explore_correlation_plot

source

explore_correlation_plot(df, *, style='heatmap', title=None, subtitle=None)

Visualise a correlation matrix (Pearson above, Spearman below the diagonal).

Parameters

Name	Type	Description	Default
df	pd.DataFrame	Data frame with at least two numeric/logical variables and five observations.	required
style	Literal['heatmap', 'ellipse']	"heatmap" (default) renders a Plotly heatmap; "ellipse" reproduces R’s corrplot(method = "ellipse") look with one ellipse glyph per cell.	'heatmap'

Returns

Name	Type	Description
	CorrelationGraphResult	df_corr/df_prob/df_n plus the Plotly fig.

Examples

Basic — a correlation heatmap for a few variables (slice the columns first, then attach labels so the axes read nicely):

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

df = ex.set_labels(
    load_kuznets()[["gini_regional", "gdp_pc", "log_gdp_pc"]],
    load_kuznets_data_def(),
)
ex.explore_correlation_plot(df).fig

Advanced — the ellipse style (R corrplot look), with the underlying correlation matrix available from .df_corr:

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

df = ex.set_labels(
    load_kuznets()[["gini_regional", "gdp_pc", "log_gdp_pc", "trade_share"]],
    load_kuznets_data_def(),
)
result = ex.explore_correlation_plot(df, style="ellipse")
result.fig
result.df_corr

	gini_regional	gdp_pc	log_gdp_pc	trade_share
gini_regional	1.000000	0.202154	-0.087637	-0.154822
gdp_pc	-0.190296	1.000000	0.824505	-0.082082
log_gdp_pc	-0.190296	1.000000	1.000000	0.027653
trade_share	-0.148334	0.058316	0.058316	1.000000