Dropping columns and rows in Pandas - Step-by-Step Execution

Concept Flow - Dropping columns and rows

Start with DataFrame

↓

Choose drop type

↓

Drop rows

↓

Specify labels

↓

Apply drop()

↓

New DataFrame without dropped parts

↓

End

Start with a DataFrame, decide to drop rows or columns by specifying labels, then apply drop() to get a new DataFrame without those rows or columns.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

new_df = df.drop('B', axis=1)

This code creates a DataFrame and drops column 'B' to create a new DataFrame without it.

Execution Table

Step	Action	Input	Axis	Resulting DataFrame
1	Create DataFrame	{'A':[1,2,3],'B':[4,5,6],'C':[7,8,9]}	N/A	A B C 0 1 4 7 1 2 5 8 2 3 6 9
2	Call drop() to remove column 'B'	'B'	1 (columns)	A C 0 1 7 1 2 8 2 3 9
3	Call drop() to remove row 1	1	0 (rows)	A B C 0 1 4 7 2 3 6 9
4	Call drop() with multiple columns ['A','C']	['A','C']	1 (columns)	B 0 4 1 5 2 6
5	Call drop() with multiple rows [0,2]	[0,2]	0 (rows)	A B C 1 2 5 8
6	Call drop() with inplace=True to remove column 'C'	'C'	1 (columns)	DataFrame df now without column 'C'
7	Call drop() with inplace=True to remove row 0	0	0 (rows)	DataFrame df now without row 0
8	Exit	No more drops	N/A	Final DataFrame after drops

💡 All specified rows or columns dropped, no more actions.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	After Step 7	Final
df	Original DataFrame with columns A,B,C and rows 0,1,2	Unchanged	Unchanged	Unchanged	Unchanged	Column 'C' dropped inplace	Row 0 dropped inplace	DataFrame with columns A,B and rows 1,2
new_df	N/A	DataFrame without column 'B'	N/A	N/A	N/A	N/A	N/A	N/A

Key Moments - 3 Insights

Why do we need to specify axis=1 to drop columns?

Because axis=0 means rows and axis=1 means columns. The drop() method uses axis to know if labels are rows or columns. See execution_table step 2 where axis=1 drops column 'B'.

What happens if we don't use inplace=True?

drop() returns a new DataFrame without changing the original. The original stays the same. See variable_tracker where df is unchanged after step 2 but changes only after step 6 with inplace=True.

How to drop multiple rows or columns at once?

Pass a list of labels to drop(), like ['A','C'] for columns or [0,2] for rows. See execution_table steps 4 and 5 for examples.

Visual Quiz - 3 Questions

Look at the execution_table step 2. What does axis=1 mean in the drop() method?

A Drop both rows and columns

B Drop columns

C Drop rows

D No effect

💡 Hint

Check the 'Axis' column in step 2 of execution_table.

At which step does the original DataFrame 'df' change permanently?

A Step 6

B Step 2

C Step 4

D Step 3

💡 Hint

Look at variable_tracker for 'df' changes after step 6.

If you want to drop row 0 and 2 at once, which step shows this?

A Step 3

B Step 4

C Step 5

D Step 7

💡 Hint

See execution_table rows for multiple rows dropped.

Concept Snapshot

pandas drop() removes rows or columns by label.
Use axis=0 for rows, axis=1 for columns.
Pass label or list of labels to drop.
By default, drop() returns new DataFrame.
Use inplace=True to modify original.
Example: df.drop('B', axis=1) drops column B.

Full Transcript

We start with a DataFrame having columns A, B, and C and rows indexed 0, 1, 2. To remove columns or rows, we use the drop() method. We specify the labels to remove and the axis: axis=1 for columns, axis=0 for rows. For example, dropping column 'B' with df.drop('B', axis=1) returns a new DataFrame without 'B'. If we want to remove rows, we specify row labels and axis=0. We can drop multiple labels by passing a list. By default, drop() returns a new DataFrame and does not change the original. To change the original DataFrame, we use inplace=True. This method helps clean data by removing unwanted rows or columns easily.