Seaborn | Categorical Plots

Last Updated : 15 Jun, 2026

Plots help visualize relationships between variables. Seaborn provides various functions for analyzing categorical data using datasets such as tips, which contains information about restaurant customers and their tips.

Implementation 

1. Import Required Libraries

  • Seaborn provides built-in categorical plotting functions.
  • Matplotlib is used to display the plots.
Python 
import seaborn as sns
import matplotlib.pyplot as plt

2. Load the Dataset

The built-in tips dataset is loaded. It contains information about restaurant customers, including bill amount, tip amount, gender, smoking status and dining time.

Python 
tips = sns.load_dataset("tips")
tips.head()
output
Dataset

3. Create a Strip Plot

  • Shows the distribution of observations.
  • Helps identify clusters and outliers.
Python 
sns.stripplot(x="day", y="total_bill", data=tips)
plt.show()

Output:

strip-plot
Strip plot

4. Create a Swarm Plot

  • Provides a clearer view of data distribution.
  • Makes individual observations easier to analyze.
Python 
sns.swarmplot(x="day", y="total_bill", data=tips)
plt.show()

Output:

swarm-plot
Swarm Plot

5. Create a Box Plot

  • Shows median and spread of data.
  • Helps detect outliers.
  • Useful for comparing categories.
Python 
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()

Output:

boxplot
Box plot

6. Create a Violin Plot

  • Shows data distribution and density.
  • Helps understand the shape of the data.
Python 
sns.violinplot(x="day", y="total_bill", data=tips)
plt.show()

Output:

violin-plot
Violin Plot

7. Create a Bar Plot

  • Compares average values across categories.
  • Useful for identifying trends.
Python 
sns.barplot(x="day", y="total_bill", data=tips)
plt.show()

Output:

barplot
Bar Plot

8. Create a Count Plot

  • Displays category frequencies.
  • Helps understand dataset composition.
Python 
sns.countplot(x="day", data=tips)
plt.show()

Output:

countplot
Countplot

Download full code from here

Comment
Article Tags:
Machine Learning
python

Explore