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.

Practice

1. What does the apply() function with a lambda do in pandas?

easy

A. It sorts the DataFrame by column values.

B. It applies a small function to each element in a DataFrame column or row.

C. It merges two DataFrames based on a key.

D. It creates a new DataFrame from scratch.

Solution

1. Step 1: Understand the purpose of apply() with lambda

   The apply() method lets you run a function on each element or row/column of a DataFrame or Series.

2. Step 2: Recognize what lambda does

   A lambda is a small anonymous function used inside apply() to specify what to do with each element.

3. Final Answer:

   It applies a small function to each element in a DataFrame column or row. -> Option B

4. Quick Check:

   apply() + lambda = element-wise function [OK]

Hint: apply() runs a function on each item quickly [OK]

Common Mistakes:

• Thinking apply() sorts data
• Confusing apply() with merge or join
• Believing apply() creates new DataFrames

2. Which of the following is the correct syntax to use apply() with a lambda on a pandas Series named data to double each value?

easy

A. data.apply(lambda x: x * 2)

B. data.apply(x => x * 2)

C. apply(data, lambda x: x * 2)

D. data.apply(lambda x x * 2)

Solution

1. Step 1: Recall correct lambda syntax in Python

   Python lambda functions use the format lambda x: expression, so lambda x: x * 2 is correct.

2. Step 2: Check apply() usage on Series

   The method call is data.apply(lambda x: x * 2). Other options have syntax errors or wrong order.

3. Final Answer:

   data.apply(lambda x: x * 2) -> Option A

4. Quick Check:

   Correct lambda syntax = data.apply(lambda x: x * 2) [OK]

Hint: Use colon after lambda parameter: lambda x: ... [OK]

Common Mistakes:

• Using arrow syntax (=>) from other languages
• Missing colon after lambda parameter
• Calling apply() as a standalone function

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]})
df['B'] = df['A'].apply(lambda x: x + 5)
print(df)

What is the output?

medium

A. Error: lambda function syntax

B. A B
0 1 5
1 2 6
2 3 7

C. A B
0 1 6
1 2 7
2 3 8

D. A B
0 6 1
1 7 2
2 8 3

Solution

1. Step 1: Understand the lambda function applied

   The lambda adds 5 to each value in column 'A'. So 1+5=6, 2+5=7, 3+5=8.

2. Step 2: Check DataFrame after assignment

   Column 'B' gets these new values, while 'A' remains unchanged.

3. Final Answer:

   A B 0 1 6 1 2 7 2 3 8 -> Option C

4. Quick Check:

   Original + 5 = New column [OK]

Hint: apply() changes each value as per lambda [OK]

Common Mistakes:

• Mixing up columns A and B
• Forgetting to add 5 inside lambda
• Expecting error from correct syntax

4. What is wrong with this code snippet?

df['C'] = df['A'].apply(lambda x x * 3)

medium

A. Missing colon after lambda parameter causes syntax error.

B. apply() cannot be used on DataFrame columns.

C. lambda functions cannot multiply values.

D. Column 'C' does not exist yet.

Solution

1. Step 1: Check lambda function syntax

   Python lambda requires a colon after parameters: lambda x: x * 3. The code misses the colon.

2. Step 2: Understand error cause

   Missing colon causes a syntax error, so code won't run.

3. Final Answer:

   Missing colon after lambda parameter causes syntax error. -> Option A

4. Quick Check:

   lambda syntax error = missing colon [OK]

Hint: Always put colon after lambda parameters [OK]

Common Mistakes:

• Forgetting colon after lambda parameter
• Thinking apply() can't be used on columns
• Assuming new columns must exist before assignment

5. You have a DataFrame df with a column 'score' containing numbers. You want to create a new column 'grade' that says 'Pass' if the score is 50 or more, and 'Fail' otherwise. Which code correctly uses apply() with a lambda to do this?

hard

A. df['grade'] = df.apply(lambda x: 'Pass' if x['score'] >= 50 else 'Fail')

B. df['grade'] = df['score'].apply(lambda x: if x >= 50 then 'Pass' else 'Fail')

C. df['grade'] = df['score'].apply(lambda x: x >= 50 ? 'Pass' : 'Fail')

D. df['grade'] = df['score'].apply(lambda x: 'Pass' if x >= 50 else 'Fail')

Solution

1. Step 1: Understand conditional expression in lambda

   Python uses value_if_true if condition else value_if_false syntax inside lambda.

2. Step 2: Check application target and syntax

   Applying to the Series df['score'] with lambda x: 'Pass' if x >= 50 else 'Fail' is correct, as x is each scalar score. Applying to the whole df (without axis=1) passes column Series to lambda, where x['score'] causes KeyError. Syntaxes like ? : ternary or if-then are invalid in Python.

3. Final Answer:

   df['grade'] = df['score'].apply(lambda x: 'Pass' if x >= 50 else 'Fail') -> Option D

4. Quick Check:

   Correct if-else in lambda = df['grade'] = df['score'].apply(lambda x: 'Pass' if x >= 50 else 'Fail') [OK]

Hint: Use Python's if-else inside lambda: val_if_true if cond else val_if_false [OK]

Common Mistakes:

• Using ?: ternary operator from other languages
• Applying lambda on whole DataFrame without axis
• Writing if-else without proper Python syntax