Reading and writing CSV data in Python - Time & Space Complexity

Time Complexity: Reading and writing CSV data

O(n)

Understanding Time Complexity

When working with CSV files, it's important to know how the time to read or write data changes as the file grows.

We want to understand how the program's speed changes when the number of rows in the CSV changes.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import csv

def read_csv(filename):
    with open(filename, newline='') as file:
        reader = csv.reader(file)
        data = []
        for row in reader:
            data.append(row)
    return data

# This function reads all rows from a CSV file into a list

This code reads each row from a CSV file and stores it in a list.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through each row in the CSV file.
• How many times: Once for every row in the file (n times).

How Execution Grows With Input

As the number of rows increases, the time to read grows in a straight line.

Input Size (n)	Approx. Operations
10	About 10 row reads
100	About 100 row reads
1000	About 1000 row reads

Pattern observation: Doubling the rows roughly doubles the work done.

Final Time Complexity

Time Complexity: O(n)

This means the time to read the CSV grows directly with the number of rows.

Common Mistake

[X] Wrong: "Reading a CSV file always takes the same time no matter how big it is."

[OK] Correct: The program reads each row one by one, so more rows mean more time.

Interview Connect

Understanding how file reading time grows helps you write efficient data processing code and explain your reasoning clearly.

Self-Check

"What if we read the CSV file but only processed every other row? How would the time complexity change?"