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

Data Visualization with Matplotlib

David Landup
David Landup

In Lesson 4. - Getting Started with Matplotlib we got acquainted with the anatomy of Matplotlib plots, how to utilize the generic plot() function with simple data and got familiar with the APIs we can use to work with Matplotlib.

In Lesson 5. - Basic Matplotlib Customization, we explored some customization operations which are commonly used, such as changing the figure and font size, setting axis ranges, changing tick frequency, adding legends, and plotting vertical lines. We've also jumped into Matplotlib text, and how it can be used, including how to add and style annotations to point out certain parts of your plots.

These operations are a primer on what we'll be generally using, but aren't the only customization options you'll ever be using. While we'll explore those in the next lesson - these should be more than enough to get you through most of your plotting needs.

In this lesson, armed with knowledge of how Matplotlib works and how we can tweak plots - let's jump into Data Visualization with Matplotlib.

We'll be covering some of the most commonly used plot types, such as Scatter Plots, Bar Plots and Box Plots, but we'll also be utilizing some more rarely used plot types such as Ridge Plots and the story of how they were conceived. Additionally, we'll be creating a custom plot such as a Joint Plot which isn't built into the Matplotlib library, but is a popularized plot type from another Data Visualization library - Seaborn.

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