info() for column types and nulls in Pandas - Time & Space Complexity

Time Complexity: info() for column types and nulls

O(n x m)

Understanding Time Complexity

We want to understand how long it takes for pandas to show column types and count missing values using info().

How does the time grow when the data has more rows or columns?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

n = 1000

df = pd.DataFrame({
    'A': range(n),
    'B': [None if i % 10 == 0 else i for i in range(n)],
    'C': ['text'] * n
})

df.info()

This code creates a DataFrame with n rows and 3 columns, then calls info() to show data types and count nulls.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: pandas scans each column to count non-null values and check data types.
• How many times: It processes each of the n rows once per column.

How Execution Grows With Input

As the number of rows grows, pandas must check more data to count nulls and types.

Input Size (n)	Approx. Operations
10	About 30 checks (3 columns x 10 rows)
100	About 300 checks
1000	About 3000 checks

Pattern observation: The work grows roughly in direct proportion to the number of rows times columns.

Final Time Complexity

Time Complexity: O(n x m)

This means the time grows linearly with the number of rows n and columns m.

Common Mistake

[X] Wrong: "Calling info() is always very fast and does not depend on data size."

[OK] Correct: Actually, info() looks at every row in each column to count nulls and types, so bigger data means more work and longer time.

Interview Connect

Knowing how info() scales helps you understand data inspection costs and prepares you to explain performance in real data projects.

Self-Check

What if we added many more columns instead of rows? How would the time complexity change?