holoviews
A high-level plotting API for the PyData ecosystem built on HoloViews.
Decision gist · record as of 2026-08-14
Yes. HoloViews is actively maintained, has no known vulnerabilities, and solves a real friction point in exploratory data analysis. The permissive BSD-3-Clause license and low install friction make it a straightforward addition to a Jupyter-based workflow. Install it if you work with structured data in notebooks and want to reduce boilerplate plotting code.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; bokeh must be installed for interactive rendering.
- Low install friction; pure Python wheel with 9 runtime dependencies including bokeh, pandas, and numpy.
- Active maintenance with a release 43 days ago and ongoing commits.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions.
last release 2026-07-02 (43 days) · last repo commit 2026-08-14 · 2,902 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,109,199 downloads/mo, #3,290 on PyPI
Alternatives
Verify before relying
pip install holoviews
import holoviews as hv
hv.extension('bokeh')
data = {'x': [1, 2, 3], 'y': [1, 2, 3]}
plot = hv.Scatter(data, 'x', 'y')
plot.show()- Whether narwhals integration enables lazy evaluation or dataframe abstraction across multiple backends
- Performance characteristics with large datasets and rendering latency
What it is and what it does
HoloViews is a declarative visualization library that shifts the mental model from 'how do I plot this' to 'what data structure am I working with and what should it show'. You annotate your data with dimension names and types, then HoloViews handles rendering to interactive plots via bokeh or other backends. It sits atop a stack of dependencies including bokeh for interactivity, pandas and numpy for data handling, and panel for dashboard integration.
The library is designed for exploratory data analysis in Jupyter environments, where you want to iterate quickly without writing boilerplate plotting code. It supports composing multiple plots, overlaying data, and linking interactions across visualizations. With 9 runtime dependencies and low install friction, it integrates cleanly into existing PyData workflows.
Use it for
- Exploratory data analysis in Jupyter notebooks where you need interactive plots without verbose code
- Composing multi-panel dashboards with linked selections and interactions using panel
- Rapid prototyping of scientific visualizations where data structure and plot type are closely coupled
- Building interactive web applications that render data-driven plots with bokeh as the backend
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
HoloViews is actively maintained, has no known vulnerabilities, and solves a real friction point in exploratory data analysis. The permissive BSD-3-Clause license and low install friction make it a straightforward addition to a Jupyter-based workflow. Install it if you work with structured data in notebooks and want to reduce boilerplate plotting code.
Install
holoviews on PyPI
Before you install
Low install friction; pure Python wheel with 9 runtime dependencies including bokeh, pandas, and numpy. Active maintenance with a release 43 days ago and ongoing commits.
Requires Python 3.10 or later; bokeh must be installed for interactive rendering.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install holoviews
import holoviews as hv
hv.extension('bokeh')
data = {'x': [1, 2, 3], 'y': [1, 2, 3]}
plot = hv.Scatter(data, 'x', 'y')
plot.show()Verify before relying
- Whether narwhals integration enables lazy evaluation or dataframe abstraction across multiple backends
- Performance characteristics with large datasets and rendering latency
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesbokehcolorcetnarwhalsnumpypandaspanelparampython-dateutilpyviz-comms |
| Maintenance | Actively maintained 43 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,109,199 / month, #3,290 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableFramework :: MatplotlibIntended Audience :: DevelopersIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: holoviews-1.23.1-py3-none-any.whl
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See also hvplot · jupyter_bokeh · bokeh · plotly-express · colorcet · datashader · pyviz-comms · matplotlib · bqplot · jupyter-dash