Further Readings
Official documentation websites
As mentioned in the introduction., the official developers’ documentation websites act as a glossary paired with very good examples and often tutorials:
- https://www.python.org/doc/, https://docs.python.org/3/
- https://matplotlib.org
- https://numpy.org/doc/
- https://pandas.pydata.org/docs/
- https://pingouin-stats.org/api.html
Community forums
A great help is also provided in community forums, e.g., stackoverflow.com ꜛ, that has a bunch of Q+A with example code snippets.
Cheat sheets
Very useful cheat sheets are provided by Datacamp.com ꜛ.
Online tutorials
Online tutorials, which go beyond the scope of our course, e.g.:
- https://python101.pythonlibrary.org/intro.html - a very good glossary and tutorial website, that gives a broader introduction into general Python functions.
- python-course.eu - a very good and detailed introduction into general Python programming with extended examples. Available in English ꜛ and in German ꜛ.
- A short introduction to scientific Python programming ꜛ by Hans Petter Langtangen and Leif Rune Hellevik.
- How to Think Like a Computer Scientist ꜛ by Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers.
- Data Analysis and visualization with Python ꜛ by Pablo Caceres ꜛ.
- IPython Interactive Computing and Visualization Cookbook ꜛ, Second Edition (2018), by Cyrille Rossant ꜛ. The online version of this book is free and it provides an excellent introduction to data analysis with Python paired with a large amount of very good code examples, that are also shared and freely available on GitHub ꜛ.
- Data All The Wayꜛ, a blog by Rohit Farmer ()/) with very good tutorials and examples on data analysis with Python, Julia and R.
Off-topics
Markdown references
You can find an overview of Markdown syntax in the Markdown guide of the General Teaching Materials. In the reference list of the guide you will find additional Markdown ressources.