---
id: arviz-plots
version: "1.3.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# arviz-plots — ArviZ-plots provides ready to use and composable plots for Bayesian Workflow.

License: permissive · Maintenance: active · Downloads: 599.8K/mo

## What it is and what it does

arviz-plots is the visualization subpackage of ArviZ, a Python library for exploratory analysis of Bayesian models. It provides ready-to-use, composable plotting functions for posterior analysis, model checking, comparison, and diagnostics. The package is intentionally minimal—it depends only on xarray, numpy, arviz-base, and arviz-stats—and does not bundle any plotting backend by default. You choose which backend to install (matplotlib, bokeh, or plotly) based on your needs, keeping the core package lightweight.

The package is designed for Bayesian practitioners at all levels, from first-time modelers to experienced researchers. It integrates with the broader ArviZ ecosystem and supports modern Python versions (3.12+). With active maintenance, recent releases, and permissive licensing, it is positioned as a stable, community-driven tool for Bayesian workflow visualization.

Use it for:

- Visualize posterior distributions and trace plots for MCMC diagnostics and convergence assessment.
- Compare multiple Bayesian models using side-by-side or comparative plotting functions.
- Generate posterior predictive check plots to validate model fit against observed data.
- Create publication-ready diagnostic plots for Bayesian model reports and papers.
- Explore high-dimensional posterior samples interactively using bokeh or plotly backends.

## Worth the install?

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

arviz-plots provides visualization functions for Bayesian model analysis, handling posterior diagnostics, model comparison, and workflow visualization through composable plot components.

Yes. arviz-plots is actively maintained, has zero known vulnerabilities, supports current Python versions, and carries permissive licensing. Install it if you are doing Bayesian modeling with ArviZ and need visualization—but remember to also install your chosen backend (e.g., `pip install arviz-plots[matplotlib]`) to actually render plots.

## Install

pip install arviz-plots
uv add arviz-plots
poetry add arviz-plots

## Installing arviz-plots

Before you install: Low friction install with a pure-Python wheel. Active maintenance with a release 3 days old. Requires arviz-base and arviz-stats as runtime dependencies, and you must separately install a plotting backend (matplotlib, bokeh, or plotly) to use it.

License in practice: Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.

Quickstart:

pip install "arviz-plots[matplotlib]"

import arviz_plots as azp
# Use plotting functions from arviz_plots with arviz-base and arviz-stats data

Requires Python 3.12 or later. A plotting backend (matplotlib, bokeh, or plotly) must be installed separately via extras; arviz-plots alone provides no visualization without one.

Verify before relying:

- Specific plot types and functions available beyond the general posterior/diagnostic/comparison categories mentioned in the description.
- Whether arviz-plots can be used standalone or requires deeper integration with arviz-base and arviz-stats beyond data input.
- Performance characteristics or scalability limits for large posterior samples or complex models.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 599.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

bayesian model visualization, posterior analysis plots, mcmc diagnostics plotting, bayesian workflow visualization, arviz plotting backend, posterior predictive plots, model comparison visualization, bayesian-inference, visualization, mcmc-diagnostics

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