ggplot
ggplot for python
Decision gist · record as of 2026-08-14
No. The package is abandoned (latest release 2016-09-29, repository archived) and almost certainly incompatible with modern Python and libraries. While it has no dependencies and a permissive license, the lack of maintenance makes it unsuitable for new projects. Consider alternatives or test thoroughly before use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- The package was last updated in 2016 and may not be compatible with modern Python or dependency versions; testing on your target environment is essential.
- Installation is straightforward with no runtime dependencies.
- However, the package is abandoned—the repository is archived and the last release was in 2016.
License · maintenance · safety
BSD (permissive) — ggplot is licensed under BSD (permissive), which allows commercial and private use with minimal restrictions. This poses no licensing barrier to adoption.
last release 2016-09-29 (3606 days) · last repo commit 2023-01-21 · 3,687 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 89,904 downloads/mo, #13,628 on PyPI
Alternatives
Verify before relying
pip install ggplot
from ggplot import ggplot, aes, geom_density, scale_color_brewer, facet_wrap
ggplot(diamonds, aes(x='price', color='clarity')) + \
geom_density() + \
scale_color_brewer(type='div', palette=7) + \
facet_wrap('cut')- Whether the package works with current Python versions or modern data science libraries.
- What specific data structures or formats are required for the input data argument.
- Whether all ggplot2 features are implemented or which major features remain unported.
What it is and what it does
ggplot is a Python port of the R ggplot2 library, implementing the grammar of graphics paradigm for building plots. Rather than calling functions to draw specific chart types, you compose plots by layering components: a data source, aesthetic mappings (which columns map to which visual properties), geometric layers (points, lines, densities), scales, and facets. The library was designed to bring R's declarative plotting approach to Python, though it does not aim for feature-parity with ggplot2.
The package has been abandoned since its latest release on 2016-09-29 and receives no maintenance. While it has no runtime dependencies and installs cleanly, its age means it was built for older Python versions (2.7 and 3.3 era) and may not work reliably with modern environments or current data science libraries. The repository is archived with a last commit on 2023-01-21.
Use it for
- Build multi-layered statistical plots by composing data, aesthetics, and geometric layers in a declarative style.
- Create faceted plots that split data across multiple subplots based on categorical variables.
- Apply consistent color scales and themes across multiple plot types using a unified grammar.
- Prototype exploratory data visualizations when you prefer a grammar-of-graphics approach over imperative plotting.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The package is abandoned (latest release 2016-09-29, repository archived) and almost certainly incompatible with modern Python and libraries. While it has no dependencies and a permissive license, the lack of maintenance makes it unsuitable for new projects. Consider alternatives or test thoroughly before use.
Install
ggplot on PyPI
Before you install
Installation is straightforward with no runtime dependencies. However, the package is abandoned—the repository is archived and the last release was in 2016. No maintenance or updates are forthcoming.
The package was last updated in 2016 and may not be compatible with modern Python or dependency versions; testing on your target environment is essential.
License in practice
ggplot is licensed under BSD (permissive), which allows commercial and private use with minimal restrictions. This poses no licensing barrier to adoption.
Quickstart
pip install ggplot
from ggplot import ggplot, aes, geom_density, scale_color_brewer, facet_wrap
ggplot(diamonds, aes(x='price', color='clarity')) + \
geom_density() + \
scale_color_brewer(type='div', palette=7) + \
facet_wrap('cut')Verify before relying
- Whether the package works with current Python versions or modern data science libraries.
- What specific data structures or formats are required for the input data argument.
- Whether all ggplot2 features are implemented or which major features remain unported.
Package facts
| License | BSD permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 3,606 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 89,904 / month, #13,628 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.3Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: ggplot-0.11.5-py2.py3-none-any.whl
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See also plotnine · mizani · bqscales · bqplot · altair · hist · glum