boost-histogram
The Boost::Histogram Python wrapper.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python ≥3.10; numpy is a runtime dependency.
- Medium install friction due to compiled C++ extensions; pre-built wheels available for Python 3.10–3.15 across macOS (x86_64 and ARM64), Linux (x86_64 and aarch64), Windows, and musl-based systems.
- Active maintenance with a release 7 days ago.
License · maintenance · safety
BSD-3-Clause AND BSL-1.0 (permissive) — Dual-licensed under BSD-3-Clause and BSL-1.0 (permissive); both allow commercial and private use with minimal restrictions, making it safe for most projects.
last release 2026-08-07 (7 days) · last repo commit 2026-08-10 · 163 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 967,952 downloads/mo, #4,616 on PyPI
Alternatives
Verify before relying
import boost_histogram as bh
import numpy as np
hist = bh.Histogram(bh.axis.Regular(10, 0, 1))
hist.fill([0.1, 0.5, 0.9])
values = hist.values()- Performance comparison claims ('one of the fastest libraries') lack quantitative benchmarks in the fact sheet.
- Thread-safety guarantees for AtomicInt64 storage and growing axes under concurrent load are not detailed.
What it is and 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.
You 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).
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
boost-histogram on PyPI
Before you install
Medium install friction due to compiled C++ extensions; pre-built wheels available for Python 3.10–3.15 across macOS (x86_64 and ARM64), Linux (x86_64 and aarch64), Windows, and musl-based systems. Active maintenance with a release 7 days ago.
Requires Python ≥3.10; numpy is a runtime dependency.
License in practice
Dual-licensed under BSD-3-Clause and BSL-1.0 (permissive); both allow commercial and private use with minimal restrictions, making it safe for most projects.
Quickstart
import boost_histogram as bh
import numpy as np
hist = bh.Histogram(bh.axis.Regular(10, 0, 1))
hist.fill([0.1, 0.5, 0.9])
values = hist.values()Verify before relying
- Performance comparison claims ('one of the fastest libraries') lack quantitative benchmarks in the fact sheet.
- Thread-safety guarantees for AtomicInt64 storage and growing axes under concurrent load are not detailed.
Package facts
| License | BSD-3-Clause AND BSL-1.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 967,952 / month, #4,616 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: C++Programming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.15Programming Language :: Python :: Free ThreadingProgramming Language :: Python :: Free Threading :: 3 - StableProgramming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: MathematicsTopic :: Scientific/Engineering :: PhysicsTopic :: Software DevelopmentTopic :: UtilitiesTyping :: Typed |
Evidence: boost_histogram-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl; boost_histogram-1.8.0-cp310-cp310-macosx_11_0_arm64.whl; boost_histogram-1.8.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; boost_histogram-1.8.0-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; boost_histogram-1.8.0-cp310-cp310-musllinux_1_2_aarch64.whl; boost_histogram-1.8.0-cp310-cp310-musllinux_1_2_x86_64.whl; boost_histogram-1.8.0-cp310-cp310-win32.whl; boost_histogram-1.8.0-cp310-cp310-win_amd64.whl; boost_histogram-1.8.0-cp310-cp310-win_arm64.whl; boost_histogram-1.8.0-cp311-cp311-macosx_10_9_x86_64.whl; boost_histogram-1.8.0-cp311-cp311-macosx_11_0_arm64.whl; boost_histogram-1.8.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; boost_histogram-1.8.0-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; boost_histogram-1.8.0-cp311-cp311-musllinux_1_2_aarch64.whl; boost_histogram-1.8.0-cp311-cp311-musllinux_1_2_x86_64.whl; boost_histogram-1.8.0-cp311-cp311-win32.whl; boost_histogram-1.8.0-cp311-cp311-win_amd64.whl; boost_histogram-1.8.0-cp311-cp311-win_arm64.whl; boost_histogram-1.8.0-cp312-cp312-macosx_10_13_x86_64.whl; boost_histogram-1.8.0-cp312-cp312-macosx_11_0_arm64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “fast histogram library python”
- boost-histogramboost-histogram provides Python bindings to Boost::Histogram, a C++14…
- uhiUHI defines and documents a standard interface for histogram objects…
- hdrhistogramRecords and analyzes high-precision latency and performance…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also hist · hdrhistogram · uhi · fastjet · histoprint · fast-array-utils · spatial_image · correctionlib