and typos in variable names](#dynamic_binding)\n", - "\n", - "- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "[] \n", + "ID (initial): 4486857224 \n", "\n", - "- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n", + "[1] \n", + "ID (append): 4486857224 \n", "\n", - "- [Creating copies of mutable objects](#copy_mutable)\n", - "\n", - "- [Key differences between Python 2 and 3](#python_differences)\n", - "\n", - "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n", - "\n", - "- [Abortive statements in `finally` blocks](#finally_blocks)\n", - "\n", - "- [Assigning types to variables as values](#variable_types)\n", - "\n", - "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n", - "\n", - "- [Keyword argument unpacking syntax - `*args` and `**kwargs`](#splat_op)\n", - "\n", - "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n", - "\n", - "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n", - "\n", - "- [Interning of compile-time constants vs. run-time expressions](#string_interning)" + "[1, 2] \n", + "ID (extend): 4486857224\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" + } + ], + "source": [ + "a_list = []\n", + "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n", + "\n", + "a_list.append(1)\n", + "print(a_list, '\\nID (append):',id(a_list), '\\n')\n", + "\n", + "a_list.extend([2])\n", + "print(a_list, '\\nID (extend):',id(a_list))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## `True` and `False` in the datetime module\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable — at least in some quarters.\" \n", + "\n", + "Please note that Python version <= 3.4.5 evaluated the first statement `bool(datetime.time(0,0,0))` as `False`, which was regarded counter-intuitive, since \"12am\" refers to \"midnight.\"\n", + "\n", + "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current python version: 3.6.4\n", + "\"datetime.time(0,0,0)\" (Midnight) -> True\n", + "\"datetime.time(1,0,0)\" (1 am) -> True\n" ] - }, + } + ], + "source": [ + "from platform import python_version\n", + "import datetime\n", + "\n", + "print(\"Current python version: \", python_version())\n", + "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0))) # Python version <= 3.4.5 evaluates this statement to False\n", + "\n", + "print('\"datetime.time(1,0,0)\" (1 am) ->', bool(datetime.time(1,0,0)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Python reuses objects for small integers - use \"==\" for equality, \"is\" for identity\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This oddity occurs, because Python keeps an array of small integer objects (i.e., integers between -5 and 256, [see the doc](https://docs.python.org/2/c-api/int.html#PyInt_FromLong))." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The C3 class resolution algorithm for multiple class inheritance" + "name": "stdout", + "output_type": "stream", + "text": [ + "a is b True\n", + "c is d False\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + } + ], + "source": [ + "a = 1\n", + "b = 1\n", + "print('a is b', bool(a is b))\n", + "True\n", + "\n", + "c = 999\n", + "d = 999\n", + "print('c is d', bool(c is d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(*I received a comment that this is in fact a CPython artifact and **must not necessarily be true** in all implementations of Python!*)\n", + "\n", + "So the take home message is: always use \"==\" for equality, \"is\" for identity!\n", + "\n", + "Here is a [nice article](http://python.net/%7Egoodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables) explaining it by comparing \"boxes\" (C language) with \"name tags\" (Python)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example demonstrates that this applies indeed for integers in the range in -5 to 256:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "256 is 257-1 True\n", + "257 is 258-1 False\n", + "-5 is -6+1 True\n", + "-7 is -6-1 False\n" ] - }, + } + ], + "source": [ + "print('256 is 257-1', 256 is 257-1)\n", + "print('257 is 258-1', 257 is 258 - 1)\n", + "print('-5 is -6+1', -5 is -6+1)\n", + "print('-7 is -6-1', -7 is -6-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### And to illustrate the test for equality (`==`) vs. identity (`is`):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we are dealing with multiple inheritance, according to the newer C3 class resolution algorithm, the following applies: \n", - "Assuming that child class C inherits from two parent classes A and B, \"class A should be checked before class B\".\n", - "\n", - "If you want to learn more, please read the [original blog](http://python-history.blogspot.ru/2010/06/method-resolution-order.html) post by Guido van Rossum.\n", - "\n", - "(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))" + "name": "stdout", + "output_type": "stream", + "text": [ + "a is b, False\n", + "a == b, True\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class A(object):\n", - " def foo(self):\n", - " print(\"class A\")\n", - "\n", - "class B(object):\n", - " def foo(self):\n", - " print(\"class B\")\n", - "\n", - "class C(A, B):\n", - " pass\n", - "\n", - "C().foo()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "class A\n" - ] - } - ], - "prompt_number": 2 - }, + } + ], + "source": [ + "a = 'hello world!'\n", + "b = 'hello world!'\n", + "print('a is b,', a is b)\n", + "print('a == b,', a == b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would think that identity would always imply equality, but this is not always true, as we can see in the next example:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So what actually happened above was that class `C` looked in the scope of the parent class `A` for the method `.foo()` first (and found it)!" + "name": "stdout", + "output_type": "stream", + "text": [ + "a is a, True\n", + "a == a, False\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I received an email containing a suggestion which uses a more nested example to illustrate Guido van Rossum's point a little bit better:" + } + ], + "source": [ + "a = float('nan')\n", + "print('a is a,', a is a)\n", + "print('a == a,', a == a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Shallow vs. deep copies if list contains other structures and objects\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Shallow copy**: \n", + "If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list. If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` or `a_list.copy()`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IDs:\n", + "list1: 4486860424\n", + "list2: 4486860424\n", + "list3: 4486818632\n", + "list4: 4486818568\n", + "\n", + "list1: [3, 2]\n", + "\n", + "list1: [3, 2]\n", + "list2: [3, 2]\n", + "list3: [4, 2]\n", + "list4: [1, 4]\n" ] - }, + } + ], + "source": [ + "list1 = [1,2]\n", + "list2 = list1 # reference\n", + "list3 = list1[:] # shallow copy\n", + "list4 = list1.copy() # shallow copy\n", + "\n", + "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\n'\n", + " .format(id(list1), id(list2), id(list3), id(list4)))\n", + "\n", + "list2[0] = 3\n", + "print('list1:', list1)\n", + "\n", + "list3[0] = 4\n", + "list4[1] = 4\n", + "print('\\nlist1:', list1)\n", + "print('list2:', list2)\n", + "print('list3:', list3)\n", + "print('list4:', list4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Deep copy** \n", + "As we have seen above, a shallow copy works fine if we want to create a new list with contents of the original list which we want to modify independently. \n", + "\n", + "However, if we are dealing with compound objects (e.g., lists that contain other lists, [read here](https://docs.python.org/2/library/copy.html) for more information) it becomes a little trickier.\n", + "\n", + "In the case of compound objects, a shallow copy would create a new compound object, but it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects \n", + "for the objects found in the original compound object. \n", + "If you follow the code, the concept should become more clear:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "class A(object):\n", - " def foo(self):\n", - " print(\"class A\")\n", - "\n", - "class B(A):\n", - " pass\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "IDs:\n", + "list1: 4486818824\n", + "list2: 4486886024\n", + "list3: 4486888200\n", "\n", - "class C(A):\n", - " def foo(self):\n", - " print(\"class C\")\n", + "list1: [[3], [2]]\n", "\n", - "class D(B,C):\n", - " pass\n", - "\n", - "D().foo()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "class C\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, class `D` searches in `B` first, which in turn inherits from `A` (note that class `C` also inherits from `A`, but has its own `.foo()` method) so that we come up with the search order: `D, B, C, A`. " + "list1: [[3], [2]]\n", + "list2: [[3], [2]]\n", + "list3: [[5], [2]]\n" ] - }, + } + ], + "source": [ + "from copy import deepcopy\n", + "\n", + "list1 = [[1],[2]]\n", + "list2 = list1.copy() # shallow copy\n", + "list3 = deepcopy(list1) # deep copy\n", + "\n", + "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\n'\n", + " .format(id(list1), id(list2), id(list3)))\n", + "\n", + "list2[0][0] = 3\n", + "print('list1:', list1)\n", + "\n", + "list3[0][0] = 5\n", + "print('\\nlist1:', list1)\n", + "print('list2:', list2)\n", + "print('list3:', list3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Picking `True` values from logical `and`s and `or`s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Logical `or`:** \n", + "\n", + "`a or b == a if a else b` \n", + "- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions. \n", + "This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n", + "\n", + "**Logical `and`:** \n", + "\n", + "`a and b == b if a else a` \n", + "- If both values in `and` expressions are `True`, Python will select the second value, since for a logical `and`, both values must be true.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" + "name": "stdout", + "output_type": "stream", + "text": [ + "2 * 7 = 14\n" ] - }, + } + ], + "source": [ + "result = (2 or 3) * (5 and 7)\n", + "print('2 * 7 =', result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Don't use mutable objects as default arguments for functions!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Don't use mutable objects (e.g., dictionaries, lists, sets, etc.) as default arguments for functions! You might expect that a new list is created every time when we call the function without providing an argument for the default parameter, but this is not the case: **Python will create the mutable object (default parameter) the first time the function is defined - not when it is called**, see the following code:\n", + "\n", + "(Original source: [http://docs.python-guide.org/en/latest/writing/gotchas/](http://docs.python-guide.org/en/latest/writing/gotchas/)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Assignment operators and lists - simple-add vs. add-AND operators" + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n", + "[1, 2]\n" ] - }, + } + ], + "source": [ + "def append_to_list(value, def_list=[]):\n", + " def_list.append(value)\n", + " return def_list\n", + "\n", + "my_list = append_to_list(1)\n", + "print(my_list)\n", + "\n", + "my_other_list = append_to_list(2)\n", + "print(my_other_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another good example showing that demonstrates that default arguments are created when the function is created (**and not when it is called!**):" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + "name": "stdout", + "output_type": "stream", + "text": [ + "1528560045.3962939\n", + "1528560045.3962939\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object directly. \n", - "\n", - "However, if we use the assigment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:" + } + ], + "source": [ + "import time\n", + "def report_arg(my_default=time.time()):\n", + " print(my_default)\n", + "\n", + "report_arg()\n", + "\n", + "time.sleep(5)\n", + "\n", + "report_arg()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Be aware of the consuming generator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Be aware of what is happening when combining `in` checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 in gen, True\n", + "3 in gen, True\n", + "1 in gen, False\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_list = []\n", - "print('ID:', id(a_list))\n", - "\n", - "a_list += [1]\n", - "print('ID (+=):', id(a_list))\n", - "\n", - "a_list = a_list + [2]\n", - "print('ID (list = list + ...):', id(a_list))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "ID: 4366496544\n", - "ID (+=): 4366496544\n", - "ID (list = list + ...): 4366495472\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just for reference, the `.append()` and `.extends()` methods are modifying the `list` object in place, just as expected." + } + ], + "source": [ + "gen = (i for i in range(5))\n", + "print('2 in gen,', 2 in gen)\n", + "print('3 in gen,', 3 in gen)\n", + "print('1 in gen,', 1 in gen) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Although this defeats the purpose of a generator (in most cases), we can convert a generator into a list to circumvent the problem. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 in l, True\n", + "3 in l, True\n", + "1 in l, True\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_list = []\n", - "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n", - "\n", - "a_list.append(1)\n", - "print(a_list, '\\nID (append):',id(a_list), '\\n')\n", - "\n", - "a_list.extend([2])\n", - "print(a_list, '\\nID (extend):',id(a_list))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[] \n", - "ID (initial): 140704077653128 \n", - "\n", - "[1] \n", - "ID (append): 140704077653128 \n", - "\n", - "[1, 2] \n", - "ID (extend): 140704077653128\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" + } + ], + "source": [ + "gen = (i for i in range(5))\n", + "a_list = list(gen)\n", + "print('2 in l,', 2 in a_list)\n", + "print('3 in l,', 3 in a_list)\n", + "print('1 in l,', 1 in a_list) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## `bool` is a subclass of `int`\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Chicken or egg? In the history of Python (Python 2.2 to be specific) truth values were implemented via 1 and 0 (similar to the old C). In order to avoid syntax errors in old (but perfectly working) Python code, `bool` was added as a subclass of `int` in Python 2.3.\n", + "\n", + "Original source: [http://www.peterbe.com/plog/bool-is-int](http://www.peterbe.com/plog/bool-is-int)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "isinstance(True, int): True\n", + "True + True: 2\n", + "3*True + True: 4\n", + "3*True - False: 3\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## `True` and `False` in the datetime module\n", - "\n" + } + ], + "source": [ + "print('isinstance(True, int):', isinstance(True, int))\n", + "print('True + True:', True + True)\n", + "print('3*True + True:', 3*True + True)\n", + "print('3*True - False:', 3*True - False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## About lambda-in-closures-and-a-loop pitfall" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember the section about the [consuming generators](#consuming_generator)? This example is somewhat related, but the result might still come as unexpected. \n", + "\n", + "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n", + "\n", + "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope. Since the list comprehension has already been constructed and evaluated when we `for-loop` through the list, the closure-variable will be set to the last value 4." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "4\n", + "4\n", + "4\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable\u2014at least in some quarters.\" \n", - "\n", - "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))" + } + ], + "source": [ + "my_list = [lambda: i for i in range(5)]\n", + "for l in my_list:\n", + " print(l())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, by using a generator expression, we can make use of its stepwise evaluation (note that the returned variable still stems from the same closure, but the value changes as we iterate over the generator)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + } + ], + "source": [ + "my_gen = (lambda: n for n in range(5))\n", + "for l in my_gen:\n", + " print(l())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And if you are really keen on using lists, there is a nifty trick that circumvents this problem as a reader nicely pointed out in the comments: We can simply pass the loop variable `i` as a default argument to the lambdas." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import datetime\n", - "\n", - "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0)))\n", - "\n", - "print('\"datetime.time(1,0,0)\" (1 am) ->', bool(datetime.time(1,0,0)))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\"datetime.time(0,0,0)\" (Midnight) -> False\n", - "\"datetime.time(1,0,0)\" (1 am) -> True\n" - ] - } - ], - "prompt_number": 8 - }, + } + ], + "source": [ + "my_list = [lambda x=i: x for i in range(5)]\n", + "for l in my_list:\n", + " print(l())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Python's LEGB scope resolution and the keywords `global` and `nonlocal`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is nothing particularly surprising about Python's LEGB scope resolution (Local -> Enclosed -> Global -> Built-in), but it is still useful to take a look at some examples!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `global` vs. `local`\n", + "\n", + "According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` `local`ly in the function's scope, it won't have an effect on the `global` `x`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" + "name": "stdout", + "output_type": "stream", + "text": [ + "in_func: 1\n", + "global: 0\n" ] - }, + } + ], + "source": [ + "x = 0\n", + "\n", + "\n", + "def in_func():\n", + " x = 1\n", + " print('in_func:', x)\n", + "\n", + "\n", + "in_func()\n", + "print('global:', x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to modify the `global` x via a function, we can simply use the `global` keyword to import the variable into the function's scope:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Python reuses objects for small integers - use \"==\" for equality, \"is\" for identity\n", - "\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "in_func: 1\n", + "global: 1\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This oddity occurs, because Python keeps an array of small integer objects (i.e., integers between -5 and 256, [see the doc](https://docs.python.org/2/c-api/int.html#PyInt_FromLong))." + } + ], + "source": [ + "x = 0\n", + "\n", + "\n", + "def in_func():\n", + " global x\n", + " x = 1\n", + " print('in_func:', x)\n", + "\n", + "\n", + "in_func()\n", + "print('global:', x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `local` vs. `enclosed`\n", + "\n", + "Now, let us take a look at `local` vs. `enclosed`. Here, we set the variable `x = 1` in the `outer` function and set `x = 1` in the enclosed function `inner`. Since `inner` looks in the local scope first, it won't modify `outer`'s `x`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "outer before: 1\n", + "inner: 2\n", + "outer after: 1\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = 1\n", - "b = 1\n", - "print('a is b', bool(a is b))\n", - "True\n", - "\n", - "c = 999\n", - "d = 999\n", - "print('c is d', bool(c is d))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "a is b True\n", - "c is d False\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(*I received a comment that this is in fact a CPython artefact and **must not necessarily be true** in all implementations of Python!*)\n", - "\n", - "So the take home message is: always use \"==\" for equality, \"is\" for identity!\n", - "\n", - "Here is a [nice article](http://python.net/%7Egoodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables) explaining it by comparing \"boxes\" (C language) with \"name tags\" (Python)." + } + ], + "source": [ + "def outer():\n", + " x = 1\n", + " print('outer before:', x)\n", + "\n", + " def inner():\n", + " x = 2\n", + " print(\"inner:\", x)\n", + " inner()\n", + " print(\"outer after:\", x)\n", + "\n", + "\n", + "outer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is where the `nonlocal` keyword comes in handy - it allows us to modify the `x` variable in the `enclosed` scope:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "outer before: 1\n", + "inner: 2\n", + "outer after: 2\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This example demonstrates that this applies indeed for integers in the range in -5 to 256:" + } + ], + "source": [ + "def outer():\n", + " x = 1\n", + " print('outer before:', x)\n", + "\n", + " def inner():\n", + " nonlocal x\n", + " x = 2\n", + " print(\"inner:\", x)\n", + " inner()\n", + " print(\"outer after:\", x)\n", + "\n", + "\n", + "outer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## When mutable contents of immutable tuples aren't so mutable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we all know, tuples are immutable objects in Python, right!? But what happens if they contain mutable objects? \n", + "\n", + "First, let us have a look at the expected behavior: a `TypeError` is raised if we try to modify immutable types in a tuple: " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('256 is 257-1', 256 is 257-1)\n", - "print('257 is 258-1', 257 is 258 - 1)\n", - "print('-5 is -6+1', -5 is -6+1)\n", - "print('-7 is -6-1', -7 is -6-1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "256 is 257-1 True\n", - "257 is 258-1 False\n", - "-5 is -6+1 True\n", - "-7 is -6-1 False\n" - ] - } - ], - "prompt_number": 11 - }, + } + ], + "source": [ + "tup = (1,)\n", + "tup[0] += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### And to illustrate the test for equality (`==`) vs. identity (`is`):" + "name": "stdout", + "output_type": "stream", + "text": [ + "tup before: ([],)\n" ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = 'hello world!'\n", - "b = 'hello world!'\n", - "print('a is b,', a is b)\n", - "print('a == b,', a == b)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "a is b, False\n", - "a == b, True\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We would think that identity would always imply equality, but this is not always true, as we can see in the next example:" + "ename": "TypeError", + "evalue": "'tuple' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = float('nan')\n", - "print('a is a,', a is a)\n", - "print('a == a,', a == a)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "a is a, True\n", - "a == a, False\n" - ] - } - ], - "prompt_number": 12 - }, + } + ], + "source": [ + "tup = ([],)\n", + "print('tup before: ', tup)\n", + "tup[0] += [1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('tup after: ', tup)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "However, **there are ways** to modify the mutable contents of the tuple without raising the `TypeError`, the solution is the `.extend()` method, or alternatively `.append()` (for lists):" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" + "name": "stdout", + "output_type": "stream", + "text": [ + "tup before: ([],)\n", + "tup after: ([1],)\n" ] - }, + } + ], + "source": [ + "tup = ([],)\n", + "print('tup before: ', tup)\n", + "tup[0].extend([1])\n", + "print('tup after: ', tup)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Shallow vs. deep copies if list contains other structures and objects\n", - "\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "tup before: ([],)\n", + "tup after: ([1],)\n" ] - }, + } + ], + "source": [ + "tup = ([],)\n", + "print('tup before: ', tup)\n", + "tup[0].append(1)\n", + "print('tup after: ', tup)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explanation\n", + "\n", + "**A. Jesse Jiryu Davis** has a nice explanation for this phenomenon (Original source: [http://emptysqua.re/blog/python-increment-is-weird-part-ii/](http://emptysqua.re/blog/python-increment-is-weird-part-ii/))\n", + "\n", + "If we try to extend the list via `+=` *\"then the statement executes `STORE_SUBSCR`, which calls the C function `PyObject_SetItem`, which checks if the object supports item assignment. In our case the object is a tuple, so `PyObject_SetItem` throws the `TypeError`. Mystery solved.\"*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 4, 5)\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Shallow copy**: \n", - "If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list. If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` or `a_list.copy()`." + } + ], + "source": [ + "my_tup = (1,)\n", + "my_tup += (4,)\n", + "my_tup = my_tup + (5,)\n", + "print(my_tup)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What happens \"behind\" the curtains is that the tuple is not modified, but a new object is generated every time, which will inherit the old \"name tag\":" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4486707912\n", + "4485211784\n", + "4486955152\n" ] - }, + } + ], + "source": [ + "my_tup = (1,)\n", + "print(id(my_tup))\n", + "my_tup += (4,)\n", + "print(id(my_tup))\n", + "my_tup = my_tup + (5,)\n", + "print(id(my_tup))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## List comprehensions are fast, but generators are faster!?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"List comprehensions are fast, but generators are faster!?\" - No, not really (or significantly, see the benchmarks below). So what's the reason to prefer one over the other?\n", + "- use lists if you want to use the plethora of list methods \n", + "- use generators when you are dealing with huge collections to avoid memory issues" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import timeit\n", + "\n", + "\n", + "def plainlist(n=100000):\n", + " my_list = []\n", + " for i in range(n):\n", + " if i % 5 == 0:\n", + " my_list.append(i)\n", + " return my_list\n", + "\n", + "\n", + "def listcompr(n=100000):\n", + " my_list = [i for i in range(n) if i % 5 == 0]\n", + " return my_list\n", + "\n", + "\n", + "def generator(n=100000):\n", + " my_gen = (i for i in range(n) if i % 5 == 0)\n", + " return my_gen\n", + "\n", + "\n", + "def generator_yield(n=100000):\n", + " for i in range(n):\n", + " if i % 5 == 0:\n", + " yield i" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To be fair to the list, let us exhaust the generators:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "list1 = [1,2]\n", - "list2 = list1 # reference\n", - "list3 = list1[:] # shallow copy\n", - "list4 = list1.copy() # shallow copy\n", + "name": "stdout", + "output_type": "stream", + "text": [ + "plain_list: 10.8 ms ± 793 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "\n", - "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\n'\n", - " .format(id(list1), id(list2), id(list3), id(list4)))\n", - "\n", - "list2[0] = 3\n", - "print('list1:', list1)\n", - "\n", - "list3[0] = 4\n", - "list4[1] = 4\n", - "print('list1:', list1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "IDs:\n", - "list1: 4346366472\n", - "list2: 4346366472\n", - "list3: 4346366408\n", - "list4: 4346366536\n", - "\n", - "list1: [3, 2]\n", - "list1: [3, 2]\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Deep copy** \n", - "As we have seen above, a shallow copy works fine if we want to create a new list with contents of the original list which we want to modify independently. \n", + "listcompr: 10 ms ± 830 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "\n", - "However, if we are dealing with compound objects (e.g., lists that contain other lists, [read here](https://docs.python.org/2/library/copy.html) for more information) it becomes a little trickier.\n", + "generator: 11.4 ms ± 1 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n", "\n", - "In the case of compound objects, a shallow copy would create a new compound object, but it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects \n", - "for the objects found in the original compound object. \n", - "If you follow the code, the concept should become more clear:" + "generator_yield: 12.3 ms ± 1.82 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from copy import deepcopy\n", - "\n", - "list1 = [[1],[2]]\n", - "list2 = list1.copy() # shallow copy\n", - "list3 = deepcopy(list1) # deep copy\n", - "\n", - "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\n'\n", - " .format(id(list1), id(list2), id(list3)))\n", - "\n", - "list2[0][0] = 3\n", - "print('list1:', list1)\n", - "\n", - "list3[0][0] = 5\n", - "print('list1:', list1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "IDs:\n", - "list1: 4377956296\n", - "list2: 4377961752\n", - "list3: 4377954928\n", - "\n", - "list1: [[3], [2]]\n", - "list1: [[3], [2]]\n" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" + } + ], + "source": [ + "def test_plainlist(plain_list):\n", + " for i in plain_list():\n", + " pass\n", + "\n", + "\n", + "def test_listcompr(listcompr):\n", + " for i in listcompr():\n", + " pass\n", + "\n", + "\n", + "def test_generator(generator):\n", + " for i in generator():\n", + " pass\n", + "\n", + "\n", + "def test_generator_yield(generator_yield):\n", + " for i in generator_yield():\n", + " pass\n", + "\n", + "\n", + "print('plain_list: ', end='')\n", + "%timeit test_plainlist(plainlist)\n", + "print('\\nlistcompr: ', end='')\n", + "%timeit test_listcompr(listcompr)\n", + "print('\\ngenerator: ', end='')\n", + "%timeit test_generator(generator)\n", + "print('\\ngenerator_yield: ', end='')\n", + "%timeit test_generator_yield(generator_yield)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Public vs. private class methods and name mangling\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class (i.e., by the API user). \n", + "All we can do is indicate methods as private to make clear that they are not to be used outside the class, but it really is up to the class user, since \"we are all consenting adults here\"! \n", + "So, when we want to mark a class method as private, we can put a single underscore in front of it. \n", + "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n", + "\n", + "This doesn't prevent the class users to access this class member though, but they have to know the trick and also know that it is at their own risk...