Adding and removing columns in Data Analysis Python - Step-by-Step Execution

Concept Flow - Adding and removing columns

Start with DataFrame

↓

Add Column

↓

Check DataFrame

↓

Remove Column

↓

Check DataFrame

↓

End

Start with a DataFrame, add a new column, check the result, then remove a column and check again.

Execution Sample

Data Analysis Python

import pandas as pd

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

Create a DataFrame, add a new column 'C' as sum of 'A' and 'B', then remove column 'B' and print the result.

Execution Table

Step	Action	DataFrame Columns	DataFrame Content	Output
1	Create DataFrame	['A', 'B']	[[1, 3], [2, 4]]	Initial DataFrame with columns A and B
2	Add column 'C' as A+B	['A', 'B', 'C']	[[1, 3, 4], [2, 4, 6]]	Column C added with sums
3	Remove column 'B'	['A', 'C']	[[1, 4], [2, 6]]	Column B removed
4	Print DataFrame	['A', 'C']	[[1, 4], [2, 6]]	Printed final DataFrame

💡 All steps completed; DataFrame now has columns A and C only.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df.columns.tolist()	N/A	['A', 'B']	['A', 'B', 'C']	['A', 'C']	['A', 'C']
df.values.tolist()	N/A	[[1, 3], [2, 4]]	[[1, 3, 4], [2, 4, 6]]	[[1, 4], [2, 6]]	[[1, 4], [2, 6]]

Key Moments - 2 Insights

Why does the new column 'C' appear after step 2?

Because we assigned df['C'] = df['A'] + df['B'], which creates a new column 'C' with the sum of columns 'A' and 'B' as shown in execution_table row 2.

Why does the DataFrame no longer have column 'B' after step 3?

Because we used df.drop('B', axis=1) which removes column 'B' from the DataFrame, as shown in execution_table row 3.

Visual Quiz - 3 Questions

Look at the execution table, what columns does the DataFrame have after step 2?

A ['A', 'B', 'C']

B ['A', 'C']

C ['A', 'B']

D ['B', 'C']

💡 Hint

Check the 'DataFrame Columns' column in execution_table row 2.

At which step is column 'B' removed from the DataFrame?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Action' and 'DataFrame Columns' in execution_table rows.

If we did not specify axis=1 in df.drop('B', axis=1), what would happen?

A Column 'B' would still be removed

B An error would occur

C Row 'B' would be removed instead

D Nothing would change

💡 Hint

Refer to pandas drop method requirements for axis parameter.

Concept Snapshot

Adding and removing columns in pandas:
- Add column: df['new_col'] = values
- Remove column: df = df.drop('col_name', axis=1)
- axis=1 means column, axis=0 means row
- Changes update DataFrame structure
- Use print(df) to see changes

Full Transcript

We start with a DataFrame with columns A and B. Then we add a new column C by summing columns A and B. This adds column C to the DataFrame. Next, we remove column B using the drop method with axis=1 to specify column removal. Finally, we print the DataFrame which now has columns A and C only. This shows how to add and remove columns step-by-step.