Distribution Plots (histplot, kdeplot)

Visualizing data distributions helps you see how values are spread, identify trends, and detect outliers.

Seaborn provides two primary functions for this:

• histplot() – Displays the frequency distribution of a variable.
• kdeplot() – Plots a smooth curve representing the estimated probability density.

The histplot() function creates a histogram — a plot that shows how many data points fall within each numeric range, or bin.

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import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
sns.histplot(data=tips, x="total_bill")
plt.title("Distribution of Total Bills")
plt.show()

Key points:

• x defines the variable to visualize.
• The X-axis is divided into bins (numeric intervals).
• The height of each bar indicates the count of observations in that bin.

The kdeplot() function draws a smooth curve that estimates the probability density of the dataset.

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sns.kdeplot(data=tips, x="total_bill")
plt.title("KDE of Total Bills")
plt.show()

Key points:

• KDE stands for Kernel Density Estimate, a technique that generates a smooth curve from the data.
• Ideal for analyzing continuous distributions and overall trends.
• Often combined with histplot() to visualize both frequency and density together.

You can display both a histogram and a KDE curve in one plot by setting kde=True inside histplot():

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sns.histplot(data=tips, x="total_bill", kde=True)
plt.title("Total Bill Distribution with KDE")
plt.show()