\n", + "\n", + "Let the following example illustrate what I mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello public world!\n", + "Hello private world!\n", + "Hello private world!\n" ] - }, + } + ], + "source": [ + "class my_class():\n", + " def public_method(self):\n", + " print('Hello public world!')\n", + "\n", + " def __private_method(self):\n", + " print('Hello private world!')\n", + "\n", + " def call_private_method_in_class(self):\n", + " self.__private_method()\n", + "\n", + "\n", + "my_instance = my_class()\n", + "\n", + "my_instance.public_method()\n", + "my_instance._my_class__private_method()\n", + "my_instance.call_private_method_in_class()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The consequences of modifying a list when looping through it" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It can be really dangerous to modify a list when iterating through it - this is a very common pitfall that can cause unintended behavior! \n", + "Look at the following examples, and for a fun exercise: try to figure out what is going on before you skip to the solution!" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Picking `True` values from logical `and`s and `or`s" + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 3, 5]\n" ] - }, + } + ], + "source": [ + "a = [1, 2, 3, 4, 5]\n", + "for i in a:\n", + " if not i % 2:\n", + " a.remove(i)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 5]\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Logical `or`:** \n", - "\n", - "`a or b == a if a else b` \n", - "- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions. \n", - "This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n", - "\n", - "**Logical `and`:** \n", + } + ], + "source": [ + "b = [2, 4, 5, 6]\n", + "for i in b:\n", + " if not i % 2:\n", + " b.remove(i)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing. Look at the following example and it will become clear:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2\n", + "1 5\n", + "2 6\n", + "[4, 5]\n" + ] + } + ], + "source": [ + "b = [2, 4, 5, 6]\n", + "for index, item in enumerate(b):\n", + " print(index, item)\n", + " if not item % 2:\n", + " b.remove(item)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dynamic binding and typos in variable names\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Be careful, dynamic binding is convenient, but can also quickly become dangerous!" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "first list:\n", + "0\n", + "1\n", + "2\n", "\n", - "`a and b == b if a else a` \n", - "- If both values in `and` expressions are `True`, Python will select the second value, since for a logical `and`, both values must be true.\n" + "second list:\n", + "2\n", + "2\n", + "2\n" ] - }, + } + ], + "source": [ + "print('first list:')\n", + "for i in range(3):\n", + " print(i)\n", + " \n", + "print('\\nsecond list:')\n", + "for j in range(3):\n", + " print(i) # I (intentionally) made typo here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## List slicing using indexes that are \"out of range\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we have all encountered it 1 (x10000) time(s) in our lives, the infamous `IndexError`:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "list index out of range", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ], + "source": [ + "my_list = [1, 2, 3, 4, 5]\n", + "print(my_list[5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But suprisingly, it is not raised when we are doing list slicing, which can be a real pain when debugging:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_list = [1, 2, 3, 4, 5]\n", + "print(my_list[5:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Reusing global variable names and `UnboundLocalErrors`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usually, it is no problem to access global variables in the local scope of a function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_func():\n", + " print(var)\n", + "\n", + "\n", + "var = 'global'\n", + "my_func()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And is also no problem to use the same variable name in the local scope without affecting the local counterpart: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_func():\n", + " var = 'locally changed'\n", + "\n", + "\n", + "var = 'global'\n", + "my_func()\n", + "print(var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But we have to be careful if we use a variable name that occurs in the global scope, and we want to access it in the local function scope if we want to reuse this name:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_func():\n", + " print(var) # want to access global variable\n", + " var = 'locally changed' # but Python thinks we forgot to define the local variable!\n", + "\n", + "\n", + "var = 'global'\n", + "my_func()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we have to use the `global` keyword!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def my_func():\n", + " global var\n", + " print(var) # want to access global variable\n", + " var = 'locally changed' # changes the gobal variable\n", + "\n", + "\n", + "var = 'global'\n", + "\n", + "my_func()\n", + "print(var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating copies of mutable objects\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create an independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_list1 = [[1, 2, 3]] * 2\n", + "\n", + "print('initially ---> ', my_list1)\n", + "\n", + "# modify the 1st element of the 2nd sublist\n", + "my_list1[1][0] = 'a'\n", + "print(\"after my_list1[1][0] = 'a' ---> \", my_list1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "In this case, we should better create \"new\" objects:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "my_list2 = [[1, 2, 3] for i in range(2)]\n", + "\n", + "print('initially: ---> ', my_list2)\n", + "\n", + "# modify the 1st element of the 2nd sublist\n", + "my_list2[1][0] = 'a'\n", + "print(\"after my_list2[1][0] = 'a': ---> \", my_list2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "And here is the proof:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for a, b in zip(my_list1, my_list2):\n", + " print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Key differences between Python 2 and 3\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are some good articles already that are summarizing the differences between Python 2 and 3, e.g., \n", + "- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)\n", + "- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)\n", + "- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)\n", + "- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html) \n", + "etc.\n", + "\n", + "But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!\n", + "(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overview - Key differences between Python 2 and 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "- [Unicode](#unicode)\n", + "- [The print statement](#print)\n", + "- [Integer division](#integer_div)\n", + "- [xrange()](#xrange)\n", + "- [Raising exceptions](#raising_exceptions)\n", + "- [Handling exceptions](#handling_exceptions)\n", + "- [next() function and .next() method](#next_next)\n", + "- [Loop variables and leaking into the global scope](#loop_leak)\n", + "- [Comparing unorderable types](#compare_unorder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unicode..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### Python 2: \n", + "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n", + "#### Python 3: \n", + "Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "code_folding": [] + }, + "outputs": [], + "source": [ + "#############\n", + "# Python 2 #\n", + "#############\n", + "\n", + ">>> type(unicode('is like a python3 str()'))\n", + "\n", + "\n", + ">>> type(b'byte type does not exist')\n", + "\n", + "\n", + ">>> 'they are really' + b' the same'\n", + "'they are really the same'\n", + "\n", + ">>> type(bytearray(b'bytearray oddly does exist though'))\n", + "\n", + "\n", + "#############\n", + "# Python 3\n", + "#############\n", + "\n", + ">>> print('strings are now utf-8 \\u03BCnico\\u0394é!')\n", + "strings are now utf-8 μnicoΔé!\n", + "\n", + "\n", + ">>> type(b' and we have byte types for storing data')\n", + "\n", + "\n", + ">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n", + "\n", + "\n", + ">>> 'string' + b'bytes for data'\n", + "Traceback (most recent call last):s\n", + " File \"\", line 1, in \n", + "TypeError: Can't convert 'bytes' object to str implicitly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The print statement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> print 'Hello, World!'\n", + "Hello, World!\n", + ">>> print('Hello, World!')\n", + "Hello, World!\n", + "\n", + "# Python 3\n", + ">>> print('Hello, World!')\n", + "Hello, World!\n", + ">>> print 'Hello, World!'\n", + " File \"\", line 1\n", + " print 'Hello, World!'\n", + " ^\n", + "SyntaxError: invalid syntax" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=\"\"` in Python 3:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> print \"line 1\", ; print 'same line'\n", + "line 1 same line\n", + "\n", + "# Python 3\n", + ">>> print(\"line 1\", end=\"\") ; print (\" same line\")\n", + "line 1 same line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Integer division" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed. \n", + "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> 3 / 2\n", + "1\n", + ">>> 3 // 2\n", + "1\n", + ">>> 3 / 2.0\n", + "1.5\n", + ">>> 3 // 2.0\n", + "1.0\n", + "\n", + "# Python 3\n", + ">>> 3 / 2\n", + "1.5\n", + ">>> 3 // 2\n", + "1\n", + ">>> 3 / 2.0\n", + "1.5\n", + ">>> 3 // 2.0\n", + "1.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `xrange()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch! \n", + "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> python -m timeit 'for i in range(1000000):' ' pass'\n", + "10 loops, best of 3: 66 msec per loop\n", + "\n", + " > python -m timeit 'for i in xrange(1000000):' ' pass'\n", + "10 loops, best of 3: 27.8 msec per loop\n", + "\n", + "# Python 3\n", + ">>> python3 -m timeit 'for i in range(1000000):' ' pass'\n", + "10 loops, best of 3: 51.1 msec per loop\n", + "\n", + ">>> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n", + "Traceback (most recent call last):\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n", + " x = t.timeit(number)\n", + " File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 178, in timeit\n", + " timing = self.inner(it, self.timer)\n", + " File \"\", line 6, in inner\n", + " for i in xrange(1000000):\n", + "NameError: name 'xrange' is not defined" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Raising exceptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> raise IOError, \"file error\"\n", + "Traceback (most recent call last):\n", + " File \"\", line 1, in \n", + "IOError: file error\n", + ">>> raise IOError(\"file error\")\n", + "Traceback (most recent call last):\n", + " File \"\", line 1, in \n", + "IOError: file error\n", + "\n", + " \n", + "# Python 3 \n", + ">>> raise IOError, \"file error\"\n", + " File \"\", line 1\n", + " raise IOError, \"file error\"\n", + " ^\n", + "SyntaxError: invalid syntax\n", + ">>> raise IOError(\"file error\")\n", + "Traceback (most recent call last):\n", + " File \"\", line 1, in \n", + "OSError: file error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Handling exceptions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> try:\n", + "... blabla\n", + "... except NameError, err:\n", + "... print err, '--> our error msg'\n", + "... \n", + "name 'blabla' is not defined --> our error msg\n", + "\n", + "# Python 3\n", + ">>> try:\n", + "... blabla\n", + "... except NameError as err:\n", + "... print(err, '--> our error msg')\n", + "... \n", + "name 'blabla' is not defined --> our error msg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The `next()` function and `.next()` method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remains in Python 3!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python 2\n", + ">>> my_generator = (letter for letter in 'abcdefg')\n", + ">>> my_generator.next()\n", + "'a'\n", + ">>> next(my_generator)\n", + "'b'\n", + "\n", + "# Python 3\n", + ">>> my_generator = (letter for letter in 'abcdefg')\n", + ">>> next(my_generator)\n", + "'a'\n", + ">>> my_generator.next()\n", + "Traceback (most recent call last):\n", + " File \"\", line 1, in \n", + "AttributeError: 'generator' object has no attribute 'next'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### In Python 3.x for-loop variables don't leak into the global namespace anymore" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n", + "\n", + "*\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\"*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + ">>> from platform import python_version\n", + ">>> print 'This code cell was executed in Python', python_version()\n", + "'This code cell was executed in Python 2.7.6'\n", + ">>> i = 1\n", + ">>> print [i for i in range(5)]\n", + "'[0, 1, 2, 3, 4]'\n", + ">>> print i, '-> i in global'\n", + "'4 -> i in global'" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This code cell was executed in Python 3.6.4\n", + "[0, 1, 2, 3, 4]\n", + "1 -> i in global\n" + ] + } + ], + "source": [ + "%%python3\n", + "from platform import python_version\n", + "print('This code cell was executed in Python', python_version())\n", + "\n", + "i = 1\n", + "print([i for i in range(5)])\n", + "print(i, '-> i in global')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Python 3.x prevents us from comparing unorderable types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to Python 2.x vs 3.x overview](#py23_overview)]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "result = (2 or 3) * (5 and 7)\n", - "print('2 * 7 =', result)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "2 * 7 = 14\n" - ] - } - ], - "prompt_number": 9 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Couldn't find program: 'python2'\n" + ] + } + ], + "source": [ + ">>> from platform import python_version\n", + ">>> print 'This code cell was executed in Python', python_version()\n", + "'This code cell was executed in Python 2.7.6'\n", + ">>> print [1, 2] > 'foo'\n", + "'False'\n", + ">>> print (1, 2) > 'foo'\n", + "'True'\n", + ">>> print [1, 2] > (1, 2)\n", + "'False'" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" + "name": "stdout", + "output_type": "stream", + "text": [ + "This code cell was executed in Python 3.6.4\n" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Don't use mutable objects as default arguments for functions!" + "ename": "TypeError", + "evalue": "'>' not supported between instances of 'list' and 'str'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'list' and 'str'" ] - }, + } + ], + "source": [ + "from platform import python_version\n", + "print('This code cell was executed in Python', python_version())\n", + "\n", + "print([1, 2] > 'foo')\n", + "print((1, 2) > 'foo')\n", + "print([1, 2] > (1, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Function annotations - What are those `->`'s in my Python code?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Have you ever seen any Python code that used colons inside the parantheses of a function definition?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def foo1(x: 'insert x here', y: 'insert x^2 here'):\n", + " print('Hello, World')\n", + " return" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And what about the fancy arrow here?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def foo2(x, y) -> 'Hi!':\n", + " print('Hello, World')\n", + " return" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Q: Is this valid Python syntax? \n", + "A: Yes!\n", + " \n", + " \n", + "Q: So, what happens if I *just call* the function? \n", + "A: Nothing!\n", + " \n", + "Here is the proof!" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, World\n" ] - }, + } + ], + "source": [ + "foo1(1,2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Don't use mutable objects (e.g., dictionaries, lists, sets, etc.) as default arguments for functions! You might expect that a new list is created every time when we call the function without providing an argument for the default parameter, but this is not the case: **Python will create the mutable object (default parameter) the first time the function is defined - not when it is called**, see the following code:\n", - "\n", - "(Original source: [http://docs.python-guide.org/en/latest/writing/gotchas/](http://docs.python-guide.org/en/latest/writing/gotchas/)" + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, World\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def append_to_list(value, def_list=[]):\n", - " def_list.append(value)\n", - " return def_list\n", - "\n", - "my_list = append_to_list(1)\n", - "print(my_list)\n", - "\n", - "my_other_list = append_to_list(2)\n", - "print(my_other_list)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1]\n", - "[1, 2]\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another good example showing that demonstrates that default arguments are created when the function is created (**and not when it is called!**):" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import time\n", - "def report_arg(my_default=time.time()):\n", - " print(my_default)\n", - "\n", - "report_arg()\n", - "\n", - "time.sleep(5)\n", - "\n", - "report_arg()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1397764090.456688\n", - "1397764090.456688" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Be aware of the consuming generator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Be aware of what is happening when combining \"`in`\" checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gen = (i for i in range(5))\n", - "print('2 in gen,', 2 in gen)\n", - "print('3 in gen,', 3 in gen)\n", - "print('1 in gen,', 1 in gen) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "2 in gen, True\n", - "3 in gen, True\n", - "1 in gen, False\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Although this defeats the purpose of an generator (in most cases), we can convert a generator into a list to circumvent the problem. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gen = (i for i in range(5))\n", - "a_list = list(gen)\n", - "print('2 in l,', 2 in a_list)\n", - "print('3 in l,', 3 in a_list)\n", - "print('1 in l,', 1 in a_list) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "2 in l, True\n", - "3 in l, True\n", - "1 in l, True\n" - ] - } - ], - "prompt_number": 27 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## `bool` is a subclass of `int`\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Chicken or egg? In the history of Python (Python 2.2 to be specific) truth values were implemented via 1 and 0 (similar to the old C). In order to avoid syntax errors in old (but perfectly working) Python code, `bool` was added as a subclass of `int` in Python 2.3.\n", - "\n", - "Original source: [http://www.peterbe.com/plog/bool-is-int](http://www.peterbe.com/plog/bool-is-int)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('isinstance(True, int):', isinstance(True, int))\n", - "print('True + True:', True + True)\n", - "print('3*True + True:', 3*True + True)\n", - "print('3*True - False:', 3*True - False)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "isinstance(True, int): True\n", - "True + True: 2\n", - "3*True + True: 4\n", - "3*True - False: 3\n" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## About lambda-in-closures-and-a-loop pitfall" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember the section about the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n", - "\n", - "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n", - "\n", - "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list is already constructed when we `for-loop` through the list, it will be set to the last value 4." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_list = [lambda: i for i in range(5)]\n", - "for l in my_list:\n", - " print(l())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "4\n", - "4\n", - "4\n", - "4\n", - "4\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This, however, does not apply to generators:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_gen = (lambda: n for n in range(5))\n", - "for l in my_gen:\n", - " print(l())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And if you are really keen on using lists, there is a nifty trick that circumvents this problem as a reader nicely pointed out in the comments: We can simply pass the loop variable `i` as a default argument to the lambdas." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_list = [lambda x=i: x for i in range(5)]\n", - "for l in my_list:\n", - " print(l())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Python's LEGB scope resolution and the keywords `global` and `nonlocal`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is nothing particularly surprising about Python's LEGB scope resolution (Local -> Enclosed -> Global -> Built-in), but it is still useful to take a look at some examples!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `global` vs. `local`\n", - "\n", - "According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` `local`ly in the function's scope, it won't have an effect on the `global` `x`." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = 0\n", - "def in_func():\n", - " x = 1\n", - " print('in_func:', x)\n", - " \n", - "in_func()\n", - "print('global:', x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "in_func: 1\n", - "global: 0\n" - ] - } - ], - "prompt_number": 31 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to modify the `global` x via a function, we can simply use the `global` keyword to import the variable into the function's scope:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "x = 0\n", - "def in_func():\n", - " global x\n", - " x = 1\n", - " print('in_func:', x)\n", - " \n", - "in_func()\n", - "print('global:', x)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "in_func: 1\n", - "global: 1\n" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### `local` vs. `enclosed`\n", - "\n", - "Now, let us take a look at `local` vs. `enclosed`. Here, we set the variable `x = 1` in the `outer` function and set `x = 1` in the enclosed function `inner`. Since `inner` looks in the local scope first, it won't modify `outer`'s `x`." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def outer():\n", - " x = 1\n", - " print('outer before:', x)\n", - " def inner():\n", - " x = 2\n", - " print(\"inner:\", x)\n", - " inner()\n", - " print(\"outer after:\", x)\n", - "outer()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "outer before: 1\n", - "inner: 2\n", - "outer after: 1\n" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is where the `nonlocal` keyword comes in handy - it allows us to modify the `x` variable in the `enclosed` scope:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def outer():\n", - " x = 1\n", - " print('outer before:', x)\n", - " def inner():\n", - " nonlocal x\n", - " x = 2\n", - " print(\"inner:\", x)\n", - " inner()\n", - " print(\"outer after:\", x)\n", - "outer()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "outer before: 1\n", - "inner: 2\n", - "outer after: 2\n" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## When mutable contents of immutable tuples aren't so mutable" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we all know, tuples are immutable objects in Python, right!? But what happens if they contain mutable objects? \n", - "\n", - "First, let us have a look at the expected behavior: a `TypeError` is raised if we try to modify immutable types in a tuple: " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tup = (1,)\n", - "tup[0] += 1" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "'tuple' object does not support item assignment", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" - ] - } - ], - "prompt_number": 41 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tup = ([],)\n", - "print('tup before: ', tup)\n", - "tup[0] += [1]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "tup before: ([],)\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'tuple' object does not support item assignment", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('tup after: ', tup)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "tup after: ([1],)\n" - ] - } - ], - "prompt_number": 43 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "However, **there are ways** to modify the mutable contents of the tuple without raising the `TypeError`, the solution is the `.extend()` method, or alternatively `.append()` (for lists):" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tup = ([],)\n", - "print('tup before: ', tup)\n", - "tup[0].extend([1])\n", - "print('tup after: ', tup)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "tup before: ([],)\n", - "tup after: ([1],)\n" - ] - } - ], - "prompt_number": 44 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "tup = ([],)\n", - "print('tup before: ', tup)\n", - "tup[0].append(1)\n", - "print('tup after: ', tup)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "tup before: ([],)\n", - "tup after: ([1],)\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Explanation\n", - "\n", - "**A. Jesse Jiryu Davis** has a nice explanation for this phenomenon (Original source: [http://emptysqua.re/blog/python-increment-is-weird-part-ii/](http://emptysqua.re/blog/python-increment-is-weird-part-ii/))\n", - "\n", - "If we try to extend the list via `+=` *\"then the statement executes `STORE_SUBSCR`, which calls the C function `PyObject_SetItem`, which checks if the object supports item assignment. In our case the object is a tuple, so `PyObject_SetItem` throws the `TypeError`. Mystery solved.\"*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_tup = (1,)\n", - "my_tup += (4,)\n", - "my_tup = my_tup + (5,)\n", - "print(my_tup)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(1, 4, 5)\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What happens \"behind\" the curtains is that the tuple is not modified, but every time a new object is generated, which will inherit the old \"name tag\":" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_tup = (1,)\n", - "print(id(my_tup))\n", - "my_tup += (4,)\n", - "print(id(my_tup))\n", - "my_tup = my_tup + (5,)\n", - "print(id(my_tup))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "4337381840\n", - "4357415496\n", - "4357289952\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## List comprehensions are fast, but generators are faster!?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"List comprehensions are fast, but generators are faster!?\" - No, not really (or significantly, see the benchmarks below). So what's the reason to prefer one over the other?\n", - "- use lists if you want to use the plethora of list methods \n", - "- use generators when you are dealing with huge collections to avoid memory issues" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import timeit\n", - "\n", - "def plainlist(n=100000):\n", - " my_list = []\n", - " for i in range(n):\n", - " if i % 5 == 0:\n", - " my_list.append(i)\n", - " return my_list\n", - "\n", - "def listcompr(n=100000):\n", - " my_list = [i for i in range(n) if i % 5 == 0]\n", - " return my_list\n", - "\n", - "def generator(n=100000):\n", - " my_gen = (i for i in range(n) if i % 5 == 0)\n", - " return my_gen\n", - "\n", - "def generator_yield(n=100000):\n", - " for i in range(n):\n", - " if i % 5 == 0:\n", - " yield i" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### To be fair to the list, let us exhaust the generators:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def test_plainlist(plain_list):\n", - " for i in plain_list():\n", - " pass\n", - "\n", - "def test_listcompr(listcompr):\n", - " for i in listcompr():\n", - " pass\n", - "\n", - "def test_generator(generator):\n", - " for i in generator():\n", - " pass\n", - "\n", - "def test_generator_yield(generator_yield):\n", - " for i in generator_yield():\n", - " pass\n", - "\n", - "print('plain_list: ', end = '')\n", - "%timeit test_plainlist(plainlist)\n", - "print('\\nlistcompr: ', end = '')\n", - "%timeit test_listcompr(listcompr)\n", - "print('\\ngenerator: ', end = '')\n", - "%timeit test_generator(generator)\n", - "print('\\ngenerator_yield: ', end = '')\n", - "%timeit test_generator_yield(generator_yield)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "plain_list: 10 loops, best of 3: 22.4 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - "listcompr: 10 loops, best of 3: 20.8 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - "generator: 10 loops, best of 3: 22 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "\n", - "generator_yield: 10 loops, best of 3: 21.9 ms per loop" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Public vs. private class methods and name mangling\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class (i.e., by the API user). \n", - "All we can do is to indicate methods as private to make clear that they are better not used outside the class, but it is really up to the class user, since \"we are all consenting adults here\"! \n", - "So, when we want to mark a class method as private, we can put a single underscore in front of it. \n", - "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n", - "\n", - "This doesn't prevent the class user to access this class member though, but he has to know the trick and also knows that it his own risk...