The Python visualization landscape can seem daunting at first. These overviews attempt to shine light on common patterns and use cases, comparing or discussing multiple plotting libraries. Note that some of the projects discussed in the overviews are no longer maintained, so be sure to check the list of dormant projects before choosing that library.
Adaptation of Jake VanderPlas' graphic about the Python visualization landscape, by Nicolas P. Rougier
Data Visualization with Streamlit, Dash, and Panel. Part 1 and Part 2, 20 September 2023: Patryk Młynarek. Comparing Panel, Dash, and Streamlit.
Interactive Dashboards in Python 2023, July 8 2023: Mark Topacio. Comparing Streamlit, Solara, Dash, Datasette, and Shiny for Python.
One library to rule them all? Geospatial visualisation tools in Python, November 2022: Gregor Herda. Comparing Altair, Bokeh, Cartopy, Datashader, GeoPandas, Geoplot, GeoViews, hvPlot, and Plotly.
What Are the Best Python Plotting Libraries?, May 2022: Will Norris. Comparing Matplotlib, Seaborn, Plotly, and Folium.
Python Dashboarding Shootout and Showdown | PyData Global 2021 October 2021: James Bednar, Nicolas Kruchten, Marc Skov Madsen, Sylvain Corlay and Adrien Treuille
Why *Interactive* Data Visualization Matters for Data Science in Python | PyData Global 2021 October 2021: Nicolas Kruchten
Beyond Matplotlib and Seaborn: Python Data Visualization Tools That Work 1 Feb 2021 Stephanie Kirmer. Comparing Matplotlib, Seaborn, Bokeh, Altair, Plotnine, and Plotly, with example github repo for code.
Plotly vs. Bokeh: Interactive Python Visualisation Pros and Cons 7 June 2020 Paul Iacomi. In-depth comparison of Bokeh and Plotly+Dash for dashboarding.
Complete Guide to Data Visualization with Python 29 Feb 2020 Albert Sanchez Lafuente. Example code for Pandas tables, Matplotlib, Seaborn, Bokeh, Altair, and Folium.
Python Visualization Landscape 24 Oct 2019 Sophia Yang. High-level overview of various categories of Python viz libraries, without example code.
Python Grids: Data Visualization 19 Sep 2019 Jared Chung. Table comparing stats on 14 Python plotting libraries.
Python Data Visualization 2018 15 Nov 2018 - 14 Dec 2018 James A. Bednar, Anaconda, Inc. Three blog posts surveying the history and breadth of several dozen Python viz libraries, without example code. Updated in 2019 as an eBook.
pythonplot.com 23 Jun 2017 - 12 Jun 2019 Timothy Hopper. Website with examples of plots made with Pandas+Matplotlib, Seaborn, plotnine, plotly, and R ggplot2, with output and Python code.
Plotting business locations on maps using multiple Plotting libraries in Python 30 Apr 2018 Karan Bhanot. Blog post comparing plotting business locations using gmplot, geopandas, plotly, and bokeh.
Python Data Visualization — Comparing 5 Tools 6 Dec 2017 Elena Kirzhner, Codeburst. Blog post with simple comparisons of Pandas, Seaborn, Bokeh, Pygal, and Plotly code and output.
10 Heatmaps 10 Libraries 10 Sep 2017 Luke Shulman. Comparing heatmap code across 10 different viz libraries.
Overview of Python Visualization Tools 20 Jan 2015 - 25 Apr 2017 Chris Moffitt, Practical Business Python. Three blog posts with examples of using pandas, seaborn, ggplot, bokeh, pygal, plotly, altair, matplotlib.
A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair) 02 Oct 2016 Dan Saber. Comparison of Matplotlib, Pandas .plot(), Seaborn, ggplot/ggpy (now superseded by plotnine), and Altair, with example code.
10 Useful Python Data Visualization Libraries for Any Discipline 8 Jun 2016 Melissa Bierly, Mode.com. Blog post briefly describing matplotlib, seaborn, ggplot, bokeh, pygal, plotly, geoplotlib, gleam, missingno, and leather (now retired), with examples running on the Mode server.
Comparing 7 Tools For Data Visualization in Python 12 Nov 2015 Vik Paruchuri, Dataquest. Blog post illustrating usage of matplotlib, vispy, bokeh, seaborn, pygal, folium, and networkx, with code, for an airport/flight dataset.