Top 10 growing data visualization libraries in Python in 2023

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • pygwalker

    PyGWalker: Turn your pandas dataframe into an interactive UI for visual analysis

  • The most popular data visualization python library in 2023. It turns your dataframe into an interactive data exploration app like tableau/powerBI with one line of code. It provides simple drag-and-drop/chat interface for you to build charts. It can run in juypter notebook, which means you do not need to switch between your code and the visualization app. Besides, you can also build interactive spitial visualization on maps with it. And it also has Javascript and R version.

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  • matplotlib

    matplotlib: plotting with Python

  • Github: https://github.com/matplotlib/matplotlib

  • plotly

    The interactive graphing library for Python :sparkles: This project now includes Plotly Express!

  • Github: https://github.com/plotly/plotly.py

  • bokeh

    Interactive Data Visualization in the browser, from Python

  • Github: https://github.com/bokeh/bokeh

  • seaborn

    Statistical data visualization in Python

  • Github: https://github.com/mwaskom/seaborn

  • pyecharts

    🎨 Python Echarts Plotting Library

  • Github: https://github.com/pyecharts/pyecharts

  • Altair

    Declarative statistical visualization library for Python

  • Github: Altair

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • plotnine

    A Grammar of Graphics for Python

  • Github: https://github.com/has2k1/plotnine

  • holoviews

    With Holoviews, your data visualizes itself.

  • Github: https://github.com/holoviz/holoviews

  • VisPy

    Main repository for Vispy

  • Github: https://github.com/vispy/vispy

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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