\n", - "\n", - "Let the following example illustrate what I mean:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class my_class():\n", - " def public_method(self):\n", - " print('Hello public world!')\n", - " def __private_method(self):\n", - " print('Hello private world!')\n", - " def call_private_method_in_class(self):\n", - " self.__private_method()\n", - " \n", - "my_instance = my_class()\n", - "\n", - "my_instance.public_method()\n", - "my_instance._my_class__private_method()\n", - "my_instance.call_private_method_in_class()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hello public world!\n", - "Hello private world!\n", - "Hello private world!\n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The consequences of modifying a list when looping through it" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It can be really dangerous to modify a list when iterating through it - this is a very common pitfall that can cause unintended behavior! \n", - "Look at the following examples, and for a fun exercise: try to figure out what is going on before you skip to the solution!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = [1, 2, 3, 4, 5]\n", - "for i in a:\n", - " if not i % 2:\n", - " a.remove(i)\n", - "print(a)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 3, 5]\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "b = [2, 4, 5, 6]\n", - "for i in b:\n", - " if not i % 2:\n", - " b.remove(i)\n", - "print(b)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[4, 5]\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing, look at the following example, and it will become clear:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "b = [2, 4, 5, 6]\n", - "for index, item in enumerate(b):\n", - " print(index, item)\n", - " if not item % 2:\n", - " b.remove(item)\n", - "print(b)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0 2\n", - "1 5\n", - "2 6\n", - "[4, 5]\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dynamic binding and typos in variable names\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Be careful, dynamic binding is convenient, but can also quickly become dangerous!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('first list:')\n", - "for i in range(3):\n", - " print(i)\n", - " \n", - "print('\\nsecond list:')\n", - "for j in range(3):\n", - " print(i) # I (intentionally) made typo here!" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "first list:\n", - "0\n", - "1\n", - "2\n", - "\n", - "second list:\n", - "2\n", - "2\n", - "2\n" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## List slicing using indexes that are \"out of range\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we have all encountered it 1 (x10000) time(s) in our live, the infamous `IndexError`:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_list = [1, 2, 3, 4, 5]\n", - "print(my_list[5])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "IndexError", - "evalue": "list index out of range", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: list index out of range" - ] - } - ], - "prompt_number": 15 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But suprisingly, it is not raised when we are doing list slicing, which can be a really pain for debugging:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_list = [1, 2, 3, 4, 5]\n", - "print(my_list[5:])" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[]\n" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Reusing global variable names and `UnboundLocalErrors`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Usually, it is no problem to access global variables in the local scope of a function:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def my_func():\n", - " print(var)\n", - "\n", - "var = 'global'\n", - "my_func()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "global\n" - ] - } - ], - "prompt_number": 37 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And is also no problem to use the same variable name in the local scope without affecting the local counterpart: " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def my_func():\n", - " var = 'locally changed'\n", - "\n", - "var = 'global'\n", - "my_func()\n", - "print(var)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "global\n" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But we have to be careful if we use a variable name that occurs in the global scope, and we want to access it in the local function scope if we want to reuse this name:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def my_func():\n", - " print(var) # want to access global variable\n", - " var = 'locally changed' # but Python thinks we forgot to define the local variable!\n", - " \n", - "var = 'global'\n", - "my_func()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "UnboundLocalError", - "evalue": "local variable 'var' referenced before assignment", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# want to access global variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'locally changed'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'var' referenced before assignment" - ] - } - ], - "prompt_number": 40 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we have to use the `global` keyword!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def my_func():\n", - " global var\n", - " print(var) # want to access global variable\n", - " var = 'locally changed' # changes the gobal variable\n", - "\n", - "var = 'global'\n", - "\n", - "my_func()\n", - "print(var)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "global\n", - "locally changed\n" - ] - } - ], - "prompt_number": 43 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating copies of mutable objects\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_list1 = [[1, 2, 3]] * 2\n", - "\n", - "print('initially ---> ', my_list1)\n", - "\n", - "# modify the 1st element of the 2nd sublist\n", - "my_list1[1][0] = 'a'\n", - "print(\"after my_list1[1][0] = 'a' ---> \", my_list1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "initially ---> [[1, 2, 3], [1, 2, 3]]\n", - "after my_list1[1][0] = 'a' ---> [['a', 2, 3], ['a', 2, 3]]\n" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "In this case, we should better create \"new\" objects:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "my_list2 = [[1, 2, 3] for i in range(2)]\n", - "\n", - "print('initially: ---> ', my_list2)\n", - "\n", - "# modify the 1st element of the 2nd sublist\n", - "my_list2[1][0] = 'a'\n", - "print(\"after my_list2[1][0] = 'a': ---> \", my_list2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "initially: ---> [[1, 2, 3], [1, 2, 3]]\n", - "after my_list2[1][0] = 'a': ---> [[1, 2, 3], ['a', 2, 3]]\n" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "And here is the proof:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for a,b in zip(my_list1, my_list2):\n", - " print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "id my_list1: 4350764680, id my_list2: 4350766472\n", - "id my_list1: 4350764680, id my_list2: 4350766664\n" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Key differences between Python 2 and 3\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are some good articles already that are summarizing the differences between Python 2 and 3, e.g., \n", - "- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)\n", - "- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)\n", - "- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)\n", - "- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html) \n", - "etc.\n", - "\n", - "But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!\n", - "(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Overview - Key differences between Python 2 and 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "- [Unicode](#unicode)\n", - "- [The print statement](#print)\n", - "- [Integer division](#integer_div)\n", - "- [xrange()](#xrange)\n", - "- [Raising exceptions](#raising_exceptions)\n", - "- [Handling exceptions](#handling_exceptions)\n", - "- [next() function and .next() method](#next_next)\n", - "- [Loop variables and leaking into the global scope](#loop_leak)\n", - "- [Comparing unorderable types](#compare_unorder)\n", - "\n", - "
\n", - "
\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unicode..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "####- Python 2: \n", - "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n", - "####- Python 3: \n", - "Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#############\n", - "# Python 2\n", - "#############\n", - "\n", - ">>> type(unicode('is like a python3 str()'))\n", - "\n", - "\n", - ">>> type(b'byte type does not exist')\n", - "\n", - "\n", - ">>> 'they are really' + b' the same'\n", - "'they are really the same'\n", - "\n", - ">>> type(bytearray(b'bytearray oddly does exist though'))\n", - "\n", - "\n", - "#############\n", - "# Python 3\n", - "#############\n", - "\n", - ">>> print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')\n", - "strings are now utf-8 \u03bcnico\u0394\u00e9!\n", - "\n", - "\n", - ">>> type(b' and we have byte types for storing data')\n", - "\n", - "\n", - ">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n", - "\n", - "\n", - ">>> 'string' + b'bytes for data'\n", - "Traceback (most recent call last):s\n", - " File \"\", line 1, in \n", - "TypeError: Can't convert 'bytes' object to str implicitly" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The print statement" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - ">>> print 'Hello, World!'\n", - "Hello, World!\n", - ">>> print('Hello, World!')\n", - "Hello, World!\n", - "\n", - "# Python 3\n", - ">>> print('Hello, World!')\n", - "Hello, World!\n", - ">>> print 'Hello, World!'\n", - " File \"\", line 1\n", - " print 'Hello, World!'\n", - " ^\n", - "SyntaxError: invalid syntax" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=\"\"` in Python 3:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - ">>> print \"line 1\", ; print 'same line'\n", - "line 1 same line\n", - "\n", - "# Python 3\n", - ">>> print(\"line 1\", end=\"\") ; print (\" same line\")\n", - "line 1 same line" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integer division" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed. \n", - "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - ">>> 3 / 2\n", - "1\n", - ">>> 3 // 2\n", - "1\n", - ">>> 3 / 2.0\n", - "1.5\n", - ">>> 3 // 2.0\n", - "1.0\n", - "\n", - "# Python 3\n", - ">>> 3 / 2\n", - "1.5\n", - ">>> 3 // 2\n", - "1\n", - ">>> 3 / 2.0\n", - "1.5\n", - ">>> 3 // 2.0\n", - "1.0" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###`xrange()` " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n", - "`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch! \n", - "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - "> python -m timeit 'for i in range(1000000):' ' pass'\n", - "10 loops, best of 3: 66 msec per loop\n", - "\n", - " > python -m timeit 'for i in xrange(1000000):' ' pass'\n", - "10 loops, best of 3: 27.8 msec per loop\n", - "\n", - "# Python 3\n", - "> python3 -m timeit 'for i in range(1000000):' ' pass'\n", - "10 loops, best of 3: 51.1 msec per loop\n", - "\n", - "> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n", - "Traceback (most recent call last):\n", - " File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n", - " x = t.timeit(number)\n", - " File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 178, in timeit\n", - " timing = self.inner(it, self.timer)\n", - " File \"\", line 6, in inner\n", - " for i in xrange(1000000):\n", - "NameError: name 'xrange' is not defined" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Raising exceptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - ">>> raise IOError, \"file error\"\n", - "Traceback (most recent call last):\n", - " File \"\", line 1, in \n", - "IOError: file error\n", - ">>> raise IOError(\"file error\")\n", - "Traceback (most recent call last):\n", - " File \"\", line 1, in \n", - "IOError: file error\n", - "\n", - " \n", - "# Python 3 \n", - ">>> raise IOError, \"file error\"\n", - " File \"\", line 1\n", - " raise IOError, \"file error\"\n", - " ^\n", - "SyntaxError: invalid syntax\n", - ">>> raise IOError(\"file error\")\n", - "Traceback (most recent call last):\n", - " File \"\", line 1, in \n", - "OSError: file error" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Handling exceptions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - ">>> try:\n", - "... blabla\n", - "... except NameError, err:\n", - "... print err, '--> our error msg'\n", - "... \n", - "name 'blabla' is not defined --> our error msg\n", - "\n", - "# Python 3\n", - ">>> try:\n", - "... blabla\n", - "... except NameError as err:\n", - "... print(err, '--> our error msg')\n", - "... \n", - "name 'blabla' is not defined --> our error msg" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "
\n", - "
" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The `next()` function and `.next()` method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Python 2\n", - ">>> my_generator = (letter for letter in 'abcdefg')\n", - ">>> my_generator.next()\n", - "'a'\n", - ">>> next(my_generator)\n", - "'b'\n", - "\n", - "# Python 3\n", - ">>> my_generator = (letter for letter in 'abcdefg')\n", - ">>> next(my_generator)\n", - "'a'\n", - ">>> my_generator.next()\n", - "Traceback (most recent call last):\n", - " File \"\", line 1, in \n", - "AttributeError: 'generator' object has no attribute 'next'" - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### In Python 3.x for-loop variables don't leak into the global namespace anymore" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n", - "\n", - "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\"" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from platform import python_version\n", - "print('This code cell was executed in Python', python_version())\n", - "\n", - "i = 1\n", - "print([i for i in range(5)])\n", - "print(i, '-> i in global')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "This code cell was executed in Python 3.3.5\n", - "[0, 1, 2, 3, 4]\n", - "1 -> i in global\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from platform import python_version\n", - "print 'This code cell was executed in Python', python_version()\n", - "\n", - "i = 1\n", - "print [i for i in range(5)]\n", - "print i, '-> i in global' " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "This code cell was executed in Python 2.7.6\n", - "[0, 1, 2, 3, 4]\n", - "4 -> i in global\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Python 3.x prevents us from comparing unorderable types" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to Python 2.x vs 3.x overview](#py23_overview)]" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from platform import python_version\n", - "print 'This code cell was executed in Python', python_version()\n", - "\n", - "print [1, 2] > 'foo'\n", - "print (1, 2) > 'foo'\n", - "print [1, 2] > (1, 2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "This code cell was executed in Python 2.7.6\n", - "False\n", - "True\n", - "False\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from platform import python_version\n", - "print('This code cell was executed in Python', python_version())\n", - "\n", - "print([1, 2] > 'foo')\n", - "print((1, 2) > 'foo')\n", - "print([1, 2] > (1, 2))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "This code cell was executed in Python 3.3.5\n" - ] - }, - { - "ename": "TypeError", - "evalue": "unorderable types: list() > str()", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Function annotations - What are those `->`'s in my Python code?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Have you ever seen any Python code that used colons inside the parantheses of a function definition?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def foo1(x: 'insert x here', y: 'insert x^2 here'):\n", - " print('Hello, World')\n", - " return" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And what about the fancy arrow here?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def foo2(x, y) -> 'Hi!':\n", - " print('Hello, World')\n", - " return" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Q: Is this valid Python syntax? \n", - "A: Yes!\n", - " \n", - " \n", - "Q: So, what happens if I *just call* the function? \n", - "A: Nothing!\n", - " \n", - "Here is the proof!" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "foo1(1,2)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hello, World\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "foo2(1,2) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hello, World\n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**So, those are function annotations ... ** \n", - "- the colon for the function parameters \n", - "- the arrow for the return value \n", - "\n", - "You probably will never make use of them (or at least very rarely). Usually, we write good function documentations below the function as a docstring - or at least this is how I would do it (okay this case is a little bit extreme, I have to admit):" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def is_palindrome(a):\n", - " \"\"\"\n", - " Case-and punctuation insensitive check if a string is a palindrom.\n", - " \n", - " Keyword arguments:\n", - " a (str): The string to be checked if it is a palindrome.\n", - " \n", - " Returns `True` if input string is a palindrome, else False.\n", - " \n", - " \"\"\"\n", - " stripped_str = [l for l in my_str.lower() if l.isalpha()]\n", - " return stripped_str == stripped_str[::-1]\n", - " " - ], - "language": "python", - "metadata": {}, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very very rarely.\n", - "\n", - "As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n", - "\n", - "1. Function annotations, both for parameters and return values, are completely optional.\n", - "\n", - "2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The nice thing about function annotations is their `__annotations__` attribute, which is dictionary of all the parameters and/or the `return` value you annotated." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "foo1.__annotations__" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 17, - "text": [ - "{'y': 'insert x^2 here', 'x': 'insert x here'}" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "foo2.__annotations__" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 18, - "text": [ - "{'return': 'Hi!'}" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**When are they useful?**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function annotations can be useful for a couple of things \n", - "- Documentation in general\n", - "- pre-condition testing\n", - "- [type checking](http://legacy.python.org/dev/peps/pep-0362/#annotation-checker)\n", - " \n", - "..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Abortive statements in `finally` blocks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python's `try-except-finally` blocks are very handy for catching and handling errors. The `finally` block is always executed whether an `exception` has been raised or not as illustrated in the following example." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def try_finally1():\n", - " try:\n", - " print('in try:')\n", - " print('do some stuff')\n", - " float('abc')\n", - " except ValueError:\n", - " print('an error occurred')\n", - " else:\n", - " print('no error occurred')\n", - " finally:\n", - " print('always execute finally')\n", - " \n", - "try_finally1()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "in try:\n", - "do some stuff\n", - "an error occurred\n", - "always execute finally\n" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "But can you also guess what will be printed in the next code cell?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def try_finally2():\n", - " try:\n", - " print(\"do some stuff in try block\")\n", - " return \"return from try block\"\n", - " finally:\n", - " print(\"do some stuff in finally block\")\n", - " return \"always execute finally\"\n", - " \n", - "print(try_finally2())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "do some stuff in try block\n", - "do some stuff in finally block\n", - "always execute finally\n" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Here, the abortive `return` statement in the `finally` block simply overrules the `return` in the `try` block, since **`finally` is guaranteed to always be executed.** So, be careful using abortive statements in `finally` blocks!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Assigning types to variables as values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I am not yet sure in which context this can be useful, but it is a nice fun fact to know that we can assign types as values to variables." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = str\n", - "a_var(123)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 1, - "text": [ - "'123'" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from random import choice\n", - "\n", - "a, b, c = float, int, str\n", - "for i in range(5):\n", - " j = choice([a,b,c])(i)\n", - " print(j, type(j))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0 \n", - "1 \n", - "2.0 \n", - "3 \n", - "4 \n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Only the first clause of generators is evaluated immediately" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n", - "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gen_fails = (i for i in 1/0)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "ZeroDivisionError", - "evalue": "division by zero", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" - ] - } - ], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "gen_succeeds = (i for i in range(5) for j in 1/0)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('But obviously fails when we iterate ...')\n", - "for i in gen_succeeds:\n", - " print(i)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "ZeroDivisionError", - "evalue": "division by zero", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "But obviously fails when we iterate ...\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Keyword argument unpacking syntax - `*args` and `**kwargs`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Python has a very convenient \"keyword argument unpacking syntax\" (often also referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Single-asterisk (*args)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def a_func(*args):\n", - " print('type of args:', type(args))\n", - " print('args contents:', args)\n", - " print('1st argument:', args[0])\n", - "\n", - "a_func(0, 1, 'a', 'b', 'c')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "type of args: \n", - "args contents: (0, 1, 'a', 'b', 'c')\n", - "1st argument: 0\n" - ] - } - ], - "prompt_number": 55 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Double-asterisk (**kwargs)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def b_func(**kwargs):\n", - " print('type of kwargs:', type(kwargs))\n", - " print('kwargs contents: ', kwargs)\n", - " print('value of argument a:', kwargs['a'])\n", - " \n", - "b_func(a=1, b=2, c=3, d=4)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "type of kwargs: \n", - "kwargs contents: {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n", - "value of argument a: 1\n" - ] - } - ], - "prompt_number": 56 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (Partially) unpacking of iterables\n", - "Another useful application of the \"unpacking\"-operator is the unpacking of lists and other other iterables." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "val1, *vals = [1, 2, 3, 4, 5]\n", - "print('val1:', val1)\n", - "print('vals:', vals)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "val1: 1\n", - "vals: [2, 3, 4, 5]\n" - ] - } - ], - "prompt_number": 57 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Metaclasses - What creates a new instance of a class?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + } + ], + "source": [ + "foo2(1,2) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**So, those are function annotations ... ** \n", + "- the colon for the function parameters \n", + "- the arrow for the return value \n", + "\n", + "You probably will never make use of them (or at least very rarely). Usually, we write good function documentations below the function as a docstring - or at least this is how I would do it (okay this case is a little bit extreme, I have to admit):" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def is_palindrome(a):\n", + " \"\"\"\n", + " Case-and punctuation insensitive check if a string is a palindrom.\n", + "\n", + " Keyword arguments:\n", + " a (str): The string to be checked if it is a palindrome.\n", + "\n", + " Returns `True` if input string is a palindrome, else False.\n", + "\n", + " \"\"\"\n", + " stripped_str = [l for l in my_str.lower() if l.isalpha()]\n", + " return stripped_str == stripped_str[::-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very, very rarely.\n", + "\n", + "As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n", + "\n", + "1. *Function annotations, both for parameters and return values, are completely optional.*\n", + "\n", + "2. *Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.*\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The nice thing about function annotations is their `__annotations__` attribute, which is a dictionary of all the parameters and/or the `return` value you annotated." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'x': 'insert x here', 'y': 'insert x^2 here'}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo1.__annotations__" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'return': 'Hi!'}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "foo2.__annotations__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**When are they useful?**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function annotations can be useful for a couple of things \n", + "- Documentation in general\n", + "- pre-condition testing\n", + "- [type checking](http://legacy.python.org/dev/peps/pep-0362/#annotation-checker)\n", + " \n", + "..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Abortive statements in `finally` blocks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python's `try-except-finally` blocks are very handy for catching and handling errors. The `finally` block is always executed whether an `exception` has been raised or not as illustrated in the following example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def try_finally1():\n", + " try:\n", + " print('in try:')\n", + " print('do some stuff')\n", + " float('abc')\n", + " except ValueError:\n", + " print('an error occurred')\n", + " else:\n", + " print('no error occurred')\n", + " finally:\n", + " print('always execute finally')\n", + "\n", + "\n", + "try_finally1()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "But can you also guess what will be printed in the next code cell?" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "do some stuff in try block\n", + "do some stuff in finally block\n", + "always execute finally\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n", - "More specifically, this is what is returned: \n", - "`return super(, cls).__new__(subcls, *args, **kwargs)` \n", - "\n", - "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n", - "\n", - "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:" + } + ], + "source": [ + "def try_finally2():\n", + " try:\n", + " print(\"do some stuff in try block\")\n", + " return \"return from try block\"\n", + " finally:\n", + " print(\"do some stuff in finally block\")\n", + " return \"always execute finally\"\n", + "\n", + "\n", + "print(try_finally2())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "Here, the abortive `return` statement in the `finally` block simply overrules the `return` in the `try` block, since **`finally` is guaranteed to always be executed.** So, be careful using abortive statements in `finally` blocks!