Python Pandas - Draw a swarm plot and control swarm order by passing an explicit order with Seaborn

A swarm plot in Seaborn creates a categorical scatterplot with non-overlapping points, making it ideal for visualizing the distribution of values across categories. You can control the order of categories using the order parameter to customize how data appears on the plot.

Creating Sample Data

Let's create sample cricket data to demonstrate swarm plot ordering ?

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

# Create sample cricket data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia', 'Victoria', 
                'Western Australia', 'South Australia', 'Victoria', 'Western Australia',
                'South Australia', 'Victoria', 'Western Australia', 'South Australia'],
    'Matches': [25, 30, 15, 40, 35, 20, 28, 45, 18, 32, 38, 22]
}

df = pd.DataFrame(data)
print(df.head())

        Academy  Matches
0       Victoria       25
1  Western Australia       30
2   South Australia       15
3       Victoria       40
4  Western Australia       35

Basic Swarm Plot

First, let's create a basic swarm plot without specifying order ?

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

# Create sample data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia'] * 4,
    'Matches': [25, 30, 15, 40, 35, 20, 28, 45, 18, 32, 38, 22]
}

df = pd.DataFrame(data)

# Create basic swarm plot
plt.figure(figsize=(8, 5))
sns.swarmplot(data=df, x='Academy', y='Matches')
plt.title('Basic Swarm Plot')
plt.show()

Controlling Swarm Order

Now let's control the order of academies using the order parameter ?

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

# Create sample data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia'] * 4,
    'Matches': [25, 30, 15, 40, 35, 20, 28, 45, 18, 32, 38, 22]
}

df = pd.DataFrame(data)

# Create swarm plot with custom order
plt.figure(figsize=(8, 5))
sns.swarmplot(data=df, x='Academy', y='Matches', 
              order=['Victoria', 'Western Australia', 'South Australia'])
plt.title('Swarm Plot with Custom Order')
plt.show()

Enhancing the Plot

You can further customize the swarm plot with colors and styling ?

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

# Create sample data with additional column for color mapping
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia'] * 4,
    'Matches': [25, 30, 15, 40, 35, 20, 28, 45, 18, 32, 38, 22],
    'Performance': ['Good', 'Excellent', 'Average', 'Excellent', 'Good', 'Average',
                   'Good', 'Excellent', 'Average', 'Good', 'Excellent', 'Average']
}

df = pd.DataFrame(data)

# Enhanced swarm plot
plt.figure(figsize=(10, 6))
sns.set_theme(style="whitegrid")
sns.swarmplot(data=df, x='Academy', y='Matches', hue='Performance',
              order=['Victoria', 'Western Australia', 'South Australia'],
              palette='viridis')
plt.title('Enhanced Swarm Plot with Custom Order and Colors')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

Key Parameters

Conclusion

Use the order parameter in sns.swarmplot() to control category display order. This is particularly useful for logical ordering like performance rankings or chronological sequences.