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

Getting Started with Matplotlib

David Landup
David Landup

Now that we've covered everything you need to know about Pandas, its data structures, how to manipulate create and export them from and to various data types, as well as gotten a good view of Pandas' own data visualization capabilities, it's finally time to jump into the famed Matplotlib library.

What is Matplotlib?

Matplotlib is the de-facto most popular visualization engine. Note the usage of "visualization engine" here.

Matplotlib isn't just a standalone library for itself - it carries much more on its shoulders. Other libraries, such as Pandas and Seaborn rely on Matplotlib to perform the actual visualizations. Seaborn can construct and create beautiful plots, but ultimately relies on Matplotlib to actually visualize it.

GeoPandas is another library, specialized for creating, manipulating and visualizing geospatial data, based on Pandas, and thus, Matplotlib.

Originally, Matplotlib was released back in 2003, and has seen worldwide adoption since, with regular updates to this day. Year-over-year, it's cemented itself as one of the key and core libraries for visualization, and isn't likely to be dethroned soon, given how deeply engrained it is with other extremely popular libraries, alongside its own popularity.

During this time, the team behind Matplotlib, including the community, has expanded it to include a plethora of visualization tools and plot types - from simple static 2D plots to more advanced, animated 3D plots, widgets and event-handling. It even offers support for integration with the popular PyQt and TKinter frameworks, used to create GUI applications in Python, allowing developers to integrate powerful visualizations in their applications.

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