Pandas DataFrame.aggregate: Aggregate Operations on a DataFrame

Pandas DataFrame.aggregate

The DataFrame.aggregate method in pandas is used to perform aggregation operations on a DataFrame. It allows you to apply one or more aggregation functions to the rows or columns of a DataFrame. This method is an alias for DataFrame.agg.

Syntax

The syntax for DataFrame.aggregate is:

DataFrame.aggregate(func=None, axis=0, *args, **kwargs)

Here, DataFrame refers to the pandas DataFrame on which the aggregation is performed.

Parameters

ParameterDescription
funcThe aggregation function(s) to apply. This can be a string, function, list of functions, or dictionary mapping columns to functions.
axisSpecifies the axis along which the aggregation is performed. Use 0 or 'index' to apply the function to each column, and 1 or 'columns' to apply the function to each row. Defaults to 0.
*argsPositional arguments to pass to the aggregation function.
**kwargsKeyword arguments to pass to the aggregation function.

Returns

A DataFrame, Series, or scalar value resulting from the aggregation operation, depending on the input parameters.

Examples

Applying a Single Aggregation Function to a DataFrame

This example demonstrates how to use aggregate to apply a single aggregation function (e.g., sum) to each column of a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Apply the sum aggregation function to each column
result = df.aggregate('sum')
print(result)

Output

A     6
B    15
C    24
dtype: int64

Applying Multiple Aggregation Functions to a DataFrame

This example shows how to use aggregate to apply multiple aggregation functions (e.g., sum and mean) to each column of a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Apply multiple aggregation functions to each column
result = df.aggregate(['sum', 'mean'])
print(result)

Output

         A    B    C
sum    6.0  15.0  24.0
mean   2.0   5.0   8.0

Applying Different Aggregation Functions to Different Columns of a DataFrame

This example demonstrates how to use aggregate with a dictionary to apply different aggregation functions to different columns of a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Apply different aggregation functions to different columns
result = df.aggregate({'A': 'sum', 'B': 'mean', 'C': 'max'})
print(result)

Output

A     6.0
B     5.0
C     9.0
dtype: float64

Applying Aggregation Functions to Rows of a DataFrame

This example shows how to use aggregate to apply aggregation functions to rows of a DataFrame by setting axis=1.

Python Program

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Apply the sum aggregation function to each row
result = df.aggregate('sum', axis=1)
print(result)

Output

0    12
1    15
2    18
dtype: int64

Summary

In this tutorial, we explored the DataFrame.aggregate method in pandas. Key takeaways include:

  • Using aggregate to apply single or multiple aggregation functions to a DataFrame.
  • Applying different aggregation functions to different columns using a dictionary.
  • Performing aggregation operations on rows by setting axis=1.
  • Understanding that aggregate is an alias for agg.

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