bqplot
Interactive plotting for the Jupyter notebook, using d3.js and ipywidgets.
Decision gist · record as of 2026-08-14
Yes. bqplot is actively maintained, has low install friction, carries no known vulnerabilities, and solves a specific problem—interactive 2-D plotting in Jupyter—with a mature, well-established dependency stack. Install it if you need to build interactive visualizations or dashboards inside Jupyter notebooks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Jupyter notebook or JupyterLab environment; JupyterLab <= 2 requires a separate labextension install via jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot
- Low install friction with a pure-Python wheel and six well-established runtime dependencies.
- Actively maintained with a recent release; the repository shows 3694 stars and the last commit was 2026-05-07.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-05-07 (99 days) · last repo commit 2026-05-07 · 3,694 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 415,747 downloads/mo, #6,823 on PyPI
Alternatives
Verify before relying
pip install bqplot
from bqplot import pyplot as plt
import numpy as np
plt.plot(np.arange(10), np.random.randn(10))
plt.show()- Whether JupyterLab 3+ requires a labextension install or if the widget loads automatically
- Performance characteristics when plotting large datasets with pandas
- Exact compatibility matrix for different JupyterLab and Jupyter Notebook versions
What it is and what it does
bqplot is a 2-D visualization system built on the Grammar of Graphics principle, designed to work natively inside Jupyter notebooks. Every element of a plot—axes, scales, marks, interactions—is an interactive widget, so you can bind them to other ipywidgets controls to create responsive, integrated GUIs without leaving the notebook.
The library depends on ipywidgets, traitlets, traittypes, numpy, pandas, and bqscales to handle the widget infrastructure, trait-based configuration, and data handling. It renders plots using d3.js on the front end, giving you both the expressiveness of a grammar-of-graphics system and the interactivity of Jupyter's widget ecosystem. You can use either a pyplot-style API or an object-model API depending on your workflow.
Use it for
- Build interactive dashboards in Jupyter by linking plots to sliders, dropdowns, and other ipywidgets controls
- Explore pandas DataFrames interactively with linked plots and real-time filtering without writing a web app
- Create educational notebooks where students can manipulate plot parameters and see results update instantly
- Prototype data visualizations quickly in a notebook before moving to a production web framework
- Combine multiple plots with shared scales and linked selection for exploratory data analysis
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
bqplot is actively maintained, has low install friction, carries no known vulnerabilities, and solves a specific problem—interactive 2-D plotting in Jupyter—with a mature, well-established dependency stack. Install it if you need to build interactive visualizations or dashboards inside Jupyter notebooks.
Install
bqplot on PyPI
Before you install
Low install friction with a pure-Python wheel and six well-established runtime dependencies. Actively maintained with a recent release; the repository shows 3694 stars and the last commit was 2026-05-07.
Requires Jupyter notebook or JupyterLab environment; JupyterLab <= 2 requires a separate labextension install via jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot
License in practice
Licensed under Apache-2.0 (permissive), allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install bqplot
from bqplot import pyplot as plt
import numpy as np
plt.plot(np.arange(10), np.random.randn(10))
plt.show()Verify before relying
- Whether JupyterLab 3+ requires a labextension install or if the widget loads automatically
- Performance characteristics when plotting large datasets with pandas
- Exact compatibility matrix for different JupyterLab and Jupyter Notebook versions
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesipywidgetstraitletstraittypesnumpypandasbqscales |
| Maintenance | Actively maintained 99 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 415,747 / month, #6,823 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: bqplot-0.13.1-py2.py3-none-any.whl
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