# Python interactive web charting libraries

> Zoomable, hoverable charts for notebooks and the browser. 6 tools, ranked by GitHub stars.

Python libraries that output interactive charts for Jupyter and the browser, with zooming, panning and tooltips. Plotly (Python) and Bokeh are the most widely used, bqplot is built on Jupyter widgets, and pyecharts and ipychart wrap the ECharts and Chart.js JavaScript libraries.

| # | Tool | GitHub stars | Commits (12 mo) | License | Description |
|---:|---|---:|---:|---|---|
| 1 | [Bokeh](https://awesomedataviz.com/tools/bokeh/) | 20,468 | 423 | BSD-3-Clause | Interactive Web Plotting for Python. |
| 2 | [Plotly (Python)](https://awesomedataviz.com/tools/plotly-python/) | 18,868 | 816 | MIT | Interactive web based visualization built on top of plotly.js |
| 3 | [pyecharts](https://awesomedataviz.com/tools/pyecharts/) | 15,782 | 1 | MIT | Python binding for Echarts library. |
| 4 | [bqplot](https://awesomedataviz.com/tools/bqplot/) | 3,693 | 31 | Apache-2.0 | Plotting library for IPython/Jupyter notebooks. |
| 5 | [Chartify](https://awesomedataviz.com/tools/chartify/) | 3,656 | 0 | Apache-2.0 | Bokeh wrapper that makes it easy for data scientists to create charts. |
| 6 | [ipychart](https://awesomedataviz.com/tools/ipychart/) | 133 | 0 | MIT | The power of Chart.js in Jupyter Notebook. |

Source: https://awesomedataviz.com/categories/python-interactive-charts/ · Data updated 2026-11-01 · Content: CC BY 4.0, Awesome Dataviz (https://awesomedataviz.com).