https://pyviz.org/overviews/index.html

OverviewsP

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of the projects discussed in the overviews are no longer maintained,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

-The Essential Guide to R and Python Libraries for Data Visualization
, 16 Dec 2024: Sarah Lea. Comparing Matplotlib, Seaborn, Plotly,
Pandas .plot(), Bokeh, Altair, HoloViews, and Folium.

  * A Survey of Python Frameworks, 25 Sep 2024: Ellie Ko. Comparing
    Streamlit, Shiny for Python, Panel, Flask, Chainlit, Dash, Voila,
    and Gradio.

  * The Power of Pandas Plots: Backends, 29 Aug 2024: Pierre-Etienne
    Toulemonde. Comparing matplotlib, plotly, and hvPlot for plotting
    with Pandas.

  * 7 Best Python Libraries For Data Visualisation, 25 Jan 2024:
    inVerita. Comparing Matplotlib, Seaborn, Plotly, Bokeh, Altair,
    and HoloViews.

  * Top-5 Python Frontend Libraries for Data Science, part 2, 31 Mar
    2024: Artem Shelamanov. Comparing Voila, PyWebIO, Gradio, Panel,
    and Dash.

  * Top-5 Python Frontend Libraries for Data Science, part 1, 24 Dec
    2023: Artem Shelamanov. Comparing Streamlit, Solara, Trame,
    ReactPy, and PyQt.

  * Declarative vs. Imperative Plotting: An overview for Python
    beginners, 9 January 2024: Lee Vaughan. Comparing Matplotlib,
    Seaborn, Plotly Express, and hvPlot/HoloViews.

  * Is Matplotlib Still the Best Python Library for Static Plots?, 19
    January 2024: Mike Clayton. Comparing Matplotlib, Seaborn,
    plotnine, Altair, and Plotly.

  * Top-5 Python Frontend Libraries for Data Science, 24 December
    2023: Artem Shelamanov. Comparing Streamlit, Solara, Trame,
    ReactPy, and PyQt.

  * Python on the Web, 11 October 2023: Pier Paolo Ippolito.
    Comparing Panel, Shiny for Python, and PyScript.

  * Data Visualization with Streamlit, Dash, and Panel. Part 1 and
    Part 2, 20 September 2023: Patryk Mlynarek. Comparing Panel,
    Dash, and Streamlit.

  * Low Code With Dash, Streamlit, and Panel, 9 July 2023: Petrica
    Leuca. Comparing Dash, Streamlit, and Panel. Separate followups
    focus individually on Dash, Streamlit, and Panel.

  * Interactive Dashboards in Python 2023, 8 July 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.

  * The Python Visualization Landscape 20 May 2017 Jake VanderPlas,
    U. Washington. 30-minute talk surveying the history and breadth
    of Python viz libraries. [slides].

  * Python Graph Gallery 30 Apr 2017 - 7 Jan 2018 Yan Holtz. Website
    with examples of plots made with Seaborn, Matplotlib, Pandas,
    with output and Python code, used in data-to-viz.com.

  * 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.

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