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tidydraws

A tidybayes-inspired data layer for declarative Bayesian visualisation in Python

tidydraws turns MCMC output (ArviZ) into tidy Polars frames that are ready to plot — one .to_pandas() away from any ggplot-like backend. It does no plotting itself. Three functions, three spaces:

Function	Space	Plot archetype
parameter_draws()	parameter	density, forest, scatter
prediction_draws()	prediction	ribbon + line, fit + data
compare_draws()	comparison	prior vs posterior, intervals

Install

With uv:

uv add tidydraws

With pip:

pip install tidydraws

If you want the latest functionality merged into main but not yet released, install directly from GitHub:

pip install git+https://github.com/drbenvincent/tidydraws.git

Or with uv:

uv add git+https://github.com/drbenvincent/tidydraws.git

Why tidydraws?

Plotting MCMC output in Python means manually slicing xarray dimensions, iterating groups, and aligning coordinates — imperative, verbose, error-prone. R's tidybayes solved this with a data layer that respects parameter space vs prediction space. tidydraws brings that to Python on Polars.

Backend-agnostic: tidydraws returns Polars DataFrames. Call .to_pandas() to bridge to lets-plot, plotnine, or any library that takes pandas. See the examples for both lets-plot and plotnine versions.

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Inspired by tidybayes for R.