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Hist is an analyst-friendly front-end for boost-histogram, designed for Python 3.7+ (3.6 users get version 2.4). See what's new.

Slideshow of features. See docs/ for text if the image is not readable.


You can install this library from PyPI with pip:

python3 -m pip install "hist[plot]"

If you do not need the plotting features, you can skip the [plot] extra.


Hist currently provides everything boost-histogram provides, and the following enhancements:

  • Hist augments axes with names:

    • name= is a unique label describing each axis.
    • label= is an optional string that is used in plotting (defaults to name if not provided).
    • Indexing, projection, and more support named axes.
    • Experimental NamedHist is a Hist that disables most forms of positional access, forcing users to use only names.
  • The Hist class augments bh.Histogram with simpler construction:

    • flow=False is a fast way to turn off flow for the axes on construction.
    • Storages can be given by string.
    • storage= can be omitted, strings and storages can be positional.
    • data= can initialize a histogram with existing data.
    • Hist.from_columns can be used to initialize with a DataFrame or dict.
    • You can cast back and forth with boost-histogram (or any other extensions).
  • Hist support QuickConstruct, an import-free construction system that does not require extra imports:

    • Use<axis>().<axis>().<storage>().
    • Axes names can be full (Regular) or short (Reg).
    • Histogram arguments (like data=) can go in the storage.
  • Extended Histogram features:

    • Direct support for .name and .label, like axes.
    • .density() computes the density as an array.
    • .profile(remove_ax) can convert a ND COUNT histogram into a (N-1)D MEAN histogram.
    • .sort(axis) supports sorting a histogram by a categorical axis. Optionally takes a function to sort by.
  • Hist implements UHI+; an extension to the UHI (Unified Histogram Indexing) system designed for import-free interactivity:

    • Uses j suffix to switch to data coordinates in access or slices.
    • Uses j suffix on slices to rebin.
    • Strings can be used directly to index into string category axes.
  • Quick plotting routines encourage exploration:

    • .plot() provides 1D and 2D plots (or use plot1d(), plot2d())
    • .plot2d_full() shows 1D projects around a 2D plot.
    • .plot_ratio(...) make a ratio plot between the histogram and another histogram or callable.
    • .plot_pull(...) performs a pull plot.
    • .plot_pie() makes a pie plot.
    • .show() provides a nice str printout using Histoprint.
  • Stacks: work with groups of histograms with identical axes

    • Stacks can be created with h.stack(axis), using index or name of an axis (StrCategory axes ideal).
    • You can also create with hist.stacks.Stack(h1, h2, ...), or use from_iter or from_dict.
    • You can index a stack, and set an entry with a matching histogram.
    • Stacks support .plot() and .show(), with names (plot labels default to original axes info).
    • Stacks pass through .project, *, +, and -.
  • New modules

    • intervals supports frequentist coverage intervals.
  • Notebook ready: Hist has gorgeous in-notebook representation.

    • No dependencies required


from hist import Hist

# Quick construction, no other imports needed:
h = (
  .Reg(10, 0 ,1, name="x", label="x-axis")
  .Var(range(10), name="y", label="y-axis")

# Filling by names is allowed:
h.fill(y=[1, 4, 6], x=[3, 5, 2])

# Names can be used to manipulate the histogram:
h[{"y": 0.5j + 3, "x": 5j}]

# You can access data coordinates or rebin with a `j` suffix:
h[.3j:, ::2j] # x from .3 to the end, y is rebinned by 2

# Elegant plotting functions:


From a git checkout, either use nox, or run:

python -m pip install -e .[dev]

See Contributing guidelines for information on setting up a development environment.


We would like to acknowledge the contributors that made this project possible (emoji key):

Henry Schreiner

🚧 💻 📖

Nino Lau

🚧 💻 📖

Chris Burr


Nick Amin


Eduardo Rodrigues


Andrzej Novak


Matthew Feickert


Kyle Cranmer


Daniel Antrim


Nicholas Smith


Michael Eliachevitch


Jonas Eschle


This project follows the all-contributors specification.



This library was primarily developed by Henry Schreiner and Nino Lau.

Support for this work was provided by the National Science Foundation cooperative agreement OAC-1836650 (IRIS-HEP) and OAC-1450377 (DIANA/HEP). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.