Convert JSON to CSV in Notepad++: Step-by-Step Guide

Convert JSON arrays to CSV format in Notepad++ using manual find/replace, plugins, or Python scripts. This method is best for transforming structured data into spreadsheet-compatible format.

Example JSON to CSV

Input JSON:

[
  {"name": "Alice", "age": 25, "city": "New York"},
  {"name": "Bob", "age": 30, "city": "San Francisco"}
]

Output CSV:

name,age,city
Alice,25,New York
Bob,30,San Francisco

Method 1: Manual Conversion

For small JSON files with simple structure, manual conversion works well:

1. Identify the keys in your JSON objects (these become column headers)
2. Create header row in new Notepad++ tab:
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   name,age,city

3. Extract values from each JSON object and format as CSV rows
4. Use Find & Replace (Ctrl+H) to remove JSON syntax like
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   "name": "

5. Save as .csv file

Good for: Small files (< 50 records), learning the process

Limitations: Time-consuming for large files

Method 2: JSON Viewer Plugin

Use Notepad++ plugin for visual conversion:

1. Install JSON Viewer plugin: Plugins → Plugins Admin → Search "JSON Viewer"
2. Open JSON file and go to Plugins → JSON Viewer → Show JSON Viewer
3. Right-click root node in viewer panel → Copy as CSV
4. Paste into new tab and save as .csv

Good for: Medium files with flat structure

Limitations: Struggles with deeply nested objects

Method 3: Python Script (Recommended)

For large files or regular conversions, use a Python script:

Create convert_json_to_csv.py:

import json
import csv

# Change these filenames as needed
json_file = 'data.json'
csv_file = 'data.csv'

with open(json_file, 'r') as f:
    data = json.load(f)

# Extract headers from first object
headers = list(data[0].keys())

# Write to CSV
with open(csv_file, 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(headers)
    for item in data:
        writer.writerow([item[header] for header in headers])

print(f"CSV file '{csv_file}' created successfully!")

Run the script:

python convert_json_to_csv.py

Good for: Large files, complex data, regular conversions

Advantages: Handles encoding, commas, missing fields automatically

Handle Nested JSON

For JSON with nested objects, flatten the structure:

def flatten_json(item):
    flat = {}
    for key, value in item.items():
        if isinstance(value, dict):
            for subkey, subvalue in value.items():
                flat[f"{key}_{subkey}"] = subvalue
        else:
            flat[key] = value
    return flat

# Use in main script:
flat_data = [flatten_json(item) for item in data]
headers = list(flat_data[0].keys())

Method Comparison

Common Issues

Invalid JSON: Use JSONLint to validate syntax

Missing fields: Some objects lack certain keys - use

.get()

Encoding problems: Save as UTF-8 in Notepad++

Commas in data: Python csv module handles this automatically

Alternative Tools

• Online converters: ConvertCSV for quick conversions
• Excel import: Data → Get Data → From JSON (Excel 2019+)
• Command line: Use
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  jq

  tool for complex transformations

Key Points

1. Manual method works for small, simple JSON files
2. JSON Viewer plugin provides visual approach for medium files
3. Python script is most reliable for large or complex data
4. Always validate JSON before attempting conversion
5. Handle nested objects by flattening to flat CSV structure

Related Guides

• JSON to Excel converter - direct Excel output
• How to format JSON in Notepad - JSON beautification
• Best JSON editors for Windows - alternative editors