Skip to main content
Practice

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.

Using histplot()​

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

Basic Histogram
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.

Using kdeplot()​

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

Basic KDE Plot
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.

Combining Histogram and KDE​

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

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

Want to learn more?

Join CodeFriends Plus membership or enroll in a course to start your journey.