I learnt python by first jumping into two complicated packages (OSMNX and NetworkX). Some things you learn as you go: Linux, LaTeX, NetLogo, Octave, Git/Github. But for some, you cannot start without a complete set of basics, else risk getting stuck at it for too long and end up procrastinating. This holds true for Data Science and Complex Systems (will post on this soon!): You can only gain mastery in python (or R) quickly by doing a project in it, but you still need to know the basic python to be able to handle any data that you have. Data Science is an ocean! You learn a ton, forget some, adapt quickly and learn more new packages, have fun solving challenging problems, but the base of coding remains the same.
How is this different from so many other quick tutorials out there? Most courses out there go into details of each topic - making it seem like there is a lot (there is, but it doesn’t have to be at the start). This has all you need to know summarized into tiny chunks to get started - similar to and taking inspiration from what SWIRL is for R. Its all in one place - it can also be used as cheatsheets - at least until you are more comfortable with it. And after having learnt both R (easily) and Python (long, winding way, then all of a sudden), I wish I had this course to learn from and not get intimidated by Python at all. This is designed by me for a workshop which got super positive feedback and was an intro to some further advanced coding session. Learning to code (or learning anything) shouldn’t be like jumping right into the ocean. It should start with a bucket of water, lake with a life jacket on, then learning swimming to finally jumping into the ocean to learn to your heart’s content and some more!
Learn Python Quickly
You can find the whole course set here.
Anaconda is the easiest way to get python and dependent modules/packages; to get started right away. Anaconda comes with a few IDEs pre-installed: Jupyter Notebook (I generally work on this), Jupyter Lab (my new favorite) and Spyder (similar to R-Studio).
Install Anaconda and open Jupyter Notebook. Download the whole repository and test it out.
It starts with basic python codes and its data types (int, float, string, etc.) and then goes on to the two most important and most used packages: numpy and pandas. Pandas also touches upon time series as this was an intro for large multi-dimentional data set (spatial and time). And some intro to visualization through matplotlib.