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Assigning types to variables as values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I am not yet sure in which context this can be useful, but it is a nice fun fact to know that we can assign types as values to variables." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'123'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a_var = str\n", + "a_var(123)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 \n", + "1.0 \n", + "2 \n", + "3.0 \n", + "4.0 \n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class a_class(object):\n", - " def __new__(clss, *args, **kwargs):\n", - " print('excecuted __new__')\n", - " return None\n", - " def __init__(self, an_arg):\n", - " print('excecuted __init__')\n", - " self.an_arg = an_arg\n", - " \n", - "a_object = a_class(1)\n", - "print('Type of a_object:', type(a_object))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "excecuted __new__\n", - "Type of a_object: \n" - ] - } - ], - "prompt_number": 53 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object. \n", - "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":" + } + ], + "source": [ + "from random import choice\n", + "\n", + "a, b, c = float, int, str\n", + "for i in range(5):\n", + " j = choice([a,b,c])(i)\n", + " print(j, type(j))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Only the first clause of generators is evaluated immediately" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n", + "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "class a_class(object):\n", - " def __new__(cls, *args, **kwargs):\n", - " print('excecuted __new__')\n", - " inst = super(a_class, cls).__new__(cls)\n", - " return inst\n", - " def __init__(self, an_arg):\n", - " print('excecuted __init__')\n", - " self.an_arg = an_arg\n", - " \n", - "a_object = a_class(1)\n", - "print('Type of a_object:', type(a_object))\n", - "print('a_object.an_arg: ', a_object.an_arg)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "excecuted __new__\n", - "excecuted __init__\n", - "Type of a_object: \n", - "a_object.an_arg: 1\n" - ] - } - ], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(5):\n", - " if i == 1:\n", - " print('in for')\n", - "else:\n", - " print('in else')\n", - "print('after for-loop')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "in for\n", - "in else\n", - "after for-loop\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(5):\n", - " if i == 1:\n", - " break\n", - "else:\n", - " print('in else')\n", - "print('after for-loop')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "after for-loop\n" - ] - } - ], - "prompt_number": 6 - }, + } + ], + "source": [ + "gen_fails = (i for i in 1/0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "gen_succeeds = (i for i in range(5) for j in 1/0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" + "name": "stdout", + "output_type": "stream", + "text": [ + "But obviously fails when we iterate ...\n" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Else-clauses: \"conditional else\" and \"completion else\"" + "ename": "ZeroDivisionError", + "evalue": "division by zero", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + } + ], + "source": [ + "print('But obviously fails when we iterate ...')\n", + "for i in gen_succeeds:\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Keyword argument unpacking syntax - `*args` and `**kwargs`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python has a very convenient \"keyword argument unpacking syntax\" (often referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Single-asterisk (*args)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type of args: \n", + "args contents: (0, 1, 'a', 'b', 'c')\n", + "1st argument: 0\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later). \n", - "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with. \n" + } + ], + "source": [ + "def a_func(*args):\n", + " print('type of args:', type(args))\n", + " print('args contents:', args)\n", + " print('1st argument:', args[0])\n", + "\n", + "\n", + "a_func(0, 1, 'a', 'b', 'c')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Double-asterisk (**kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type of kwargs: \n", + "kwargs contents: {'a': 1, 'b': 2, 'c': 3, 'd': 4}\n", + "value of argument a: 1\n" ] - }, + } + ], + "source": [ + "def b_func(**kwargs):\n", + " print('type of kwargs:', type(kwargs))\n", + " print('kwargs contents: ', kwargs)\n", + " print('value of argument a:', kwargs['a'])\n", + "\n", + "\n", + "b_func(a=1, b=2, c=3, d=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (Partially) unpacking of iterables\n", + "Another useful application of the \"unpacking\"-operator is the unpacking of lists and other other iterables." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Conditional else:" + "name": "stdout", + "output_type": "stream", + "text": [ + "val1: 1\n", + "vals: [2, 3, 4, 5]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# conditional else\n", - "\n", - "a_list = [1,2]\n", - "if a_list[0] == 1:\n", - " print('Hello, World!')\n", - "else:\n", - " print('Bye, World!')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Hello, World!\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# conditional else\n", - "\n", - "a_list = [1,2]\n", - "if a_list[0] == 2:\n", - " print('Hello, World!')\n", - "else:\n", - " print('Bye, World!')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Bye, World!\n" - ] - } - ], - "prompt_number": 4 - }, + } + ], + "source": [ + "val1, *vals = [1, 2, 3, 4, 5]\n", + "print('val1:', val1)\n", + "print('vals:', vals)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metaclasses - What creates a new instance of a class?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n", + "More specifically, this is what is returned: \n", + "`return super(, cls).__new__(subcls, *args, **kwargs)` \n", + "\n", + "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n", + "\n", + "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both. \n", - "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block. \n", - "\n", - "### Completion else\n", - "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished. \n", - "To show you an example, let us use `else` for error-handling:" + "name": "stdout", + "output_type": "stream", + "text": [ + "excecuted __new__\n", + "Type of a_object: \n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Completion else (try-except)" + } + ], + "source": [ + "class a_class(object):\n", + " def __new__(clss, *args, **kwargs):\n", + " print('excecuted __new__')\n", + " return None\n", + "\n", + " def __init__(self, an_arg):\n", + " print('excecuted __init__')\n", + " self.an_arg = an_arg\n", + "\n", + "\n", + "a_object = a_class(1)\n", + "print('Type of a_object:', type(a_object))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object. \n", + "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "excecuted __new__\n", + "excecuted __init__\n", + "Type of a_object: \n", + "a_object.an_arg: 1\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "try:\n", - " print('first element:', a_list[0])\n", - "except IndexError:\n", - " print('raised IndexError')\n", - "else:\n", - " print('no error in try-block')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "first element: 1\n", - "no error in try-block\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "try:\n", - " print('third element:', a_list[2])\n", - "except IndexError:\n", - " print('raised IndexError')\n", - "else:\n", - " print('no error in try-block')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "raised IndexError\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".** \n", - "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below." + } + ], + "source": [ + "class a_class(object):\n", + " def __new__(cls, *args, **kwargs):\n", + " print('excecuted __new__')\n", + " inst = super(a_class, cls).__new__(cls)\n", + " return inst\n", + "\n", + " def __init__(self, an_arg):\n", + " print('excecuted __init__')\n", + " self.an_arg = an_arg\n", + "\n", + "\n", + "a_object = a_class(1)\n", + "print('Type of a_object:', type(a_object))\n", + "print('a_object.an_arg: ', a_object.an_arg)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "in for\n", + "in else\n", + "after for-loop\n" ] - }, + } + ], + "source": [ + "for i in range(5):\n", + " if i == 1:\n", + " print('in for')\n", + "else:\n", + " print('in else')\n", + "print('after for-loop')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Completion else (while-loop)" + "name": "stdout", + "output_type": "stream", + "text": [ + "after for-loop\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "i = 0\n", - "while i < 2:\n", - " print(i)\n", - " i += 1\n", - "else:\n", - " print('in else')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "in else\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "i = 0\n", - "while i < 2:\n", - " print(i)\n", - " i += 1\n", - " break\n", - "else:\n", - " print('completed while-loop')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n" - ] - } - ], - "prompt_number": 8 - }, + } + ], + "source": [ + "for i in range(5):\n", + " if i == 1:\n", + " break\n", + "else:\n", + " print('in else')\n", + "print('after for-loop')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Else-clauses: \"conditional else\" and \"completion else\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I would claim that the conditional `else` is every programmer's daily bread and butter. However, there is a second flavor of `else`-clauses in Python, which I will call \"completion else\" (for reason that will become clear later). \n", + "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conditional else:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Completion else (for-loop)" + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, World!\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(2):\n", - " print(i)\n", - "else:\n", - " print('completed for-loop')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n", - "1\n", - "completed for-loop\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for i in range(2):\n", - " print(i)\n", - " break\n", - "else:\n", - " print('completed for-loop')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "0\n" - ] - } - ], - "prompt_number": 10 - }, + } + ], + "source": [ + "# conditional else\n", + "\n", + "a_list = [1,2]\n", + "if a_list[0] == 1:\n", + " print('Hello, World!')\n", + "else:\n", + " print('Bye, World!')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" + "name": "stdout", + "output_type": "stream", + "text": [ + "Bye, World!\n" ] - }, + } + ], + "source": [ + "# conditional else\n", + "\n", + "a_list = [1,2]\n", + "if a_list[0] == 2:\n", + " print('Hello, World!')\n", + "else:\n", + " print('Bye, World!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both. \n", + "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block. \n", + "
\n", + "### Completion else\n", + "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished. \n", + "To show you an example, let us use `else` for error-handling:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Completion else (try-except)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interning of compile-time constants vs. run-time expressions" + "name": "stdout", + "output_type": "stream", + "text": [ + "first element: 1\n", + "no error in try-block\n" ] - }, + } + ], + "source": [ + "try:\n", + " print('first element:', a_list[0])\n", + "except IndexError:\n", + " print('raised IndexError')\n", + "else:\n", + " print('no error in try-block')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + "name": "stdout", + "output_type": "stream", + "text": [ + "raised IndexError\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n", - "\n", - "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))" + } + ], + "source": [ + "try:\n", + " print('third element:', a_list[2])\n", + "except IndexError:\n", + " print('raised IndexError')\n", + "else:\n", + " print('no error in try-block')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".** \n", + "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Completion else (while-loop)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "in else\n" ] - }, + } + ], + "source": [ + "i = 0\n", + "while i < 2:\n", + " print(i)\n", + " i += 1\n", + "else:\n", + " print('in else')" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity." + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "hello1 = 'Hello'\n", - "\n", - "hello2 = 'Hell' + 'o'\n", - "\n", - "hello3 = 'Hell'\n", - "hello3 = hello3 + 'o'\n", - "\n", - "print('hello1 is hello2:', hello1 is hello2)\n", - "print('hello1 is hello3:', hello1 is hello3)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "hello1 is hello2: True\n", - "hello1 is hello3: False\n" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, how does it come that the first expression evaluates to true, but the second does not? To answer this question, we need to take a closer look at the underlying byte codes:" + } + ], + "source": [ + "i = 0\n", + "while i < 2:\n", + " print(i)\n", + " i += 1\n", + " break\n", + "else:\n", + " print('completed while-loop')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Completion else (for-loop)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "completed for-loop\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import dis\n", - "def hello1_func():\n", - " s = 'Hello'\n", - " return s\n", - "dis.dis(hello1_func)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 3 0 LOAD_CONST 1 ('Hello')\n", - " 3 STORE_FAST 0 (s)\n", - "\n", - " 4 6 LOAD_FAST 0 (s)\n", - " 9 RETURN_VALUE\n" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def hello2_func():\n", - " s = 'Hell' + 'o'\n", - " return s\n", - "dis.dis(hello2_func)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 2 0 LOAD_CONST 3 ('Hello')\n", - " 3 STORE_FAST 0 (s)\n", - "\n", - " 3 6 LOAD_FAST 0 (s)\n", - " 9 RETURN_VALUE\n" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def hello3_func():\n", - " s = 'Hell'\n", - " s = s + 'o'\n", - " return s\n", - "dis.dis(hello3_func)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - " 2 0 LOAD_CONST 1 ('Hell')\n", - " 3 STORE_FAST 0 (s)\n", - "\n", - " 3 6 LOAD_FAST 0 (s)\n", - " 9 LOAD_CONST 2 ('o')\n", - " 12 BINARY_ADD\n", - " 13 STORE_FAST 0 (s)\n", - "\n", - " 4 16 LOAD_FAST 0 (s)\n", - " 19 RETURN_VALUE\n" - ] - } - ], - "prompt_number": 40 - }, + } + ], + "source": [ + "for i in range(2):\n", + " print(i)\n", + "else:\n", + " print('completed for-loop')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version \n", - "`s = 'Hell'` \n", - "`s = s + 'o'` seems to be not interned. Let us quickly confirm the behavior with the following code:" + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(hello1_func() is hello2_func())\n", - "print(hello1_func() is hello3_func())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "True\n", - "False\n" - ] - } - ], - "prompt_number": 42 - }, + } + ], + "source": [ + "for i in range(2):\n", + " print(i)\n", + " break\n", + "else:\n", + " print('completed for-loop')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interning of compile-time constants vs. run-time expressions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n", + "\n", + "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:" + "name": "stdout", + "output_type": "stream", + "text": [ + "hello1 is hello2: True\n", + "hello1 is hello3: False\n" ] - }, + } + ], + "source": [ + "hello1 = 'Hello'\n", + "\n", + "hello2 = 'Hell' + 'o'\n", + "\n", + "hello3 = 'Hell'\n", + "hello3 = hello3 + 'o'\n", + "\n", + "print('hello1 is hello2:', hello1 is hello2)\n", + "print('hello1 is hello3:', hello1 is hello3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, how does it come that the first expression evaluates to true, but the second does not? To answer this question, we need to take a closer look at the underlying byte codes:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "import sys\n", + "name": "stdout", + "output_type": "stream", + "text": [ + " 3 0 LOAD_CONST 1 ('Hello')\n", + " 2 STORE_FAST 0 (s)\n", "\n", - "print(hello1_func() is sys.intern(hello3_func()))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "True\n" - ] - } - ], - "prompt_number": 45 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
\n", - "
\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Changelog" + " 4 4 LOAD_FAST 0 (s)\n", + " 6 RETURN_VALUE\n" ] - }, + } + ], + "source": [ + "import dis\n", + "def hello1_func():\n", + " s = 'Hello'\n", + " return s\n", + "dis.dis(hello1_func)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[back to top](#sections)]" + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 0 LOAD_CONST 3 ('Hello')\n", + " 2 STORE_FAST 0 (s)\n", + "\n", + " 3 4 LOAD_FAST 0 (s)\n", + " 6 RETURN_VALUE\n" ] - }, + } + ], + "source": [ + "def hello2_func():\n", + " s = 'Hell' + 'o'\n", + " return s\n", + "dis.dis(hello2_func)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 05/24/2014\n", - "- new section: unorderable types in Python 2\n", - "- table of contents for the Python 2 vs. Python 3 topic\n", - " \n", - "#### 05/03/2014\n", - "- new section: else clauses: conditional vs. completion\n", - "- new section: Interning of compile-time constants vs. run-time expressions\n", + "name": "stdout", + "output_type": "stream", + "text": [ + " 2 0 LOAD_CONST 1 ('Hell')\n", + " 2 STORE_FAST 0 (s)\n", "\n", - "#### 05/02/2014\n", - "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n", - "- new section: Metaclasses - What creates a new instance of a class? \n", + " 3 4 LOAD_FAST 0 (s)\n", + " 6 LOAD_CONST 2 ('o')\n", + " 8 BINARY_ADD\n", + " 10 STORE_FAST 0 (s)\n", "\n", - "#### 05/01/2014\n", - "- new section: keyword argument unpacking syntax\n", - "\n", - "#### 04/27/2014\n", - "- minor fixes of typos \n", - "- new section: \"Only the first clause of generators is evaluated immediately\"" + " 4 12 LOAD_FAST 0 (s)\n", + " 14 RETURN_VALUE\n" ] - }, + } + ], + "source": [ + "def hello3_func():\n", + " s = 'Hell'\n", + " s = s + 'o'\n", + " return s\n", + "dis.dis(hello3_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version \n", + "`s = 'Hell'` \n", + "`s = s + 'o'` seems to not be interned. Let us quickly confirm the behavior with the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "print(hello1_func() is hello2_func())\n", + "print(hello1_func() is hello3_func())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] } ], - "metadata": {} + "source": [ + "import sys\n", + "\n", + "print(hello1_func() is sys.intern(hello3_func()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
\n", + "
\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Changelog" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 06/09/2018\n", + "- pep8 spacing\n", + "- fixed minor typos\n", + "- fixed minor markdown formatting\n", + "- fixed broken page jumps\n", + "\n", + "#### 07/16/2014\n", + "- slight change of wording in the [lambda-closure section](#lambda_closure)\n", + "\n", + "#### 05/24/2014\n", + "- new section: unorderable types in Python 2\n", + "- table of contents for the Python 2 vs. Python 3 topic\n", + " \n", + "#### 05/03/2014\n", + "- new section: else clauses: conditional vs. completion\n", + "- new section: Interning of compile-time constants vs. run-time expressions\n", + "\n", + "#### 05/02/2014\n", + "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n", + "- new section: Metaclasses - What creates a new instance of a class? \n", + "\n", + "#### 05/01/2014\n", + "- new section: keyword argument unpacking syntax\n", + "\n", + "#### 04/27/2014\n", + "- minor fixes of typos \n", + "- new section: \"Only the first clause of generators is evaluated immediately\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/tutorials/numpy_nan_quickguide.ipynb b/tutorials/numpy_nan_quickguide.ipynb new file mode 100644 index 0000000..acbbeed --- /dev/null +++ b/tutorials/numpy_nan_quickguide.ipynb @@ -0,0 +1,770 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:b2597ea4263c11dd6774b227e7a3a5626197c4863e6895002657fd55d02b55d9" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to python_reference](https://github.com/rasbt/python_reference)]" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%load_ext watermark" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%watermark -v -p numpy -d -u" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Last updated: 31/07/2014 \n", + "\n", + "CPython 3.4.1\n", + "IPython 2.1.0\n", + "\n", + "numpy 1.8.1\n" + ] + } + ], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Quick guide for dealing with missing numbers in NumPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just a quick overview of how to deal with missing values (i.e., \"NaN\"s for \"Not-a-Number\") in NumPy and I am happy to expand it over time. Yes, and there will also be a separate one for pandas some time!\n", + "\n", + "I would be happy to hear your comments and suggestions. \n", + "Please feel free to drop me a note via\n", + "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [Sample data from a CSV file](#Sample-data-from-a-CSV-file)\n", + "- [Determining if a value is missing](#Determining-if-a-value-is-missing)\n", + "- [Counting the number of missing values](#Counting-the-number-of-missing-values)\n", + "- [Calculating the sum of an array that contains NaNs](#Calculating the sum of an array that contains NaNs)\n", + "- [Removing all rows that contain missing values](#Removing-all-rows-that-contain-missing-values)\n", + "- [Convert missing values to 0](#Convert-missing-values-to-0)\n", + "- [Converting certain numbers to NaN](#Converting-certain-numbers-to-NaN)\n", + "- [Remove all missing elements from an array](#Remove-all-missing-elements-from-an-array)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Sample data from a CSV file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's assume that we have a CSV file with missing elements like the one shown below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%%file example.csv\n", + "1,2,3,4\n", + "5,6,,8\n", + "10,11,12," + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "Writing example.csv\n" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `np.genfromtxt` function has a `missing_values` parameters which translates missing values into `np.nan` objects by default. This allows us to construct a new NumPy `ndarray` object, even if elements are missing." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "ary = np.genfromtxt('./example.csv', delimiter=',')\n", + "\n", + "print('%s x %s array:\\n' %(ary.shape[0], ary.shape[1]))\n", + "print(ary)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "3 x 4 array:\n", + "\n", + "[[ 1. 2. 3. 4.]\n", + " [ 5. 6. nan 8.]\n", + " [ 10. 11. 12. nan]]\n" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Determining if a value is missing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A handy function to test whether a value is a `NaN` or not is to use the `np.isnan` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.isnan(np.nan)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "True" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It is especially useful to create boolean masks for the so-called \"fancy indexing\" of NumPy arrays, which we will come back to later." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.isnan(ary)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 6, + "text": [ + "array([[False, False, False, False],\n", + " [False, False, True, False],\n", + " [False, False, False, True]], dtype=bool)" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Counting the number of missing values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to find out how many elements are missing in our array, we can use the `np.isnan` function that we have seen in the previous section. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.count_nonzero(np.isnan(ary))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 7, + "text": [ + "2" + ] + } + ], + "prompt_number": 7 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to determine the number of non-missing elements, we can simply revert the returned `Boolean` mask via the handy \"tilde\" sign." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.count_nonzero(~np.isnan(ary))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 8, + "text": [ + "10" + ] + } + ], + "prompt_number": 8 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Calculating the sum of an array that contains `NaN`s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we will find out via the following code snippet, we can't use NumPy's regular `sum` function to calculate the sum of an array." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "np.sum(ary)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 9, + "text": [ + "nan" + ] + } + ], + "prompt_number": 9 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the `np.sum` function does not work, use `np.nansum` instead:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print('total sum:', np.nansum(ary))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "total sum: 62.0\n" + ] + } + ], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print('column sums:', np.nansum(ary, axis=0))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "column sums: [ 16. 19. 15. 12.]\n" + ] + } + ], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "print('row sums:', np.nansum(ary, axis=1))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "row sums: [ 10. 19. 33.]\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Removing all rows that contain missing values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we will use the `Boolean mask` again to return only those rows that DON'T contain missing values. And if we want to get only the rows that contain `NaN`s, we could simply drop the `~`." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ary[~np.isnan(ary).any(1)]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 14, + "text": [ + "array([[ 1., 2., 3., 4.]])" + ] + } + ], + "prompt_number": 14 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Convert missing values to 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Certain operations, algorithms, and other analyses might not work with `NaN` objects in our data array. But that's not a problem: We can use the convenient `np.nan_to_num` function will convert it to the value 0." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ary0 = np.nan_to_num(ary)\n", + "ary0" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 15, + "text": [ + "array([[ 1., 2., 3., 4.],\n", + " [ 5., 6., 0., 8.],\n", + " [ 10., 11., 12., 0.]])" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Converting certain numbers to NaN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vice versa, we can also convert any number to a `np.NaN` object. Here, we use the array that we created in the previous section and convert the `0`s back to `np.nan` objects." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ary0[ary0==0] = np.nan\n", + "ary0" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 16, + "text": [ + "array([[ 1., 2., 3., 4.],\n", + " [ 5., 6., nan, 8.],\n", + " [ 10., 11., 12., nan]])" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Remove all missing elements from an array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to top](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is one is a little bit more tricky. We can remove missing values via a combination of the `Boolean` mask and fancy indexing, however, this will have the disadvantage that it will flatten our array (we can't just punch holes into a NumPy array)." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "ary[~np.isnan(ary)]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 17, + "text": [ + "array([ 1., 2., 3., 4., 5., 6., 8., 10., 11., 12.])" + ] + } + ], + "prompt_number": 17 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thus, this is a method that would better work on individual rows:" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "x = np.array([1,2,np.nan])\n", + "\n", + "x[~np.isnan(np.array(x))]" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "array([ 1., 2.])" + ] + } + ], + "prompt_number": 21 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb index a47fd1d..58adb8a 100644 --- a/tutorials/scope_resolution_legb_rule.ipynb +++ b/tutorials/scope_resolution_legb_rule.ipynb @@ -1,1148 +1,1159 @@ { - "metadata": { - "name": "", - "signature": "sha256:b33e0c5563d80d68580ea6ce62ae2703856ccde40aec8ff9fb1364ac70d70521" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[Sebastian Raschka](http://www.sebastianraschka.com) \n", - "\n", - "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n", - "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%load_ext watermark" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "%watermark -a 'Sebastian Raschka' -v -d" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Sebastian Raschka 04/07/2014 \n", - "\n", - "CPython 3.3.5\n", - "IPython 2.1.0\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "I would be happy to hear your comments and suggestions. \n", - "Please feel free to drop me a note via\n", - "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download this IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Sections " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "- [Introduction to namespaces and scopes](#introduction) \n", - "- [1. LG - Local and Global scopes](#section_1) \n", - "- [2. LEG - Local, Enclosed, and Global scope](#section_2) \n", - "- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3) \n", - "- [Self-assessment exercise](#assessment)\n", - "- [Conclusion](#conclusion) \n", - "- [Solutions](#solutions)\n", - "- [Warning: For-loop variables \"leaking\" into the global namespace](#for_loop)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Objectives\n", - "- Namespaces and scopes - where does Python look for variable names?\n", - "- Can we define/reuse variable names for multiple objects at the same time?\n", - "- In which order does Python search different namespaces for variable names?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to namespaces and scopes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Namespaces" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object. \n", - "Such a \"name-to-object\" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = \"Hello string\"`, we created a reference to the `\"Hello string\"` object, and henceforth we can access via its variable name `a_string`.\n", - "\n", - "We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., \n", - "\n", - "
