How to Use Multiple Aggregations with GroupBy in pandas

Use groupby() on your DataFrame and then apply agg() with a list or dictionary of aggregation functions to perform multiple aggregations on grouped data. You can specify different functions for different columns by passing a dictionary to agg().

Syntax

The basic syntax to perform multiple aggregations after grouping data in pandas is:

• df.groupby('column').agg(['func1', 'func2']): Applies multiple functions to all grouped columns.
• df.groupby('column').agg({'col1': ['func1', 'func2'], 'col2': 'func3'}): Applies different functions to specific columns.

Here, groupby() splits data by the given column, and agg() applies aggregation functions like sum, mean, or count.

python

df.groupby('group_column').agg(['sum', 'mean'])
df.groupby('group_column').agg({'col1': ['sum', 'mean'], 'col2': 'count'})

Example

This example shows how to group data by a column and apply multiple aggregation functions to different columns.

python

import pandas as pd

data = {
    'Team': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Points': [10, 15, 10, 20, 15, 10],
    'Assists': [5, 7, 8, 6, 7, 5]
}
df = pd.DataFrame(data)

result = df.groupby('Team').agg({'Points': ['sum', 'mean'], 'Assists': ['mean', 'max']})
print(result)

Output

      Points       Assists
         sum  mean    mean  max
Team                             
A         25  12.5     6.0    7
B         30  15.0     7.0    8
C         25  12.5     6.0    7

Common Pitfalls

Common mistakes when using multiple aggregations with groupby include:

• Passing a list of functions directly to agg() without specifying columns, which applies all functions to all columns and may cause unexpected results.
• Using aggregation function names as strings incorrectly (e.g., typos).
• Not resetting the index after aggregation if you want a flat DataFrame.

Always check the output structure because multiple aggregations create multi-level column headers.

python

import pandas as pd

data = {'Category': ['X', 'X', 'Y', 'Y'], 'Value': [1, 2, 3, 4]}
df = pd.DataFrame(data)

# Wrong: applying list of functions without specifying columns
wrong = df.groupby('Category').agg(['sum', 'mean'])
print(wrong)

# Right: specify columns with functions
right = df.groupby('Category').agg({'Value': ['sum', 'mean']})
print(right)

Output

          Value     
           sum mean
Category           
X             3  1.5
Y             7  3.5
          Value     
           sum mean
Category           
X             3  1.5
Y             7  3.5

Quick Reference

Summary tips for multiple aggregations with groupby:

• Use agg() with a list to apply multiple functions to all columns.
• Use a dictionary in agg() to apply different functions to specific columns.
• Aggregation functions can be strings like 'sum', 'mean', or custom functions.
• Result columns have multi-level headers; use reset_index() and df.columns = df.columns.map('_'.join) to flatten.

Key Takeaways

Use df.groupby().agg() with lists or dictionaries to apply multiple aggregation functions.

Specify functions per column with a dictionary for precise control.

Multiple aggregations create multi-level column headers; flatten if needed.

Common errors include applying functions to all columns unintentionally.

Check function names carefully to avoid typos in aggregation.