Checking data types in Data Analysis Python - Time & Space Complexity

Time Complexity: Checking data types

O(n)

Understanding Time Complexity

We want to understand how long it takes to check data types in a dataset as it grows.

How does the time needed change when we have more data?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

def check_types(df):
    types = []
    for col in df.columns:
        types.append(df[col].dtype)
    return types

This code checks the data type of each column in a DataFrame and collects them in a list.

Identify Repeating Operations

• Primary operation: Looping over each column in the DataFrame.
• How many times: Once for each column, so as many times as there are columns.

How Execution Grows With Input

As the number of columns grows, the time to check all types grows in the same way.

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

Pattern observation: The time grows directly with the number of columns.

Final Time Complexity

Time Complexity: O(n)

This means the time to check data types grows in a straight line with the number of columns.

Common Mistake

[X] Wrong: "Checking data types depends on the number of rows in the data."

[OK] Correct: The code only looks at column types, which are stored as metadata, so rows do not affect the time.

Interview Connect

Understanding how operations scale with data size helps you explain your code clearly and shows you think about efficiency.

Self-Check

"What if we checked the data type of every single cell instead of just columns? How would the time complexity change?"