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mapclassify

Classification Schemes for Choropleth Maps.

mapclassify v2.11.0 913.7K downloads/30d#4,737 on PyPI151
Permissive license BSD 3-Clause Active released

What it is and what it does

Mapclassify solves the problem of how to divide continuous geographic data into discrete classes for color-coded choropleth maps. It focuses on the algorithmic side—determining how many classes to use and which observations belong in each—leaving the actual map rendering to downstream visualization packages. The package is built on a stack of scientific Python tools: numpy for numerical operations, pandas for data handling, scikit-learn and scipy for statistical methods, and networkx for graph-based algorithms that some classification schemes may require.

Typically used in geographic data science workflows, it sits between raw spatial data and visualization, taking continuous values and producing class assignments that make choropleth maps readable and meaningful. The package is maintained as part of the PySAL ecosystem and is actively developed.

Use it for:

  • Determine optimal class boundaries for a choropleth map before rendering with downstream visualization tools.
  • Assign spatial data to color classes using different classification algorithms to compare visual patterns.
  • Classify continuous geographic data into discrete categories for thematic mapping.
  • Experiment with multiple classification schemes on the same dataset to find the most effective visualization.
  • Automate class assignment in batch geographic data processing pipelines.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Mapclassify implements classification schemes for choropleth maps, determining the optimal number of classes and assigning observations to them for use with geovisualization packages.

Yes. Mapclassify is actively maintained, has no known vulnerabilities, uses a permissive BSD 3-Clause license, and solves a specific problem in geographic data visualization. Install it if you are building choropleth maps and need to classify continuous spatial data into discrete color classes.

Install

mapclassify on PyPI

pip

pip install mapclassify

uv

uv add mapclassify

poetry

poetry add mapclassify

Installing mapclassify

Before you install

Low install friction with a pure Python wheel. Active maintenance with a recent release and steady development. Requires Python 3.12 or later.

License in practice

BSD 3-Clause permissive license allows commercial and private use with minimal restrictions, requiring only license and copyright notice retention.

Quickstart

pip install mapclassify
import mapclassify
# Pass data and classification scheme to mapclassify for class assignment

Requires Python 3.12 or later.

Verify before relying

  • Whether the package supports all common choropleth classification methods beyond what the description implies.
  • Performance characteristics when classifying large spatial datasets.
  • Whether it integrates directly with upstream packages or requires manual data passing.

Package facts

License BSD 3-Clause (permissive)
Python support supports the current Python release (>=3.12)
Install friction low — pure-Python wheel
Runtime dependencies 5 — networkx, numpy, pandas, scikit-learn, scipy
Maintenance actively maintained — 3 days since the last release
Last repo commit
First released
Downloads 913,662/month — #4,737 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: mapclassify-2.11.0-py3-none-any.whl

Keywords: spatial statistics, geovisualization

Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: GIS

Tags

choropleth map classificationgeographic data classificationmap class schemesspatial data binninggeovisualization classificationchoropleth color assignmentgeographic data quantization
geospatialcartographydata-classification

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