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

Point Plot in Seaborn is used to show point estimates and confidence intervals using scatter plot glyphs. The seaborn.pointplot() function creates these visualizations, and you can control the order of categories using the order parameter.

Syntax

seaborn.pointplot(x, y, data, order=None, ...)

Parameters

Key parameters for controlling order:

• x, y: Column names for x and y axes
• data: DataFrame containing the data
• order: List specifying the order of categorical levels

Example with Sample Data

Let's create a point plot with explicit ordering using sample cricket academy data ?

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

# Create sample data
data = {
    'Academy': ['Tasmania', 'Victoria', 'South Australia', 'Tasmania', 'Victoria', 
                'South Australia', 'Tasmania', 'Victoria', 'South Australia'],
    'Age': [22, 25, 23, 24, 26, 21, 23, 24, 25]
}

dataFrame = pd.DataFrame(data)
print("Sample Data:")
print(dataFrame.head())

Sample Data:
        Academy  Age
0      Tasmania   22
1      Victoria   25
2  South Australia   23
3      Tasmania   24
4      Victoria   26

Creating Point Plot with Default Order

First, let's see the default ordering (alphabetical) ?

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

# Sample data
data = {
    'Academy': ['Tasmania', 'Victoria', 'South Australia', 'Tasmania', 'Victoria', 
                'South Australia', 'Tasmania', 'Victoria', 'South Australia'],
    'Age': [22, 25, 23, 24, 26, 21, 23, 24, 25]
}

dataFrame = pd.DataFrame(data)

sb.set_theme(style="darkgrid")

# Point plot with default order
plt.figure(figsize=(8, 5))
sb.pointplot(x='Academy', y='Age', data=dataFrame)
plt.title('Point Plot - Default Order')
plt.show()

Using Explicit Order Parameter

Now, let's control the order by specifying a custom sequence ?

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

# Sample data
data = {
    'Academy': ['Tasmania', 'Victoria', 'South Australia', 'Tasmania', 'Victoria', 
                'South Australia', 'Tasmania', 'Victoria', 'South Australia'],
    'Age': [22, 25, 23, 24, 26, 21, 23, 24, 25]
}

dataFrame = pd.DataFrame(data)

sb.set_theme(style="darkgrid")

# Point plot with explicit order
plt.figure(figsize=(8, 5))
sb.pointplot(x='Academy', y='Age', data=dataFrame, 
             order=["Tasmania", "South Australia", "Victoria"])
plt.title('Point Plot - Custom Order')
plt.show()

Comparison of Ordering Methods

Advanced Example with Multiple Categories

You can also use ordering with additional categorical variables ?

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

# Extended sample data with hue
data = {
    'Academy': ['Tasmania', 'Victoria', 'South Australia'] * 6,
    'Age': [22, 25, 23, 24, 26, 21, 23, 24, 25, 26, 27, 22, 25, 24, 26, 23, 25, 24],
    'Level': ['Junior', 'Senior'] * 9
}

dataFrame = pd.DataFrame(data)

sb.set_theme(style="darkgrid")

plt.figure(figsize=(10, 6))
sb.pointplot(x='Academy', y='Age', hue='Level', data=dataFrame,
             order=["Tasmania", "South Australia", "Victoria"])
plt.title('Point Plot with Hue and Custom Order')
plt.show()

Conclusion

Use the order parameter in seaborn.pointplot() to control the sequence of categories on your plot. This is essential for creating logical, meaningful visualizations that emphasize relationships in your data.