# bmabey/pyLDAvis

Python library for interactive topic model visualization. Port of the R LDAvis package.

Repository: https://github.com/bmabey/pyLDAvis
Canonical: https://ross.abutalabs.com/products/pyldavis
Language: Jupyter Notebook
License: BSD-3-Clause
License Family: permissive
Last push: 2025-12-04T20:42:58+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 55, release rhythm 8, longevity 100
- inputs: {"age_days": 4164, "days_push": 272, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1850, forks 354 (observed 2026-08-28T04:05:44.340590+00:00)

## What it is
pyLDAvis is a Python library for interactive visualization of fitted LDA topic models, ported from the R LDAvis package. It renders web-based visualizations designed for Jupyter notebooks that can also be exported as standalone HTML files.

## Use cases
- visualize topics from a fitted LDA topic model
- interpret and explore topic model results in a Jupyter notebook
- export an interactive topic visualization as a standalone HTML file
- explain topic model outputs to non-technical stakeholders
- compare topic relevance and term distributions across topics

## When to choose
- you have a trained LDA topic model and need to interpret its topics
- you work in Jupyter notebooks and want interactive exploration
- you want a shareable HTML visualization of topic models

## When to avoid
- you use topic models other than LDA-style models without adaptation
- you need static publication-quality plots rather than interactive HTML
- you are not working in Python or a notebook environment

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, nlp, machine-learning
- domain: data-visualization, data-science, machine-learning
- platform: python, jvm-scripting
- tags: topic-modeling, lda, jupyter-notebook, interactive-visualization, html-export, natural-language-processing

## Member repositories
- bmabey/pyLDAvis (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:44.340590+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:16:56.985709+00:00, confidence not recorded.
  - readme: https://github.com/bmabey/pyLDAvis (fetched 2026-08-28T04:05:44.340590+00:00, sha ec3f3ed22dc4)
  - registry_pypi: https://pypi.org/pypi/pyldavis/json (fetched 2026-08-29T10:56:25.237862+00:00, sha e832114bbda3)
- Data as of 2026-08-30T08:39:29.467469+00:00.