Group by Multiple columns in Pandas

Last update on April 11 2025 12:51:03 (UTC/GMT +8 hours)

1. Grouping by Multiple columns

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' columns.
• Sum the grouped data.
• Print the result.

For more Practice: Solve these Related Problems:

• Write a Pandas program to group a DataFrame by two categorical columns and calculate the mean of a numeric column for each group.
• Write a Pandas program to group data by multiple columns and then compute both the sum and count for each group simultaneously.
• Write a Pandas program to group a dataset by multiple keys and filter out groups that do not meet a minimum group size threshold.
• Write a Pandas program to group data by two columns and then pivot the grouped results to display multi-level headers.

Python Code Editor:

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