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

Bar plots in Seaborn display point estimates and confidence intervals as rectangular bars. The seaborn.barplot() function allows you to control the ordering of categories by passing an explicit order using the order parameter.

Importing Required Libraries

First, import the necessary libraries for data visualization ?

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

Creating Sample Data

Let's create a sample dataset to demonstrate bar plot ordering ?

# Create sample cricket data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia', 'Tasmania', 
                'Victoria', 'Western Australia', 'South Australia', 'Tasmania'],
    'Matches': [25, 30, 20, 15, 28, 32, 18, 12]
}

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

         Academy  Matches
0        Victoria       25
1  Western Australia       30
2   South Australia       20
3        Tasmania       15
4        Victoria       28
5  Western Australia       32
6   South Australia       18
7        Tasmania       12

Basic Bar Plot

Create a basic bar plot without specifying order ?

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

# Create sample data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia', 'Tasmania', 
                'Victoria', 'Western Australia', 'South Australia', 'Tasmania'],
    'Matches': [25, 30, 20, 15, 28, 32, 18, 12]
}
dataFrame = pd.DataFrame(data)

# Basic bar plot
plt.figure(figsize=(10, 6))
sb.barplot(data=dataFrame, x='Academy', y='Matches')
plt.title('Cricket Matches by Academy (Default Order)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Controlling Order with order Parameter

Control the display order by passing an explicit order to the order parameter ?

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

# Create sample data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia', 'Tasmania', 
                'Victoria', 'Western Australia', 'South Australia', 'Tasmania'],
    'Matches': [25, 30, 20, 15, 28, 32, 18, 12]
}
dataFrame = pd.DataFrame(data)

# Bar plot with custom order
plt.figure(figsize=(10, 6))
custom_order = ['Victoria', 'Western Australia', 'South Australia', 'Tasmania']
sb.barplot(data=dataFrame, x='Academy', y='Matches', order=custom_order)
plt.title('Cricket Matches by Academy (Custom Order)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Ordering by Values

You can also order bars by their values (ascending or descending) ?

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

# Create sample data
data = {
    'Academy': ['Victoria', 'Western Australia', 'South Australia', 'Tasmania', 
                'Victoria', 'Western Australia', 'South Australia', 'Tasmania'],
    'Matches': [25, 30, 20, 15, 28, 32, 18, 12]
}
dataFrame = pd.DataFrame(data)

# Calculate mean matches per academy and sort
academy_order = dataFrame.groupby('Academy')['Matches'].mean().sort_values(ascending=False).index

# Bar plot ordered by values (descending)
plt.figure(figsize=(10, 6))
sb.barplot(data=dataFrame, x='Academy', y='Matches', order=academy_order)
plt.title('Cricket Matches by Academy (Ordered by Mean Matches)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Key Parameters

The order parameter accepts a list specifying the desired category order ?

Conclusion

Use the order parameter in seaborn.barplot() to control category ordering. You can specify custom order lists or sort categories by their values for better data visualization.