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‎source/rst/about_py.md‎

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```
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# About Python
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### Syntax and Design
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One nice feature of Python is its elegant syntax --- we'll see many examples later on.
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## Scientific Programming
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Python has become one of the core languages of scientific computing.
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### Numerical Programming
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Fundamental matrix and array processing capabilities are provided by the excellent [NumPy](http://www.numpy.org/) library.
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### Graphics
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```{index} single: Matplotlib
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The most popular and comprehensive Python library for creating figures and graphs is [Matplotlib](http://matplotlib.org/), with functionality including
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It's useful to be able to manipulate symbolic expressions, as in Mathematica or Maple.
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The [SymPy](http://www.sympy.org/) library provides this functionality from within the Python shell.
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#### Pandas
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```
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One of the most popular libraries for working with data is [pandas](http://pandas.pydata.org/).
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#### Other Useful Statistics Libraries
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* [statsmodels](http://statsmodels.sourceforge.net/) --- various statistical routines
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* [scikit-learn](http://scikit-learn.org/) --- machine learning in Python (sponsored by Google, among others)
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* [pyMC](http://pymc-devs.github.io/pymc/) --- for Bayesian data analysis
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* [pystan](https://pystan.readthedocs.org/en/latest/) Bayesian analysis based on [stan](http://mc-stan.org/)
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Python has many libraries for studying graphs.
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One well-known example is [NetworkX](http://networkx.github.io/).
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### Cloud Computing
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Running your Python code on massive servers in the cloud is becoming easier and easier.
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A nice example is [Anaconda Enterprise](https://www.anaconda.com/enterprise/).
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See also
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* [Amazon Elastic Compute Cloud](http://aws.amazon.com/ec2/)
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* The [Google App Engine](https://cloud.google.com/appengine/) (Python, Java, PHP or Go)
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* [Pythonanywhere](https://www.pythonanywhere.com/)
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* [Sagemath Cloud](https://cloud.sagemath.com/)
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### Parallel Processing
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Apart from the cloud computing options listed above, you might like to consider
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* [Parallel computing through IPython clusters](http://ipython.org/ipython-doc/stable/parallel/parallel_demos.html).
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* The [Starcluster](http://star.mit.edu/cluster/) interface to Amazon's EC2.
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* GPU programming through [PyCuda](https://wiki.tiker.net/PyCuda), [PyOpenCL](https://mathema.tician.de/software/pyopencl/), [Theano](http://deeplearning.net/software/theano/) or similar.
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Some representative examples include
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* [Jupyter](http://jupyter.org/) --- Python in your browser with interactive code cells, embedded images and other useful features.
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* [Numba](http://numba.pydata.org/) --- Make Python run at the same speed as native machine code!
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* [Blaze](http://blaze.pydata.org/) --- a generalization of NumPy.
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* [PyTables](http://www.pytables.org) --- manage large data sets.
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* [CVXPY](https://github.com/cvxgrp/cvxpy) --- convex optimization in Python.
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* Visit the [Python Package Index](https://pypi.org/).
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‎source/rst/debugging.md‎

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# Debugging
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### The `debug` Magic
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‎source/rst/functions.md‎

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# Functions
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Our function `generate_data()` is rather limited.
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‎source/rst/getting_started.md‎

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# Setting up Your Python Environment
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[Jupyter](http://jupyter.org/) notebooks are one of the many possible ways to interact with Python and the scientific libraries.
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Once you have installed Anaconda, you can start the Jupyter notebook.
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This exercise will familiarize you with git and GitHub.

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