altair
Vega-Altair: A declarative statistical visualization library for Python.
Decision gist · record as of 2026-08-14
Yes. Altair is a mature, actively maintained library with no security issues, low install friction, and a permissive license. It is well-suited for anyone doing exploratory data analysis or building interactive visualizations in Python, especially in Jupyter environments. The declarative API is more readable and less error-prone than imperative plotting libraries for most statistical graphics tasks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel distribution.
- The package is actively maintained with a recent release and has been in production use since 2016.
- All five runtime dependencies are lightweight and widely used.
License · maintenance · safety
permissive license (permissive) — BSD 3-Clause permissive license allows commercial and private use with minimal restrictions; you must retain copyright and license notices in redistributions.
last release 2026-06-23 (52 days) · last repo commit 2026-08-13 · 10,451 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 54,458,936 downloads/mo, #543 on PyPI
Alternatives
Verify before relying
import altair as alt
from altair.datasets import data
cars = data.cars()
alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin'
)- Whether narwhals integration enables lazy evaluation or other dataframe-agnostic features beyond what the description states.
- Specific performance characteristics when rendering large datasets or complex interactive specifications.
What it is and what it does
Altair is a Python wrapper around the Vega-Lite declarative visualization grammar, letting you build interactive statistical charts by describing what you want to see rather than how to draw it. You write simple, readable Python code that specifies data encodings (which columns map to which visual properties), marks (points, bars, lines), and interactions (selections, filters, linked views), and Altair compiles your specification into JSON that Vega-Lite renders in Jupyter notebooks, VS Code, GitHub, and browsers.
The library depends on jinja2 for templating, jsonschema for validation, packaging for version handling, typing-extensions for type hints, and narwhals for dataframe abstraction. It supports modern Python versions (3.10 through 3.14) and is actively maintained with no known security vulnerabilities. The API is designed to be intuitive and consistent, making it accessible for exploratory data analysis while remaining powerful enough for publication-quality visualizations.
Use it for
- Build interactive scatter plots, histograms, and bar charts in Jupyter notebooks for exploratory data analysis without writing rendering code.
- Create linked visualizations where selections in one chart filter or highlight data in another, enabling interactive drill-down exploration.
- Export statistical graphics as PNG, SVG, or standalone HTML for reports, presentations, or web embedding.
- Prototype data-driven dashboards and interactive applications that run in notebooks or web environments.
- Serialize visualization specifications as JSON for programmatic manipulation or sharing with non-Python tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Altair is a mature, actively maintained library with no security issues, low install friction, and a permissive license. It is well-suited for anyone doing exploratory data analysis or building interactive visualizations in Python, especially in Jupyter environments. The declarative API is more readable and less error-prone than imperative plotting libraries for most statistical graphics tasks.
Install
altair on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. The package is actively maintained with a recent release and has been in production use since 2016. All five runtime dependencies are lightweight and widely used.
License in practice
BSD 3-Clause permissive license allows commercial and private use with minimal restrictions; you must retain copyright and license notices in redistributions.
Quickstart
import altair as alt
from altair.datasets import data
cars = data.cars()
alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin'
)Verify before relying
- Whether narwhals integration enables lazy evaluation or other dataframe-agnostic features beyond what the description states.
- Specific performance characteristics when rendering large datasets or complex interactive specifications.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesjinja2jsonschemanarwhalspackagingtyping-extensions |
| Maintenance | Actively maintained 52 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 54,458,936 / month, #543 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/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Typing :: Typed |
Evidence: altair-6.2.2-py3-none-any.whl
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