# Python data visualization libraries

> From Matplotlib and Seaborn to interactive and 3D plotting. 48 tools, ranked by GitHub stars.

Python has one of the richest data visualization ecosystems of any language. Matplotlib is the foundation for static plots and Seaborn builds statistical graphics on top of it. Plotly (Python), Bokeh and HoloViews produce interactive charts for notebooks and the web, and Vega-Altair offers a declarative grammar based on Vega-Lite.

For 3D and GPU-accelerated scientific visualization, look at VisPy, PyVista and Mayavi. For quick looks at a whole dataset, profiling tools such as ydata-profiling generate exploratory reports in one call.

| # | Tool | GitHub stars | Commits (12 mo) | License | Description |
|---:|---|---:|---:|---|---|
| 1 | [Streamlit](https://awesomedataviz.com/tools/streamlit/) | 45,895 | 2,413 | Apache-2.0 | Framework for turning Python scripts into interactive data apps. |
| 2 | [Dash](https://awesomedataviz.com/tools/dash/) | 24,441 | 1,348 | MIT | Framework for building data apps and dashboards in Python, built on Plotly.js and React. |
| 3 | [Matplotlib](https://awesomedataviz.com/tools/matplotlib/) | 23,323 | 2,023 | PSF-2.0 | 2D plotting library. |
| 4 | [Bokeh](https://awesomedataviz.com/tools/bokeh/) | 20,455 | 344 | BSD-3-Clause | Interactive Web Plotting for Python. |
| 5 | [Taipy](https://awesomedataviz.com/tools/taipy/) | 19,443 | 69 | Apache-2.0 | Python framework for building data and AI web applications with interactive charts and dashboards. |
| 6 | [Plotly (Python)](https://awesomedataviz.com/tools/plotly-python/) | 18,822 | 739 | MIT | Interactive web based visualization built on top of plotly.js |
| 7 | [PyGWalker](https://awesomedataviz.com/tools/pygwalker/) | 15,983 | 154 | Apache-2.0 | Turns dataframes into a drag-and-drop visual analysis UI in Jupyter, based on Graphic Walker. |
| 8 | [pyecharts](https://awesomedataviz.com/tools/pyecharts/) | 15,774 | 2 | MIT | Python binding for Echarts library. |
| 9 | [Seaborn](https://awesomedataviz.com/tools/seaborn/) | 14,056 | 18 | BSD-3-Clause | A library for making attractive and informative statistical graphics. |
| 10 | [ydata-profiling](https://awesomedataviz.com/tools/ydata-profiling/) | 13,719 | 8 | MIT | Generates statistical analytic reports with visualization for quick data analysis (formerly pandas-profiling). |
| 11 | [Rerun](https://awesomedataviz.com/tools/rerun/) | 11,543 | 3,095 | Apache-2.0 | An SDK for logging computer vision and robotics data paired with a visualizer for exploring that data over time. |
| 12 | [WordCloud](https://awesomedataviz.com/tools/wordcloud/) | 10,536 | 5 | MIT | Word cloud generator for Python. |
| 13 | [Vega-Altair](https://awesomedataviz.com/tools/altair/) | 10,491 | 153 | BSD-3-Clause | Declarative statistical visualizations, based on Vega-Lite. |
| 14 | [SciencePlots](https://awesomedataviz.com/tools/scienceplots/) | 9,270 | 10 | MIT | Matplotlib styles for scientific figures and journal publications. |
| 15 | [folium](https://awesomedataviz.com/tools/folium/) | 7,409 | 161 | MIT | Builds interactive Leaflet.js maps from Python data. |
| 16 | [Voilà](https://awesomedataviz.com/tools/voila/) | 5,947 | 12 | BSD | Turns Jupyter notebooks into standalone interactive web applications. |
| 17 | [Panel](https://awesomedataviz.com/tools/panel/) | 5,782 | 392 | BSD-3-Clause | Data exploration and web app framework for Python that works with many plotting libraries. |
| 18 | [plotnine](https://awesomedataviz.com/tools/plotnine/) | 4,770 | 490 | MIT | Grammar of graphics for Python, based on R's ggplot2. |
| 19 | [mplfinance](https://awesomedataviz.com/tools/mplfinance/) | 4,439 | 0 | BSD-style | Financial market data visualization (candlestick, OHLC, volume) using matplotlib. |
| 20 | [PyQtGraph](https://awesomedataviz.com/tools/pyqtgraph/) | 4,421 | 355 | MIT | Interactive and realtime 2D/3D/Image plotting and science/engineering widgets. |
| 21 | [missingno](https://awesomedataviz.com/tools/missingno/) | 4,211 | 0 | MIT | Provides flexible toolset of data-visualization utilities that allows quick visual summary of the completeness of your dataset, based on matplotlib. |
