06. Visualization in Python

Python Data Visualization Libraries

In this course, you will make use of the following libraries for creating data visualizations:

• Matplotlib: a versatile library for visualizations, but it can take some code effort to put together common visualizations.
• Seaborn: built on top of matplotlib, adds a number of functions to make common statistical visualizations easier to generate.
• pandas: while this library includes some convenient methods for visualizing data that hook into matplotlib, we'll mainly be using it for its main purpose as a general tool for working with data.

All together, these libraries will allow you to visualize data in a balance of productivity and flexibility, for both exploratory as well as explanatory analyses.

A Quick Note about Library Versions

Some of the things that you see in this course might not work the same depending on which version of the Python packages you have. For clarity, as of August 2018, here are the library versions that were used to create the course materials:

• NumPy: Workspaces use v1.12.1, content created with v1.14.0
• pandas: Workspaces use v0.20.3, content created with v0.22.0
• Matplotlib: Workspaces use v2.1.0, content created with v2.1.2
• Seaborn: Workspaces and content both created with v0.8.1