a_namespace = {'name_a':object_1, 'name_b':object_2, ...}
\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:\n", - "\n", - "
a_namespace = {'name_a':object_1, 'name_b':object_2, ...}\n",
-      "b_namespace = {'name_a':object_3, 'name_b':object_4, ...}
\n", - "\n", - "For example, everytime we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called \"scope\"), which we will discuss in more detail in the next section." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scope" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of \"scope\". The \"scope\" in Python defines the \"hierarchy level\" in which we search namespaces for certain \"name-to-object\" mappings. \n", - "For example, let us consider the following code:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "i = 1\n", - "\n", - "def foo():\n", - " i = 5\n", - " print(i, 'in foo()')\n", - "\n", - "print(i, 'global')\n", - "\n", - "foo()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 global\n", - "5 in foo()\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we just defined the variable name `i` twice, once on the `foo` function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- `foo_namespace = {'i':object_3, ...}` \n", - "- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So, how does Python know which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tip:\n", - "If we want to print out the dictionary mapping of the global and local variables, we can use the\n", - "the functions `global()` and `local()`" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "#print(globals()) # prints global namespace\n", - "#print(locals()) # prints local namespace\n", - "\n", - "glob = 1\n", - "\n", - "def foo():\n", - " loc = 5\n", - " print('loc in foo():', 'loc' in locals())\n", - "\n", - "foo()\n", - "print('loc in global:', 'loc' in globals()) \n", - "print('glob in global:', 'foo' in globals())" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "loc in foo(): True\n", - "loc in global: False\n", - "glob in global: True\n" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scope resolution for variable names via the LEGB rule." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The \"scope\" defines on which hierarchy level Python searches for a particular \"variable name\" for its associated object. Now, the next question is: \"In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?\" \n", - "To answer is: It uses the LEGB-rule, which stands for\n", - "\n", - "**Local -> Enclosed -> Global -> Built-in**, \n", - "\n", - "where the arrows should denote the direction of the namespace-hierarchy search order. \n", - "\n", - "- *Local* can be inside a function or class method, for example. \n", - "- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function. \n", - "- *Global* refers to the uppermost level of the executing script itself, and \n", - "- *Built-in* are special names that Python reserves for itself. \n", - "\n", - "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the built-in namespace (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n", - "\n", - "**Note**: \n", - "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy\n", - "import math\n", - "import scipy\n", - "\n", - "print(math.pi, 'from the math module')\n", - "print(numpy.pi, 'from the numpy package')\n", - "print(scipy.pi, 'from the scipy package')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3.141592653589793 from the math module\n", - "3.141592653589793 from the numpy package\n", - "3.141592653589793 from the scipy package\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "![LEGB figure](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/scope_resolution_1.png)\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. LG - Local and Global scopes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 1.1** \n", - "As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes. \n", - "What does the following code print?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 'global variable'\n", - "\n", - "def a_func():\n", - " print(a_var, '[ a_var inside a_func() ]')\n", - "\n", - "a_func()\n", - "print(a_var, '[ a_var outside a_func() ]')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**a)**\n", - "
raises an error
\n", - "\n", - "**b)** \n", - "
\n",
-      "global value [ a_var outside a_func() ]
\n", - "\n", - "**c)** \n", - "
global value [ a_var in a_func() ]  \n",
-      "global value [ a_var outside a_func() ]
\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[go to solution](#solutions)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Here is why:\n", - "\n", - "We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 1.2** \n", - "Now, let us define the variable `a_var` in the global and the local scope. \n", - "Can you guess what the following code will produce?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 'global value'\n", - "\n", - "def a_func():\n", - " a_var = 'local value'\n", - " print(a_var, '[ a_var inside a_func() ]')\n", - "\n", - "a_func()\n", - "print(a_var, '[ a_var outside a_func() ]')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**a)**\n", - "
raises an error
\n", - "\n", - "**b)** \n", - "
local value [ a_var in a_func() ]\n",
-      "global value [ a_var outside a_func() ]
\n", - "\n", - "**c)** \n", - "
global value [ a_var in a_func() ]  \n",
-      "global value [ a_var outside a_func() ]
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[go to solution](#solutions)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Here is why:\n", - "\n", - "When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 'global value'\n", - "\n", - "def a_func():\n", - " global a_var\n", - " a_var = 'local value'\n", - " print(a_var, '[ a_var inside a_func() ]')\n", - "\n", - "print(a_var, '[ a_var outside a_func() ]')\n", - "a_func()\n", - "print(a_var, '[ a_var outside a_func() ]')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "global value [ a_var outside a_func() ]\n", - "local value [ a_var inside a_func() ]\n", - "local value [ a_var outside a_func() ]\n" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 1\n", - "\n", - "def a_func():\n", - " a_var = a_var + 1\n", - " print(a_var, '[ a_var inside a_func() ]')\n", - "\n", - "print(a_var, '[ a_var outside a_func() ]')\n", - "a_func()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "ename": "UnboundLocalError", - "evalue": "local variable 'a_var' referenced before assignment", - "output_type": "pyerr", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var outside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "1 [ a_var outside a_func() ]\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. LEG - Local, Enclosed, and Global scope\n", - "\n", - "\n", - "\n", - "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 2.1**" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 'global value'\n", - "\n", - "def outer():\n", - " a_var = 'enclosed value'\n", - " \n", - " def inner():\n", - " a_var = 'local value'\n", - " print(a_var)\n", - " \n", - " inner()\n", - "\n", - "outer()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**a)**\n", - "
global value
\n", - "\n", - "**b)** \n", - "
enclosed value
\n", - "\n", - "**c)** \n", - "
local value
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[go to solution](#solutions)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Here is why:\n", - "\n", - "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value. \n", - "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 'global value'\n", - "\n", - "def outer():\n", - " a_var = 'local value'\n", - " print('outer before:', a_var)\n", - " def inner():\n", - " nonlocal a_var\n", - " a_var = 'inner value'\n", - " print('in inner():', a_var)\n", - " inner()\n", - " print(\"outer after:\", a_var)\n", - "outer()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "outer before: local value\n", - "in inner(): inner value\n", - "outer after: inner value\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. LEGB - Local, Enclosed, Global, Built-in\n", - "\n", - "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Example 3**" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 'global variable'\n", - "\n", - "def len(in_var):\n", - " print('called my len() function')\n", - " l = 0\n", - " for i in in_var:\n", - " l += 1\n", - " return l\n", - "\n", - "def a_func(in_var):\n", - " len_in_var = len(in_var)\n", - " print('Input variable is of length', len_in_var)\n", - "\n", - "a_func('Hello, World!')" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**a)**\n", - "
raises an error (conflict with in-built `len()` function)
\n", - "\n", - "**b)** \n", - "
called my len() function\n",
-      "Input variable is of length 13
\n", - "\n", - "**c)** \n", - "
Input variable is of length 13
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[go to solution](#solutions)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Here is why:\n", - "\n", - "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Self-assessment exercise" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a = 'global'\n", - "\n", - "def outer():\n", - " \n", - " def len(in_var):\n", - " print('called my len() function: ', end=\"\")\n", - " l = 0\n", - " for i in in_var:\n", - " l += 1\n", - " return l\n", - " \n", - " a = 'local'\n", - " \n", - " def inner():\n", - " global len\n", - " nonlocal a\n", - " a += ' variable'\n", - " inner()\n", - " print('a is', a)\n", - " print(len(a))\n", - "\n", - "\n", - "outer()\n", + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Sebastian Raschka](http://www.sebastianraschka.com) \n", + "\n", + "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n", + "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext watermark" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sebastian Raschka 01/27/2016 \n", "\n", - "print(len(a))\n", - "print('a is', a)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 59 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[[go to solution](#solutions)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Conclusion" + "CPython 3.5.1\n", + "IPython 4.0.3\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### A rule of thumb" + } + ], + "source": [ + "%watermark -a 'Sebastian Raschka' -v -d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "I would be happy to hear your comments and suggestions. \n", + "Please feel free to drop me a note via\n", + "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download this IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sections " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "- [Introduction to namespaces and scopes](#introduction) \n", + "- [1. LG - Local and Global scopes](#section_1) \n", + "- [2. LEG - Local, Enclosed, and Global scope](#section_2) \n", + "- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3) \n", + "- [Self-assessment exercise](#assessment)\n", + "- [Conclusion](#conclusion) \n", + "- [Solutions](#solutions)\n", + "- [Warning: For-loop variables \"leaking\" into the global namespace](#for_loop)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Objectives\n", + "- Namespaces and scopes - where does Python look for variable names?\n", + "- Can we define/reuse variable names for multiple objects at the same time?\n", + "- In which order does Python search different namespaces for variable names?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to namespaces and scopes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Namespaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object. \n", + "Such a \"name-to-object\" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = \"Hello string\"`, we created a reference to the `\"Hello string\"` object, and henceforth we can access via its variable name `a_string`.\n", + "\n", + "We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., \n", + "\n", + "
a_namespace = {'name_a':object_1, 'name_b':object_2, ...}
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:\n", + "\n", + "
a_namespace = {'name_a':object_1, 'name_b':object_2, ...}\n",
+    "b_namespace = {'name_a':object_3, 'name_b':object_4, ...}
\n", + "\n", + "For example, everytime we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called \"scope\"), which we will discuss in more detail in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scope" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of \"scope\". The \"scope\" in Python defines the \"hierarchy level\" in which we search namespaces for certain \"name-to-object\" mappings. \n", + "For example, let us consider the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 global\n", + "5 in foo()\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug. \n", - "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value. \n", - "For example:" + } + ], + "source": [ + "i = 1\n", + "\n", + "def foo():\n", + " i = 5\n", + " print(i, 'in foo()')\n", + "\n", + "print(i, 'global')\n", + "\n", + "foo()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we just defined the variable name `i` twice, once on the `foo` function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `foo_namespace = {'i':object_3, ...}` \n", + "- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, how does Python know which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Tip:\n", + "If we want to print out the dictionary mapping of the global and local variables, we can use the\n", + "the functions `global()` and `local()`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loc in foo(): True\n", + "loc in global: False\n", + "glob in global: True\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "a_var = 2\n", - "\n", - "def a_func(some_var):\n", - " return 2**3\n", - "\n", - "a_var = a_func(a_var)\n", - "print(a_var)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "8\n" - ] - } - ], - "prompt_number": 42 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "
\n", - "
" + } + ], + "source": [ + "#print(globals()) # prints global namespace\n", + "#print(locals()) # prints local namespace\n", + "\n", + "glob = 1\n", + "\n", + "def foo():\n", + " loc = 5\n", + " print('loc in foo():', 'loc' in locals())\n", + "\n", + "foo()\n", + "print('loc in global:', 'loc' in globals()) \n", + "print('glob in global:', 'foo' in globals())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Scope resolution for variable names via the LEGB rule." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The \"scope\" defines on which hierarchy level Python searches for a particular \"variable name\" for its associated object. Now, the next question is: \"In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?\" \n", + "To answer is: It uses the LEGB-rule, which stands for\n", + "\n", + "**Local -> Enclosed -> Global -> Built-in**, \n", + "\n", + "where the arrows should denote the direction of the namespace-hierarchy search order. \n", + "\n", + "- *Local* can be inside a function or class method, for example. \n", + "- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function. \n", + "- *Global* refers to the uppermost level of the executing script itself, and \n", + "- *Built-in* are special names that Python reserves for itself. \n", + "\n", + "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the built-in namespace (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n", + "\n", + "**Note**: \n", + "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.141592653589793 from the math module\n", + "3.141592653589793 from the numpy package\n", + "3.141592653589793 from the scipy package\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solutions\n", - "\n", - "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:" + } + ], + "source": [ + "import numpy\n", + "import math\n", + "import scipy\n", + "\n", + "print(math.pi, 'from the math module')\n", + "print(numpy.pi, 'from the numpy package')\n", + "print(scipy.pi, 'from the scipy package')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "![LEGB figure](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/scope_resolution_1.png)\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. LG - Local and Global scopes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 1.1** \n", + "As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes. \n", + "What does the following code print?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a_var = 'global variable'\n", + "\n", + "def a_func():\n", + " print(a_var, '[ a_var inside a_func() ]')\n", + "\n", + "a_func()\n", + "print(a_var, '[ a_var outside a_func() ]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**a)**\n", + "
raises an error
\n", + "\n", + "**b)** \n", + "
\n",
+    "global value [ a_var outside a_func() ]
\n", + "\n", + "**c)** \n", + "
global value [ a_var inside a_func() ]  \n",
+    "global value [ a_var outside a_func() ]
\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[go to solution](#solutions)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Here is why:\n", + "\n", + "We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 1.2** \n", + "Now, let us define the variable `a_var` in the global and the local scope. \n", + "Can you guess what the following code will produce?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a_var = 'global value'\n", + "\n", + "def a_func():\n", + " a_var = 'local value'\n", + " print(a_var, '[ a_var inside a_func() ]')\n", + "\n", + "a_func()\n", + "print(a_var, '[ a_var outside a_func() ]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**a)**\n", + "
raises an error
\n", + "\n", + "**b)** \n", + "
local value [ a_var inside a_func() ]\n",
+    "global value [ a_var outside a_func() ]
\n", + "\n", + "**c)** \n", + "
global value [ a_var inside a_func() ]  \n",
+    "global value [ a_var outside a_func() ]
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[go to solution](#solutions)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Here is why:\n", + "\n", + "When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "global value [ a_var outside a_func() ]\n", + "local value [ a_var inside a_func() ]\n", + "local value [ a_var outside a_func() ]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Example 1.1:', chr(int('01100011',2)))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Example 1.2:', chr(int('01100010',2)))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 7 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Example 2.1:', chr(int('01100011',2)))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print('Example 3.1:', chr(int('01100010',2)))" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 9 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Execute to run the self-assessment solution\n", - "\n", - "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n", - "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n", - "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n", - "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n", - "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n", - "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n", - "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n", - "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n", - "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n", - "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n", - "\"1101100\"\n", - "\n", - "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n", - "for line in sol_str.split('\\n'):\n", - " print(line)" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 58 - }, + } + ], + "source": [ + "a_var = 'global value'\n", + "\n", + "def a_func():\n", + " global a_var\n", + " a_var = 'local value'\n", + " print(a_var, '[ a_var inside a_func() ]')\n", + "\n", + "print(a_var, '[ a_var outside a_func() ]')\n", + "a_func()\n", + "print(a_var, '[ a_var outside a_func() ]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "
\n", - "" + "ename": "UnboundLocalError", + "evalue": "local variable 'a_var' referenced before assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var outside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment" ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Warning: For-loop variables \"leaking\" into the global namespace" + "name": "stdout", + "output_type": "stream", + "text": [ + "1 [ a_var outside a_func() ]\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n" + } + ], + "source": [ + "a_var = 1\n", + "\n", + "def a_func():\n", + " a_var = a_var + 1\n", + " print(a_var, '[ a_var inside a_func() ]')\n", + "\n", + "print(a_var, '[ a_var outside a_func() ]')\n", + "a_func()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. LEG - Local, Enclosed, and Global scope\n", + "\n", + "\n", + "\n", + "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 2.1**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a_var = 'global value'\n", + "\n", + "def outer():\n", + " a_var = 'enclosed value'\n", + " \n", + " def inner():\n", + " a_var = 'local value'\n", + " print(a_var)\n", + " \n", + " inner()\n", + "\n", + "outer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**a)**\n", + "
global value
\n", + "\n", + "**b)** \n", + "
enclosed value
\n", + "\n", + "**c)** \n", + "
local value
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[go to solution](#solutions)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Here is why:\n", + "\n", + "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value. \n", + "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "outer before: local value\n", + "in inner(): inner value\n", + "outer after: inner value\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "for a in range(5):\n", - " if a == 4:\n", - " print(a, '-> a in for-loop')\n", - "print(a, '-> a in global')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "4 -> a in for-loop\n", - "4 -> a in global\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**This also applies if we explicitly defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:" + } + ], + "source": [ + "a_var = 'global value'\n", + "\n", + "def outer():\n", + " a_var = 'local value'\n", + " print('outer before:', a_var)\n", + " def inner():\n", + " nonlocal a_var\n", + " a_var = 'inner value'\n", + " print('in inner():', a_var)\n", + " inner()\n", + " print(\"outer after:\", a_var)\n", + "outer()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. LEGB - Local, Enclosed, Global, Built-in\n", + "\n", + "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example 3**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a_var = 'global variable'\n", + "\n", + "def len(in_var):\n", + " print('called my len() function')\n", + " l = 0\n", + " for i in in_var:\n", + " l += 1\n", + " return l\n", + "\n", + "def a_func(in_var):\n", + " len_in_var = len(in_var)\n", + " print('Input variable is of length', len_in_var)\n", + "\n", + "a_func('Hello, World!')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**a)**\n", + "
raises an error (conflict with in-built `len()` function)
\n", + "\n", + "**b)** \n", + "
called my len() function\n",
+    "Input variable is of length 13
\n", + "\n", + "**c)** \n", + "
Input variable is of length 13
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[go to solution](#solutions)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Here is why:\n", + "\n", + "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope (G) first before it attempts to search the built-in (B) namespace." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Self-assessment exercise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a = 'global'\n", + "\n", + "def outer():\n", + " \n", + " def len(in_var):\n", + " print('called my len() function: ', end=\"\")\n", + " l = 0\n", + " for i in in_var:\n", + " l += 1\n", + " return l\n", + " \n", + " a = 'local'\n", + " \n", + " def inner():\n", + " global len\n", + " nonlocal a\n", + " a += ' variable'\n", + " inner()\n", + " print('a is', a)\n", + " print(len(a))\n", + "\n", + "\n", + "outer()\n", + "\n", + "print(len(a))\n", + "print('a is', a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[go to solution](#solutions)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### A rule of thumb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug. \n", + "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value. \n", + "For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "b = 1\n", - "for b in range(5):\n", - " if b == 4:\n", - " print(b, '-> b in for-loop')\n", - "print(b, '-> b in global')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "4 -> b in for-loop\n", - "4 -> b in global\n" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):" + } + ], + "source": [ + "a_var = 2\n", + "\n", + "def a_func(some_var):\n", + " return 2**3\n", + "\n", + "a_var = a_func(a_var)\n", + "print(a_var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solutions\n", + "\n", + "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print('Example 1.1:', chr(int('01100011',2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print('Example 1.2:', chr(int('01100010',2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print('Example 2.1:', chr(int('01100011',2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print('Example 3.1:', chr(int('01100010',2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Execute to run the self-assessment solution\n", + "\n", + "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n", + "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n", + "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n", + "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n", + "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n", + "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n", + "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n", + "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n", + "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n", + "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n", + "\"1101100\"\n", + "\n", + "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n", + "for line in sol_str.split('\\n'):\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Warning: For-loop variables \"leaking\" into the global namespace" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 -> a in for-loop\n", + "4 -> a in global\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "i = 1\n", - "print([i for i in range(5)])\n", - "print(i, '-> i in global')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[0, 1, 2, 3, 4]\n", - "1 -> i in global\n" - ] - } - ], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n", - "\n", - "
\n",
-      "4 -> i in global\n",
-      "
"
+    }
+   ],
+   "source": [
+    "for a in range(5):\n",
+    "    if a == 4:\n",
+    "        print(a, '-> a in for-loop')\n",
+    "print(a, '-> a in global')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**This also applies if we explicitly defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 -> b in for-loop\n",
+      "4 -> b in global\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
-      "\n",
-      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+    }
+   ],
+   "source": [
+    "b = 1\n",
+    "for b in range(5):\n",
+    "    if b == 4:\n",
+    "        print(b, '-> b in for-loop')\n",
+    "print(b, '-> b in global')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 2, 3, 4]\n",
+      "1 -> i in global\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
-   "metadata": {}
+   "source": [
+    "i = 1\n",
+    "print([i for i in range(5)])\n",
+    "print(i, '-> i in global')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
+    "\n",
+    "
\n",
+    "4 -> i in global\n",
+    "
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+    "\n",
+    "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
diff --git a/tutorials/scope_resolution_legb_rule.md b/tutorials/scope_resolution_legb_rule.md
deleted file mode 100644
index 6722604..0000000
--- a/tutorials/scope_resolution_legb_rule.md
+++ /dev/null
@@ -1,579 +0,0 @@
-# A Beginner's Guide to Python's Namespaces, Scope Resolution, and the LEGB Rule #
-
-
-This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download it as IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb).
