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Practice

Distribution Plots (histplot, kdeplot)

Visualizing data distributions helps you understand how your data is spread, spot patterns, and detect potential outliers.

Seaborn provides two main functions for this:

  • histplot(): Shows the frequency distribution of a dataset.
  • kdeplot(): Shows a smooth curve of the estimated probability density.

Using histplot()​

The histplot() function creates a histogram that shows how many data points fall into each range (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 specifies the variable to plot.
  • The plot is divided into bins (intervals) along the X-axis.
  • The height of each bar shows how many observations fall into that bin.

Using kdeplot()​

The kdeplot() function displays a smooth curve that represents the estimated probability density of the data.

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, which creates a smooth curve from the data.
  • Useful for understanding trends in continuous data.
  • Can be combined with histplot() for better context.

Combining Histogram and KDE​

You can combine both in a single histplot() by setting kde=True:

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

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