How to Use Aggregate Function in pandas for Data Analysis

In pandas, use the aggregate() method to apply one or more summary functions like sum, mean, or max on DataFrame columns. It allows flexible aggregation by passing a single function, a list of functions, or a dictionary mapping columns to functions.

Syntax

The aggregate() method syntax is:

• DataFrame.aggregate(func, axis=0, *args, **kwargs)

Where:

• func: a function, list of functions, or dict specifying aggregation(s)
• axis: 0 to aggregate columns (default), 1 to aggregate rows

python

df.aggregate(func, axis=0, *args, **kwargs)

Example

This example shows how to use aggregate() to get the sum and mean of numeric columns in a DataFrame.

python

import pandas as pd

data = {'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': ['x', 'y', 'z', 'w']}
df = pd.DataFrame(data)

# Aggregate sum and mean for columns A and B
result = df.aggregate({'A': ['sum', 'mean'], 'B': ['sum', 'mean']})
print(result)

Output

     A     B
sum  10    26
mean 2.5  6.5

Common Pitfalls

Common mistakes include:

• Passing non-aggregatable columns without specifying functions causes errors.
• Using agg() and aggregate() interchangeably is fine, but be consistent.
• Not specifying a dictionary when different functions are needed per column.

Example of wrong and right usage:

python

# Wrong: applying sum to a non-numeric column
import pandas as pd
data = {'A': [1, 2], 'B': ['x', 'y']}
df = pd.DataFrame(data)

try:
    print(df.aggregate('sum'))
except Exception as e:
    print(f'Error: {e}')

# Right: specify functions only for numeric columns
print(df.aggregate({'A': 'sum'}))

Output

Error: unsupported operand type(s) for +: 'int' and 'str'
A    3
dtype: int64

Quick Reference

Usage	Description	Example
Single function	Apply one function to all columns	df.aggregate('sum')
Multiple functions	Apply list of functions to all columns	df.aggregate(['sum', 'mean'])
Dict of functions	Apply different functions per column	df.aggregate({'A': 'sum', 'B': ['min', 'max']})
Axis parameter	Aggregate over rows instead of columns	df.aggregate('sum', axis=1)

Key Takeaways

Use aggregate() to apply one or more summary functions on DataFrame columns.

Pass a function, list of functions, or a dictionary mapping columns to functions for flexible aggregation.

Specify axis=1 to aggregate across rows instead of columns.

Avoid applying aggregation functions to non-numeric columns without specifying functions explicitly.

Both agg() and aggregate() are interchangeable methods in pandas.