Arithmetic operations on columns in Pandas - Step-by-Step Execution

Concept Flow - Arithmetic operations on columns

Start with DataFrame

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Select columns

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Apply arithmetic operation (+, -, *, /)

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Create new column or update existing

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Resulting DataFrame with updated columns

Start with a DataFrame, select columns, perform arithmetic operations, then store results in new or existing columns.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'A': [10, 20, 30], 'B': [1, 2, 3]})
df['C'] = df['A'] + df['B']
print(df)

Adds columns 'A' and 'B' element-wise and stores the result in a new column 'C'.

Execution Table

Step	Action	Columns involved	Operation	Resulting 'C' column
1	Create DataFrame	A, B	N/A	N/A
2	Select columns 'A' and 'B'	A, B	N/A	N/A
3	Add columns element-wise	A, B	Addition	[11, 22, 33]
4	Assign result to new column 'C'	C	Store result	[11, 22, 33]
5	Print DataFrame	A, B, C	Display	A: [10, 20, 30], B: [1, 2, 3], C: [11, 22, 33]

💡 All rows processed; arithmetic operation completed and stored in new column 'C'.

Variable Tracker

Variable	Start	After Step 3	After Step 4	Final
df['A']	[10, 20, 30]	[10, 20, 30]	[10, 20, 30]	[10, 20, 30]
df['B']	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]	[1, 2, 3]
df['C']	N/A	N/A	[11, 22, 33]	[11, 22, 33]

Key Moments - 2 Insights

Why does df['C'] not exist before the operation but appears after?

Because df['C'] is created by assigning the result of df['A'] + df['B'] in step 4, as shown in the execution_table rows 3 and 4.

What happens if columns 'A' and 'B' have different lengths?

Pandas will raise an error or align by index, which can cause NaN values. This example assumes equal lengths as shown in the execution_table.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the result of adding columns 'A' and 'B'?

A [9, 18, 27]

B [10, 20, 30]

C [11, 22, 33]

D [1, 2, 3]

💡 Hint

Check the 'Resulting 'C' column' value in row 3 of the execution_table.

At which step is the new column 'C' created in the DataFrame?

A Step 2

B Step 4

C Step 3

D Step 5

💡 Hint

Look at the 'Action' column in the execution_table where the result is assigned to 'C'.

If we changed the operation to df['C'] = df['A'] * df['B'], what would be the value of df['C'] after step 4?

A [10, 40, 90]

B [11, 22, 33]

C [9, 18, 27]

D [1, 2, 3]

💡 Hint

Multiply each element of 'A' by corresponding element of 'B' as in variable_tracker.

Concept Snapshot

Arithmetic operations on columns in pandas:
- Select columns by name (e.g., df['A'])
- Use operators (+, -, *, /) for element-wise operations
- Assign result to new or existing column (df['C'] = ...)
- Resulting column has same length as original columns
- Works best when columns have matching indexes

Full Transcript

This visual execution shows how to perform arithmetic operations on pandas DataFrame columns. We start with a DataFrame containing columns 'A' and 'B'. We select these columns and add them element-wise. The result is stored in a new column 'C'. The execution table traces each step, showing the creation of the DataFrame, selection of columns, the addition operation, and assignment to 'C'. The variable tracker shows how 'C' changes from non-existent to holding the sum values. Key moments clarify why 'C' appears only after assignment and what happens if column lengths differ. The quiz tests understanding of the addition result, the step when 'C' is created, and the effect of changing the operation to multiplication.