{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"}],"enrichment":{"capability":"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.","skillfed_tags":["topic-modeling","visualization","nlp"],"use_cases":["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."],"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\u2014useful 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.\n\nThe 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.","worth_installing":"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\u2014the 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."},"id":"pyldavis","links":{"html":"https://skillfed.io/packages/pyldavis","md":"https://skillfed.io/packages/pyldavis.md","pypi":"https://pypi.org/project/pyldavis/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2023-04-23","license_spdx":null,"license_treatment":"permissive","name":"pyLDAvis","python_support":"supports_current","summary":"Interactive topic model visualization. Port of the R package."},"popularity":{"monthly_downloads":90311,"position":13602,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.4.1"}