applymap() for DataFrame-wide operations in Pandas - Step-by-Step Execution

Concept Flow - applymap() for DataFrame-wide operations

Start with DataFrame

↓

Define function to apply

↓

Call applymap() with function

↓

Function applied to each cell

↓

New DataFrame with transformed values

↓

End

applymap() takes a function and applies it to every cell in the DataFrame, returning a new DataFrame with the results.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2],
    'B': [3, 4]
})

result = df.applymap(lambda x: x * 10)

This code multiplies every cell in the DataFrame by 10 using applymap.

Execution Table

Step	Cell (row,col)	Original Value	Function Applied	Result Value
1	(0, 'A')	1	1 * 10	10
2	(0, 'B')	3	3 * 10	30
3	(1, 'A')	2	2 * 10	20
4	(1, 'B')	4	4 * 10	40
5	All cells processed	-	-	New DataFrame created with transformed values

💡 All cells processed, applymap returns new DataFrame with each cell multiplied by 10.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	Final
df	[[1, 3], [2, 4]]	[[1, 3], [2, 4]]	[[1, 3], [2, 4]]	[[1, 3], [2, 4]]	[[1, 3], [2, 4]]	[[1, 3], [2, 4]]
result	None	None	None	None	None	[[10, 30], [20, 40]]

Key Moments - 2 Insights

Why does applymap apply the function to each cell instead of each row or column?

applymap is designed to work element-wise on every cell, unlike apply which works on rows or columns. See execution_table rows 1-4 where each cell is processed individually.

Does applymap modify the original DataFrame?

No, applymap returns a new DataFrame with transformed values. The original DataFrame 'df' remains unchanged as shown in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution table, what is the result value for cell (1, 'A') after applying the function?

A 10

B 2

C 20

D 40

💡 Hint

Check step 3 in the execution_table where cell (1, 'A') is processed.

At which step does the function get applied to the cell at row 0, column 'B'?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the execution_table row for cell (0, 'B').

If the function was changed to add 5 instead of multiply by 10, what would be the result value for cell (0, 'A')?

A 6

B 15

C 5

D 10

💡 Hint

Original value is 1; adding 5 means 1 + 5 = 6.

Concept Snapshot

applymap() applies a function to each cell in a DataFrame.
It returns a new DataFrame with transformed values.
Use it for element-wise operations.
Does not modify the original DataFrame.
Syntax: df.applymap(function).

Full Transcript

The applymap() function in pandas applies a given function to every cell in a DataFrame. Starting with the original DataFrame, you define a function, then call applymap with that function. The function runs on each cell individually, producing a new DataFrame with the results. The original DataFrame stays the same. For example, multiplying each cell by 10 changes 1 to 10, 3 to 30, and so on. This process is shown step-by-step in the execution table. Remember, applymap works cell-by-cell, unlike apply which works on rows or columns.