Pandas Series agg() Method (original) (raw)

Last Updated : 11 Jul, 2025

**Pandasis one of those packages and makes importing and analyzing data much easier. Pandas Series.agg() is used to pass a function or list of functions to be applied on a series or even each element of the series separately. In the case of a list of functions, multiple results are returned by the Series.agg() method.

Pandas Series Aggregate Syntax

**Syntax: Series.agg(func, axis=0)

**Parameters:

• **func: Function, list of function or string of function name to be called on Series.
• ****axis:**0 or ‘index’ for row wise operation and 1 or ‘columns’ for column wise operation.

**Return Type: The return type depends on return type of function passed as parameter.

Aggregate the Series Elements in Pandas

In Pandas, series elements can be aggregated by computing statistical measures such as sum, mean, min, max, and count. These functions can provide insights into the dataset's characteristics.

Python3 `

import pandas as pd s = pd.Series([89,99,78,70]) s.agg('min') s.agg(['min', 'max'])

`

**Output:

min 70 max 99

**Example 1: In this example, a Python lambda function is passed which simply adds 2 to each value of the series. Since the function will be applied to each value of the series, the return type is also a series. A random series of 10 elements is generated by passing an array generated using Numpy random method.

Python3 `

importing pandas module

import pandas as pd

importing numpy module

import numpy as np

creating random arr of 10 elements

arr = np.random.randn(10)

creating series from array

series = pd.Series(arr)

calling .agg() method

result = series.agg(lambda num: num + 2)

display

print('Array before operation: \n', series, '\n\nArray after operation: \n', result)

`

**Output:

As shown in the output, the function was applied to each value and 2 was added to each value of the series.

Array before operation: 0 -0.178400 1 -0.014408 2 -2.185778 3 0.335517 4 1.013446 5 0.897206 6 0.116324 7 -1.046006 8 -0.918818 9 0.552542 dtype: float64 Array after operation: 0 1.821600 1 1.985592 2 -0.185778 3 2.335517 4 3.013446 5 2.897206 6 2.116324 7 0.953994 8 1.081182 9 2.552542 dtype: float64

**Example 2: Passing List of functions In this example, a list of some of Python's default functions is passed and multiple results are returned by Pandas Series.agg() method into multiple variables.

Python3 `

importing pandas module

import pandas as pd

importing numpy module

import numpy as np

creating random arr of 10 elements

arr = np.random.randn(10)

creating series from array

series = pd.Series(arr)

creating list of function names

func_list = [min, max, sorted]

calling .agg() method

passing list of functions

result1, result2, result3 = series.agg(func_list)

display

print('Series before operation: \n', series) print('\nMin = {}\n\nMax = {},
\n\nSorted Series:\n{}'.format(result1, result2, result3))

`

**Output:

As shown in the output, multiple results were returned. Min, Max, and Sorted array were returned into different variables result1, result2, and result3 respectively.

Series before operation: 0 -1.493851 1 -0.658618 2 0.265253 3 -0.503875 4 1.419182 5 0.221025 6 -0.712019 7 -1.462868 8 -0.341504 9 -0.338337 dtype: float64 Min = -1.4938513079840412 Max = 1.4191824761086351,
Sorted Series: [-1.4938513079840412, -1.462868259420631, -0.7120191767162937, -0.6586184541010776, -0.5038754446324809, -0.34150351227216663, -0.33833663286234356, 0.22102480822109685, 0.2652526809574672, 1.4191824761086351]