{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"}],"enrichment":{"capability":"Computes optimal class boundaries for numerical data using the Fisher-Jenks algorithm, partitioning a list or array into natural groups with minimal within-class variance.","skillfed_tags":["data-binning","statistical-classification","geospatial"],"use_cases":["Classify geographic or demographic data into natural groups for choropleth mapping or statistical analysis.","Discretize continuous variables into optimal bins for machine learning feature engineering.","Identify natural thresholds in sensor or time-series data for alerting or segmentation.","Group numerical measurements into categories with minimal within-group variation for reporting.","Partition income, age, or other population data into statistically meaningful brackets."],"what_it_does":"Jenkspy implements the Fisher-Jenks algorithm, a deterministic optimization method that finds the best way to partition numerical data into a specified number of classes by minimizing variance within each class. It accepts lists, tuples, arrays, or numpy arrays of integers or floats and returns the break points that define class boundaries. The package provides two interfaces: a simple `jenks_breaks()` function for direct break computation, and a scikit-learn-style `JenksNaturalBreaks` class for fitting, prediction, and grouping workflows.\n\nThe library is built as a C extension and ships with pre-compiled wheels for major platforms and Python versions, eliminating compilation for most users. It depends only on numpy and is marked Production/Stable with MIT licensing, making it suitable for integration into data analysis and geospatial classification pipelines.","worth_installing":"Yes. Jenkspy is a stable, production-ready library with no known vulnerabilities, permissive MIT licensing, and solid adoption. Install friction is moderate due to C compilation, but wheels eliminate this for most users. Use it if you need deterministic, optimal class boundaries for numerical data."},"id":"jenkspy","links":{"html":"https://skillfed.io/packages/jenkspy","md":"https://skillfed.io/packages/jenkspy.md","pypi":"https://pypi.org/project/jenkspy/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2024-06-03","license_spdx":null,"license_treatment":"permissive","name":"jenkspy","python_support":"supports_current","summary":"Compute Natural Breaks (Fisher-Jenks algorithm)"},"popularity":{"monthly_downloads":207474,"position":9550,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.1"}