10 Python Data Visualization Libraries for Multi‑Disciplinary Use

Today we introduce ten Python data‑visualization libraries suitable for multiple disciplines, ranging from well‑known to lesser‑known options.

1. matplotlib

matplotlib is the cornerstone of Python visualization, heavily inspired by MATLAB and still the most widely used library after more than a decade of development.

Many other libraries, such as pandas and Seaborn, are built on or call matplotlib under the hood.

While matplotlib easily provides a rough view of data, producing publication‑ready charts can be complex, as noted by Chris Moffitt.

The default style has a strong 1990s feel, but the upcoming matplotlib 2.0 promises more modern aesthetics.

Developer: John D. Hunter

2. Seaborn

Seaborn leverages matplotlib to create attractive charts with concise code.

Its default styles and color palettes are more modern than matplotlib’s.

Because Seaborn builds on matplotlib, understanding matplotlib is helpful for tweaking Seaborn’s parameters.

Developer: Michael Waskom

3. ggplot

ggplot brings the R package ggplot2 to Python, based on the Grammar of Graphics.

It allows layering of plot elements (axes, points, lines, trend lines, etc.) to build a figure.

Users accustomed to matplotlib may need time to adapt to this layered approach.

ggplot sacrifices some customization for simplicity and integrates tightly with pandas.

Developer: ŷhat

4. Bokeh

Bokeh follows the Grammar of Graphics like ggplot but is pure Python.

It excels at creating interactive, web‑ready visualizations that can be exported as JSON, HTML, or interactive apps.

Bokeh offers three levels of control: high (quick charts), medium (similar to matplotlib), and low (full element‑by‑element definition).

Developer: Continuum Analytics

5. pygal

pygal, like Bokeh and Plotly, provides interactive charts embeddable in browsers.

Its main distinction is outputting SVG format, which is ideal for small datasets but can become slow with thousands of points.

With concise code and attractive default styles, pygal makes it easy to generate beautiful charts.

Developer: Florian Mounier

6. Plotly

Plotly can be used from Python to create interactive visualizations, offering chart types rarely found elsewhere, such as contour, treemap, and 3‑D plots.

Developer: Plotly

7. geoplotlib

geoplotlib is a toolbox for creating maps and geographic visualizations, supporting choropleths, heatmaps, and point‑density plots.

It requires the Pyglet OOP interface and fills the gap left by most Python visualization tools that lack mapping capabilities.

Developer: Andrea Cuttone

8. Gleam

Gleam draws inspiration from R’s Shiny, allowing Python‑only creation of interactive web applications without needing HTML, CSS, or JavaScript knowledge.

It can work with any Python visualization library and lets users add interactive domains for sorting and filtering data.

Developer: David Robinson

9. missingno

missingno visualizes missing data, enabling quick assessment of data completeness through nullity matrices, heatmaps, and tree maps.

It helps users sort, filter, or decide how to handle incomplete data.

Developer: Aleksey Bilogur

10. Leather

Leather, defined by its author Christopher Groskopf, targets users who need a chart immediately and are not concerned with perfection.

It generates SVG charts for any data type, preserving quality when resizing.

Developer: Christopher Groskopf

Original source: https://blog.csdn.net/weixin_44208569