Boolean filtering in Data Analysis Python - Step-by-Step Execution

Concept Flow - Boolean filtering

Start with DataFrame

↓

Create Boolean Condition

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Apply Condition to DataFrame

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Get Filtered DataFrame

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End

We start with a data table, create a true/false condition for each row, then keep only rows where the condition is true.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Age': [18, 22, 15, 30]})
filtered = df[df['Age'] >= 18]
print(filtered)

This code keeps only rows where Age is 18 or more.

Execution Table

Step	DataFrame Rows	Condition (Age >= 18)	Filtered Rows
1	[18, 22, 15, 30]	[True, True, False, True]	[18, 22, 30]
2	Filtered DataFrame ready	N/A	Rows with True condition kept

💡 Filtering done, only rows where Age >= 18 remain

Variable Tracker

Variable	Start	After Filtering	Final
df['Age']	[18, 22, 15, 30]	[18, 22, 15, 30]	[18, 22, 15, 30]
condition	N/A	[True, True, False, True]	[True, True, False, True]
filtered	N/A	[18, 22, 30]	[18, 22, 30]

Key Moments - 2 Insights

Why does the filtered DataFrame only have 3 rows instead of 4?

Because the condition 'Age >= 18' is False for the row with Age 15, so that row is excluded (see execution_table step 1).

What type of values does the condition produce?

It produces a list of True/False values, one for each row, indicating if the row meets the condition (see 'Condition' column in execution_table).

Visual Quiz - 3 Questions

Look at the execution table, what is the condition value for the row where Age is 15?

A True

B False

C None

D Error

💡 Hint

Check the 'Condition (Age >= 18)' column in execution_table at step 1

At which step do we get the filtered DataFrame with only rows where Age >= 18?

A Step 1

B Step 2

C Before Step 1

D No step

💡 Hint

Look at the 'Filtered Rows' column in execution_table

If we change the condition to 'Age > 20', how would the filtered rows change?

A [15, 18, 22, 30]

B [18, 22, 30]

C [22, 30]

D [15]

💡 Hint

Think about which ages are strictly greater than 20

Concept Snapshot

Boolean filtering in pandas:
- Create a condition that returns True/False per row
- Use df[condition] to keep only True rows
- Result is a smaller DataFrame
- Useful to select data by criteria
- Condition can be any comparison or logical expression

Full Transcript

Boolean filtering means selecting rows from a data table where a condition is true. We start with a DataFrame, create a condition like 'Age >= 18' that returns True or False for each row. Then we use this condition to keep only rows where it is True. For example, if ages are 18, 22, 15, and 30, only rows with 18, 22, and 30 remain after filtering. The condition is a list of True/False values matching each row. This method helps us focus on data that meets our criteria.