Introduction to Python - available from https://github.com/milaan9/Python4DataScience
Python is a modern, robust, high level programming language. It is very easy to pick up even if you are completely new to programming.
Python, similar to other languages like matlab or R, is interpreted hence runs slowly compared to C++, Fortran or Java. However writing programs in Python is very quick. Python has a very large collection of libraries for everything from scientific computing to web services. It caters for object oriented and functional programming with module system that allows large and complex applications to be developed in Python.
These lectures are using jupyter notebooks which mix Python code with documentation. The python notebooks can be run on a webserver or stand-alone on a computer.
To give an indication of what Python code looks like, here is a simple bit of code that defines a set
N={1,3,4,5,7}
print('The sum of ∑_i∈N i*i =',sum( i**2 for i in N ) )The sum of ∑_i∈N i*i = 100
This course is broken up into a number of notebooks (chapters).
- 00 This introduction with additional information below on how to get started in running python
- 01 Basic data types and operations (numbers, strings)
- 02 String manipulation
- 03 Data structures: Lists and Tuples
- 04 Data structures (continued): dictionaries
- 05 Control statements: if, for, while, try statements
- 06 Functions
- 07 Classes and basic object oriented programming
- 08 Scipy: libraries for arrays (matrices) and plotting
- 09 Mixed Integer Linear Programming using the mymip library
- 10 Networks and graphs under python - a very brief introduction
- 11 Using the numba library for fast numerical computing.
This is a tutorial style introduction to Python. For a quick reminder / summary of Python syntax the following Quick Reference Card may be useful. A longer and more detailed tutorial style introduction to python is available from the python site at: https://docs.python.org/3/tutorial/
- Press the start button (if prompted by the system)
- Use the menu of the jupyter system to upload a .ipynb python notebook file or to start a new notebook.
Python runs on windows, linux, mac and other environments. There are many python distributions available. However the recommended way to install python under Microsoft Windows or Linux is to use the Anaconda distribution available at [https://www.continuum.io/downloads]. Make sure to get the Python 3.5 version, not 2.7. This distribution comes with the SciPy collection of scientific python tools as well as the iron python notebook. For developing python code without notebooks consider using spyder (also included with Anaconda)
To open a notebook with anaconda installed, from the terminal run:
ipython notebook
Download all the notebooks from https://github.com/milaan9/Python4DataScience
Launch ipython notebook from the folder which contains the notebooks. Open each one of them
Cell > All Output > Clear
This will clear all the outputs and now you can understand each statement and learn interactively.
This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/