---
id: corner
version: "2.3.0"
license: BSD 2-Clause License
license_treatment: permissive
maintenance: active
---

# corner — Make some beautiful corner plots

License: permissive · Maintenance: active · Downloads: 572.6K/mo

## What it is and what it does

Corner is a Python plotting library that creates corner plots—a standard visualization in Bayesian inference and parameter estimation where each panel shows either a 1D histogram (diagonal) or 2D scatterplot (off-diagonal) for a set of variables. It wraps matplotlib to handle the repetitive layout and styling work, letting you focus on your data rather than plot construction.

The package is designed for scientists and statisticians working with multivariate analyses. It takes a data array and produces a publication-ready figure showing marginal distributions and pairwise correlations across all parameter dimensions at once, making it easy to spot relationships and validate results.

Use it for:

- Visualize posterior samples from Bayesian inference to inspect parameter correlations and marginal constraints.
- Create corner plots from parameter estimation output to diagnose convergence and identify parameter relationships.
- Generate publication-quality figures for scientific papers showing multivariate distributions and covariances.
- Explore high-dimensional data by examining all pairwise relationships and individual marginals in one plot.
- Compare parameter estimates across different models or inference methods side-by-side.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Generates publication-quality corner plots (scatterplot matrices) for visualizing multivariate data distributions and correlations, commonly used in Bayesian analysis and parameter estimation.

Yes. Corner is actively maintained, has no known vulnerabilities, installs with minimal friction, and solves a specific visualization problem for statistical and scientific analysis workflows. Its permissive BSD 2-Clause License and stable API (Production/Stable status) make it a low-risk, reliable choice for anyone working with multivariate data visualization.

## Install

pip install corner
uv add corner
poetry add corner

## Installing corner

Before you install: Low friction install with a single runtime dependency (matplotlib). Active maintenance with recent releases; last commit 2026-08-10 and 573 repository stars indicate ongoing support.

License in practice: BSD 2-Clause License is permissive and imposes minimal restrictions; you can use, modify, and distribute this package with only attribution and liability disclaimer requirements.

Quickstart:

pip install corner
import corner
import matplotlib.pyplot as plt

fig = corner.corner(data)

Requires Python 3.9 or later; matplotlib must be installed and configured for your display environment.

Verify before relying:

- Whether the package handles very large datasets efficiently or has known performance limits.
- Support for interactive features or customization options beyond what the documentation excerpt describes.
- Specific use cases and typical workflows the package is designed for.

## Package facts

- License: BSD 2-Clause License (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 572.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

corner plots scatterplot matrix, multivariate distribution visualization, parameter posterior plots, bayesian analysis plotting, correlation matrix visualization, mcmc chain visualization, triangle plots python, visualization, scientific-computing, statistical-analysis

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