Pandas is a great numerical libraries for data analysis. However, this convenience comes at the cost of a complex installation process (usually suggesting that you install anaconda to sidestep the issue), and several megabytes of extra dependencies. In many cases, the extra efficiency provided by pandas isn’t required, and a few lines of utility code can allow you to forgo pandas all together.
Using pure Python code snippets and examples from my data analysis library meza, this talk will walk you through some of the common tabular data tasks. In the process, I will teach you alternatives to their Pandas counterparts.
By attending, you will learn: