apply() with lambda functions in Pandas

Introduction

We use apply() with lambda functions to quickly change or calculate new values for each item in a column or row of a table.

You want to add 10 to every number in a list of ages.

You need to change all names in a list to uppercase letters.

You want to create a new column based on a simple rule from existing columns.

You want to check if values in a column meet a condition and mark them True or False.

Syntax

Pandas

DataFrame['column_name'].apply(lambda x: expression_using_x)

The lambda x: part means 'for each item x in the column'.

The expression after the colon is what you want to do with each item.

Examples

Adds 10 to each value in the 'age' column and saves it in a new column 'age_plus_10'.

Pandas

df['age_plus_10'] = df['age'].apply(lambda x: x + 10)

Changes all names to uppercase letters.

Pandas

df['name_upper'] = df['name'].apply(lambda x: x.upper())

Creates a True/False column showing if age is 18 or more.

Pandas

df['is_adult'] = df['age'].apply(lambda x: x >= 18)

Sample Program

This code creates a small table with names and ages. Then it adds 5 to each age, makes names uppercase, and checks if each person is an adult (18 or older). Finally, it prints the updated table.

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'age': [17, 22, 15]}
df = pd.DataFrame(data)

# Add 5 years to each age
df['age_plus_5'] = df['age'].apply(lambda x: x + 5)

# Make names uppercase
df['name_upper'] = df['name'].apply(lambda x: x.upper())

# Check if person is adult
df['is_adult'] = df['age'].apply(lambda x: x >= 18)

print(df)

Important Notes

Lambda functions are small, quick functions you write inside apply().

Use apply() when you want to change or check each item in a column easily.

Summary

apply() with lambda lets you quickly change or check each value in a column.

You write a small function with lambda x: to say what to do with each item.

This is useful for simple calculations, text changes, or condition checks on data.