set_roles

source

set_roles(df, *, outcome=None, covariates=None)

Declare the main outcome and covariates on df and return it.

The roles are stored under df.attrs["expdpy_roles"] so that subsequent explore_* / analyze_* calls (and the no-code apps) can default to them when their primary variable argument is omitted. Explicit arguments to those functions still take precedence.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	The data frame (modified in place — its `attrs` are updated and the same object is returned).	required
outcome	str \| None	Name of the main outcome (dependent) variable, or `None` to leave it unset.	`None`
covariates	str \| Sequence[str] \| None	Name(s) of the main covariate(s) — a single column or a sequence — or `None` to leave them unset.	`None`

Returns

Name	Type	Description
	pandas.DataFrame	The same `df`, with `df.attrs["expdpy_roles"]` updated.

Examples

Declare the key variables once, then explore/analyze without repeating them:

import expdpy as ex
from expdpy.data import load_kuznets

df = ex.set_panel(load_kuznets(), entity="country", time="year")
df = ex.set_roles(df, outcome="gini_regional", covariates=["log_gdp_pc"])
ex.explore_scatter_plot(df).fig          # x = log_gdp_pc, y = gini_regional
ex.analyze_regression_table(df).etable    # gini_regional ~ log_gdp_pc

	gini_regional
	(1)
coef
log_gdp_pc	-0.005** (0.002)
Intercept	0.323*** (0.019)
stats
Observations	880
R²	0.008
Significance levels: * p < 0.05, p < 0.01, * p < 0.001. Format of coefficient cell: Coefficient (Std. Error)