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Heatmaps for Correlation

Correlation recap

Correlation describes how two numeric variables move together.

+1: strong positive relationship
-1: strong negative relationship
0: no linear relationship

Correlation heatmap

Correlation heatmap

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
penguins = sns.load_dataset("penguins").dropna()
 
corr = penguins[[
    "bill_length_mm",
    "bill_depth_mm",
    "flipper_length_mm",
    "body_mass_g",
]].corr()
 
plt.figure(figsize=(6, 4))
sns.heatmap(corr, annot=True, cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Penguins correlation")
plt.tight_layout()
plt.show()

Correlation heatmap

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
 
penguins = sns.load_dataset("penguins").dropna()
 
corr = penguins[[
    "bill_length_mm",
    "bill_depth_mm",
    "flipper_length_mm",
    "body_mass_g",
]].corr()
 
plt.figure(figsize=(6, 4))
sns.heatmap(corr, annot=True, cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Penguins correlation")
plt.tight_layout()
plt.show()

Tips

Correlation is not causation.
Outliers can inflate correlations.
For non-linear relationships, correlation can be misleading.

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