polars-statistics¶

High-performance statistical testing and regression for Polars DataFrames, powered by Rust.

Features¶

Native Polars Expressions — Full support for group_by, over, and lazy evaluation
Statistical Tests — Parametric, non-parametric, distributional, correlation, categorical, and TOST equivalence tests
Regression Models — OLS, Ridge, Elastic Net, WLS, Quantile, Isotonic, GLMs, ALM (24+ distributions)
Formula Syntax — R-style formulas with polynomial and interaction effects
Diagnostics — Condition number, quasi-separation detection, count sparsity checks
High Performance — Rust-powered with zero-copy data transfer and automatic parallelization

Installation¶

pip install polars-statistics

Quick Example¶

import polars as pl
import polars_statistics as ps

df = pl.DataFrame({
    "group": ["A"] * 50 + ["B"] * 50,
    "y": [...],
    "x1": [...],
    "x2": [...],
})

# OLS regression per group
result = df.group_by("group").agg(
    ps.ols("y", "x1", "x2").alias("model")
)

# Extract results
result.with_columns(
    pl.col("model").struct.field("r_squared"),
    pl.col("model").struct.field("coefficients"),
)

Use from Rust¶

polars-statistics builds as both a Python extension (cdylib) and a Rust library (rlib). Other Rust crates can depend on it directly and call the same statistical and regression code that the Python plugin uses — no Python boundary, no FFI overhead.

[dependencies]
polars = { version = "0.52", features = ["lazy", "partition_by"] }
polars-statistics = { version = "0.5", default-features = false }

default-features = false disables the python feature, so pyo3 and numpy are not linked. Every Polars expression has a public Rust counterpart named <name>_fit (e.g. ols_fit, vif_fit, logistic_predict_fit) under polars_statistics::expressions. See the Use from Rust section of the README for a full runnable example and the complete list of _fit entry points.

Examples¶

Example	Description
Hypothesis Testing	Check assumptions, choose tests, interpret results
Regression Workflow	Fit, summarize, predict, diagnose
Group-wise Analysis	`group_by` and `over` patterns
A/B Testing	Proportions, equivalence, per-segment analysis

What's in the Docs¶

Section	Description
Getting Started	Installation and first examples
API Conventions	Common patterns across all functions
Statistical Tests	30+ hypothesis tests
Regression	Linear, GLM, ALM, dynamic models
Model Classes	Direct Python class access
R Validation	R-vs-Rust numerical agreement with reference values
Output Structures	Return type definitions