Binning continuous data into categories using pd.cut() in Pandas

Last update on April 09 2025 12:54:16 (UTC/GMT +8 hours)

7. Binning Data into Categories

Write a Pandas program to bin data into categories.

This exercise demonstrates how to bin continuous numerical data into discrete categories using pd.cut() method.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with continuous values
df = pd.DataFrame({
    'Age': [25, 30, 22, 45, 35, 28, 40]
})

# Bin ages into categories: 'Young', 'Middle-aged', 'Old'
bins = [0, 25, 35, 100]
labels = ['Young', 'Middle-aged', 'Old']
df['Age_Group'] = pd.cut(df['Age'], bins=bins, labels=labels)

# Output the result
print(df)

Output:

   Age    Age_Group
0   25        Young
1   30  Middle-aged
2   22        Young
3   45          Old
4   35  Middle-aged
5   28  Middle-aged
6   40          Old

Explanation:

• Created a DataFrame with continuous 'Age' values.
• Used pd.cut() to bin the ages into discrete categories ('Young', 'Middle-aged', 'Old').
• Added a new column 'Age_Group' with the corresponding category for each age.

For more Practice: Solve these Related Problems:

• Write a Pandas program to bin a numeric column into four categories based on quantile ranges.
• Write a Pandas program to create custom bins for a numeric column and assign descriptive labels to each bin.
• Write a Pandas program to bin data and then group by the bin labels to calculate summary statistics.
• Write a Pandas program to dynamically generate bins based on the distribution of values in a DataFrame column.

Python-Pandas Code Editor:

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