How to Convert a CSV File to JSON Using Python

Working with data often requires converting between different formats. One common task is converting CSV (Comma-Separated Values) files to JSON (JavaScript Object Notation), which is widely used in web applications and APIs. Python makes this conversion simple with its built-in libraries.

Why Convert CSV to JSON?

CSV is great for tabular data, but JSON offers more flexibility with nested structures, making it ideal for APIs and configuration files. Converting CSV to JSON helps in:

Integrating data with web applications.
Storing hierarchical or nested data.
Improving readability for complex datasets.

Prerequisites

Before proceeding, ensure you have:

Python installed (version 3.6 or higher recommended).
A CSV file to convert (e.g., data.csv).

Required Python Module

The most popular and widely used module for handling CSV and JSON in Python is the built-in csv and json libraries. No additional installation is needed.

Step-by-Step Conversion Process

Step 1: Read the CSV File

Use Python's csv module to read the CSV file:

import csv

with open('data.csv', mode='r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    data = [row for row in csv_reader]

Step 2: Convert CSV Data to JSON

Use the json module to dump the data into a JSON file:

import json

with open('output.json', mode='w') as json_file:
    json.dump(data, json_file, indent=4)

Step 3: Verify the Output

Check the generated output.json file to ensure the conversion was successful.

Complete Example

Here’s a full script combining all steps:

import csv
import json

# Read CSV and convert to JSON
with open('data.csv', mode='r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    data = [row for row in csv_reader]

# Write JSON to a file
with open('output.json', mode='w') as json_file:
    json.dump(data, json_file, indent=4)

print("CSV successfully converted to JSON!")

Handling Edge Cases

Consider these scenarios for robust conversion:

Empty CSV files: Add a check to handle empty inputs.
Custom delimiters: Use csv.reader with delimiter=';' if needed.
Large files: Process data in chunks to avoid memory issues.

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