ContextLab/hypertools

Health v2 · maintenance only

Adoption not part of the score

1887 stars · 164 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

HyperTools is a Python library for visualizing and exploring high-dimensional data via dimensionality reduction, built on matplotlib, scikit-learn, and seaborn. It reduces datasets and produces plots in a single function call, with optional interactive plotly backends and tools for clustering, alignment, and text vectorization.

Use cases

• visualize high-dimensional data in 2D or 3D
• reduce dimensionality of a dataset and plot it in one call
• explore time-series trajectories in a lower-dimensional space
• cluster high-dimensional data with mixture models
• hyperalign multiple datasets to a common space
• visualize topic models of text corpora
• create interactive plots of embeddings in Colab or Kaggle

When to choose

• you want quick, single-call dimensionality-reduction visualizations of numeric or text data
• you work in Python with scikit-learn-style models and want plotting integrated with them
• you need to compare or align multiple high-dimensional datasets
• you want both static matplotlib and interactive plotly output

When to avoid

• you need production-grade dashboards or business intelligence reporting rather than exploratory plots
• you need fine-grained control over every plot aesthetic - use matplotlib or seaborn directly
• you work outside Python or need GPU-accelerated visualization of massive datasets
• you need real-time or streaming data visualization

Facets

library · maturity active

data-visualization machine-learning nlp data-visualization data-science machine-learning python cross-platform dimensionality-reduction high-dimensional-data matplotlib plotly clustering hyperalignment pca umap time-series topic-modeling

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ContextLab/hypertools")