Simple and fast histogramming in Python accelerated with OpenMP with help from pybind11.

pygram11 provides functions for very fast histogram calculations (and the variance in each bin) in one and two dimensions. The API is very simple; documentation can be found here (you'll also find some benchmarks there).

Binary wheels are provided for Linux, macOS, and Windows. They can be installed from PyPI via pip:

pip install pygram11

For installation via the conda package manager pygram11 is part of conda-forge.

conda install pygram11 -c conda-forge

You need is a C++14 compiler and OpenMP. If you are using a relatively modern GCC release on Linux then you probably don't have to worry about the OpenMP dependency. If you are on macOS, you can install libomp from Homebrew (pygram11 does compile on Apple Silicon devices with Python version >= 3.9 and libomp installed from Homebrew). With those dependencies met, simply run:

git clone https://github.com/douglasdavis/pygram11.git --recurse-submodules
cd pygram11
pip install .

Or let pip handle the cloning procedure:

pip install git+https://github.com/douglasdavis/pygram11.git@main

Tests are run on Python versions >= 3.8 and binary wheels are provided for those versions.

A histogram (with fixed bin width) of weighted data in one dimension:

>>> rng = np.random.default_rng(123)
>>> x = rng.standard_normal(10000)
>>> w = rng.uniform(0.8, 1.2, x.shape[0])
>>> h, err = pygram11.histogram(x, bins=40, range=(-4, 4), weights=w)

A histogram with fixed bin width which saves the under and overflow in the first and last bins:

>>> x = rng.standard_normal(1000000)
>>> h, __ = pygram11.histogram(x, bins=20, range=(-3, 3), flow=True)

where we've used __ to catch the None returned when weights are absent. A histogram in two dimensions with variable width bins:

>>> x = rng.standard_normal(1000)
>>> y = rng.standard_normal(1000)
>>> xbins = [-2.0, -1.0, -0.5, 1.5, 2.0, 3.1]
>>> ybins = [-3.0, -1.5, -0.1, 0.8, 2.0, 2.8]
>>> h, err = pygram11.histogram2d(x, y, bins=[xbins, ybins])

Manually controlling OpenMP acceleration with context managers:

>>> with pygram11.omp_disabled():  # disable all thresholds.
...     result, _ = pygram11.histogram(x, bins=10, range=(-3, 3))
...
>>> with pygram11.omp_forced(key="thresholds.var1d"):  # force a single threshold.
...     result, _ = pygram11.histogram(x, bins=[-3, -2, 0, 2, 3])
...

Histogramming multiple weight variations for the same data, then putting the result in a DataFrame (the input pandas DataFrame will be interpreted as a NumPy array):

>>> N = 10000
>>> weights = pd.DataFrame({"weight_a": np.abs(rng.standard_normal(N)),
...                         "weight_b": rng.uniform(0.5, 0.8, N),
...                         "weight_c": rng.uniform(0.0, 1.0, N)})
>>> data = rng.standard_normal(N)
>>> count, err = pygram11.histogram(data, bins=20, range=(-3, 3), weights=weights, flow=True)
>>> count_df = pd.DataFrame(count, columns=weights.columns)
>>> err_df = pd.DataFrame(err, columns=weights.columns)

I also wrote a blog post with some simple examples.

• boost-histogram provides Pythonic object oriented histograms.
• Simple and fast histogramming in Python using the NumPy C API: fast-histogram (no variance or overflow support).
• To calculate histograms in Python on a GPU, see cupy.histogram.

If there is something you'd like to see in pygram11, please open an issue or pull request.