Reading JSON (read_json) in Data Analysis Python - Time & Space Complexity

Time Complexity: Reading JSON (read_json)

O(n)

Understanding Time Complexity

We want to understand how the time to read a JSON file changes as the file size grows.

How does the reading time grow when the JSON data gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

data = pd.read_json('data.json')

This code reads a JSON file into a DataFrame for analysis.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Parsing each element in the JSON file.
• How many times: Once for each item in the JSON data.

How Execution Grows With Input

As the JSON file gets bigger, the time to read it grows roughly in direct proportion to its size.

Input Size (n)	Approx. Operations
10	About 10 parsing steps
100	About 100 parsing steps
1000	About 1000 parsing steps

Pattern observation: Doubling the data roughly doubles the work needed.

Final Time Complexity

Time Complexity: O(n)

This means the time to read the JSON grows linearly with the number of items in the file.

Common Mistake

[X] Wrong: "Reading JSON is always instant no matter the size."

[OK] Correct: Larger JSON files take more time because each item must be read and parsed.

Interview Connect

Understanding how data reading scales helps you explain performance in real projects clearly and confidently.

Self-Check

"What if the JSON file contains nested objects? How would the time complexity change?"