explore_histogram

source

explore_histogram(
    df,
    var=None,
    *,
    bins=30,
    kde=False,
    normal=False,
    title=None,
    subtitle=None,
)

Histogram of a numeric variable, optionally with density overlays.

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Data frame containing `var`.	required
var	str \| None	Numeric column to bin. Defaults to the declared main outcome (:func:`expdpy.set_roles`) when omitted.	`None`
bins	int	Number of equal-width bins.	`30`
kde	bool	Overlay a Gaussian kernel-density estimate of the distribution.	`False`
normal	bool	Overlay a normal curve with the sample mean and standard deviation.	`False`

Returns

Name	Type	Description
	HistogramResult	`df` (columns `bin_left`, `bin_right`, `count`) and the Plotly `fig`.

Notes

The density overlays are drawn on the Density scale, so requesting either one opens the figure in Density view; the built-in Count/Density toggle hides the overlays in Count view and shows them in Density view.

Examples

Basic — a 30-bin histogram of a numeric variable, with readable labels from the data dictionary:

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_histogram(df, "gini_regional").fig

Advanced — overlay a kernel-density estimate and a normal curve:

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_histogram(df, "log_gdp_pc", bins=40, kde=True, normal=True).fig