Python Pandas - Group the swarms by two categorical variables with Seaborn

Swarm Plot in Seaborn is used to draw a categorical scatterplot with non-overlapping points. The seaborn.swarmplot() function is used for this. To group the swarms by two categorical variables, set those variables in the swarmplot() using the x, y or hue parameters.

Sample Dataset

We'll create a sample cricket dataset to demonstrate grouping by two categorical variables ?

import seaborn as sb
import pandas as pd
import matplotlib.pyplot as plt

# Create sample cricket data
data = {
    'Role': ['Batsman', 'Batsman', 'Bowler', 'Bowler', 'All-rounder', 'All-rounder',
             'Batsman', 'Bowler', 'All-rounder', 'Batsman', 'Bowler', 'All-rounder'],
    'Matches': [45, 32, 28, 41, 38, 29, 52, 35, 44, 39, 31, 47],
    'Academy': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']
}

dataFrame = pd.DataFrame(data)
print(dataFrame)

         Role  Matches Academy
0     Batsman       45       A
1     Batsman       32       B
2      Bowler       28       A
3      Bowler       41       B
4  All-rounder       38       A
5  All-rounder       29       B
6     Batsman       52       A
7      Bowler       35       B
8  All-rounder       44       A
9     Batsman       39       B
10     Bowler       31       A
11 All-rounder       47       B

Basic Swarm Plot with Two Categorical Variables

Use the x, y, and hue parameters to group by two categorical variables ?

import seaborn as sb
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Role': ['Batsman', 'Batsman', 'Bowler', 'Bowler', 'All-rounder', 'All-rounder',
             'Batsman', 'Bowler', 'All-rounder', 'Batsman', 'Bowler', 'All-rounder'],
    'Matches': [45, 32, 28, 41, 38, 29, 52, 35, 44, 39, 31, 47],
    'Academy': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']
}

dataFrame = pd.DataFrame(data)

# Set the theme
sb.set_theme(style="whitegrid")

# Create swarm plot grouped by Role (x-axis) and Academy (hue)
plt.figure(figsize=(8, 6))
sb.swarmplot(x="Role", y="Matches", hue="Academy", data=dataFrame)
plt.title("Cricket Matches by Player Role and Academy")
plt.show()

Customizing the Swarm Plot

You can customize colors, size, and other properties of the swarm plot ?

import seaborn as sb
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Role': ['Batsman', 'Batsman', 'Bowler', 'Bowler', 'All-rounder', 'All-rounder',
             'Batsman', 'Bowler', 'All-rounder', 'Batsman', 'Bowler', 'All-rounder'],
    'Matches': [45, 32, 28, 41, 38, 29, 52, 35, 44, 39, 31, 47],
    'Academy': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']
}

dataFrame = pd.DataFrame(data)

# Create customized swarm plot
plt.figure(figsize=(10, 6))
sb.swarmplot(x="Role", y="Matches", hue="Academy", data=dataFrame, 
             palette="Set2", size=8, alpha=0.8)

plt.title("Player Performance by Role and Academy")
plt.xlabel("Player Role")
plt.ylabel("Number of Matches")
plt.legend(title="Academy")
plt.show()

Alternative Grouping Approach

You can also swap the categorical variables to see different perspectives ?

import seaborn as sb
import pandas as pd
import matplotlib.pyplot as plt

# Create sample data
data = {
    'Role': ['Batsman', 'Batsman', 'Bowler', 'Bowler', 'All-rounder', 'All-rounder',
             'Batsman', 'Bowler', 'All-rounder', 'Batsman', 'Bowler', 'All-rounder'],
    'Matches': [45, 32, 28, 41, 38, 29, 52, 35, 44, 39, 31, 47],
    'Academy': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']
}

dataFrame = pd.DataFrame(data)

# Group by Academy (x-axis) and Role (hue)
plt.figure(figsize=(8, 6))
sb.swarmplot(x="Academy", y="Matches", hue="Role", data=dataFrame)
plt.title("Matches by Academy and Player Role")
plt.show()

Key Parameters

Conclusion

Seaborn's swarmplot() effectively groups data by two categorical variables using x and hue parameters. This creates clear visualizations showing distributions across multiple categories without overlapping points.