Reading HTML tables in Data Analysis Python - Time & Space Complexity

Time Complexity: Reading HTML tables

O(n * m)

Understanding Time Complexity

When we read HTML tables into data, we want to know how long it takes as the table size grows.

We ask: How does the time to read change when the table has more rows or columns?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

url = 'https://example.com/sample-table.html'
tables = pd.read_html(url)
df = tables[0]

This code reads all HTML tables from a webpage and selects the first table as a DataFrame.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Parsing each row and column of the HTML table to build the DataFrame.
• How many times: Once for each cell in the table (rows x columns).

How Execution Grows With Input

As the number of rows and columns grows, the work grows with the total number of cells.

Input Size (rows x columns)	Approx. Operations
10 x 5 = 50	About 50 operations
100 x 10 = 1,000	About 1,000 operations
1,000 x 20 = 20,000	About 20,000 operations

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

Final Time Complexity

Time Complexity: O(n * m)

This means the time to read the table grows proportionally to the number of rows (n) times the number of columns (m).

Common Mistake

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

[OK] Correct: The code must process each cell, so bigger tables take more time to read.

Interview Connect

Understanding how data reading time grows helps you explain your code's efficiency clearly and confidently.

Self-Check

"What if the HTML table has nested tables inside cells? How would the time complexity change?"