{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/4"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"}],"enrichment":{"capability":"boost-histogram provides Python bindings to Boost::Histogram, a C++14 library for fast histogram creation and manipulation with support for multiple axis types, storage modes, and advanced indexing.","skillfed_tags":["histogram-binning","scientific-computing","data-analysis"],"use_cases":["Build multidimensional histograms for physics or astronomy data analysis with fast filling and slicing.","Accumulate weighted or mean-valued samples using specialized storage types for statistical analysis.","Create histograms with custom axis transforms (log, sqrt, power) for non-linear binning schemes.","Project and rebin histograms on the fly to explore data along different dimensions.","Integrate histogram operations into Dask workflows for distributed data processing.","Plot histograms via UHI-compatible libraries like mplhep without manual conversion."],"what_it_does":"boost-histogram is a Python wrapper around Boost::Histogram, a high-performance C++ histogram library. It lets you create and manipulate histograms with flexible axis types (regular, variable-width, categorical, boolean), multiple storage backends (double, integer, weighted, mean), and advanced indexing following the UHI (Universal Histogram Indexing) protocol. The package is designed for scientific computing and data analysis workflows where speed and expressiveness matter.\n\nYou compose histograms by specifying axes and storage types, fill them with data (including weighted or sample data), and query results via NumPy array views or specialized accessors. It supports slicing, projection, rebinning, and arithmetic operations on histograms. The library integrates with the broader scikit-HEP ecosystem, including Hist (an analyst-friendly wrapper), mplhep (plotting), and dask-histogram (distributed computing).","worth_installing":"Yes. boost-histogram is actively maintained, permissively licensed, widely available as pre-built wheels, and has no known vulnerabilities. It is a solid choice if you need fast, flexible histogramming for scientific or data analysis work. Install friction is moderate due to C++ compilation, but wheels mitigate this for common platforms and Python versions."},"id":"boost-histogram","links":{"html":"https://skillfed.io/packages/boost-histogram","md":"https://skillfed.io/packages/boost-histogram.md","pypi":"https://pypi.org/project/boost-histogram/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":"BSD-3-Clause AND BSL-1.0","license_treatment":"permissive","name":"boost-histogram","python_support":"supports_current","summary":"The Boost::Histogram Python wrapper."},"popularity":{"monthly_downloads":967952,"position":4616,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.8.0"}