hist
Hist classes and utilities
Decision gist · record as of 2026-08-14
Yes. Hist is actively maintained, has no known vulnerabilities, installs with low friction, and is BSD-3-Clause licensed. It is well-suited for anyone doing data binning or histogram analysis in Python, especially those working in scientific or physics domains. The named-axes interface and plotting integration make it more approachable than raw boost-histogram for exploratory work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction installation as a pure Python wheel.
- Actively maintained with a release 4 days ago.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is a permissive license allowing commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
last release 2026-08-10 (4 days) · last repo commit 2026-08-13 · 139 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 744,588 downloads/mo, #5,172 on PyPI
Alternatives
Verify before relying
pip install hist
from hist import Hist
h = Hist.new.Reg(10, 0, 1, name="x").Int64()
h.fill(x=[0.1, 0.5, 0.9])
print(h)- Whether optional extras like [plot] and [fit] add significant dependencies or install friction.
- Performance characteristics when working with very large histograms or high-dimensional data.
- Compatibility with WebAssembly environments beyond the noted [fit] limitation.
What it is and what it does
Hist is a front-end library for boost-histogram that simplifies histogram creation and manipulation for data analysis workflows. It extends boost-histogram with named axes, simpler construction syntax, and convenience methods for common operations like density computation, projection, and sorting. The library is designed for analysts and researchers who need to bin, aggregate, and visualize data without wrestling with low-level histogram APIs.
The package includes plotting routines for 1D and 2D histograms, support for histogram stacks, and an extended indexing system (UHI+) that allows data-coordinate slicing and rebinning. It depends on boost-histogram for the core binning engine, numpy for array operations, and optional extras for plotting and fitting. The library is actively maintained, supports modern Python versions (3.10+), and is part of the Scikit-HEP ecosystem.
Use it for
- Exploratory data analysis: quickly bin and visualize distributions with named axes and automatic plotting.
- Physics analysis: work with multidimensional histograms using named axes and UHI+ indexing for event selection.
- Statistical summaries: compute density arrays, project histograms to lower dimensions, or convert to profile histograms.
- Stacked comparisons: group related histograms and plot them together with automatic legend generation.
- Interactive notebooks: leverage built-in Jupyter representation and quick plotting for iterative analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Hist is actively maintained, has no known vulnerabilities, installs with low friction, and is BSD-3-Clause licensed. It is well-suited for anyone doing data binning or histogram analysis in Python, especially those working in scientific or physics domains. The named-axes interface and plotting integration make it more approachable than raw boost-histogram for exploratory work.
Install
hist on PyPI
Before you install
Low friction installation as a pure Python wheel. Actively maintained with a release 4 days ago. Requires Python 3.10 or later and five runtime dependencies including boost-histogram and numpy, all standard scientific packages.
Requires Python 3.10 or later.
License in practice
BSD-3-Clause is a permissive license allowing commercial use, modification, and distribution with minimal restrictions—suitable for most projects.
Quickstart
pip install hist
from hist import Hist
h = Hist.new.Reg(10, 0, 1, name="x").Int64()
h.fill(x=[0.1, 0.5, 0.9])
print(h)Verify before relying
- Whether optional extras like [plot] and [fit] add significant dependencies or install friction.
- Performance characteristics when working with very large histograms or high-dimensional data.
- Compatibility with WebAssembly environments beyond the noted [fit] limitation.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesboost-histogramhistoprintnumpypackagingtyping-extensions |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 744,588 / month, #5,172 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 :: Science/ResearchOperating System :: OS IndependentProgramming 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 :: 3.15Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: MathematicsTopic :: Scientific/Engineering :: PhysicsTyping :: Typed |
Evidence: hist-2.11.0-py3-none-any.whl
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See also boost-histogram · uhi · histoprint · hdrhistogram · hvplot · plotext · ggplot · termplotlib