-
-
-
- -## Objectives -- Namespaces and scopes - where does Python look for variable names? -- Can we define/reuse variable names for multiple objects at the same time? -- In which order does Python search different namespaces for variable names? - -
-
- -## Sections -- [Introduction to namespaces and scopes](#introduction) -- [1. LG - Local and Global scopes](#section_1) -- [2. LEG - Local, Enclosed, and Global scope](#section_2) -- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3) -- [Self-assessment exercise](#assessment) -- [Conclusion](#conclusion) -- [Solutions](#solutions) -- [Warning: For-loop variables "leaking" into the global namespace](#for_loop) - - -
-
- -##Introduction to Namespaces and Scopes - -
- -###Namespaces -
- -Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object. -Such a "name-to-object" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = "Hello string"`, we created a reference to the `"Hello string"` object, and henceforth we can access via its variable name `a_string`. - -We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., - -
a_namespace = {'name_a':object_1, 'name_b':object_2, ...}
- - -Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example: - -
a_namespace = {'name_a':object_1, 'name_b':object_2, ...}
-b_namespace = {'name_a':object_3, 'name_b':object_4, ...}
- -For example, every time we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called "scope"), which we will discuss in more detail in the next section. - -
-
- -### Scope - - -In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of "scope". The "scope" in Python defines the "hierarchy level" in which we search namespaces for certain "name-to-object" mappings. -For example, let us consider the following code: - -`Input:` -
i = 1
-
-def foo():
-    i = 5
-    print(i, 'in foo()')
-print(i, 'global')
-
-foo()
-
- -`Output:` -
1 global
-5 in foo()
-
- -
-
-Here, we just defined the variable name `i` twice, once on the `foo` function. - -- `foo_namespace = {'i':object_3, ...}` -- `global_namespace = {'i':object_1, 'name_b':object_2, ...}` - -So, how does Python now which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section. - -
-### Tip: -If we want to print out the dictionary mapping of the global and local variables, we can use the -the functions `global()` and `local() - -`Input:` -
#print(globals()) # prints global namespace
-#print(locals()) # prints local namespace
-
-glob = 1
-
-def foo():
-    loc = 5
-    print('loc in foo():', 'loc' in locals())
-
-foo()
-print('loc in global:', 'loc' in globals())    
-print('glob in global:', 'foo' in globals())
-
- -`Output:` -
loc in foo(): True
-loc in global: False
-glob in global: True
-
- -
-
- -### Scope resolution for variable names via the LEGB rule. - -We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The "scope" defines on which hierarchy level Python searches for a particular "variable name" for its associated object. Now, the next question is: "In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?" -To answer is: It uses the LEGB-rule, which stands for - -**Local -> Enclosed -> Global -> Built-in**, - -where the arrows should denote the direction of the namespace-hierarchy search order. - -- *Local* can be inside a function or class method, for example. -- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function. -- *Global* refers to the uppermost level of the executing script itself, and -- *Built-in* are special names that Python reserves for itself. - -So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the global namespaces (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised). - -**Note**: -Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block: - -`Input:` -
import numpy
-import math
-import scipy
-
-print(math.pi, 'from the math module')
-print(numpy.pi, 'from the numpy package')
-print(scipy.pi, 'from the scipy package')
-
- -`Output:` -
3.141592653589793 from the math module
-3.141592653589793 from the numpy package
-3.141592653589793 from the scipy package
-
-
-
-(This is also why we have to be careful if we import modules via "`from a_module import *`", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names) - -
- -
-
-![LEGB figure](../Images/scope_resolution_1.png) -
-
- - -
-
- -## 1. LG - Local and Global scopes - - -**Example 1.1** -As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes. -What does the following code print? - -
a_var = 'global variable'
-
-def a_func():
-    print(a_var, '[ a_var inside a_func() ]')
-
-a_func()
-print(a_var, '[ a_var outside a_func() ]')
-
- -**a)** -
raises an error
- -**b)** -
-global value [ a_var outside a_func() ]
- -**c)** -
global value [ a_var in a_func() ]  
-global value [ a_var outside a_func() ]
- -[[go to solution](#solutions)] - -### Here is why: - -We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously. -
-
- - -**Example 1.2** -Now, let us define the variable `a_var` in the global and the local scope. -Can you guess what the following code will produce? - -
a_var = 'global value'
-
-def a_func():
-    a_var = 'local value'
-    print(a_var, '[ a_var inside a_func() ]')
-
-a_func()
-print(a_var, '[ a_var outside a_func() ]')
-
- -**a)** -
raises an error
- -**b)** -
local value [ a_var in a_func() ]
-global value [ a_var outside a_func() ]
- -**c)** -
global value [ a_var in a_func() ]  
-global value [ a_var outside a_func() ]
- - -[[go to solution](#solutions)] - -### Here is why: - -When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope. - -
-However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate: - -`Input:` -
a_var = 'global value'
-
-def a_func():
-    global a_var
-    a_var = 'local value'
-    print(a_var, '[ a_var inside a_func() ]')
-
-print(a_var, '[ a_var outside a_func() ]')
-a_func()
-print(a_var, '[ a_var outside a_func() ]')
-
- -`Output:` -
**a)**
-<pre>raises an error</pre>
-
-**b)** 
-<pre>
-global value [ a_var outside a_func() ]</pre>
-
-**c)** 
-<pre>global value [ a_var in a_func() ]  
-global value [ a_var outside a_func() ]</pre>
-
- -But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first): - -`Input:` -
a_var = 1
-
-def a_func():
-    a_var = a_var + 1
-    print(a_var, '[ a_var inside a_func() ]')
-
-print(a_var, '[ a_var outside a_func() ]')
-a_func()
-
-`Output:` -
---------------------------------------------------------------------------
-UnboundLocalError                         Traceback (most recent call last)
-<ipython-input-4-a6cdd0ee9a55> in <module>()
-      6 
-      7 print(a_var, '[ a_var outside a_func() ]')
-----> 8 a_func()
-
-<ipython-input-4-a6cdd0ee9a55> in a_func()
-      2 
-      3 def a_func():
-----> 4     a_var = a_var + 1
-      5     print(a_var, '[ a_var inside a_func() ]')
-      6 
-
-UnboundLocalError: local variable 'a_var' referenced before assignment
-
-1 [ a_var outside a_func() ]
-
- -
-
- - -
-
- -## 2. LEG - Local, Enclosed, and Global scope - - - -Now, let us introduce the concept of the enclosed (E) scope. Following the order "Local -> Enclosed -> Global", can you guess what the following code will print? - - -**Example 2.1** - -
a_var = 'global value'
-
-def outer():
-    a_var = 'enclosed value'
-    
-    def inner():
-        a_var = 'local value'
-        print(a_var)
-    
-    inner()
-
-outer()
-
-**a)** -
global value
- -**b)** -
enclosed value
- -**c)** -
local value
- -[[go to solution](#solutions)] - -### Here is why: - -Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope. - -Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value. -Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet). - -`Input:` -
a_var = 'global value'
-
-def outer():
-       a_var = 'local value'
-       print('outer before:', a_var)
-       def inner():
-           nonlocal a_var
-           a_var = 'inner value'
-           print('in inner():', a_var)
-       inner()
-       print("outer after:", a_var)
-outer()
-
-`Output:` -
outer before: local value
-in inner(): inner value
-outer after: inner value
-
- - -
-
-
- - -## 3. LEGB - Local, Enclosed, Global, Built-in - -To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our "own" length-function, which happens to bear the same name as the in-built `len()` function. What outcome do you expect if we'd execute the following code? - - - -**Example 3** - -
a_var = 'global variable'
-
-def len(in_var):
-    print('called my len() function')
-    l = 0
-    for i in in_var:
-        l += 1
-    return l
-
-def a_func(in_var):
-    len_in_var = len(in_var)
-    print('Input variable is of length', len_in_var)
-
-a_func('Hello, World!')
-
- -**a)** -
raises an error (conflict with in-built `len()` function)
- -**b)** -
called my len() function
-Input variable is of length 13
- -**c)** -
Input variable is of length 13
- -[[go to solution](#solutions)] - -### Here is why: - -Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration purposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts - - - -
-
- -# Self-assessment exercise - -Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out? - -
a = 'global'
-
-def outer():
-    
-    def len(in_var):
-        print('called my len() function: ', end="")
-        l = 0
-        for i in in_var:
-            l += 1
-        return l
-    
-    a = 'local'
-    
-    def inner():
-        global len
-        nonlocal a
-        a += ' variable'
-    inner()
-    print('a is', a)
-    print(len(a))
-
-outer()
-
-print(len(a))
-print('a is', a)
-
- - -
- -[[go to solution](#solutions)] - -# Conclusion - -I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes. - -#### A rule of thumb - -In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug. -If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value. -For example: - -`Input:` -
a_var = 2
-
-def a_func(some_var):
-    return 2**3
-
-a_var = a_func(a_var)
-print(a_var)
-
-`Output:` -
8
-
- - -
-
-
- -## Solutions - -In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code: - -
print('Example 1.1:', chr(int('01100011',2)))
-
- -[[back to example 1.1](#example1.1)] - -
print('Example 1.2:', chr(int('01100001',2)))
-
- -[[back to example 1.2](#example1.2)] - -
print('Example 2:', chr(int('01100011',2)))
-
- -[[back to example 2](#example2)] - -
print('Example 3:', chr(int('01100010',2)))
-
- -[[back to example 3](#example3)] - -
# Solution to the self-assessment exercise
-sol = "000010100110111101110101011101000110010101110010001010"\
-"0000101001001110100000101000001010011000010010000001101001011100110"\
-"0100000011011000110111101100011011000010110110000100000011101100110"\
-"0001011100100110100101100001011000100110110001100101000010100110001"\
-"1011000010110110001101100011001010110010000100000011011010111100100"\
-"1000000110110001100101011011100010100000101001001000000110011001110"\
-"1010110111001100011011101000110100101101111011011100011101000100000"\
-"0011000100110100000010100000101001100111011011000110111101100010011"\
-"0000101101100001110100000101000001010001101100000101001100001001000"\
-"0001101001011100110010000001100111011011000110111101100010011000010"\
-"1101100"
-
-sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))
-for line in sol_str.split('\n'):
-    print(line)
-
- -[[back to self-assessment exercise](#assessment)] - - - - -
-
- - -## Warning: For-loop variables "leaking" into the global namespace - -In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind. - -`Input:` -
for a in range(5):
-    if a == 4:
-        print(a, '-> a in for-loop')
-print(a, '-> a in global')
-
-`Output:` -
4 -> a in for-loop
-4 -> a in global
-
- -**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable: - -`Input:` -
b = 1
-for b in range(5):
-    if b == 4:
-        print(b, '-> b in for-loop')
-print(b, '-> b in global')
-
- -`Output:` -
4 -> b in for-loop
-4 -> b in global
-
- -However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4): - -`Input:` -
i = 1
-print([i for i in range(5)])
-print(i, '-> i in global')
-
-`Output:` -
[0, 1, 2, 3, 4]
-1 -> i in global
-
- -Why did I mention "Python 3.x"? Well, as it happens, the same code executed in Python 2.x would print: - -
-4 -> i in global
-
- -This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows: - -"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope." \ No newline at end of file diff --git a/tutorials/setup.py b/tutorials/setup.py deleted file mode 100644 index c92789a..0000000 --- a/tutorials/setup.py +++ /dev/null @@ -1,14 +0,0 @@ - -import numpy as np -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext - -setup( - cmdclass = {'build_ext': build_ext}, - ext_modules = [ - Extension("cython_bubblesort_nomagic", - ["cython_bubblesort_nomagic.pyx"], - include_dirs=[np.get_include()]) - ] -) \ No newline at end of file diff --git a/tutorials/sqlite3_howto/README.md b/tutorials/sqlite3_howto/README.md index 7ef88e9..c596dfc 100644 --- a/tutorials/sqlite3_howto/README.md +++ b/tutorials/sqlite3_howto/README.md @@ -29,7 +29,7 @@ _\-- written by Sebastian Raschka_ on March 7, 2014 • Conclusion The complete Python code that I am using in this tutorial can be downloaded -from my GitHub repository: [https://github.com/rasbt/python_reference/tutorials/sqlite3_howto](https://github.com/rasbt/python_reference/tutorials/sqlite3_howto) +from my GitHub repository: [https://github.com/rasbt/python_reference/tree/master/tutorials/sqlite3_howto](https://github.com/rasbt/python_reference/tree/master/tutorials/sqlite3_howto) * * * @@ -97,7 +97,7 @@ there is more information about PRIMARY KEYs further down in this section). - mport sqlite3 + import sqlite3 sqlite_file = 'my_first_db.sqlite' # name of the sqlite database file table_name1 = 'my_table_1' # name of the table to be created @@ -123,7 +123,7 @@ there is more information about PRIMARY KEYs further down in this section). conn.close() -Download the script: [create_new_db.py](https://raw.github.com/rasbt/python_reference/master/tutorials/code/create_new_db.py) +Download the script: [create_new_db.py](https://github.com/rasbt/python_reference/blob/master/tutorials/sqlite3_howto/code/create_new_db.py) * * * @@ -207,7 +207,7 @@ Let's have a look at some code: conn.close() -Download the script: [add_new_column.py](https://raw.github.com/rasbt/python_reference/master/tutorials/code/add_new_column.py) +Download the script: [add_new_column.py](https://github.com/rasbt/python_reference/blob/master/tutorials/sqlite3_howto/code/add_new_column.py) @@ -270,8 +270,7 @@ But let us first have a look at the example code: conn.close() -Download the script: [update_or_insert_records.py](https://raw.github.com/rasb -t/python_sqlite_code/master/code/update_or_insert_records.py) +Download the script: [update_or_insert_records.py](code/update_or_insert_records.py) ![3_sqlite3_insert_update.png](../../Images/3_sqlite3_insert_update.png) @@ -335,8 +334,7 @@ drop the index, which is also shown in the code below. conn.close() -Download the script: [create_unique_index.py](https://raw.github.com/rasbt/pyt -hon_sqlite_code/master/code/create_unique_index.py) +Download the script: [create_unique_index.py](code/create_unique_index.py) ![4_sqlite3_unique_index.png](../../Images/4_sqlite3_unique_index.png) @@ -401,8 +399,7 @@ row entries for all or some columns if they match certain criteria. conn.close() -Download the script: [selecting_entries.py](https://raw.github.com/rasbt/pytho -n_sqlite_code/master/code/selecting_entries.py) +Download the script: [selecting_entries.py](code/selecting_entries.py) ![4_sqlite3_unique_index.png](../../Images/4_sqlite3_unique_index.png) @@ -542,8 +539,7 @@ that have been added xxx days ago. conn.close() -Download the script: [date_time_ops.py](https://raw.github.com/rasbt/python_sq -lite_code/master/code/date_time_ops.py) +Download the script: [date_time_ops.py](code/date_time_ops.py) @@ -590,7 +586,7 @@ syntax applies to simple dates or simple times only, too. #### Update Mar 16, 2014: -If'd we are interested to calulate the hours between two `DATETIME()` +If'd we are interested to calculate the hours between two `DATETIME()` timestamps, we can could use the handy `STRFTIME()` function like this @@ -645,8 +641,7 @@ column names): conn.close() -Download the script: [get_columnnames.py](https://raw.github.com/rasbt/python_ -sqlite_code/master/code/get_columnnames.py) +Download the script: [get_columnnames.py](code/get_columnnames.py) ![7_sqlite3_get_colnames_1.png](../../Images/7_sqlite3_get_colnames_1.png) @@ -682,53 +677,58 @@ convenient script to print a nice overview of SQLite database tables: import sqlite3 - + + def connect(sqlite_file): """ Make connection to an SQLite database file """ conn = sqlite3.connect(sqlite_file) c = conn.cursor() return conn, c - + + def close(conn): """ Commit changes and close connection to the database """ # conn.commit() conn.close() - + + def total_rows(cursor, table_name, print_out=False): """ Returns the total number of rows in the database """ - c.execute('SELECT COUNT(*) FROM {}'.format(table_name)) - count = c.fetchall() + cursor.execute('SELECT COUNT(*) FROM {}'.format(table_name)) + count = cursor.fetchall() if print_out: print('\nTotal rows: {}'.format(count[0][0])) return count[0][0] - + + def table_col_info(cursor, table_name, print_out=False): - """ - Returns a list of tuples with column informations: - (id, name, type, notnull, default_value, primary_key) - + """ Returns a list of tuples with column informations: + (id, name, type, notnull, default_value, primary_key) """ - c.execute('PRAGMA TABLE_INFO({})'.format(table_name)) - info = c.fetchall() - + cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name)) + info = cursor.fetchall() + if print_out: print("\nColumn Info:\nID, Name, Type, NotNull, DefaultVal, PrimaryKey") for col in info: print(col) return info - + + def values_in_col(cursor, table_name, print_out=True): - """ Returns a dictionary with columns as keys and the number of not-null - entries as associated values. + """ Returns a dictionary with columns as keys + and the number of not-null entries as associated values. """ - c.execute('PRAGMA TABLE_INFO({})'.format(table_name)) - info = c.fetchall() + cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name)) + info = cursor.fetchall() col_dict = dict() for col in info: col_dict[col[1]] = 0 for col in col_dict: - c.execute('SELECT ({0}) FROM {1} WHERE {0} IS NOT NULL'.format(col, table_name)) - # In my case this approach resulted in a better performance than using COUNT + c.execute('SELECT ({0}) FROM {1} ' + 'WHERE {0} IS NOT NULL'.format(col, table_name)) + # In my case this approach resulted in a + # better performance than using COUNT number_rows = len(c.fetchall()) col_dict[col] = number_rows if print_out: @@ -736,23 +736,22 @@ convenient script to print a nice overview of SQLite database tables: for i in col_dict.items(): print('{}: {}'.format(i[0], i[1])) return col_dict - - + + if __name__ == '__main__': - + sqlite_file = 'my_first_db.sqlite' table_name = 'my_table_3' - + conn, c = connect(sqlite_file) total_rows(c, table_name, print_out=True) table_col_info(c, table_name, print_out=True) - values_in_col(c, table_name, print_out=True) # slow on large data bases - + # next line might be slow on large databases + values_in_col(c, table_name, print_out=True) + close(conn) - -Download the script: [print_db_info.py](https://raw.github.com/rasbt/python_sq -lite_code/master/code/print_db_info.py) +Download the script: [print_db_info.py](code/print_db_info.py) ![8_sqlite3_print_db_info_1.png](../../Images/8_sqlite3_print_db_info_1.png) diff --git a/tutorials/sqlite3_howto/code/print_db_info.py b/tutorials/sqlite3_howto/code/print_db_info.py index 22b72a8..285a635 100644 --- a/tutorials/sqlite3_howto/code/print_db_info.py +++ b/tutorials/sqlite3_howto/code/print_db_info.py @@ -22,52 +22,57 @@ import sqlite3 + def connect(sqlite_file): """ Make connection to an SQLite database file """ conn = sqlite3.connect(sqlite_file) c = conn.cursor() return conn, c + def close(conn): """ Commit changes and close connection to the database """ - #conn.commit() + # conn.commit() conn.close() + def total_rows(cursor, table_name, print_out=False): """ Returns the total number of rows in the database """ - c.execute('SELECT COUNT(*) FROM {}'.format(table_name)) - count = c.fetchall() + cursor.execute('SELECT COUNT(*) FROM {}'.format(table_name)) + count = cursor.fetchall() if print_out: print('\nTotal rows: {}'.format(count[0][0])) return count[0][0] + def table_col_info(cursor, table_name, print_out=False): - """ - Returns a list of tuples with column informations: - (id, name, type, notnull, default_value, primary_key) - + """ Returns a list of tuples with column informations: + (id, name, type, notnull, default_value, primary_key) """ - c.execute('PRAGMA TABLE_INFO({})'.format(table_name)) - info = c.fetchall() - + cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name)) + info = cursor.fetchall() + if print_out: print("\nColumn Info:\nID, Name, Type, NotNull, DefaultVal, PrimaryKey") for col in info: print(col) return info + def values_in_col(cursor, table_name, print_out=True): - """ Returns a dictionary with columns as keys and the number of not-null - entries as associated values. + """ Returns a dictionary with columns as keys + and the number of not-null entries as associated values. """ - c.execute('PRAGMA TABLE_INFO({})'.format(table_name)) - info = c.fetchall() + cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name)) + info = cursor.fetchall() col_dict = dict() for col in info: col_dict[col[1]] = 0 for col in col_dict: - c.execute('SELECT ({0}) FROM {1} WHERE {0} IS NOT NULL'.format(col, table_name)) - # In my case this approach resulted in a better performance than using COUNT + c.execute('SELECT ({0}) FROM {1} ' + 'WHERE {0} IS NOT NULL'.format(col, table_name)) + # In my case this approach resulted in a + # better performance than using COUNT number_rows = len(c.fetchall()) col_dict[col] = number_rows if print_out: @@ -85,7 +90,7 @@ def values_in_col(cursor, table_name, print_out=True): conn, c = connect(sqlite_file) total_rows(c, table_name, print_out=True) table_col_info(c, table_name, print_out=True) - values_in_col(c, table_name, print_out=True) # slow on large data bases - - close(conn) + # next line might be slow on large databases + values_in_col(c, table_name, print_out=True) + close(conn) diff --git a/tutorials/sqlite3_howto/code/update_or_insert_records.py b/tutorials/sqlite3_howto/code/update_or_insert_records.py index 37292a5..ee461ec 100644 --- a/tutorials/sqlite3_howto/code/update_or_insert_records.py +++ b/tutorials/sqlite3_howto/code/update_or_insert_records.py @@ -1,6 +1,6 @@ # Sebastian Raschka, 2014 # Update records or insert them if they don't exist. -# Note that this is a workaround to accomodate for missing +# Note that this is a workaround to accommodate for missing # SQL features in SQLite. import sqlite3 diff --git a/tutorials/standardized_data.pkl b/tutorials/standardized_data.pkl deleted file mode 100644 index b81fb23..0000000 Binary files a/tutorials/standardized_data.pkl and /dev/null differ diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb index 639753b..1245132 100644 --- a/tutorials/table_of_contents_ipython.ipynb +++ b/tutorials/table_of_contents_ipython.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:ba20b7e6666952b09ad936f7b9cb32fc0e9ed680a00aeff02035be51895c8e45" + "signature": "sha256:34307c4f0973ebef511e97c036657231fc4e230e7627cfe073d89f4046f9ce9f" }, "nbformat": 3, "nbformat_minor": 0, @@ -13,10 +13,7 @@ "metadata": {}, "source": [ "[Sebastian Raschka](http://sebastianraschka.com) \n", - "last updated: 05/18/2014\n", - "\n", - "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb) \n", - "- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)\n" + "last updated: 05/29/2014" ] }, { @@ -215,6 +212,15 @@ "\n", "
