stats3

R's statistical tests on Polars, for Polars, pandas and tidy3 data. Write a test the way R users do, run it once per group, and get R's numbers back as a table in the same kind of frame you passed in.

pip install stats3              # Polars data
pip install "stats3[pandas]"    # pandas data too

stats3 needs Python 3.12+, Polars and SciPy 1.18+ (the first SciPy whose distribution functions match R to full precision).

Every test takes R's arguments and defaults, drops missing values as R does, raises R's errors and warnings, and is checked against R 4.6.

One test per group

import polars as pl
from stats3 import *

penguins = pl.read_csv(
    "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/main/inst/extdata/penguins.csv",
    null_values="NA",
)

t_test(penguins, "body_mass_g ~ sex", by="species")

┌───────────┬─────────────┬────────────┬────────────┬────────────┬─────────────┬─────────────┐
│ species   ┆ estimate    ┆ statistic  ┆ df         ┆ p_value    ┆ conf_low    ┆ conf_high   │
│ ---       ┆ ---         ┆ ---        ┆ ---        ┆ ---        ┆ ---         ┆ ---         │
│ str       ┆ f64         ┆ f64        ┆ f64        ┆ f64        ┆ f64         ┆ f64         │
╞═══════════╪═════════════╪════════════╪════════════╪════════════╪═════════════╪═════════════╡
│ Adelie    ┆ -674.657534 ┆ -13.126286 ┆ 135.693579 ┆ 6.4023e-26 ┆ -776.301162 ┆ -573.013906 │
│ Chinstrap ┆ -411.764706 ┆ -5.207734  ┆ 62.575105  ┆ 0.000002   ┆ -569.790344 ┆ -253.739068 │
│ Gentoo    ┆ -805.094686 ┆ -14.761368 ┆ 116.643504 ┆ 1.8678e-28 ┆ -913.113043 ┆ -697.07633  │
└───────────┴─────────────┴────────────┴────────────┴────────────┴─────────────┴─────────────┘

(The table also has estimate1, estimate2, method and alternative.) by= is dplyr's .by: one test per group, one row per test, groups in order.

Any frame in, the same frame out

as_="polars", "pandas" or "tidy3" asks for another type. Factor levels come from the data: pandas category and Polars Enum columns order the levels of y ~ g (the first level is estimate1) and of the groups.

Each test can be called with the data first, or piped:

t_test(penguins, "body_mass_g ~ sex", by="species")       # data first
penguins >> t_test("body_mass_g ~ sex", by="species")     # pipe
penguins_pd.pipe(t_test("body_mass_g ~ sex", by="species"))  # pandas .pipe

With tidy3 the tests are pipe steps like any verb, and its groups and factor levels carry through:

from tidy3 import *

tidy(penguins)
>> group_by(species, island)
>> wilcox_test(body_mass_g ~ sex)
>> group_by(species)
>> mutate(p_adj = p_adjust(p_value, "BH"))

In a notebook: R's shorthand

In Jupyter or IPython, after from stats3 import * (or %load_ext stats3.jupyter), from the next cell on formulas need no quotes and columns are bare names, inside stats3's calls:

penguins >> t_test(body_mass_g ~ sex, by=species)
penguins >> binom_test(sex == "male")
cor_test(penguins, bill_length_mm, bill_depth_mm, method="kendall")

A name the notebook defines keeps its value; any other bare name in a stats3 call is a column. With tidy3 loaded the two packages share the shorthand. In a .py script write "body_mass_g ~ sex" and pl.col("sex") == "male".

