Reading Excel files (read_excel) in Data Analysis Python - Time & Space Complexity

Time Complexity: Reading Excel files (read_excel)

O(n x m)

Understanding Time Complexity

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

We ask: How does reading time grow when the Excel file has more rows or columns?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

data = pd.read_excel('data.xlsx')

# data now holds the Excel file content as a DataFrame

This code reads an Excel file into a table-like structure for analysis.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Reading each cell in the Excel file to load data.
• 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 by 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 10 = 10,000	About 10,000 cell reads

Pattern observation: The time grows roughly in direct proportion to the total number of cells.

Final Time Complexity

Time Complexity: O(n * m)

This means the time to read grows roughly with the number of rows times the number of columns.

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 data tasks clearly and confidently.

Self-Check

"What if the Excel file has many empty cells? Would the time complexity change?"