polars-sdist
Statistical distributions as native Polars expressions. Built with Rust and PyO3 for zero-copy, vectorized computation over Polars DataFrames.
Install
pip install polars-sdist
Quick start
import polars as pl
from polars_sdist import SdistNamespace as sdist
df = pl.DataFrame({"x": [-1.96, 0.0, 1.96]})
# CDF, PDF, PPF, SF, ln_pdf — all as expressions
df.with_columns(
cdf=sdist(pl.col("x")).normal_cdf(mu=0, sigma=1),
pdf=sdist(pl.col("x")).normal_pdf(mu=0, sigma=1),
sf=sdist(pl.col("x")).normal_sf(mu=0, sigma=1),
)
Inverse CDF (PPF)
df = pl.DataFrame({"p": [0.025, 0.5, 0.975]})
df.with_columns(
z=sdist(pl.col("p")).normal_ppf(mu=0, sigma=1),
)
Discrete distributions
df = pl.DataFrame({"k": [0, 1, 2, 3, 4, 5]}).cast({"k": pl.Float64})
df.with_columns(
pmf=sdist(pl.col("k")).poisson_pmf(lambda_=3.0),
cdf=sdist(pl.col("k")).poisson_cdf(lambda_=3.0),
)
Random sampling (fixed parameters)
import polars_sdist
s = polars_sdist.sample_normal(n=10_000, mu=0, sigma=1, seed=42)
s = polars_sdist.sample_beta(n=10_000, alpha=2, beta=5, seed=42)
Random sampling (column parameters)
df = pl.DataFrame({
"mu": [0.0, 10.0, -10.0],
"sigma": [1.0, 0.5, 2.0],
})
df.with_columns(
sample=sdist(pl.col("mu")).sample_normal(sigma=pl.col("sigma"), seed=42),
)
Supported distributions
Continuous
| Distribution | Parameters | CDF | PPF | SF | ln_pdf | |
|---|---|---|---|---|---|---|
| Normal | mu, sigma |
x | x | x | x | x |
| Log-Normal | mu, sigma |
x | x | x | x | x |
| Beta | alpha, beta |
x | x | x | x | x |
| Gamma | shape, rate |
x | x | x | x | x |
| Exponential | lambda_ |
x | x | x | x | x |
| Cauchy | location, scale |
x | x | x | x | x |
| Chi-Squared | df |
x | x | x | x | x |
| Student's t | df |
x | x | x | x | x |
| Fisher-Snedecor (F) | d1, d2 |
x | x | x | x | x |
| Gumbel | location, scale |
x | x | x | x | x |
| Inverse Gamma | shape, scale |
x | x | x | x | x |
| Laplace | location, scale |
x | x | x | x | x |
| Pareto | shape, scale |
x | x | x | x | x |
| Triangular | min, max, mode |
x | x | x | x | x |
| Uniform | a, b |
x | x | x | x | x |
| Weibull | shape, scale |
x | x | x | x | x |
Discrete
| Distribution | Parameters | PMF | CDF | PPF | SF |
|---|---|---|---|---|---|
| Bernoulli | p |
x | x | x | x |
| Binomial | n, p |
x | x | x | x |
| Geometric | p |
x | x | x | x |
| Hypergeometric | pop_size, success_states, draws |
x | x | x | x |
| Negative Binomial | r, p |
x | x | x | x |
| Poisson | lambda_ |
x | x | x | x |
| Discrete Uniform | a, b |
x | x | x | x |
Sampling-only
These distributions support random sampling but not PDF/CDF:
PERT, Skew-Normal, Inverse Gaussian, Frechet, Zeta, Zipf
Performance
Benchmarked end-to-end against scipy/numpy on 1M elements. Median of 5 runs after warmup.
| Distribution | Operation | polars-sdist | scipy | Speedup |
|---|---|---|---|---|
| normal | 8.6 ms | 36.8 ms | 4.3x | |
| normal | ppf | 20.9 ms | 48.4 ms | 2.3x |
| normal | sample | 3.6 ms | 7.6 ms | 2.1x |
| normal | cdf | 23.3 ms | 37.2 ms | 1.6x |
| lognormal | 17.1 ms | 63.8 ms | 3.7x | |
| lognormal | sample | 7.2 ms | 12.7 ms | 1.8x |
| lognormal | ppf | 27.8 ms | 42.3 ms | 1.5x |
| lognormal | cdf | 32.5 ms | 41.4 ms | 1.3x |
| binomial | pmf | 32.0 ms | 86.2 ms | 2.7x |
| binomial | sample | 20.1 ms | 30.5 ms | 1.5x |
| chi-squared | sample | 10.0 ms | 16.7 ms | 1.7x |
| chi-squared | 49.2 ms | 60.9 ms | 1.2x | |
| chi-squared | cdf | 90.3 ms | 110.9 ms | 1.2x |
| beta | ppf | 517.8 ms | 601.1 ms | 1.2x |
| beta | 80.3 ms | 89.5 ms | 1.1x |
polars-sdist is faster in 15 of 19 benchmarks. Areas where scipy is faster (beta CDF/sample, chi-squared PPF, binomial CDF) use highly optimized C/Fortran special-function implementations.
Run it yourself:
pip install scipy
python benchmarks/bench_distributions.py --sizes 1000000
License
MIT
Metadata
Release files for polars-sdist 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| polars_sdist-0.1.1.tar.gz | 38.1 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| polars_sdist-0.1.1-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | PyPy 3.11 | PyPy 3.11 7.3 | Linux glibc 2.17+ x86-64 | Details |
| polars_sdist-0.1.1-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| polars_sdist-0.1.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ x86-64 | Details |
| polars_sdist-0.1.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| polars_sdist-0.1.1-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| polars_sdist-0.1.1-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 25.2 MB
Release files / polars_sdist-0.1.1.tar.gz
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