Boolean indexing in Pandas - Time & Space Complexity

Time Complexity: Boolean indexing

O(n)

Understanding Time Complexity

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

How does the filtering time grow when we have more rows in our data?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.DataFrame({
    'age': [23, 45, 12, 36, 27],
    'score': [88, 92, 79, 95, 85]
})

filtered = df[df['age'] > 25]

This code creates a table with ages and scores, then selects rows where age is greater than 25.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Checking each row's 'age' value against 25.
• How many times: Once for every row in the table.

How Execution Grows With Input

As the number of rows grows, the time to check each row grows too, roughly in direct proportion.

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

Pattern observation: Doubling the rows doubles the number of checks needed.

Final Time Complexity

Time Complexity: O(n)

This means the time to filter grows linearly with 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 write efficient data code and explain your choices clearly.

Self-Check

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