Boolean indexing in Pandas - Step-by-Step Execution

Concept Flow - Boolean indexing

Start with DataFrame

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Create Boolean Condition

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Apply Condition to DataFrame

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Select Rows where Condition is True

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Return Filtered DataFrame

Boolean indexing filters rows in a DataFrame by applying a True/False condition to select only matching rows.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({'Age': [21, 35, 42, 18], 'Name': ['Ann', 'Bob', 'Cara', 'Dan']})

filtered = df[df['Age'] > 30]
print(filtered)

This code filters the DataFrame to keep only rows where Age is greater than 30.

Execution Table

Step	Action	Condition Evaluation	Rows Selected	Output DataFrame
1	Create DataFrame	N/A	All rows	[{'Age':21,'Name':'Ann'}, {'Age':35,'Name':'Bob'}, {'Age':42,'Name':'Cara'}, {'Age':18,'Name':'Dan'}]
2	Evaluate condition df['Age'] > 30	[False, True, True, False]	Rows 1 and 2 (0-based index)	N/A
3	Apply boolean indexing df[df['Age'] > 30]	N/A	Rows 1 and 2	[{'Age':35,'Name':'Bob'}, {'Age':42,'Name':'Cara'}]
4	Print filtered DataFrame	N/A	Rows 1 and 2	Age Name 1 35 Bob 2 42 Cara

💡 All rows evaluated; only rows with Age > 30 selected.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
df	[{'Age':21,'Name':'Ann'}, {'Age':35,'Name':'Bob'}, {'Age':42,'Name':'Cara'}, {'Age':18,'Name':'Dan'}]	Same	Same	Same
condition	N/A	[False, True, True, False]	[False, True, True, False]	[False, True, True, False]
filtered	N/A	N/A	[{'Age':35,'Name':'Bob'}, {'Age':42,'Name':'Cara'}]	[{'Age':35,'Name':'Bob'}, {'Age':42,'Name':'Cara'}]

Key Moments - 3 Insights

Why does the filtered DataFrame only show some rows, not all?

Because boolean indexing selects only rows where the condition is True, as shown in execution_table step 3 where only rows with Age > 30 are kept.

What does the condition df['Age'] > 30 return?

It returns a Series of True/False values for each row, indicating if the Age is greater than 30, as shown in execution_table step 2.

Can the original DataFrame 'df' change after filtering?

No, the original DataFrame stays the same; filtering creates a new DataFrame 'filtered' with only selected rows, as shown in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the condition value for the first row (index 0)?

A True

B None

C False

D Error

💡 Hint

Check the 'Condition Evaluation' column in step 2 of the execution_table.

At which step does the filtered DataFrame get created?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Look for when boolean indexing is applied to create 'filtered' in the execution_table.

If the condition was changed to df['Age'] > 40, how would the number of selected rows change?

A Decrease to 1 row

B Stay the same at 2 rows

C Increase to 3 rows

D No rows selected

💡 Hint

Refer to the condition evaluation logic and original ages in variable_tracker.

Concept Snapshot

Boolean indexing filters DataFrame rows using a True/False condition.
Syntax: filtered = df[condition]
Condition returns a boolean Series matching DataFrame rows.
Only rows with True condition are kept.
Original DataFrame stays unchanged.
Useful for quick data filtering.

Full Transcript

Boolean indexing in pandas lets you filter rows by applying a condition that returns True or False for each row. You start with a DataFrame, create a condition like df['Age'] > 30, which returns a series of True/False values. Then you use this condition inside df[condition] to select only rows where the condition is True. The result is a new DataFrame with filtered rows. The original DataFrame remains unchanged. This method is simple and powerful for selecting data based on criteria.