| 22 | [PyVista](https://awesomedataviz.com/tools/pyvista/) | 3,832 | 884 | MIT | 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK) |
| 23 | [leafmap](https://awesomedataviz.com/tools/leafmap/) | 3,782 | 147 | MIT | Interactive mapping and geospatial analysis in Jupyter with multiple mapping backends. |
| 24 | [bqplot](https://awesomedataviz.com/tools/bqplot/) | 3,693 | 21 | Apache-2.0 | Plotting library for IPython/Jupyter notebooks. |
| 25 | [ggpy](https://awesomedataviz.com/tools/ggpy/) | 3,688 | 0 | BSD-2-Clause | Plotting system based on R's ggplot2. |
| 26 | [Chartify](https://awesomedataviz.com/tools/chartify/) | 3,653 | 0 | Apache-2.0 | Bokeh wrapper that makes it easy for data scientists to create charts. |
| 27 | [VisPy](https://awesomedataviz.com/tools/vispy/) | 3,603 | 62 | BSD-3-Clause | High-performance scientific visualization based on OpenGL. |
| 28 | [Datashader](https://awesomedataviz.com/tools/datashader/) | 3,562 | 48 | BSD-3-Clause | Renders very large datasets into accurate images by rasterizing them. |
| 29 | [VTK](https://awesomedataviz.com/tools/vtk/) | 3,211 | 5,558 | BSD | Open-source library for 3d Graphics, image processing and visualization. |
| 30 | [HoloViews](https://awesomedataviz.com/tools/holoviews/) | 2,910 | 321 | BSD-3-Clause | Complex and declarative visualizations from annotated data. |
| 31 | [napari](https://awesomedataviz.com/tools/napari/) | 2,771 | 641 | BSD-3-Clause | Fast, interactive viewer for multi-dimensional images in Python. |
| 32 | [vedo](https://awesomedataviz.com/tools/vedo/) | 2,272 | 263 | MIT | Library for scientific analysis and visualization of 3D objects based on VTK. |
| 33 | [Plotext](https://awesomedataviz.com/tools/plotext/) | 2,204 | 27 | MIT | Plots data directly in the terminal with a matplotlib-like syntax. |
| 34 | [Lets-Plot](https://awesomedataviz.com/tools/lets-plot/) | 1,783 | 606 | MIT | Grammar of graphics plotting library for Python and Kotlin, by JetBrains. |
| 35 | [Shiny for Python](https://awesomedataviz.com/tools/shiny-for-python/) | 1,756 | 217 | MIT | Python version of the Shiny reactive framework for interactive data apps. |
| 36 | [Cartopy](https://awesomedataviz.com/tools/cartopy/) | 1,621 | 212 | BSD-3-Clause | Cartographic projections and geospatial data plotting with matplotlib. |
| 37 | [Mayavi](https://awesomedataviz.com/tools/mayavi/) | 1,412 | 43 | BSD-3-Clause | Interactive scientific data visualization and 3D plotting in Python. |
| 38 | [glumpy](https://awesomedataviz.com/tools/glumpy/) | 1,280 | 0 | BSD-3-Clause | OpenGL scientific visualizations library. |
| 39 | [Veusz](https://awesomedataviz.com/tools/veusz/) | 1,051 | 41 | GPL-2.0 | Python multiplatform GUI plotting tool and graphing library |
| 40 | [pptk](https://awesomedataviz.com/tools/pptk/) | 635 | 0 | MIT | Visualize and work with 2D/3D pointclouds |
| 41 | [yt](https://awesomedataviz.com/tools/yt/) | 559 | 400 | BSD-3-Clause | Toolkit for analysis and visualization of volumetric data. |
| 42 | [uniplot](https://awesomedataviz.com/tools/uniplot/) | 460 | 31 | MIT | Lightweight plotting to the terminal. 4x resolution via Unicode. |
| 43 | [Toyplot](https://awesomedataviz.com/tools/toyplot/) | 449 | 26 | BSD | The kid-sized plotting toolkit for Python with grownup-sized goals. |
| 44 | [diagram](https://awesomedataviz.com/tools/diagram/) | 408 | 0 | MIT | Text mode diagrams using UTF-8 characters |
| 45 | [Quibbler](https://awesomedataviz.com/tools/quibbler/) | 332 | 0 | MIT | Your data and anything you plot is effortlessly live and interactive. |
| 46 | [ipychart](https://awesomedataviz.com/tools/ipychart/) | 133 | 0 | MIT | The power of Chart.js in Jupyter Notebook. |
| 47 | [three.py](https://awesomedataviz.com/tools/three-py/) | 121 | 0 | MIT | Easy to use 3D library based on PyOpenGL. Inspired by Three.js. |
| 48 | [syd](https://awesomedataviz.com/tools/syd/) | 30 | 12 | GPL-3.0 | A package for making GUIs around matplotlib figures easy, fast, and streamlined. |

Source: https://awesomedataviz.com/categories/python/ · Data updated 2026-10-04 · Content: CC BY 4.0, Awesome Dataviz (https://awesomedataviz.com).