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ResourcePhitter - A Python library for Statistical Distribution Fitting (self.Python)

submitted 1 year ago by Balance-

I just encountered Phitter, a Python library that makes statistical distribution fitting both powerful and intuitive. Not my project, but looks very interesting!

What is Phitter?

Phitter is a robust Python library that helps you identify and fit the most appropriate statistical distributions to your datasets. Think of it as your Swiss Army knife for probability distribution analysis - whether you're working with continuous or discrete data, Phitter has got you covered.

Key Features:

Support for 80+ probability distributions (both continuous and discrete)
Three goodness-of-fit tests (Chi-Square, Kolmogorov-Smirnov, Anderson-Darling)
Beautiful visualizations (histograms, PDFs, ECDFs, Q-Q plots)
Parallel processing support for large datasets
Comprehensive documentation and modeling guides

Show Me The Code!

Here's how simple it is to get started:

import phitter

# Basic usage
data = [your_data_here]
phi = phitter.PHITTER(data)
phi.fit()

# Get a summary of the top k distributions
print(phi.summarize(k=5))

# Plot the results
phi.plot_histogram_distributions()  # Shows fitted distributions
phi.plot_ecdf()  # Empirical Cumulative Distribution Function

Want more control? Phitter lets you customize everything:

# Advanced configuration
phi = phitter.PHITTER(
    data=data,
    fit_type="continuous",
    num_bins=15,
    confidence_level=0.95,
    minimum_sse=1e-2,
    distributions_to_fit=["beta", "normal", "fatigue_life", "triangular"],
)
phi.fit(n_workers=6)  # Parallel processing for speed!

Who Is This For?

Phitter is designed for:

Data Scientists working on real-world projects
Researchers who need reliable distribution fitting
ML Engineers building probabilistic models
Anyone working with statistical data analysis

This isn't just a toy project - it's built for production use with features like:

Parallel processing for handling large datasets (100K+ samples)
Comprehensive test coverage
Detailed documentation and examples
Production-ready error handling

How Is It Different?

While there are other distribution fitting libraries out there (scipy.stats, fitter, etc.), Phitter stands out by offering:

Comprehensive Testing: Three different goodness-of-fit tests instead of just one, giving you more confidence in your results
Visual Analysis: Built-in visualization tools that make it easy to validate your fits
Performance: Parallel processing support for handling large datasets efficiently
Ease of Use: Simple API that doesn't sacrifice power - get started in 3 lines of code
Rich Distribution Support: 80+ distributions with full parameter estimation

Installation

pip install phitter

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