apply() with lambda functions in Pandas - Step-by-Step Execution

Concept Flow - apply() with lambda functions

Start with DataFrame

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Define lambda function

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Call apply() on DataFrame/Series

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apply() sends each element/row/column to lambda

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Lambda processes element and returns result

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apply() collects all results

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Return new Series/DataFrame with results

apply() takes a lambda function and runs it on each element, row, or column of a DataFrame or Series, returning the processed results.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = df['A'].apply(lambda x: x * 2)
print(result)

This code doubles each value in column 'A' using apply() with a lambda function.

Execution Table

Step	Input to lambda (x)	Lambda operation	Lambda output	Result Series state
1	1	1 * 2	2	0: 2
2	2	2 * 2	4	0: 2, 1: 4
3	End of Series	No operation	N/A	Final Series: [2, 4]

💡 All elements in Series 'A' processed; apply() returns new Series with doubled values.

Variable Tracker

Variable	Start	After 1	After 2	Final
x	N/A	1	2	N/A
result	Empty	[2]	[2, 4]	[2, 4]

Key Moments - 2 Insights

Why does apply() return a new Series instead of modifying the original?

apply() creates a new Series with the results of the lambda function, leaving the original data unchanged, as shown in execution_table rows 1-3.

What does the lambda function receive as input during apply()?

The lambda receives each element of the Series one by one, as seen in execution_table column 'Input to lambda (x)'.

Visual Quiz - 3 Questions

Look at the execution table, what is the lambda output when x is 2?

A 2

B 4

C 6

D 8

💡 Hint

Check the row where Input to lambda (x) is 2 in the execution_table.

At which step does the result Series first contain the value 2?

A Step 1

B Step 2

C Step 3

D Never

💡 Hint

Look at the 'Result Series state' column in the execution_table.

If the lambda was changed to 'lambda x: x + 1', what would be the final result Series?

A [3, 4]

B [1, 2]

C [2, 3]

D [4, 5]

💡 Hint

Add 1 to each original value in variable_tracker for 'x' after each step.

Concept Snapshot

apply() runs a lambda on each element of a Series or DataFrame.
Syntax: df['col'].apply(lambda x: operation)
Returns a new Series with results.
Original data stays unchanged.
Useful for quick element-wise transformations.

Full Transcript

We start with a DataFrame and want to change values in one column. We define a lambda function that doubles a number. Using apply(), we send each value in the column to the lambda. The lambda returns the doubled value. apply() collects these results into a new Series. The original column stays the same. This process is step-by-step shown in the execution table and variable tracker. Beginners often wonder why the original data doesn't change or what the lambda receives. The quiz questions help check understanding of these steps.