Python | Pandas.Series.apply()

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.

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.

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%.

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.

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.