What happens to arrays in JSON?

Options: 1) Join with delimiter: ["a","b"] → "a|b". 2) Take first/last value. 3) Expand into multiple rows (each array element creates a row). 4) Create multiple columns: tags_0, tags_1, etc.

How do I handle missing keys?

JSON objects may have different keys. The converter collects all unique keys across all objects for headers, then uses empty string or null for missing values in each row.

What about special characters in values?

Per RFC 4180: values containing commas, quotes, or newlines must be quoted. Quotes inside quoted fields are doubled: "She said ""hello""". Most libraries handle this automatically.

Can I specify column order?

JSON object key order isn't guaranteed. To control column order: 1) Specify column array explicitly, 2) Use ordered structure (array of arrays with header row), or 3) Sort keys alphabetically.

JSON to CSV Converter

Guide & TutorialOffline

Convert JSON array data to CSV format

Convert JSON to CSV format instantly with our free online JSON to CSV converter. This tool helps developers and data analysts transform JSON arrays into CSV spreadsheet format for Excel and data analysis. Simply paste your JSON and get CSV output in real-time.

Include header row

Input JSON

0 characters, 1 lines

CSV Output

About JSON to CSV Conversion

Convert JSON array data to CSV (Comma-Separated Values) format. The input must be a JSON array containing objects with consistent properties.

Requirements:

Input must be a valid JSON array
Each item should be an object with the same structure
All objects should have the same keys for best results

About JSON to CSV Converter

JSON to CSV conversion flattens structured JSON data into tabular format suitable for spreadsheets, databases, and data analysis tools. This transformation enables exporting API data to Excel, creating reports, or preparing data for tools that don't support JSON.

How It Works

The converter analyzes JSON objects to determine all unique keys (columns), then iterates through each object to extract values for each key. Nested objects are flattened using dot notation or JSON string representation. Arrays may be joined or expanded into multiple rows.

Technical Details

JSON arrays of objects convert naturally: keys become headers, values become cells. Nested objects can be flattened (address.city), stringified, or expanded. Arrays within objects require decisions: join with delimiter, take first value, or create multiple rows. Null values become empty cells.

Common Use Cases

Report Generation

Export API data to CSV for spreadsheet analysis.

[{"name":"John","age":30}] → name,age\nJohn,30

Data Export

Allow users to download application data as CSV.

Database Import

Prepare JSON data for import into SQL databases via CSV.

Excel Compatibility

Convert JSON for users who prefer spreadsheet tools.

Data Analysis

Export data for analysis in tools like R, SPSS, or Tableau.

Code Examples

JavaScript

javascript

// Simple JSON to CSV
function jsonToCsv(data) {
  if (!data.length) return '';

  const headers = Object.keys(data[0]);
  const rows = data.map(obj =>
    headers.map(h => {
      const val = obj[h] ?? '';
      // Escape quotes and wrap if needed
      const str = String(val).replace(/"/g, '""');
      return str.includes(',') || str.includes('"') || str.includes('\n')
        ? `"${str}"` : str;
    }).join(',')
  );

  return [headers.join(','), ...rows].join('\n');
}

// Using Papa Parse (handles edge cases)
import Papa from 'papaparse';
const csv = Papa.unparse(jsonArray);

// With custom columns
const csv = Papa.unparse({
  fields: ['name', 'email', 'age'],
  data: jsonArray
});

Papa Parse handles quoting, escaping, and edge cases automatically. For simple data, manual conversion works.

Python

python

import csv
import json

# Using csv module
def json_to_csv(data):
    if not data:
        return ''

    output = []
    writer = csv.DictWriter(
        output,
        fieldnames=data[0].keys(),
        quoting=csv.QUOTE_MINIMAL
    )
    writer.writeheader()
    writer.writerows(data)
    return '\n'.join(output)

# Using pandas (handles nested, more features)
import pandas as pd

df = pd.json_normalize(data)  # Flattens nested objects
csv_string = df.to_csv(index=False)

# With specific columns
df[['name', 'email']].to_csv('export.csv', index=False)

pandas.json_normalize flattens nested structures. csv.DictWriter handles proper CSV formatting.

Tips & Best Practices

Decide upfront how to handle nested objects and arrays
Specify column order explicitly for consistent output
Test with data containing commas, quotes, and newlines
Consider BOM (Byte Order Mark) for Excel UTF-8 compatibility
For large datasets, use streaming to avoid memory issues

Common Mistakes to Avoid

Not escaping commas and quotes in values
Assuming all objects have the same keys
Losing data from nested structures without explicit handling
UTF-8 encoding issues with Excel (add BOM for compatibility)

When to Use This Tool

Use JSON to CSV for data export features, report generation, preparing API data for spreadsheet analysis, or converting JSON for tools that require tabular data format.

Frequently Asked Questions

Common approaches: 1) Flatten with dot notation: {"address":{"city":"NYC"}} → address.city column with "NYC" value. 2) JSON stringify: store nested object as JSON string in cell. 3) Ignore nested objects. Choose based on your use case.

// Simple JSON to CSV function jsonToCsv(data) { if (!data.length) return ''; const headers = Object.keys(data[0]); const rows = data.map(obj => headers.map(h => { const val = obj[h] ?? ''; // Escape quotes and wrap if needed const str = String(val).replace(/"/g, '""'); return str.includes(',') || str.includes('"') || str.includes('\n') ? `"${str}"` : str; }).join(',') ); return [headers.join(','), ...rows].join('\n'); } // Using Papa Parse (handles edge cases) import Papa from 'papaparse'; const csv = Papa.unparse(jsonArray); // With custom columns const csv = Papa.unparse({ fields: ['name', 'email', 'age'], data: jsonArray });

import csv import json # Using csv module def json_to_csv(data): if not data: return '' output = [] writer = csv.DictWriter( output, fieldnames=data[0].keys(), quoting=csv.QUOTE_MINIMAL ) writer.writeheader() writer.writerows(data) return '\n'.join(output) # Using pandas (handles nested, more features) import pandas as pd df = pd.json_normalize(data) # Flattens nested objects csv_string = df.to_csv(index=False) # With specific columns df[['name', 'email']].to_csv('export.csv', index=False)