---
id: ridgeplot
version: "0.6.0"
license: The MIT License (MIT) Copyright (c) 2021-2024 Tomas Pereira de Vasconcelos Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# ridgeplot — Beautiful ridgeline plots in python

License: permissive · Maintenance: active · Downloads: 135.2K/mo

## What it is and what it does

ridgeplot is a Python visualization library that wraps Plotly to simplify creation of ridgeline plots—a technique for displaying multiple probability distributions or time-series densities in a single, compact figure. It handles kernel density estimation (KDE) and layout automatically, letting you focus on data rather than plotting mechanics.

The package is built on numpy for numerical work, statsmodels for KDE computation, and Plotly for rendering. You pass in arrays of samples (one per row or category), and ridgeplot generates an interactive figure with sensible defaults for bandwidth, spacing, and color. The result is a Plotly Figure object, so you can extend it with standard Plotly methods for customization. It's designed for exploratory data analysis, scientific visualization, and publication-quality graphics.

Use it for:

- Visualize temperature or weather patterns across months or years as overlapping density curves.
- Compare probability distributions across survey response categories or demographic groups.
- Display time-series density evolution (e.g., stock price distributions by year or month).
- Create publication-ready ridgeline plots for scientific papers or reports with minimal code.
- Explore multimodal or skewed distributions across many conditions in a single, readable plot.

## Worth the install?

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

ridgeplot creates interactive ridgeline plots using Plotly, allowing you to visualize distributions across multiple categories or time periods with a simple interface.

Yes. ridgeplot is actively maintained, has no known vulnerabilities, installs with low friction, and solves a specific visualization task well. The MIT license is unrestricted. Use it if you need ridgeline plots; it's more convenient than hand-coding KDE and layout in raw Plotly.

## Install

pip install ridgeplot
uv add ridgeplot
poetry add ridgeplot

## Installing ridgeplot

Before you install: Low friction: pure Python wheel with four runtime dependencies (numpy, plotly, statsmodels, typing-extensions). Actively maintained with recent releases; last commit 2026-08-10.

License in practice: MIT license (permissive): you can use, modify, and distribute ridgeplot freely in commercial and private projects with minimal restrictions, provided you include the license notice.

Quickstart:

pip install ridgeplot

import numpy as np
from ridgeplot import ridgeplot

my_samples = [np.random.normal(n, size=900) for n in range(6, 0, -2)]
fig = ridgeplot(samples=my_samples)
fig.show()

Requires Python 3.10 or later.

Verify before relying:

- Performance characteristics with very large datasets or high-dimensional samples.
- Accessibility features or export formats beyond Plotly's standard interactive HTML output.

## Package facts

- License: The MIT License (MIT) Copyright (c) 2021-2024 Tomas Pereira de Vasconcelos Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 135.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ridgeline plots python, ridge plot visualization, plotly ridgeplot, distribution density plots, joyplot python, multiple distribution plots, kde ridge visualization, data-visualization, plotly-wrapper, statistical-graphics

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