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Versatile, high-performance histogram toolkit for Numpy.

Project description


histbook computes histograms from Numpy arrays. It differs from most other histogramming tools in that its histograms are primarily tables of numbers, rather than display graphics. Histograms can be filled and refilled iteratively through a large dataset, or in parallel and later combined with addition*. Histograms have arbitrarily many dimensions with convenient methods for selecting, rebinning, and projecting into lower-dimensional spaces.

Axis dimensions are managed by algebraic expressions, rather than string labels or index positions, so they are computable: an axis named x + y requires two Numpy arrays, x and y, which will be added before filling the histogram. Expressions in different axes or different histograms in the same “book” (a collection of named histograms) are computed in an optimized way, reusing subexpressions wherever possible for quicker filling without giving up clarity.

Histogram data may be exported to a variety of formats, such as Pandas, ROOT, and HEPData. It can also be plotted with Vega-Lite, which makes short work of projecting many dimensions of data as overlays and trellises.

(*In this respect, histbook is like histogramming packages developed for particle physics, from CERN HBOOK in the 1970’s (name similarity intended) to modern-day ROOT.)


Install histbook like any other Python package:

pip install histbook --user

or similar (use sudo, --user, virtualenv, or pip-in-conda if you wish).

Strict dependencies:


See the project homepage for a tutorial.

Interactive tutorial

Run this tutorial on Binder.

Reference documentation

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histbook-1.2.5.tar.gz (667.8 kB view hashes)

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