ehrapy.plot.stratified_table_one

ehrapy.plot.stratified_table_one(edata, *, key='stratified_table_one', n_cols=2, width=380, height=260, cmap='Category10', show_pvalues=True, **kwargs)

Plot the stratified “Table 1” baseline comparison stored by stratified_table_one().

Produces one panel per variable laid out in an n_cols-column holoviews.Layout:

• Categorical variables — stacked horizontal bars per group (percentage within group).

• Continuous variables — one horizontal bar per group annotated with the summary (e.g. mean (SD) or median [Q1, Q3] if listed in nonnormal).

Each panel title includes the variable name and, when show_pvalues=True, the per-variable p-value as reported by tableone.

Parameters:

• edata (EHRData) – Central data object containing results stored by stratified_table_one().

• key (str, default: 'stratified_table_one') – Key under which results are stored in edata.uns (matches key_added).

• n_cols (int, default: 2) – Number of columns in the panel layout.

• width (int, default: 380) – Width of each panel in pixels.

• height (int, default: 260) – Height of each panel in pixels.

• cmap (str | list[str] | None, default: 'Category10') – Colormap (name or explicit color list) used for categories.

• show_pvalues (bool, default: True) – Whether to append the p-value to each panel title.

• **kwargs – Additional .opts(...) styling forwarded to every panel.

Return type:

Layout

Returns:

HoloViews Layout of per-variable panels.

Examples

>>> import ehrdata as ed
>>> import ehrapy as ep
>>> edata = ed.dt.diabetes_130_fairlearn(
...     columns_obs_only=["gender", "race", "age", "readmit_binary", "num_procedures"]
... )
>>> ep.tl.stratified_table_one(
...     edata,
...     groupby="readmit_binary",
...     columns=["gender", "race", "age", "num_procedures"],
...     nonnormal=["num_procedures"],
... )
>>> ep.pl.stratified_table_one(edata)