How to Convert a JSON File to CSV Using Python

JSON (JavaScript Object Notation) and CSV (Comma-Separated Values) are two widely used formats for storing and exchanging data. While JSON is great for nested and hierarchical data, CSV is simpler and more compatible with spreadsheet applications like Excel. If you're working with Python, converting JSON to CSV is straightforward using the pandas library, which is the most popular tool for data manipulation.

Prerequisites

Before proceeding, ensure you have the following:

Python installed on your system (preferably Python 3.6 or later).
The pandas library installed. If not, install it using:

pip install pandas

Sample JSON File

For this tutorial, let’s assume we have a JSON file named data.json with the following content:

{
  "employees": [
    {"name": "Rahul", "age": 28, "department": "HR"},
    {"name": "Priya", "age": 32, "department": "Finance"},
    {"name": "Amit", "age": 25, "department": "IT"}
  ]
}

Step-by-Step Conversion Process

Step 1: Import the Required Libraries

First, import the pandas library in your Python script:

import pandas as pd

Step 2: Read the JSON File

Use pandas.read_json() to load the JSON file into a DataFrame:

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

If your JSON has nested structures (like the employees array in our example), you may need to flatten it:

df = pd.json_normalize(df['employees'])

Step 3: Convert the DataFrame to CSV

Now, use df.to_csv() to save the DataFrame as a CSV file:

df.to_csv('output.csv', index=False)

The index=False parameter ensures that the row numbers are not included in the CSV.

Step 4: Verify the Output

Open the generated output.csv file in a text editor or spreadsheet software. It should look like this:

name,age,department
Rahul,28,HR
Priya,32,Finance
Amit,25,IT

Handling Complex JSON Structures

If your JSON contains deeply nested objects or arrays, you may need additional preprocessing. Here’s an example of handling a more complex JSON:

{
  "company": "TechSolutions",
  "employees": [
    {
      "name": "Rahul",
      "details": {
        "age": 28,
        "role": "HR Manager"
      }
    },
    {
      "name": "Priya",
      "details": {
        "age": 32,
        "role": "Finance Head"
      }
    }
  ]
}

To flatten this structure, use:

df = pd.json_normalize(
  data['employees'],
  meta=['name'],
  record_path=['details']
)

Conclusion

Converting JSON to CSV in Python is a breeze with the pandas library. Whether your JSON is simple or nested, pandas provides flexible methods like read_json() and json_normalize() to handle the conversion efficiently.

For further reading, check out the official pandas documentation.

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