This repo was originally created by Hugh Brown (hughdbrown), who is the man when it comes to writing python (and just the man in general). I forked the project pretty quickly after him creating it and added a bunch to it.
The repo is meant to be exactly what Hugh set it up to be - a set of cheatsheets that was developed while working on the Galvanize Data Intensive. To me, that means not necessarily taking a lot of time (or space) to explain the intricacies of every numpy command, or every pandas dataframe method. Instead, it means giving a fairly comprehensive list of the libraries/modules/tools that I use on a daily basis as a data-scientist, and giving a brief explanation as to what is possible with those libraries/modules/tools.
That's what I have tried to accomplish here. While not every single method/function that I have used has been throw into this repo, a good number of them have, particularly in the numpy and pandas markdown files. As any programmer will tell you, one of the best skills you can develop is figuring out how to google stuff and read docs. Hopefully these just-enough files will guide you a little bit in searching for exactly the right function/method and navigating to the right doc. more quickly, while at the same time giving you an idea of the power of these libraries/modules/tools.
I'm hoping at some point to throw in here some basics for the regressional techniques/machine learning algorithms that we used in class. This will largely be a rundown on whats what in the scikit-learn library, but there will likely be some other files associated with that as well.
That's all that's on my current docket of things to put in, but if there's something in particular you'd like to see, drop me a line at [email protected]. Keep in mind that I won't go into detailed descriptions of anything, but try to give a pretty comprehensive overview of the functions/methods that I use the most in a given library/module/tool.