Python | Pandas.Series.apply() (original) (raw)

Last Updated : 23 Feb, 2026

Series.apply() method in Pandas is used to apply a function to each element of a Series. It allows to transform, modify or categorize data easily by running a custom function or lambda function on every value.

**Example: This example applies a function to double each value in a Series.

Python `

import pandas as pd s = pd.Series([1, 2, 3, 4]) r = s.apply(lambda x: x * 2) print(r)

`

Output

0 2 1 4 2 6 3 8 dtype: int64

**Explanation: s.apply(lambda x: x * 2) applies the function to each element of s and each value is multiplied by 2 and returned as a new Series

Syntax

Series.apply(func, convert_dtype=True, args=())

**Parameters:

• **func: Function to apply on each value of the Series
• **convert_dtype: Converts result to best possible dtype (default True)
• **args: Additional arguments passed to the function

**Return: Returns a new Pandas Series with modified values

Examples

**Example 1: This example uses apply() with a custom function to classify each mark in the Series as Pass or Fail based on a condition.

PYTHON `

import pandas as pd

s = pd.Series([35, 67, 90, 45]) def f(x): return "Pass" if x >= 50 else "Fail"

r = s.apply(f) print(r)

`

Output

0 Fail 1 Pass 2 Pass 3 Fail dtype: object

**Explanation:

• pd.Series([35, 67, 90, 45]) creates a Series of marks.
• def f(x): defines a function that checks each value.
• "Pass" if x >= 50 else "Fail" returns result based on condition.
• s.apply(f) applies function f to every element of the Series.

**Example 2: This example uses apply() with a lambda function to increase each salary value in the Series by 10%.

PYTHON `

import pandas as pd s = pd.Series([10000, 15000, 20000]) r = s.apply(lambda x: x * 1.10) print(r)

`

Output

0 11000.0 1 16500.0 2 22000.0 dtype: float64

**Explanation:

• lambda x: x * 1.10 defines a function to increase value by 10%.
• s.apply(lambda x: x * 1.10) applies this function to each salary.
• Each value is multiplied by 1.10.

**Example 3: This example uses apply() to convert each numeric value into a formatted text string.

Python `

import pandas as pd s = pd.Series([5, 10, 15]) r = s.apply(lambda x: "Value is " + str(x)) print(r)

`

Output

0 Value is 5 1 Value is 10 2 Value is 15 dtype: object

**Explanation:

• lambda x: "Value is " + str(x) converts number into text.
• str(x) converts numeric value into string.
• s.apply(...) applies this conversion to every element.