Khisto

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Optimal Binning Histograms for Python

Khisto is a Python library for creating histograms using the Khiops optimal binning algorithm. Unlike standard histograms that use fixed-width bins or simple heuristics, Khisto automatically determines the optimal number of bins and their variable widths to best represent the underlying data distribution.

Documentation is available at khiopsml.github.io/khisto-python.

Standard Gaussian Heavy-tailed Pareto
Adaptive Gaussian histogram Adaptive Pareto histogram

Installation

pip install khisto

With matplotlib support:

pip install "khisto[matplotlib]"

Quick Start

import numpy as np
import matplotlib.pyplot as plt
from khisto.matplotlib import hist

# Generate 10,000 samples from a Normal distribution
normal_data = np.random.normal(size=10000)

# Plot an adaptive histogram
n, bins, patches = hist(normal_data)
plt.show()

# Generate 10,000 samples from a Pareto distribution
long_tail_data = np.random.pareto(3, size=10000)

# Plot an adaptive histogram on logarithmic axes.
n, bins, patches = hist(long_tail_data)
plt.xscale("symlog")
plt.yscale("log")
plt.show()

Development

# Clone repository
git clone https://github.com/KhiopsML/khisto-python.git
cd khisto-python

# Install with dev dependencies
uv sync --group dev --extra all

# Run tests
uv run pytest

License

BSD 3-Clause Clear License

Project links

Data verified by PyPI on Sep 4, 2026
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Key dates

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  • Released:
Newer release available (1.0.1)

2 maintainers

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Avatar for bruno-grz from gravatar.com bruno-grz Avatar for Elouen from gravatar.com Elouen

Credits

Author: Elouen Ginat

GitHub Statistics

Data verified by PyPI on Sep 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause-Clear
View SPDX License List

Requires

Python >=3.10

Provides Extra

matplotlib all

Tags

histogram binning optimal khiops statistics data-analysis density-estimation distribution numpy matplotlib visualization

Classifiers

Report project as malware

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