Aggregate DataFrame using Operations Over an Axis in Pandas

Aggregate DataFrame using Operation(s) over an Axis

To aggregate a DataFrame using one or more operations over the specified axis, call pandas.DataFrame.agg() method and pass the required values for parameters.

In this tutorial, we will learn the syntax of DataFrame.agg() method and how to use this function to aggregate this DataFrame using specified operation(s) and axis.

Syntax

The syntax of pandas DataFrame.agg() method is

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

where

Parameter	Value	Description
func	function, str, list or dict	Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. Accepted combinations are: function string function name list of functions and/or function names. Examples: sum, ‘sum’, [sum, max, min], etc.
axis	{0 or ‘index’, 1 or ‘columns’}, default 0	If 0 or ‘index’: apply function to each column. If 1 or ‘columns’: apply function to each row.
*args		Positional arguments to pass to func.
**kwargs		Keyword arguments to pass to func.

Return Value

The return value depends on the number of functions passed for func parameter.

• Series : when DataFrame.agg() is called with a single function.
• DataFrame : when DataFrame.agg() is called with several functions.

Examples

Aggregate DataFrame with Sum Operation along Columns Axis

In the following program, we take a DataFrame and aggregate it using the sum function with axis = ‘columns’.

Example.py

import pandas as pd

df = pd.DataFrame(
    {'col_1': [10, 20, 30], 'col_2': [40, 50, 60]})

result = df.agg(sum, axis='columns')
print(result)

Output

0    50
1    70
2    90
dtype: int64

Aggregate DataFrame along Index Axis

In the following program, we take a DataFrame and aggregate it using the sum function with axis = ‘index’.

Example.py

import pandas as pd

df = pd.DataFrame(
    {'col_1': [10, 20, 30], 'col_2': [40, 50, 60]})

result = df.agg(sum, axis='index')
print(result)

Output

col_1     60
col_2    150
dtype: int64

Aggregate DataFrame with Multiple Operations

We can also aggregate a DataFrame with multiple functions. In the following program, we pass a list of functions for the func parameter.

Example.py

import pandas as pd

df = pd.DataFrame(
    {'col_1': [10, 20, 30], 'col_2': [40, 50, 60]})

result = df.agg([sum, max], axis='index')
print(result)

Output

     col_1  col_2
sum     60    150
max     30     60

Conclusion

In this Pandas Tutorial, we learned how to find the aggregate of a DataFrame with specified function(s) and axis, using pandas DataFrame.agg() method.