Distribution Plots (displot, histplot)

Why distribution plots matter

Distribution plots answer:

What values are most common?
Is the data skewed?
Are there outliers?
Do groups behave differently?

`histplothistplot` (single plot)

histplot

import seaborn as sns
import matplotlib.pyplot as plt
 
values = [55, 60, 62, 63, 65, 67, 70, 72, 76, 80, 81, 85, 90, 92, 95]
 
plt.figure(figsize=(7, 4))
sns.histplot(values, bins=8, kde=False)
plt.title("Histogram")
plt.xlabel("Value")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

histplot

import seaborn as sns
import matplotlib.pyplot as plt
 
values = [55, 60, 62, 63, 65, 67, 70, 72, 76, 80, 81, 85, 90, 92, 95]
 
plt.figure(figsize=(7, 4))
sns.histplot(values, bins=8, kde=False)
plt.title("Histogram")
plt.xlabel("Value")
plt.ylabel("Count")
plt.tight_layout()
plt.show()

Add KDE on top

histplot + kde

import seaborn as sns
import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
sns.histplot(values, bins=8, kde=True)
plt.title("Histogram + KDE")
plt.tight_layout()
plt.show()

histplot + kde

import seaborn as sns
import matplotlib.pyplot as plt
 
plt.figure(figsize=(7, 4))
sns.histplot(values, bins=8, kde=True)
plt.title("Histogram + KDE")
plt.tight_layout()
plt.show()

`displotdisplot` (figure-level)

displotdisplot can create more complex plots and handles facets.

displot

import seaborn as sns
 
# Seaborn includes sample datasets
 
tips = sns.load_dataset("tips")
 
sns.displot(data=tips, x="total_bill", bins=20, kde=True)

displot

import seaborn as sns
 
# Seaborn includes sample datasets
 
tips = sns.load_dataset("tips")
 
sns.displot(data=tips, x="total_bill", bins=20, kde=True)

Compare distributions by group

Compare groups

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.histplot(data=tips, x="total_bill", hue="sex", bins=20, kde=True, element="step")
plt.title("Total bill distribution by sex")
plt.tight_layout()
plt.show()

Compare groups

import seaborn as sns
import matplotlib.pyplot as plt
 
tips = sns.load_dataset("tips")
 
plt.figure(figsize=(7, 4))
sns.histplot(data=tips, x="total_bill", hue="sex", bins=20, kde=True, element="step")
plt.title("Total bill distribution by sex")
plt.tight_layout()
plt.show()

Tips

Use the same bin settings when comparing groups.
KDE is a smooth estimate; it can hide small details.
For heavy outliers, consider log scaling.

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