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10 Useful Python Data Visualization Libraries for Any Discipline

A great overview of 10 useful Python data visualization tools. It covers some of the big ones, like matplotlib and Seaborn, but also explores some more obscure libraries, like Gleam, Leather, and missingno.


Python data visualization - pygal

Box plot (Florian Mounier)

Like Bokeh and Plotly, pygal offers interactive plots that can be embedded in the web browser. Its prime differentiator is the ability to output charts as SVGs. As long as you’re working with smaller datasets, SVGs will do you just fine. But if you’re making charts with hundreds of thousands of data points, they’ll have trouble rendering and become sluggish.

Since each chart type is packaged into a method and the built-in styles are pretty, it’s easy to create a nice-looking chart in a few lines of code.

Created by: Florian Mounier
Where to learn more: http://www.pygal.org/en/latest/index.html


Python data visualization - Plotly

Line plot (Plotly)

You might know Plotly as an online platform for data visualization, but did you also know you can access its capabilities from a Python notebook? Like Bokeh, Plotly’s forte is making interactive plots, but it offers some charts you won’t find in most libraries, like contour plotsdendograms, and 3D charts.

Created by: Plotly
Where to learn more: https://plot.ly/python/


Python data visualization - geoplotlib

Choropleth (Andrea Cuttone)

geoplotlib is a toolbox for creating maps and plotting geographical data. You can use it to create a variety of map-types, like choropleths, heatmaps, and dot density maps. You must have Pyglet (an object-oriented programming interface) installed to use geoplotlib. Nonetheless, since most Python data visualization libraries don’t offer maps, it’s nice to have a library dedicated solely to them.

Created by: Andrea Cuttone
Where to learn more: https://github.com/andrea-cuttone/geoplotlib


Python data visualization - Gleam

Scatter plot with trend line (David Robinson)

Gleam is inspired by R’s Shiny package. It allows you to turn analyses into interactive web apps using only Python scripts, so you don’t have to know any other languages like HTML, CSS, or JavaScript. Gleam works with any Python data visualization library. Once you’ve created a plot, you can build fields on top of it so users can filter and sort data.

Created by: David Robinson
Where to learn more: https://github.com/dgrtwo/gleam


Python data visualization - missingno

Nullity matrix (Aleksey Bilogur)

Dealing with missing data is a pain. missingno allows you to quickly gauge the completeness of a dataset with a visual summary, instead of trudging through a table. You can filter and sort data based on completion or spot correlations with a heatmap or a dendrogram.

Created by: Aleksey Bilogur
Where to learn more: https://github.com/ResidentMario/missingno


Python data visualization - Leather

Chart grid with consistent scales (Christopher Groskopf)

Leather’s creator, Christopher Groskopf, puts it best: “Leather is the Python charting library for those who need charts now and don’t care if they’re perfect.” It’s designed to work with all data types and produces charts as SVGs, so you can scale them without losing image quality. Since this library is relatively new, some of the documentation is still in progress. The charts you can make are pretty basic—but that’s the intention.

Created by: Christopher Groskopf
Where to learn more: http://leather.readthedocs.io/en/latest/index.html

Original. Reposted with permission.


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