Extracting day of week and hour in Pandas - Time & Space Complexity

Time Complexity: Extracting day of week and hour

O(n)

Understanding Time Complexity

We want to see how the time to extract day of week and hour from timestamps grows as data size increases.

How does the work change when we have more rows of date-time data?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.DataFrame({
    'timestamp': pd.date_range('2023-01-01', periods=1000, freq='H')
})
df['day_of_week'] = df['timestamp'].dt.dayofweek
df['hour'] = df['timestamp'].dt.hour

This code creates a DataFrame with hourly timestamps and extracts the day of week and hour into new columns.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Extracting day of week and hour from each timestamp in the column.
• How many times: Once for each row in the DataFrame (n times).

How Execution Grows With Input

Each timestamp is processed individually to get day and hour, so work grows directly with number of rows.

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

Pattern observation: The work increases evenly as the number of timestamps increases.

Final Time Complexity

Time Complexity: O(n)

This means the time to extract day and hour grows in direct proportion to the number of timestamps.

Common Mistake

[X] Wrong: "Extracting day and hour is constant time no matter how many rows there are."

[OK] Correct: Each row needs its own extraction, so more rows mean more work.

Interview Connect

Understanding how data size affects time helps you explain your code choices clearly and confidently.

Self-Check

"What if we extracted day of week and hour from multiple timestamp columns instead of one? How would the time complexity change?"