explore_missing_values_plot

source

explore_missing_values_plot(
    df,
    *,
    time=None,
    entity=None,
    by='time',
    no_factors=False,
    binary=False,
    title=None,
    subtitle=None,
)

Heatmap of missing-value frequency by variable and panel dimension.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Data frame containing the data.	required
time	str \| None	Time identifier column. Defaults to the panel `time` declared via :func:`expdpy.set_panel`. Required when `by="time"`; must not contain missing values.	`None`
entity	str \| None	Cross-sectional (unit) identifier column. Defaults to the panel `entity`. Required when `by="entity"`; must not contain missing values.	`None`
by	Literal['time', 'entity']	Whether to aggregate missingness over `"time"` periods (the default) or over `"entity"` units.	`'time'`
no_factors	bool	If `True`, limit the plot to numeric/logical variables.	`False`
binary	bool	If `True`, show only whether values are missing (any) rather than the fraction.	`False`

Returns

Name	Type	Description
	MissingValuesResult	`df` (the missingness matrix, rows = levels, columns = variables) and `fig`.

Examples

Basic — fraction of missing values by variable and year:

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
ex.explore_missing_values_plot(df).fig

Advanced — missingness by unit, restricted to numeric variables, shown as a flag:

import expdpy as ex
from expdpy.data import load_kuznets, load_kuznets_data_def

df = ex.set_labels(load_kuznets(), load_kuznets_data_def(), set_panel=True)
ex.explore_missing_values_plot(
    df, by="entity", no_factors=True, binary=True
).fig