Correlation Heatmap#

A heatmap showing pairwise correlation between numerical features in the penguins dataset. Darker colors represent stronger positive or negative correlations.

import hvplot.pandas  # noqa

df = hvplot.sampledata.penguins("pandas")
# Compute correlation matrix
corr = df[[c for c in df.columns if c.split("_")[-1] in ("mm", "g")]].corr()
# Convert to long-form for heatmap
corr_df = corr.stack().reset_index()
corr_df.columns = ['variable_1', 'variable_2', 'correlation']

corr_df.hvplot.heatmap(
    x='variable_1',
    y='variable_2',
    C='correlation',
    cmap='coolwarm',
    clim=(-1, 1),
    title='Correlation Heatmap (Bokeh)',
    hover_tooltips=[
        ("Variable 1", "@variable_1"),
        ("Variable 2", "@variable_2"),
        ("Correlation", "@correlation")
    ]
)

import hvplot.pandas  # noqa
hvplot.extension('matplotlib')

df = hvplot.sampledata.penguins("pandas")
# Compute correlation matrix
corr = df[[c for c in df.columns if c.split("_")[-1] in ("mm", "g")]].corr()
# Convert to long-form for heatmap
corr_df = corr.stack().reset_index()
corr_df.columns = ['variable_1', 'variable_2', 'correlation']

corr_df.hvplot.heatmap(
    x='variable_1',
    y='variable_2',
    C='correlation',
    cmap='coolwarm',
    clim=(-1, 1),
    title='Correlation Heatmap (Matplotlib)',
)

corr_df.head()

	variable_1	variable_2	correlation
0	bill_length_mm	bill_length_mm	1.000000
1	bill_length_mm	bill_depth_mm	-0.235053
2	bill_length_mm	flipper_length_mm	0.656181
3	bill_length_mm	body_mass_g	0.595110
4	bill_depth_mm	bill_length_mm	-0.235053