pyLDAvis
Interactive topic model visualization. Port of the R package.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a pre-fitted LDA topic model (typically from gensim or another LDA library) and its associated corpus and dictionary objects to visualize.
- 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 · maintenance · safety
BSD-3-Clause (permissive) — 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.
last release 2023-04-23 (1209 days) · last repo commit 2025-12-04 · 1,852 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 90,311 downloads/mo, #13,602 on PyPI
Alternatives
Verify before relying
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()- 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.
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 on this page. Verify before relying on it.
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
pyldavis on PyPI
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.
Requires a pre-fitted LDA topic model (typically from gensim or another LDA library) and its associated corpus and dictionary objects to visualize.
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()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 the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesnumpyscipypandasjoblibjinja2numexprfuncyscikit-learngensimsetuptools |
| Maintenance | Aging 1,209 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 90,311 / month, #13,602 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.9 |
Evidence: pyLDAvis-3.4.1-py3-none-any.whl
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