Overviews — PyViz 0.0.1 documentation (original) (raw)

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 Note that some of the projects discussed in the overviews are no longer maintained, so be sure to check the list of dormant projects before choosing that library.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

• 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 Młynarek. 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 2021October 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 2021October 2021: Nicolas Kruchten
• Beyond Matplotlib and Seaborn: Python Data Visualization Tools That Work1 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 Cons7 June 2020 Paul Iacomi. In-depth comparison of Bokeh and Plotly+Dash for dashboarding.
• Complete Guide to Data Visualization with Python29 Feb 2020 Albert Sanchez Lafuente. Example code for Pandas tables, Matplotlib, Seaborn, Bokeh, Altair, and Folium.
• Python Visualization Landscape24 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 201815 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.com23 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 Python30 Apr 2018 Karan Bhanot. Blog post comparing plotting business locations using gmplot, geopandas, plotly, and bokeh.
• Python Data Visualization — Comparing 5 Tools6 Dec 2017 Elena Kirzhner, Codeburst. Blog post with simple comparisons of Pandas, Seaborn, Bokeh, Pygal, and Plotly code and output.
• 10 Heatmaps 10 Libraries10 Sep 2017 Luke Shulman. Comparing heatmap code across 10 different viz libraries.
• The Python Visualization Landscape20 May 2017 Jake VanderPlas, U. Washington. 30-minute talk surveying the history and breadth of Python viz libraries. [slides].
• Python Graph Gallery30 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 Tools20 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 Discipline8 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 Python12 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.