Models

lm() and glm() take R's formulas and print a summary you can read at a glance: the coefficients with their 95% intervals, then the fit.

fit = lm(penguins, "body_mass_g ~ flipper_length_mm + species")
fit

lm(body_mass_g ~ flipper_length_mm + species)   n = 342
┌───────────────────┬──────────────┬──────────────┬──────────────┬───────────┬────────────┐
│ term              ┆ estimate     ┆ conf_low     ┆ conf_high    ┆ std_error ┆ p_value    │
╞═══════════════════╪══════════════╪══════════════╪══════════════╪═══════════╪════════════╡
│ (Intercept)       ┆ -4031.476891 ┆ -5180.506847 ┆ -2882.446935 ┆ 584.15134 ┆ 2.5515e-11 │
│ flipper_length_mm ┆ 40.705401    ┆ 34.664683    ┆ 46.746119    ┆ 3.07102   ┆ 1.4016e-32 │
│ speciesChinstrap  ┆ -206.51012   ┆ -320.066724  ┆ -92.953517   ┆ 57.730646 ┆ 0.000398   │
│ speciesGentoo     ┆ 266.809603   ┆ 79.425129    ┆ 454.194077   ┆ 95.263741 ┆ 0.005392   │
└───────────────────┴──────────────┴──────────────┴──────────────┴───────────┴────────────┘
residual SE 375.5 on 338 df · R² 0.7826 · adjusted R² 0.7807
F(3, 338) = 405.7, p < 2.2e-16

broom's verbs give tables (in the data's frame type), and by= fits one model per group:

tidy(fit, conf_int=True)    # term, estimate, std_error, statistic, p_value, conf_low, conf_high
glance(fit)                 # r_squared, adj_r_squared, sigma, statistic, p_value, AIC, BIC, ...
augment(fit)                # the rows with .fitted, .resid, .hat, .sigma, .cooksd, .std_resid
anova(fit)                  # sequential sums of squares; anova(fit1, fit2) compares models
anova(fit, type=2)          # car's type II tests; type=3 with contrasts="sum"
predict(fit, new_rows, interval="prediction")

glance(lm(penguins, "body_mass_g ~ flipper_length_mm", by="species"))   # one row per species

glm(penguins, "sex ~ body_mass_g + species", family=binomial())
glm(mtcars, "carb ~ hp + offset(log(gear))", family=poisson())

Formulas follow R: a * b, a:b, (a + b)^2, 0 + / - 1, ., log(x), I(x^2), factor(cyl), offset(...), and cbind(successes, failures) for binomial models. Factors use treatment contrasts with the first level as the reference (a pandas category or Polars Enum sets the order), with R's column names (contrasts="sum" gives R's contr.sum, as type III tests want), and redundant columns are reported as aliased, as in R. For a glm, tidy(conf_int=True) gives R's profile-likelihood intervals (confint); conf_method="wald" gives confint.default. Families: gaussian, binomial, poisson, Gamma, inverse_gaussian, quasibinomial, quasipoisson, with R's links. fit >> tidy() works too, and with tidy3 loaded, tidy(data) still makes a tidy3 frame.

The engine is stats3's own: weighted least squares by QR for lm, and a port of R's glm.fit (same starting values, convergence rule and step halving) for glm. On 1,000,000 rows with 13 coefficients lm takes about 0.16 s and a Poisson glm about 0.6 s, against about 1.1 s and 1.9 s for statsmodels' formula interface.

What's in it

Next: estimated marginal means, negative binomial, robust and quantile regression, ordinal models, and mixed models.

How it works, and how it is checked

Most tests need only a few numbers per group: counts, means, variances, rank sums, cross-tabulated counts. Polars computes them for every group in one lazy query, and SciPy's distribution functions (Polars has none) turn them into p-values for all groups at once. Over 1,000 groups of 1,000 rows most tests run 4-30 times faster than calling SciPy once per group. Tests that need every value (Shapiro-Wilk, Kendall's tau) run per group. stats3 needs only Polars and SciPy.

tests/r_oracle_cases.R runs the same tests in R; pytest compares every column, to 1e-9 relative, on Polars, lazy Polars, pandas and tidy3 input. Where R's own numerics are approximate (Tukey's distribution, intervals found with uniroot, the noncentral t above ncp 37.62, the asymptotic Kolmogorov series), stats3 computes to full precision and the test compares at R's accuracy, with the reason written next to the tolerance. Without R, the SciPy comparisons still run.

pip install -e ".[dev]"
pytest