---
id: pyldavis
version: "3.4.1"
license: BSD-3-Clause
license_treatment: permissive
maintenance: aging
---

# pyLDAvis — Interactive topic model visualization. Port of the R package.

License: permissive · Maintenance: aging · Downloads: 90.3K/mo

## What it is and what it does

pyLDAvis is a Python port of an R visualization package designed to help you understand and explore topics discovered by LDA (latent Dirichlet allocation) topic modeling. It takes a fitted LDA model and generates an interactive web-based visualization that shows the topics, their key terms, and their relationships—useful for validating model quality and communicating results. The visualization works inside Jupyter notebooks for interactive exploration or can be saved as a standalone HTML file for sharing with others who don't have Python installed.

The package depends on standard data science libraries (numpy, scipy, pandas, scikit-learn, gensim, joblib) and uses Jinja2 to render the interactive HTML interface. It's marked Production/Stable and supports Python 3.9+, but maintenance has slowed since its last release in April 2023. The low install friction and permissive BSD license make it straightforward to add to existing projects.

Use it for:

- Visualize and validate topics from a gensim LDA model to assess model quality before publication or deployment.
- Explore topic distributions across a document corpus interactively in a Jupyter notebook during model development.
- Export topic model results as a standalone HTML file to share findings with non-technical stakeholders.
- Debug and interpret LDA model behavior by examining term-topic relationships and topic similarity.
- Communicate research findings on text corpora by embedding interactive topic visualizations in reports.

## Worth the install?

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

pyLDAvis creates interactive web-based visualizations for interpreting topics in fitted LDA (latent Dirichlet allocation) topic models, extracting and displaying model information for exploration in Jupyter notebooks or standalone HTML files.

Yes, if you work with LDA topic models and need to visualize or explore them. The package is stable, has low install friction, carries no known vulnerabilities, and solves a specific problem well. The aging maintenance (last release April 2023) is a minor concern but not a blocker—the core functionality is mature and the repository is not archived. Install it as part of a topic modeling workflow, but verify compatibility with your specific LDA library and Python version before relying on it in production.

## Install

pip install pyldavis
uv add pyldavis
poetry add pyldavis

## Installing pyLDAvis

Before you install: Low friction: pure Python wheel, 10 runtime dependencies including common data science libraries (numpy, scipy, pandas, scikit-learn, gensim). Aging maintenance status—last release April 2023—but repository remains active and the package is marked Production/Stable.

License in practice: BSD-3-Clause (permissive): you can use, modify, and distribute this package freely in commercial and private projects with minimal restrictions, provided you retain the license notice.

Quickstart:

pip install pyldavis

import pyldavis
# Assuming you have a fitted LDA model from gensim:
# vis = pyldavis.gensim_models.prepare(lda_model, corpus, dictionary)
# vis.show()

Requires a pre-fitted LDA topic model (typically from gensim or another LDA library) and its associated corpus and dictionary objects to visualize.

Verify before relying:

- Whether the package works with modern versions of all 10 runtime dependencies without compatibility issues.
- Current state of the Jupyter notebook integration given the aging maintenance status.
- Whether the standalone HTML export feature remains fully functional.
- Repository activity and commit frequency since the last release in April 2023.

## Package facts

- License: BSD-3-Clause (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: aging
- Downloads: 90.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

topic model visualization, LDA visualization interactive, latent dirichlet allocation viz, topic modeling exploration, text topic analysis visualization, jupyter notebook topic models, lda model interpretation, topic-modeling, visualization, nlp

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