moderndive (Python)

The Python companion package for ModernDive: Statistical Inference via Data Science — a faithful port of the R moderndive and infer packages to a modern Python data-science stack (polars, plotly, plotnine, statsmodels).

📖 Documentation (with runnable examples): https://moderndive.readthedocs.io

It is intentionally pure-Python (no compiled extensions) so it installs under Pyodide via micropip for in-browser execution.

Installation

pip install moderndive          # from PyPI (once published)
# or, from source:
pip install git+https://github.com/moderndive/moderndive-python

What's inside

• A tidy simulation-inference grammar mirroring R infer: specify → hypothesize → generate → calculate, plus fit() for multiple regression, observe(), and assume() (theoretical t/z/F/Chisq). specify() is also available as a DataFrame method, so you can write df.specify(...) just like R's df %>% specify(...). calculate(stat=...) takes the full infer vocabulary or any custom callable test statistic. Summaries via get_p_value / get_confidence_interval (percentile, SE, bias-corrected); British-spelling and short aliases included.
• Dual-engine plots: visualize / shade_p_value / shade_confidence_interval (and every plot helper) take engine="plotly" (default, interactive) or engine="plotnine" — same code, your choice of output.
• Theory-based wrapper tests: t_test, prop_test, chisq_test, t_stat, chisq_stat, plus the moderndive.theory module.
• Regression & summary helpers mirroring R moderndive: get_regression_table, get_regression_points, get_regression_summaries, get_correlation, pop_sd, tidy_summary, count_missing (built on statsmodels where relevant, returning polars frames), plus the model plots gg_parallel_slopes / geom_parallel_slopes and gg_categorical_model / geom_categorical_model, and pairplot (the GGally::ggpairs analog).
• Sampling: rep_slice_sample / rep_sample_n for sampling-distribution activities.
• 58 datasets: load_*() loaders returning polars DataFrames (the moderndive/infer, nycflights23, gapminder, ISLR2, and FiveThirtyEight datasets used in the book).

Quick start

import moderndive as md
from moderndive import get_p_value, visualize, shade_p_value

spotify = md.load_spotify_metal_deephouse()

# Observed difference in popularity rates (metal − deep house)
obs = (
    spotify
    .specify(formula="popular_or_not ~ track_genre", success="popular")
    .calculate(stat="diff in props", order=("metal", "deep-house"))
)

# Permutation null distribution
null = (
    spotify
    .specify(formula="popular_or_not ~ track_genre", success="popular")
    .hypothesize(null="independence")
    .generate(reps=1000, type="permute", seed=76)
    .calculate(stat="diff in props", order=("metal", "deep-house"))
)

get_p_value(null, obs_stat=obs, direction="right")

# Visualize — interactive plotly by default; engine="plotnine" for ggplot-style
visualize(null) + shade_p_value(obs_stat=obs, direction="right")

Development

This repo uses uv.

uv sync --extra dev          # create the environment
make test                    # run the test suite (enforces 100% coverage)
make build-data              # rebuild the bundled Parquet datasets (needs R; see tools/)
make build                   # build the wheel/sdist

The test suite is held at 100% statement coverage (enforced in CI via --cov-fail-under=100).

License

MIT. The ModernDive book content is licensed CC-BY-NC-SA 4.0; this software package is MIT-licensed.