Learn how to install a Python stack for data analysis

Learn how to use Jupyter

Use Python for a calculator!

Python and Jupyter enable you to get help quickly without resorting to searching.

Whether it is categorical data or freeform text, you will want to manipulate strings with Python.

Python makes file reading and writing easy.

Lists are a built-in type to track sequences. You can convert them to NumPy arrays or Pandas series.

Get out your Ginsu 2000 and slice and dice.

Mapping keys to values is useful for renaming or performing quick lookups.

We try and avoid loops with NumPy and Pandas, but occasionally you will need them, or their friends comprehensions.

The black box of computation. Data comes in, results go out!

We don't use these too often, but if you need to make your own utility libraries, modules are the way to go!

Classes underpin everything in Python, so it is useful to understand how they work.

Mastering exception handling will make your development go much smoother.

This library is the underpinning of all things fast in numeric Python.

Being able to pull out certain parts of your data is crucial. Slicing to the rescue!

Boolean arrays are using in NumPy and Pandas for filtering data. Grok them!

Vectorized operations make things fast!

Pandas builds on top of NumPy and makes things easy.