\n", "
\n", + "\n", + "### Solution 2: line break between the id-anchor and text:\n", + "\n", + "![img of format problem](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_remedy2.png)\n", + "\n", + "(this alternative workaround was kindly submitted by [Ryan Morshead](https://github.com/rmorshea))\n", + "\n", + "
\n", + "
\n", "
\n", "
" ] @@ -223,7 +229,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Solution 2: using header cells" + "### Solution 3: using header cells" ] }, { diff --git a/tutorials/table_of_contents_ipython.md b/tutorials/table_of_contents_ipython.md deleted file mode 100644 index 9089e1e..0000000 --- a/tutorials/table_of_contents_ipython.md +++ /dev/null @@ -1,125 +0,0 @@ -[Sebastian Raschka](http://sebastianraschka.com) -last updated: 05/18/2014 - -- [Link to this IPython Notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb) -- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day) - - -
-I would be happy to hear your comments and suggestions. -Please feel free to drop me a note via -[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227). -
- - - -# Creating a table of contents with internal links in IPython Notebooks and Markdown documents - -**Many people have asked me how I create the table of contents with internal links for my IPython Notebooks and Markdown documents on GitHub. -Well, no (IPython) magic is involved, it is just a little bit of HTML, but I thought it might be worthwhile to write this little how-to tutorial.** - -![example table](../Images/ipython_links_ex.png) - -
-
-For example, [click this link](#bottom) to jump to the bottom of the page. -
-
- -
-
- -## The two components to create an internal link - -So, how does it work? Basically, all you need are those two components: -1. the destination -2. an internal hyperlink to the destination - - -![two components](../Images/ipython_links_overview.png) - -
-###1. The destination - -To define the destination (i.e., the section on the page or the cell you want to jump to), you just need to insert an empty HTML anchor tag and give it an **`id`**, -e.g., **``** - -This anchor tag will be invisible if you render it as Markdown in the IPython Notebook. -Note that it would also work if we use the **`name`** attribute instead of **`id`**, but since the **`name`** attribute is not supported by HTML5 anymore, I would suggest to just use the **`id`** attribute, which is also shorter to type. - -
-###2. The internal hyperlink - -Now we have to create the hyperlink to the **``** anchor tag that we just created. -We can either do this in ye goode olde HTML where we put a fragment identifier in form of a hash mark (`#`) in front of the name, -for example, **`Link to the destination'`** - -Or alternatively, we can just use the slightly more convenient Markdown syntax: -**`[Link to the destination](#the_destination)`** - -**That's all!** - -
-
- -## One more piece of advice - -Of course it would make sense to place the empty anchor tags for you table of contents just on top of each cell that contains a heading. -E.g., - -`` -`###Section 2` -`some text ...` - - -And I did this for a very long time ... until I figured out that it wouldn't render the Markdown properly if you convert the IPython Notebook into HTML (for example, for printing via the print preview option). - -But instead of - - -###Section 2 - -it would be rendered as - - -`###Section 2` - -which is certainly not what we want (note that it looks normal in the IPython Notebook, but not in the converted HTML version). So my favorite remedy would be to put the `id`-anchor tag into a separate cell just above the section, ideally with some line breaks for nicer visuals. - -![img of format problem](../Images/ipython_links_format.png) - -
-
- -### Solution 1: id-anchor tag in a separate cell - -![img of format problem](../Images/ipython_links_remedy.png) - -
-
-
-
-
- - -### Solution 2: using header cells - - -To define the hyperlink anchor tag to this "header cell" is just the text content of the "header cell" connected by dashes. E.g., - -![header cell](../Images/ipython_table_header.png) - -`[link to another section](#Another-section)` -
-
-
-
-
-
- -[[Click this link and jump to the top of the page](#top)] - -You can't see it, but this cell contains a -`` -anchor tag just below this text. - diff --git a/tutorials/test_set.csv b/tutorials/test_set.csv deleted file mode 100644 index 34928b9..0000000 --- a/tutorials/test_set.csv +++ /dev/null @@ -1,54 +0,0 @@ -3.000000000000000000e+00,5.557391941977805061e-01,1.238550434380931708e+00,-5.214359630530969181e-01,1.440900834400055952e-01,-8.135811375106852816e-01,-1.108104519644248498e+00,-1.756306948722317518e+00,1.945738962393688265e+00,-1.395679990380090274e+00,3.179531790963357474e-01,-6.978181815257052945e-01,-1.202178738239686906e+00,-5.377053749563008855e-01 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-2.000000000000000000e+00,-4.596707117506483864e-01,-1.150699506508649161e+00,-3.373298713145309602e-01,-3.379378935355480951e-01,-4.186717953810183829e-02,-2.078008927484694124e-01,-2.085203060864394553e-01,-3.797707372382866375e-01,-3.339357032197764474e-01,-1.049839981442968195e+00,1.167232156863844406e+00,7.119073018705739386e-01,-9.343107579027923881e-01 diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb new file mode 100644 index 0000000..968d734 --- /dev/null +++ b/tutorials/things_in_pandas.ipynb @@ -0,0 +1,3201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Back to the GitHub repository](https://github.com/rasbt/python_reference)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sebastian Raschka 28/01/2015 \n", + "\n", + "CPython 3.4.2\n", + "IPython 2.3.1\n", + "\n", + "pandas 0.15.2\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -a 'Sebastian Raschka' -v -d -p pandas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Things in Pandas I Wish I'd Known Earlier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just a small but growing collection of pandas snippets that I find occasionally and particularly useful -- consider it as my personal notebook. Suggestions, tips, and contributions are very, very welcome!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- [Loading Some Example Data](#Loading-Some-Example-Data)\n", + "- [Renaming Columns](#Renaming-Columns)\n", + " - [Converting Column Names to Lowercase](#Converting-Column-Names-to-Lowercase)\n", + " - [Renaming Particular Columns](#Renaming-Particular-Columns)\n", + "- [Applying Computations Rows-wise](#Applying-Computations-Rows-wise)\n", + " - [Changing Values in a Column](#Changing-Values-in-a-Column)\n", + " - [Adding a New Column](#Adding-a-New-Column)\n", + " - [Applying Functions to Multiple Columns](#Applying-Functions-to-Multiple-Columns)\n", + "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n", + " - [Counting Rows with NaNs](#Counting-Rows-with-NaNs)\n", + " - [Selecting NaN Rows](#Selecting-NaN-Rows)\n", + " - [Selecting non-NaN Rows](#Selecting-non-NaN-Rows)\n", + " - [Filling NaN Rows](#Filling-NaN-Rows)\n", + "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n", + "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n", + "- [Updating Columns](#Updating-Columns)\n", + "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)\n", + "- [Column Types](#Column-Types)\n", + " - [Printing Column Types](#Printing-Column-Types)\n", + " - [Selecting by Column Type](#Selecting-by-Column-Type)\n", + " - [Converting Column Types](#Converting-Column-Types)\n", + "- [If-tests](#If-tests)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading Some Example Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I am heavily into sports prediction (via a machine learning approach) these days. So, let us use a (very) small subset of the soccer data that I am just working with." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PLAYERSALARYGPGASOTPPGP
0 Sergio Agüero\\n Forward — Manchester City $19.2m 16 14 3 34 13.12 209.98
1 Eden Hazard\\n Midfield — Chelsea $18.9m 21 8 4 17 13.05 274.04
2 Alexis Sánchez\\n Forward — Arsenal $17.6mNaN 12 7 29 11.19 223.86
3 Yaya Touré\\n Midfield — Manchester City $16.6m 18 7 1 19 10.99 197.91
4 Ángel Di María\\n Midfield — Manchester United $15.0m 13 3NaN 13 10.17 132.23
5 Santiago Cazorla\\n Midfield — Arsenal $14.8m 20 4NaN 20 9.97 NaN
6 David Silva\\n Midfield — Manchester City $14.3m 15 6 2 11 10.35 155.26
7 Cesc Fàbregas\\n Midfield — Chelsea $14.0m 20 2 14 10 10.47 209.49
8 Saido Berahino\\n Forward — West Brom $13.8m 21 9 0 20 7.02 147.43
9 Steven Gerrard\\n Midfield — Liverpool $13.8m 20 5 1 11 7.50 150.01
\n", + "" + ], + "text/plain": [ + " PLAYER SALARY GP G A SOT \\\n", + "0 Sergio Agüero\\n Forward — Manchester City $19.2m 16 14 3 34 \n", + "1 Eden Hazard\\n Midfield — Chelsea $18.9m 21 8 4 17 \n", + "2 Alexis Sánchez\\n Forward — Arsenal $17.6m NaN 12 7 29 \n", + "3 Yaya Touré\\n Midfield — Manchester City $16.6m 18 7 1 19 \n", + "4 Ángel Di María\\n Midfield — Manchester United $15.0m 13 3 NaN 13 \n", + "5 Santiago Cazorla\\n Midfield — Arsenal $14.8m 20 4 NaN 20 \n", + "6 David Silva\\n Midfield — Manchester City $14.3m 15 6 2 11 \n", + "7 Cesc Fàbregas\\n Midfield — Chelsea $14.0m 20 2 14 10 \n", + "8 Saido Berahino\\n Forward — West Brom $13.8m 21 9 0 20 \n", + "9 Steven Gerrard\\n Midfield — Liverpool $13.8m 20 5 1 11 \n", + "\n", + " PPG P \n", + "0 13.12 209.98 \n", + "1 13.05 274.04 \n", + "2 11.19 223.86 \n", + "3 10.99 197.91 \n", + "4 10.17 132.23 \n", + "5 9.97 NaN \n", + "6 10.35 155.26 \n", + "7 10.47 209.49 \n", + "8 7.02 147.43 \n", + "9 7.50 150.01 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python_reference/master/Data/some_soccer_data.csv')\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Renaming Columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting Column Names to Lowercase" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
playersalarygpgasotppgp
7 Cesc Fàbregas\\n Midfield — Chelsea $14.0m 20 2 14 10 10.47 209.49
8 Saido Berahino\\n Forward — West Brom $13.8m 21 9 0 20 7.02 147.43
9 Steven Gerrard\\n Midfield — Liverpool $13.8m 20 5 1 11 7.50 150.01
\n", + "
" + ], + "text/plain": [ + " player salary gp g a sot ppg \\\n", + "7 Cesc Fàbregas\\n Midfield — Chelsea $14.0m 20 2 14 10 10.47 \n", + "8 Saido Berahino\\n Forward — West Brom $13.8m 21 9 0 20 7.02 \n", + "9 Steven Gerrard\\n Midfield — Liverpool $13.8m 20 5 1 11 7.50 \n", + "\n", + " p \n", + "7 209.49 \n", + "8 147.43 \n", + "9 150.01 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Converting column names to lowercase\n", + "\n", + "df.columns = [c.lower() for c in df.columns]\n", + "\n", + "# or\n", + "# df.rename(columns=lambda x : x.lower())\n", + "\n", + "df.tail(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Renaming Particular Columns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepoints
7 Cesc Fàbregas\\n Midfield — Chelsea $14.0m 20 2 14 10 10.47 209.49
8 Saido Berahino\\n Forward — West Brom $13.8m 21 9 0 20 7.02 147.43
9 Steven Gerrard\\n Midfield — Liverpool $13.8m 20 5 1 11 7.50 150.01
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists \\\n", + "7 Cesc Fàbregas\\n Midfield — Chelsea $14.0m 20 2 14 \n", + "8 Saido Berahino\\n Forward — West Brom $13.8m 21 9 0 \n", + "9 Steven Gerrard\\n Midfield — Liverpool $13.8m 20 5 1 \n", + "\n", + " shots_on_target points_per_game points \n", + "7 10 10.47 209.49 \n", + "8 20 7.02 147.43 \n", + "9 11 7.50 150.01 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.rename(columns={'p': 'points', \n", + " 'gp': 'games',\n", + " 'sot': 'shots_on_target',\n", + " 'g': 'goals',\n", + " 'ppg': 'points_per_game',\n", + " 'a': 'assists',})\n", + "\n", + "df.tail(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Applying Computations Rows-wise" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Changing Values in a Column" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepoints
5 Santiago Cazorla\\n Midfield — Arsenal 14.8 20 4NaN 20 9.97 NaN
6 David Silva\\n Midfield — Manchester City 14.3 15 6 2 11 10.35 155.26
7 Cesc Fàbregas\\n Midfield — Chelsea 14.0 20 2 14 10 10.47 209.49
8 Saido Berahino\\n Forward — West Brom 13.8 21 9 0 20 7.02 147.43
9 Steven Gerrard\\n Midfield — Liverpool 13.8 20 5 1 11 7.50 150.01
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists \\\n", + "5 Santiago Cazorla\\n Midfield — Arsenal 14.8 20 4 NaN \n", + "6 David Silva\\n Midfield — Manchester City 14.3 15 6 2 \n", + "7 Cesc Fàbregas\\n Midfield — Chelsea 14.0 20 2 14 \n", + "8 Saido Berahino\\n Forward — West Brom 13.8 21 9 0 \n", + "9 Steven Gerrard\\n Midfield — Liverpool 13.8 20 5 1 \n", + "\n", + " shots_on_target points_per_game points \n", + "5 20 9.97 NaN \n", + "6 11 10.35 155.26 \n", + "7 10 10.47 209.49 \n", + "8 20 7.02 147.43 \n", + "9 11 7.50 150.01 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Processing `salary` column\n", + "\n", + "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n", + "df.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding a New Column" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
7 Cesc Fàbregas\\n Midfield — Chelsea 14.0 20 2 14 10 10.47 209.49
8 Saido Berahino\\n Forward — West Brom 13.8 21 9 0 20 7.02 147.43
9 Steven Gerrard\\n Midfield — Liverpool 13.8 20 5 1 11 7.50 150.01
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" + ], + "text/plain": [ + " player salary games goals assists \\\n", + "7 Cesc Fàbregas\\n Midfield — Chelsea 14.0 20 2 14 \n", + "8 Saido Berahino\\n Forward — West Brom 13.8 21 9 0 \n", + "9 Steven Gerrard\\n Midfield — Liverpool 13.8 20 5 1 \n", + "\n", + " shots_on_target points_per_game points position team \n", + "7 10 10.47 209.49 \n", + "8 20 7.02 147.43 \n", + "9 11 7.50 150.01 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['team'] = pd.Series('', index=df.index)\n", + "\n", + "# or\n", + "df.insert(loc=8, column='position', value='') \n", + "\n", + "df.tail(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
7 Cesc Fàbregas 14.0 20 2 14 10 10.47 209.49 Midfield Chelsea
8 Saido Berahino 13.8 21 9 0 20 7.02 147.43 Forward West Brom
9 Steven Gerrard 13.8 20 5 1 11 7.50 150.01 Midfield Liverpool
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "7 Cesc Fàbregas 14.0 20 2 14 10 \n", + "8 Saido Berahino 13.8 21 9 0 20 \n", + "9 Steven Gerrard 13.8 20 5 1 11 \n", + "\n", + " points_per_game points position team \n", + "7 10.47 209.49 Midfield Chelsea \n", + "8 7.02 147.43 Forward West Brom \n", + "9 7.50 150.01 Midfield Liverpool " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Processing `player` column\n", + "\n", + "def process_player_col(text):\n", + " name, rest = text.split('\\n')\n", + " position, team = [x.strip() for x in rest.split(' — ')]\n", + " return pd.Series([name, team, position])\n", + "\n", + "df[['player', 'team', 'position']] = df.player.apply(process_player_col)\n", + "\n", + "# modified after tip from reddit.com/user/hharison\n", + "#\n", + "# Alternative (inferior) approach:\n", + "#\n", + "#for idx,row in df.iterrows():\n", + "# name, position, team = process_player_col(row['player'])\n", + "# df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n", + " \n", + "df.tail(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Applying Functions to Multiple Columns" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
0 sergio agüero 19.2 16 14 3 34 13.12 209.98 forward manchester city
1 eden hazard 18.9 21 8 4 17 13.05 274.04 midfield chelsea
2 alexis sánchez 17.6NaN 12 7 29 11.19 223.86 forward arsenal
3 yaya touré 16.6 18 7 1 19 10.99 197.91 midfield manchester city
4 ángel di maría 15.0 13 3NaN 13 10.17 132.23 midfield manchester united
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "0 sergio agüero 19.2 16 14 3 34 \n", + "1 eden hazard 18.9 21 8 4 17 \n", + "2 alexis sánchez 17.6 NaN 12 7 29 \n", + "3 yaya touré 16.6 18 7 1 19 \n", + "4 ángel di maría 15.0 13 3 NaN 13 \n", + "\n", + " points_per_game points position team \n", + "0 13.12 209.98 forward manchester city \n", + "1 13.05 274.04 midfield chelsea \n", + "2 11.19 223.86 forward arsenal \n", + "3 10.99 197.91 midfield manchester city \n", + "4 10.17 132.23 midfield manchester united " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['player', 'position', 'team']\n", + "df[cols] = df[cols].applymap(lambda x: x.lower())\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Missing Values aka NaNs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Counting Rows with NaNs" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 rows have missing values\n" + ] + } + ], + "source": [ + "nans = df.shape[0] - df.dropna().shape[0]\n", + "\n", + "print('%d rows have missing values' % nans)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting NaN Rows" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
4 ángel di maría 15.0 13 3NaN 13 10.17 132.23 midfield manchester united
5 santiago cazorla 14.8 20 4NaN 20 9.97 NaN midfield arsenal
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "4 ángel di maría 15.0 13 3 NaN 13 \n", + "5 santiago cazorla 14.8 20 4 NaN 20 \n", + "\n", + " points_per_game points position team \n", + "4 10.17 132.23 midfield manchester united \n", + "5 9.97 NaN midfield arsenal " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Selecting all rows that have NaNs in the `assists` column\n", + "\n", + "df[df['assists'].isnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting non-NaN Rows" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
0 sergio agüero 19.2 16 14 3 34 13.12 209.98 forward manchester city
1 eden hazard 18.9 21 8 4 17 13.05 274.04 midfield chelsea
2 alexis sánchez 17.6NaN 12 7 29 11.19 223.86 forward arsenal
3 yaya touré 16.6 18 7 1 19 10.99 197.91 midfield manchester city
6 david silva 14.3 15 6 2 11 10.35 155.26 midfield manchester city
7 cesc fàbregas 14.0 20 2 14 10 10.47 209.49 midfield chelsea
8 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
9 steven gerrard 13.8 20 5 1 11 7.50 150.01 midfield liverpool
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "0 sergio agüero 19.2 16 14 3 34 \n", + "1 eden hazard 18.9 21 8 4 17 \n", + "2 alexis sánchez 17.6 NaN 12 7 29 \n", + "3 yaya touré 16.6 18 7 1 19 \n", + "6 david silva 14.3 15 6 2 11 \n", + "7 cesc fàbregas 14.0 20 2 14 10 \n", + "8 saido berahino 13.8 21 9 0 20 \n", + "9 steven gerrard 13.8 20 5 1 11 \n", + "\n", + " points_per_game points position team \n", + "0 13.12 209.98 forward manchester city \n", + "1 13.05 274.04 midfield chelsea \n", + "2 11.19 223.86 forward arsenal \n", + "3 10.99 197.91 midfield manchester city \n", + "6 10.35 155.26 midfield manchester city \n", + "7 10.47 209.49 midfield chelsea \n", + "8 7.02 147.43 forward west brom \n", + "9 7.50 150.01 midfield liverpool " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['assists'].notnull()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Filling NaN Rows" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
0 sergio agüero 19.2 16 14 3 34 13.12 209.98 forward manchester city
1 eden hazard 18.9 21 8 4 17 13.05 274.04 midfield chelsea
2 alexis sánchez 17.6 0 12 7 29 11.19 223.86 forward arsenal
3 yaya touré 16.6 18 7 1 19 10.99 197.91 midfield manchester city
4 ángel di maría 15.0 13 3 0 13 10.17 132.23 midfield manchester united
5 santiago cazorla 14.8 20 4 0 20 9.97 0.00 midfield arsenal
6 david silva 14.3 15 6 2 11 10.35 155.26 midfield manchester city
7 cesc fàbregas 14.0 20 2 14 10 10.47 209.49 midfield chelsea
8 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
9 steven gerrard 13.8 20 5 1 11 7.50 150.01 midfield liverpool
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "0 sergio agüero 19.2 16 14 3 34 \n", + "1 eden hazard 18.9 21 8 4 17 \n", + "2 alexis sánchez 17.6 0 12 7 29 \n", + "3 yaya touré 16.6 18 7 1 19 \n", + "4 ángel di maría 15.0 13 3 0 13 \n", + "5 santiago cazorla 14.8 20 4 0 20 \n", + "6 david silva 14.3 15 6 2 11 \n", + "7 cesc fàbregas 14.0 20 2 14 10 \n", + "8 saido berahino 13.8 21 9 0 20 \n", + "9 steven gerrard 13.8 20 5 1 11 \n", + "\n", + " points_per_game points position team \n", + "0 13.12 209.98 forward manchester city \n", + "1 13.05 274.04 midfield chelsea \n", + "2 11.19 223.86 forward arsenal \n", + "3 10.99 197.91 midfield manchester city \n", + "4 10.17 132.23 midfield manchester united \n", + "5 9.97 0.00 midfield arsenal \n", + "6 10.35 155.26 midfield manchester city \n", + "7 10.47 209.49 midfield chelsea \n", + "8 7.02 147.43 forward west brom \n", + "9 7.50 150.01 midfield liverpool " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filling NaN cells with default value 0\n", + "\n", + "df.fillna(value=0, inplace=True)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Appending Rows to a DataFrame" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
8 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
9 steven gerrard 13.8 20 5 1 11 7.50 150.01 midfield liverpool
10 NaN NaNNaNNaNNaNNaN NaN NaN NaN NaN
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" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "8 saido berahino 13.8 21 9 0 20 \n", + "9 steven gerrard 13.8 20 5 1 11 \n", + "10 NaN NaN NaN NaN NaN NaN \n", + "\n", + " points_per_game points position team \n", + "8 7.02 147.43 forward west brom \n", + "9 7.50 150.01 midfield liverpool \n", + "10 NaN NaN NaN NaN " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Adding an \"empty\" row to the DataFrame\n", + "\n", + "import numpy as np\n", + "\n", + "df = df.append(pd.Series(\n", + " [np.nan]*len(df.columns), # Fill cells with NaNs\n", + " index=df.columns), \n", + " ignore_index=True)\n", + "\n", + "df.tail(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
8 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
9 steven gerrard 13.8 20 5 1 11 7.50 150.01 midfield liverpool
10 new player 12.3NaNNaNNaNNaN NaN NaN NaN NaN
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "8 saido berahino 13.8 21 9 0 20 \n", + "9 steven gerrard 13.8 20 5 1 11 \n", + "10 new player 12.3 NaN NaN NaN NaN \n", + "\n", + " points_per_game points position team \n", + "8 7.02 147.43 forward west brom \n", + "9 7.50 150.01 midfield liverpool \n", + "10 NaN NaN NaN NaN " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filling cells with data\n", + "\n", + "df.loc[df.index[-1], 'player'] = 'new player'\n", + "df.loc[df.index[-1], 'salary'] = 12.3\n", + "df.tail(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sorting and Reindexing DataFrames" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
0 sergio agüero 19.2 16 14 3 34 13.12 209.98 forward manchester city
2 alexis sánchez 17.6 0 12 7 29 11.19 223.86 forward arsenal
8 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
1 eden hazard 18.9 21 8 4 17 13.05 274.04 midfield chelsea
3 yaya touré 16.6 18 7 1 19 10.99 197.91 midfield manchester city
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "0 sergio agüero 19.2 16 14 3 34 \n", + "2 alexis sánchez 17.6 0 12 7 29 \n", + "8 saido berahino 13.8 21 9 0 20 \n", + "1 eden hazard 18.9 21 8 4 17 \n", + "3 yaya touré 16.6 18 7 1 19 \n", + "\n", + " points_per_game points position team \n", + "0 13.12 209.98 forward manchester city \n", + "2 11.19 223.86 forward arsenal \n", + "8 7.02 147.43 forward west brom \n", + "1 13.05 274.04 midfield chelsea \n", + "3 10.99 197.91 midfield manchester city " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sorting the DataFrame by a certain column (from highest to lowest)\n", + "\n", + "df.sort('goals', ascending=False, inplace=True)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
1 sergio agüero 19.2 16 14 3 34 13.12 209.98 forward manchester city
2 alexis sánchez 17.6 0 12 7 29 11.19 223.86 forward arsenal
3 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
4 eden hazard 18.9 21 8 4 17 13.05 274.04 midfield chelsea
5 yaya touré 16.6 18 7 1 19 10.99 197.91 midfield manchester city
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "1 sergio agüero 19.2 16 14 3 34 \n", + "2 alexis sánchez 17.6 0 12 7 29 \n", + "3 saido berahino 13.8 21 9 0 20 \n", + "4 eden hazard 18.9 21 8 4 17 \n", + "5 yaya touré 16.6 18 7 1 19 \n", + "\n", + " points_per_game points position team \n", + "1 13.12 209.98 forward manchester city \n", + "2 11.19 223.86 forward arsenal \n", + "3 7.02 147.43 forward west brom \n", + "4 13.05 274.04 midfield chelsea \n", + "5 10.99 197.91 midfield manchester city " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Optional reindexing of the DataFrame after sorting\n", + "\n", + "df.index = range(1,len(df.index)+1)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Updating Columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
1 sergio agüero 20 16 14 3 34 13.12 209.98 forward manchester city
2 alexis sánchez 15 0 12 7 29 11.19 223.86 forward arsenal
3 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "1 sergio agüero 20 16 14 3 34 \n", + "2 alexis sánchez 15 0 12 7 29 \n", + "3 saido berahino 13.8 21 9 0 20 \n", + "\n", + " points_per_game points position team \n", + "1 13.12 209.98 forward manchester city \n", + "2 11.19 223.86 forward arsenal \n", + "3 7.02 147.43 forward west brom " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Creating a dummy DataFrame with changes in the `salary` column\n", + "\n", + "df_2 = df.copy()\n", + "df_2.loc[0:2, 'salary'] = [20.0, 15.0]\n", + "df_2.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
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" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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salarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
player
sergio agüero 19.2 16 14 3 34 13.12 209.98 forward manchester city
alexis sánchez 17.6 0 12 7 29 11.19 223.86 forward arsenal
saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
\n", + "
" + ], + "text/plain": [ + " salary games goals assists shots_on_target \\\n", + "player \n", + "sergio agüero 19.2 16 14 3 34 \n", + "alexis sánchez 17.6 0 12 7 29 \n", + "saido berahino 13.8 21 9 0 20 \n", + "\n", + " points_per_game points position team \n", + "player \n", + "sergio agüero 13.12 209.98 forward manchester city \n", + "alexis sánchez 11.19 223.86 forward arsenal \n", + "saido berahino 7.02 147.43 forward west brom " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Temporarily use the `player` columns as indices to \n", + "# apply the update functions\n", + "\n", + "df.set_index('player', inplace=True)\n", + "df_2.set_index('player', inplace=True)\n", + "df.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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salarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
player
sergio agüero 20 16 14 3 34 13.12 209.98 forward manchester city
alexis sánchez 15 0 12 7 29 11.19 223.86 forward arsenal
saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
\n", + "
" + ], + "text/plain": [ + " salary games goals assists shots_on_target \\\n", + "player \n", + "sergio agüero 20 16 14 3 34 \n", + "alexis sánchez 15 0 12 7 29 \n", + "saido berahino 13.8 21 9 0 20 \n", + "\n", + " points_per_game points position team \n", + "player \n", + "sergio agüero 13.12 209.98 forward manchester city \n", + "alexis sánchez 11.19 223.86 forward arsenal \n", + "saido berahino 7.02 147.43 forward west brom " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Update the `salary` column\n", + "df.update(other=df_2['salary'], overwrite=True)\n", + "df.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
0 sergio agüero 20 16 14 3 34 13.12 209.98 forward manchester city
1 alexis sánchez 15 0 12 7 29 11.19 223.86 forward arsenal
2 saido berahino 13.8 21 9 0 20 7.02 147.43 forward west brom
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "0 sergio agüero 20 16 14 3 34 \n", + "1 alexis sánchez 15 0 12 7 29 \n", + "2 saido berahino 13.8 21 9 0 20 \n", + "\n", + " points_per_game points position team \n", + "0 13.12 209.98 forward manchester city \n", + "1 11.19 223.86 forward arsenal \n", + "2 7.02 147.43 forward west brom " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Reset the indices\n", + "df.reset_index(inplace=True)\n", + "df.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chaining Conditions - Using Bitwise Operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
1 alexis sánchez 15 0 12 7 29 11.19 223.86 forward arsenal
3 eden hazard 18.9 21 8 4 17 13.05 274.04 midfield chelsea
7 santiago cazorla 14.8 20 4 0 20 9.97 0.00 midfield arsenal
9 cesc fàbregas 14.0 20 2 14 10 10.47 209.49 midfield chelsea
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "1 alexis sánchez 15 0 12 7 29 \n", + "3 eden hazard 18.9 21 8 4 17 \n", + "7 santiago cazorla 14.8 20 4 0 20 \n", + "9 cesc fàbregas 14.0 20 2 14 10 \n", + "\n", + " points_per_game points position team \n", + "1 11.19 223.86 forward arsenal \n", + "3 13.05 274.04 midfield chelsea \n", + "7 9.97 0.00 midfield arsenal \n", + "9 10.47 209.49 midfield chelsea " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Selecting only those players that either playing for Arsenal or Chelsea\n", + "\n", + "df[ (df['team'] == 'arsenal') | (df['team'] == 'chelsea') ]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
playersalarygamesgoalsassistsshots_on_targetpoints_per_gamepointspositionteam
1 alexis sánchez 15 0 12 7 29 11.19 223.86 forward arsenal
\n", + "
" + ], + "text/plain": [ + " player salary games goals assists shots_on_target \\\n", + "1 alexis sánchez 15 0 12 7 29 \n", + "\n", + " points_per_game points position team \n", + "1 11.19 223.86 forward arsenal " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Selecting forwards from Arsenal only\n", + "\n", + "df[ (df['team'] == 'arsenal') & (df['position'] == 'forward') ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Column Types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Printing Column Types" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{dtype('float64'): ['games',\n", + " 'goals',\n", + " 'assists',\n", + " 'shots_on_target',\n", + " 'points_per_game',\n", + " 'points'],\n", + " dtype('O'): ['player', 'salary', 'position', 'team']}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "types = df.columns.to_series().groupby(df.dtypes).groups\n", + "types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Selecting by Column Type" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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playersalarypositionteam
0 sergio agüero 20 forward manchester city
1 alexis sánchez 15 forward arsenal
2 saido berahino 13.8 forward west brom
3 eden hazard 18.9 midfield chelsea
4 yaya touré 16.6 midfield manchester city
\n", + "
" + ], + "text/plain": [ + " player salary position team\n", + "0 sergio agüero 20 forward manchester city\n", + "1 alexis sánchez 15 forward arsenal\n", + "2 saido berahino 13.8 forward west brom\n", + "3 eden hazard 18.9 midfield chelsea\n", + "4 yaya touré 16.6 midfield manchester city" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# select string columns\n", + "df.loc[:, (df.dtypes == np.dtype('O')).values].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Converting Column Types" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df['salary'] = df['salary'].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{dtype('float64'): ['salary',\n", + " 'games',\n", + " 'goals',\n", + " 'assists',\n", + " 'shots_on_target',\n", + " 'points_per_game',\n", + " 'points'],\n", + " dtype('O'): ['player', 'position', 'team']}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "types = df.columns.to_series().groupby(df.dtypes).groups\n", + "types" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# If-tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[[back to section overview](#Sections)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I was recently asked how to do an if-test in pandas, that is, how to create an array of 1s and 0s depending on a condition, e.g., if `val` less than 0.5 -> 0, else -> 1. Using the boolean mask, that's pretty simple since `True` and `False` are integers after all." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "int(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0123
02.00.304.005
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "0 2.0 0.30 4.00 5\n", + "1 0.8 0.03 0.02 5" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "a = [[2., .3, 4., 5.], [.8, .03, 0.02, 5.]]\n", + "df = pd.DataFrame(a)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1 2 3\n", + "0 False False False False\n", + "1 False True True False" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df <= 0.05\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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-3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740 -3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750 -3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835 -3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840 -3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560 diff --git a/useful_scripts/combinations.py b/useful_scripts/combinations.py new file mode 100755 index 0000000..5dbe91d --- /dev/null +++ b/useful_scripts/combinations.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python + +# Sebastian Raschka 2014 +# Functions to calculate factorial, combinations, and permutations +# bundled in an simple command line interface. + +def factorial(n): + if n == 0: + return 1 + else: + return n * factorial(n-1) + +def combinations(n, r): + numerator = factorial(n) + denominator = factorial(r) * factorial(n-r) + return int(numerator/denominator) + +def permutations(n, r): + numerator = factorial(n) + denominator = factorial(n-r) + return int(numerator/denominator) + +assert(factorial(3) == 6) +assert(combinations(20, 8) == 125970) +assert(permutations(30, 3) == 24360) + + + + +if __name__ == '__main__': + + import argparse + parser = argparse.ArgumentParser( + description='Script to calculate the number of combinations or permutations ("n choose r")', + formatter_class=argparse.RawTextHelpFormatter, + + prog='Combinations', + epilog='Example: ./combinations.py -c 20 3' + ) + + parser.add_argument('-c', '--combinations', type=int, metavar='NUMBER', nargs=2, + help='Combinations: Number of ways to combine n items with sequence length r where the item order does not matter.') + + parser.add_argument('-p', '--permutations', type=int, metavar='NUMBER', nargs=2, + help='Permutations: Number of ways to combine n items with sequence length r where the item order does not matter.') + + parser.add_argument('-f', '--factorial', type=int, metavar='NUMBER', help='n! e.g., 5! = 5*4*3*2*1 = 120.') + + parser.add_argument('--version', action='version', version='%(prog)s 1.0') + + args = parser.parse_args() + + if not any((args.combinations, args.permutations, args.factorial)): + parser.print_help() + quit() + + if args.factorial: + print(factorial(args.factorial)) + + if args.combinations: + print(combinations(args.combinations[0], args.combinations[1])) + + if args.permutations: + print(permutations(args.permutations[0], args.permutations[1])) + + if args.factorial: + print(factorial(args.factorial)) + + + + \ No newline at end of file diff --git a/useful_scripts/conc_gzip_files.py b/useful_scripts/conc_gzip_files.py index da849c9..b8d9b33 100644 --- a/useful_scripts/conc_gzip_files.py +++ b/useful_scripts/conc_gzip_files.py @@ -13,7 +13,7 @@ def conc_gzip_files(in_dir, out_file, append=False, print_progress=True): Keyword arguments: in_dir (str): Path of the directory with the gzip-files out_file (str): Path to the resulting file - append (bool): If true, it appends contents to an exisiting file, + append (bool): If true, it appends contents to an existing file, else creates a new output file. print_progress (bool): prints progress bar if true. diff --git a/useful_scripts/find_file.py b/useful_scripts/find_file.py new file mode 100644 index 0000000..8cbcc4d --- /dev/null +++ b/useful_scripts/find_file.py @@ -0,0 +1,18 @@ +# Sebastian Raschka 2014 +# +# A Python function to find files in a directory based on a substring search. + + +import os + +def find_files(substring, path): + results = [] + for f in os.listdir(path): + if substring in f: + results.append(os.path.join(path, f)) + return results + +# E.g. +# find_files('Untitled', '/Users/sebastian/Desktop/') +# returns +# ['/Users/sebastian/Desktop/Untitled0.ipynb'] \ No newline at end of file diff --git a/useful_scripts/large_csv_to_sqlite.py b/useful_scripts/large_csv_to_sqlite.py new file mode 100644 index 0000000..9932f9c --- /dev/null +++ b/useful_scripts/large_csv_to_sqlite.py @@ -0,0 +1,48 @@ +# This is a workaround snippet for reading very large CSV that exceed the +# machine's memory and dump it into an SQLite database using pandas. +# +# Sebastian Raschka, 2015 +# +# Tested in Python 3.4.2 and pandas 0.15.2 + +import pandas as pd +import sqlite3 +from pandas.io import sql +import subprocess + +# In and output file paths +in_csv = '../data/my_large.csv' +out_sqlite = '../data/my.sqlite' + +table_name = 'my_table' # name for the SQLite database table +chunksize = 100000 # number of lines to process at each iteration + +# columns that should be read from the CSV file +columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles'] + +# Get number of lines in the CSV file +nlines = subprocess.check_output(['wc', '-l', in_csv]) +nlines = int(nlines.split()[0]) + +# connect to database +cnx = sqlite3.connect(out_sqlite) + +# Iteratively read CSV and dump lines into the SQLite table +for i in range(0, nlines, chunksize): # change 0 -> 1 if your csv file contains a column header + + df = pd.read_csv(in_csv, + header=None, # no header, define column header manually later + nrows=chunksize, # number of rows to read at each iteration + skiprows=i) # skip rows that were already read + + # columns to read + df.columns = columns + + sql.to_sql(df, + name=table_name, + con=cnx, + index=False, # don't use CSV file index + index_label='molecule_id', # use a unique column from DataFrame as index + if_exists='append') +cnx.close() + diff --git a/useful_scripts/preprocess_first_last_names.py b/useful_scripts/preprocess_first_last_names.py new file mode 100644 index 0000000..b0957c2 --- /dev/null +++ b/useful_scripts/preprocess_first_last_names.py @@ -0,0 +1,79 @@ +# Sebastian Raschka 2014 +# +# A Python function to generalize first and last names. +# The typical use case of such a function to merge data that have been collected +# from different sources (e.g., names of soccer players as shown in the doctest.) +# + +import unicodedata +import string +import re + +def preprocess_names(name, output_sep=' ', firstname_output_letters=1): + """ + Function that outputs a person's name in the format + (all lowercase) + + >>> preprocess_names("Samuel Eto'o") + 'etoo s' + + >>> preprocess_names("Eto'o, Samuel") + 'etoo s' + + >>> preprocess_names("Eto'o,Samuel") + 'etoo s' + + >>> preprocess_names('Xavi') + 'xavi' + + >>> preprocess_names('Yaya Touré') + 'toure y' + + >>> preprocess_names('José Ángel Pozo') + 'pozo j' + + >>> preprocess_names('Pozo, José Ángel') + 'pozo j' + + >>> preprocess_names('Pozo, José Ángel', firstname_output_letters=2) + 'pozo jo' + + >>> preprocess_names("Eto'o, Samuel", firstname_output_letters=2) + 'etoo sa' + + >>> preprocess_names("Eto'o, Samuel", firstname_output_letters=0) + 'etoo' + + >>> preprocess_names("Eto'o, Samuel", output_sep=', ') + 'etoo, s' + + """ + + # set first and last name positions + last, first = 'last', 'first' + last_pos = -1 + + if ',' in name: + last, first = first, last + name = name.replace(',', ' ') + last_pos = 1 + + spl = name.split() + if len(spl) > 2: + name = '%s %s' % (spl[0], spl[last_pos]) + + # remove accents + name = ''.join(x for x in unicodedata.normalize('NFKD', name) if x in string.ascii_letters+' ') + + # get first and last name if applicable + m = re.match('(?P\w+)\W+(?P\w+)', name) + if m: + output = '%s%s%s' % (m.group(last), output_sep, m.group(first)[:firstname_output_letters]) + else: + output = name + return output.lower().strip() + + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/useful_scripts/principal_eigenvector.py b/useful_scripts/principal_eigenvector.py new file mode 100644 index 0000000..913cf62 --- /dev/null +++ b/useful_scripts/principal_eigenvector.py @@ -0,0 +1,20 @@ +# Select a principal eigenvector via NumPy +# to be used as a template (copy & paste) script + +import numpy as np + +# set A to be your matrix +A = np.array([[1, 2, 3], + [4, 5, 6], + [7, 8, 9]]) + + +eig_vals, eig_vecs = np.linalg.eig(A) +idx = np.absolute(eig_vals).argsort()[::-1] # decreasing order +sorted_eig_vals = eig_vals[idx] +sorted_eig_vecs = eig_vecs[:, idx] + +principal_eig_vec = sorted_eig_vecs[:, 0] # eigvec with largest eigval + +normalized_pr_eig_vec = np.real(principal_eig_vec / np.sum(principal_eig_vec)) +print(normalized_pr_eig_vec) # eigvec that sums up to one diff --git a/useful_scripts/sparsify_matrix.py b/useful_scripts/sparsify_matrix.py new file mode 100644 index 0000000..ef5e141 --- /dev/null +++ b/useful_scripts/sparsify_matrix.py @@ -0,0 +1,38 @@ +# Sebastian Raschka 2014 +# +# Sparsifying a matrix by Zeroing out all elements but the top k elements in a row. +# The matrix could be a distance or similarity matrix (e.g., kernel matrix in kernel PCA), +# where we are interested to keep the top k neighbors. + +import numpy as np + +print('Sparsify a matrix by zeroing all elements but the top 2 values in a row.\n') + +A = np.array([[1,2,3,4,5],[9,8,6,4,5],[3,1,7,8,9]]) + +print('Before:\n%s\n' %A) + + +k = 2 # keep top k neighbors +for row in A: + sort_idx = np.argsort(row)[::-1] # get indexes of sort order (high to low) + for i in sort_idx[k:]: + row[i]=0 + +print('After:\n%s\n' %A) + + +""" +Sparsify a matrix by zeroing all elements but the top 2 values in a row. + +Before: +[[1 2 3 4 5] + [9 8 6 4 5] + [3 1 7 8 9]] + +After: +[[0 0 0 4 5] + [9 8 0 0 0] + [0 0 0 8 9]] + +""" \ No newline at end of file