Boolean indexing in Data Analysis Python - Time & Space Complexity

Time Complexity: Boolean indexing

O(n)

Understanding Time Complexity

We want to understand how the time to filter data using Boolean indexing changes as the data size grows.

How does the filtering time increase when we have more data rows?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

data = pd.DataFrame({
    'age': [23, 45, 12, 36, 52],
    'score': [88, 92, 79, 94, 67]
})

filtered = data[data['age'] > 30]

This code filters rows where the 'age' column is greater than 30.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Checking each row's 'age' value against 30.
• How many times: Once for every row in the data.

How Execution Grows With Input

As the number of rows grows, the filtering checks grow at the same rate.

Input Size (n)	Approx. Operations
10	10 checks
100	100 checks
1000	1000 checks

Pattern observation: The number of operations grows directly with the number of rows.

Final Time Complexity

Time Complexity: O(n)

This means the time to filter grows in direct proportion to the number of rows in the data.

Common Mistake

[X] Wrong: "Filtering with Boolean indexing is instant no matter how big the data is."

[OK] Correct: Each row must be checked, so more rows mean more work and more time.

Interview Connect

Understanding how filtering scales helps you explain data processing speed and efficiency clearly in real projects.

Self-Check

"What if we filter using multiple conditions combined with &? How would the time complexity change?"