Khisto
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 |
|---|---|
 |
 |
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
On Windows, install Visual Studio Build Tools 2022 with the Desktop development with C++ workload before running the commands below.
# 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
Release files for khisto 1.0.2
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Total release size: 4.2 MB
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| Size | 341.9 kB |
| Tags | Python 3 Windows ARM64 |
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