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JSON to CSV

Convert JSON data to CSV format with customizable options

Sample JSON Data

About JSON to CSV Conversion

JSON to CSV conversion transforms structured JSON data into comma-separated values format, making it compatible with spreadsheet applications, databases, and data analysis tools. Perfect for data export, reporting, and integration with business intelligence platforms.

  • Convert JSON arrays and objects to CSV format
  • Handle nested objects and flatten data structures
  • Customize delimiters, headers, and formatting
  • Support for large datasets and batch processing
  • Preserve data types and handle special characters

Conversion Features

Data Handling

  • Flatten nested JSON objects
  • Handle arrays and primitive values
  • Preserve data relationships
  • Auto-generate column headers
  • Handle missing or null values

Output Options

  • Custom field delimiters (, ; | )
  • Configurable quote characters
  • Header row customization
  • Date format standardization
  • Character encoding options

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Frequently Asked Questions

How are nested JSON objects handled?

Nested objects are flattened using dot notation (e.g., "user.address.city"). Arrays are either converted to comma-separated strings or split into multiple rows depending on your preference.

What happens with missing data?

Missing or null values are typically converted to empty CSV cells or a placeholder value like "N/A". You can customize how null values are represented in the output.

Can I customize the CSV delimiter?

Yes! You can use comma (,), semicolon (;), tab ( ), or pipe (|) as delimiters. This is useful for different regional CSV standards or specific application requirements.

How large JSON files can I convert?

The tool can handle moderately large JSON files in the browser. For very large datasets (>100MB), consider using server-side processing or splitting the data into smaller chunks.

Conversion Examples

Simple JSON Array:

JSON Input:

[
  {
    "name": "John Doe",
    "age": 30,
    "city": "New York"
  },
  {
    "name": "Jane Smith",
    "age": 25,
    "city": "Los Angeles"
  }
]

CSV Output:

name,age,city
"John Doe",30,"New York"
"Jane Smith",25,"Los Angeles"

Nested JSON Objects:

JSON Input:

[
  {
    "user": {
      "name": "Alice",
      "contact": {
        "email": "alice@example.com",
        "phone": "555-0123"
      }
    },
    "order": {
      "id": "ORDER-001",
      "total": 99.99
    }
  }
]

CSV Output:

user.name,user.contact.email,user.contact.phone,order.id,order.total
"Alice","alice@example.com","555-0123","ORDER-001",99.99

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Programming Examples

JavaScript JSON to CSV:

function jsonToCsv(jsonData, options = {}) {
  const {
    delimiter = ',',
    includeHeaders = true,
    flattenObjects = true
  } = options;
  
  if (!Array.isArray(jsonData) || jsonData.length === 0) {
    throw new Error('Input must be a non-empty array');
  }
  
  // Flatten nested objects
  const flattenObject = (obj, prefix = '') => {
    const flattened = {};
    for (const key in obj) {
      const newKey = prefix ? `${prefix}.${key}` : key;
      if (typeof obj[key] === 'object' && obj[key] !== null && !Array.isArray(obj[key])) {
        Object.assign(flattened, flattenObject(obj[key], newKey));
      } else {
        flattened[newKey] = obj[key];
      }
    }
    return flattened;
  };
  
  // Process data
  const processedData = flattenObjects ? 
    jsonData.map(item => flattenObject(item)) : jsonData;
  
  // Get all unique headers
  const headers = [...new Set(processedData.flatMap(Object.keys))];
  
  // Escape CSV values
  const escapeValue = (value) => {
    if (value === null || value === undefined) return '';
    const str = String(value);
    if (str.includes(delimiter) || str.includes('"') || str.includes('
')) {
      return '"' + str.replace(/"/g, '""') + '"';
    }
    return str;
  };
  
  // Build CSV
  const csvRows = [];
  
  if (includeHeaders) {
    csvRows.push(headers.map(escapeValue).join(delimiter));
  }
  
  processedData.forEach(row => {
    const values = headers.map(header => escapeValue(row[header]));
    csvRows.push(values.join(delimiter));
  });
  
  return csvRows.join('
');
}

// Usage
const data = [
  { name: 'John', age: 30, city: 'NYC' },
  { name: 'Jane', age: 25, city: 'LA' }
];

console.log(jsonToCsv(data));

Python JSON to CSV:

import json
import csv
import io
from typing import List, Dict, Any

def flatten_json(obj: Dict[str, Any], parent_key: str = '', sep: str = '.') -> Dict[str, Any]:
    """Flatten nested JSON object"""
    items = []
    for k, v in obj.items():
        new_key = f"{parent_key}{sep}{k}" if parent_key else k
        if isinstance(v, dict):
            items.extend(flatten_json(v, new_key, sep=sep).items())
        elif isinstance(v, list):
            # Convert list to comma-separated string
            items.append((new_key, ', '.join(map(str, v))))
        else:
            items.append((new_key, v))
    return dict(items)

def json_to_csv(json_data: List[Dict[str, Any]], 
                delimiter: str = ',',
                include_headers: bool = True) -> str:
    """Convert JSON data to CSV format"""
    
    if not json_data:
        return ""
    
    # Flatten all objects
    flattened_data = [flatten_json(item) for item in json_data]
    
    # Get all unique fieldnames
    fieldnames = set()
    for item in flattened_data:
        fieldnames.update(item.keys())
    fieldnames = sorted(fieldnames)
    
    # Create CSV in memory
    output = io.StringIO()
    writer = csv.DictWriter(
        output, 
        fieldnames=fieldnames, 
        delimiter=delimiter,
        quoting=csv.QUOTE_NONNUMERIC
    )
    
    if include_headers:
        writer.writeheader()
    
    for row in flattened_data:
        writer.writerow(row)
    
    return output.getvalue()

# Usage example
data = [
    {"name": "Alice", "details": {"age": 30, "city": "Boston"}},
    {"name": "Bob", "details": {"age": 25, "city": "Seattle"}}
]

csv_result = json_to_csv(data)
print(csv_result)

Best Practices

  • Validate JSON: Ensure JSON is valid before conversion
  • Handle Arrays Consistently: Decide how to represent array data (flatten vs. serialize)
  • Escape Special Characters: Properly escape quotes, commas, and newlines
  • Choose Appropriate Delimiters: Use semicolons for European locales, tabs for TSV
  • Preserve Data Types: Consider how numbers, booleans, and dates are represented
  • Test with Sample Data: Verify output with your target application
  • Consider Memory Usage: Use streaming for very large datasets

Common Use Cases

  • API data export to spreadsheets
  • Database migration and imports
  • Business intelligence reporting
  • Data analysis and visualization prep
  • CRM and ERP system integration
  • Log file analysis and processing
  • E-commerce data transformation
  • Research data compilation

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