Data Visualization in Python with Matplotlib and Pandas - Advanced Matplotlib Customization

David Landup

In Lesson 5, we've taken a look at some of the basic customization we'll be doing fairly commonly when doing Data Visualization. These included the basic operations of changing figure sizes, tick frequency and scales. These features have gotten us to a point, though, even in the last lesson - these weren't enough for some of the plots we've been producing.

When recreating the Joy Division album cover, using a Ridge Plot in 3D - we couldn't just set the background to black. When creating a Joint Plot, simply adding multiple Axes instances looked bad so we opted to use a GridSpec instead. We've also used Colormaps in several Surface Plot examples.

We haven't fully delved into these or explained how they work - they were covered on a need-to-know basis. Now, this is the lesson in which we'll be exploring these functionalities in detail.

## Understanding Matplotlib Stylesheets

First off, a relatively easy, but still really nice feature - Matplotlib Stylesheets. A stylesheet contains a set of parameters that change the look of Matplotlib's elements. These were hand-crafted by the Matplotlib team and are designed to have colors and palettes that work together. The default stylesheet doesn't look bad - but it could most certainly look better, slicker.

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