Reading JSON with read_json in Pandas - Time & Space Complexity

Time Complexity: Reading JSON with read_json

O(n)

Understanding Time Complexity

When we read JSON data into pandas, we want to know how long it takes as the data grows.

We ask: How does the time to load data change when the JSON file gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

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

This code reads a JSON file into a pandas DataFrame.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: pandas reads and parses each JSON element (like each object or array item).
• How many times: Once for each item in the JSON data, so it depends on the number of records.

How Execution Grows With Input

As the number of JSON records grows, the time to read and parse grows roughly in direct proportion.

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

Pattern observation: The work grows linearly as the JSON data size increases.

Final Time Complexity

Time Complexity: O(n)

This means the time to read JSON grows in a straight line with the number of records.

Common Mistake

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

[OK] Correct: Larger JSON files take more time because pandas must parse each record one by one.

Interview Connect

Understanding how data loading time grows helps you write better data pipelines and explain your choices clearly.

Self-Check

"What if we read JSON data from a compressed file? How would the time complexity change?"