Reading Excel files with read_excel in Pandas - Time & Space Complexity

Time Complexity: Reading Excel files with read_excel

O(n x m)

Understanding Time Complexity

When we read Excel files using pandas, we want to know how the time it takes changes as the file gets bigger.

We ask: How does reading more rows or columns affect the time needed?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.read_excel('data.xlsx')
print(df.head())
    

This code reads an Excel file into a DataFrame and prints the first few rows.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Reading each cell from the Excel file and converting it into DataFrame format.
• How many times: Once for every cell in the file (rows x columns).

How Execution Grows With Input

As the number of rows and columns grows, the time to read grows roughly in proportion to the total number of cells.

Input Size (rows x columns)	Approx. Operations
10 x 5 = 50	About 50 cell reads
100 x 5 = 500	About 500 cell reads
1000 x 5 = 5000	About 5000 cell reads

Pattern observation: Doubling rows or columns roughly doubles the work, so time grows linearly with total cells.

Final Time Complexity

Time Complexity: O(n x m)

This means the time grows in direct proportion to the number of rows (n) times the number of columns (m) in the Excel file.

Common Mistake

[X] Wrong: "Reading an Excel file takes the same time no matter how big it is."

[OK] Correct: The program reads every cell, so bigger files with more rows or columns take more time.

Interview Connect

Understanding how file size affects reading time helps you explain performance in real projects and shows you think about efficiency.

Self-Check

"What if we only read a specific sheet or a few columns? How would the time complexity change?"