Jupyter Notebook best practices in Data Analysis Python - Time & Space Complexity

Time Complexity: Jupyter Notebook best practices

O(n)

Understanding Time Complexity

We want to understand how the time it takes to run code in a Jupyter Notebook grows as the notebook gets bigger or more complex.

How does adding more cells or data affect the time to run the notebook?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

data = pd.read_csv('large_data.csv')

for i in range(len(data)):
    row = data.iloc[i]
    # process each row
    print(row['value'])

This code reads a large CSV file and then processes each row one by one.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through each row of the data.
• How many times: Once for every row in the dataset.

How Execution Grows With Input

As the number of rows grows, the time to process grows roughly the same amount.

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

Pattern observation: The time grows directly with the number of rows. Double the rows, double the work.

Final Time Complexity

Time Complexity: O(n)

This means the time to run the code grows in a straight line with the size of the data.

Common Mistake

[X] Wrong: "Running more cells or adding comments does not affect execution time."

[OK] Correct: While comments do not affect time, adding many cells with heavy computations or repeated data loading can increase total run time significantly.

Interview Connect

Understanding how code execution time grows in notebooks helps you write efficient data analysis and explain your approach clearly in real projects or interviews.

Self-Check

"What if we replaced the loop with vectorized operations using pandas? How would the time complexity change?"