Welcome to the World of Python!
This is a crash course in Python, with Notebook as the User Interface (IDE), prepared by Sourav Sen Gupta.
Python is a fun language to learn, and really easy to pick up even if you are new to programming. Quite often, Python is easier to pick up if you do not have any programming experience whatsoever. Python is a high level language, targeted at students and professionals from diverse backgrounds. It offers an amazing array of Useful Libraries.
This set of examples are in Python 3, written and executed in the beautifully simple IDE Jupyter Notebook. Note that Jupyter sets up a localhost:8888 server to render the notebook in your computer's browser. It can render anything thereafter -- should be fun! Once you are familiar with the Jupyter Notebook environment, and the basic Python syntax in Module 0, feel free to explore the rest of the Python Modules.
Module 0 : Python in Jupyter
Module 1 : Data Types in Python
Module 2 : Data Structures in Python
Module 3 : Conditions and Loops in Python
Module 4 : Functions and Modules in Python
Module 5 : Computing with Python
Module 6 : Data Analysis with Python
To get the most out of this crash course, you'll need to download and use the Notebooks for individual Modules posted in this repository (or git clone the repo). Before that, prepare your computer by installing the following.
Platform : Anaconda for Python 3 (https://www.anaconda.com/)
Core Engine : Python 3 (it will get installed with Anaconda)
User Interface (IDE) : Jupyter Notebook (comes with Anaconda)
You may also choose Visual Studio Code as your IDE, and set it up for Python programming. Visual Studio Code will allow you to write and execute both python Scripts (.py files) and python Notebooks (.ipynb files). You will also be able to write more complex (multi-file) programs to run on your own computer.
In case installing Anaconda for Python 3 is too heavy for you, and you know how to manage conda environments and packages, you may also download and install the MiniConda (https://docs.conda.io/en/latest/miniconda.html) package for Python 3. It's lightweight, but you will have to install all required packages individually, on your own.
Gratitude : This set of introductory material for Python is heavily inspired from two wonderful lecture series in Python, as follows, with new material targeted at data handling and basic data science inspired from the books mentioned below.
- Python4Maths by Andreas Ernst : https://gitlab.erc.monash.edu.au/andrease/Python4Maths
- Python Lectures by Rajath Kumar : https://github.com/rajathkmp/Python-Lectures
In case you want to learn and practice Python programming on your own, or want to refer to more theoretical content, feel free to read the following books. There are several other books and resources (primarily videos) available online for your reference.
- Think Python 2e : How to Think Like a Computer Scientist (2nd Edition) by Allen B. Downey. Available online at https://greenteapress.com/wp/think-python-2e/
- A Whirlwind Tour of Python (O’Reilly) by Jake VanderPlas. Available online at https://jakevdp.github.io/WhirlwindTourOfPython/
- Python Data Science Handbook (O'Reilly) by Jake VanderPlas. Available online at https://jakevdp.github.io/PythonDataScienceHandbook/
Highly recommended : Every Python user needs to know about the following resources.
- Python 3 Cheat Sheet by Laurent Pointal. This 2-pager is a life-saver -- print and frame it!
- Data Science & Machine Learning by Chris Albon. This is a true treasure trove -- go explore!
License Declaration : Following the lead from the inspirations for this material, and the spirit of Python education and development, all modules of this work are licensed under the Creative Commons Attribution 3.0 Unported License.