Grouping and Aggregating with multiple Index Levels in Pandas

Last update on May 03 2025 12:57:21 (UTC/GMT +8 hours)

8. Grouping and Aggregating with Multiple Index Levels

Write a Pandas program to perform grouping and aggregation operations using multiple index levels.

Sample Solution:

Python Code :

import pandas as pd
# Sample DataFrame
data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Type': ['X', 'Y', 'X', 'Y', 'X', 'Y'],
        'Value': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)
print("Sample DataFrame:")
print(df)
# Group by 'Category' and 'Type'
print("\nGroup by 'Category' and 'Type':")
grouped = df.groupby(['Category', 'Type']).sum()
print(grouped)

Output:

Sample DataFrame:
  Category Type  Value
0        A    X      1
1        A    Y      2
2        B    X      3
3        B    Y      4
4        C    X      5
5        C    Y      6

Group by 'Category' and 'Type':
               Value
Category Type       
A        X         1
         Y         2
B        X         3
         Y         4
C        X         5
         Y         6

Explanation:

Import pandas.
Create a sample DataFrame.
Group by 'Category' and 'Type'.
Sum the grouped data.
Print the result.

For more Practice: Solve these Related Problems:

Write a Pandas program to group data by two columns and create a multi-level index from the grouped result.
Write a Pandas program to perform groupby operations on a DataFrame with a hierarchical index and then aggregate with multiple functions.
Write a Pandas program to group data, set multiple index levels, and then unstack the result to obtain a pivot table.
Write a Pandas program to group data on multiple levels and compute aggregations that preserve the multi-index structure.

Go to:

Previous: Using GroupBy with Lambda functions in Pandas.
Next: Apply different functions to different columns with GroupBy.

Python Code Editor:

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