---
id: holoviews
version: "1.23.1"
license: BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# holoviews — A high-level plotting API for the PyData ecosystem built on HoloViews.

License: permissive · Maintenance: active · Downloads: 2.1M/mo

## 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 above — verify before relying.

HoloViews lets you declare data structure and visualization intent together, then render interactive plots through bokeh or other backends without writing explicit plotting code.

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

pip install holoviews
uv add holoviews
poetry add holoviews

## Installing holoviews

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.

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()

Requires Python 3.10 or later; bokeh must be installed for interactive rendering.

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_current
- Install friction: low
- Maintenance: active
- Downloads: 2.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

declarative data visualization, interactive plotting for jupyter, bokeh visualization wrapper, data annotation and rendering, high-level plotting API, jupyter-native, interactive-visualization, data-exploration

[View on SkillFed](https://skillfed.io/packages/holoviews) · [View on PyPI](https://pypi.org/project/holoviews/)