JSON to CSV Converter Tool

A powerful, feature-rich tool for converting JSON data to CSV format with advanced customization options and data analysis capabilities.

Overview

The JSON to CSV Converter transforms JSON data into CSV format for easier data analysis, spreadsheet import, and data manipulation. It supports complex nested objects, arrays, and provides extensive customization options.

Features

Core Functionality

• Real-time Conversion: Auto-converts JSON to CSV as you type
• Nested Object Flattening: Handles complex nested JSON structures
• Array Support: Processes JSON arrays and nested arrays
• Multiple Delimiters: Support for comma, semicolon, tab, and pipe delimiters
• Header Control: Option to include/exclude headers in CSV output

Display Options

• Table View: Interactive table display with sorting and filtering
• CSV Text View: Raw CSV text output for copying/editing
• Data Preview: Side-by-side comparison of JSON and CSV formats

Advanced Features

• Data Analysis: Real-time statistics (rows, columns, processing time, file size)
• Schema Detection: Automatic detection of data types and structure
• Search & Filter: Filter data by column or search terms
• Batch Processing: Upload and merge multiple JSON files
• Export Options: Download as CSV, TSV, Excel, or JSON

Data Transformation

• JSON Formatting: Auto-format and beautify JSON input
• Data Sorting: Sort data by any column
• Data Slicing: Preview with top 10 rows option
• Null Handling: Proper handling of null and undefined values

How to Use

Basic Usage

1. Input JSON Data

   • Paste JSON data into the input textarea
   • Upload JSON files using the file upload button
   • Use the sample data button to load example JSON

2. Configure Settings

   • Show As: Choose between table or CSV text view
   • Delimiter: Select CSV delimiter (comma, semicolon, tab, pipe)
   • Options:
     • Top 10 Row: Show only first 10 rows in table view
     • Flatten Nested Objects: Convert nested objects to flat structure
     • Include Headers: Add column headers to CSV output

3. View Results

   • Table view: Interactive table with sorting and filtering
   • CSV view: Raw CSV text ready for copying or download

Advanced Usage

Batch Processing

1. Click "Batch Upload" button
2. Select multiple JSON files
3. Files are automatically merged into a single dataset
4. Convert the combined data to CSV

Data Filtering

1. Enter search terms in the search box
2. Select specific columns to filter
3. Click "Apply" to filter the data
4. Export filtered results

Export Options

• CSV: Standard comma-separated values
• TSV: Tab-separated values
• Excel: Excel-compatible format
• JSON: Export processed JSON data

Configuration Options

Display Settings

showas: 'table' | 'csv'  // Default: 'table'

CSV Formatting

delimiter: ',' | ';' | 'tab' | '|'  // Default: ','

Processing Options

options: [
  'slice',           // Show top 10 rows only
  'flattenNested',   // Flatten nested objects
  'includeHeaders'   // Include CSV headers
]

Sample JSON Data

{
  "users": [
    {
      "id": 1,
      "name": "John Doe",
      "email": "john@example.com",
      "age": 30,
      "address": {
        "street": "123 Main St",
        "city": "New York",
        "zipCode": "10001"
      },
      "hobbies": ["reading", "swimming"]
    },
    {
      "id": 2,
      "name": "Jane Smith",
      "email": "jane@example.com",
      "age": 25,
      "address": {
        "street": "456 Oak Ave",
        "city": "Los Angeles",
        "zipCode": "90210"
      },
      "hobbies": ["coding", "hiking"]
    }
  ]
}

Output Examples

With Flattened Objects

id,name,email,age,address.street,address.city,address.zipCode,hobbies
1,John Doe,john@example.com,30,123 Main St,New York,10001,"[""reading"",""swimming""]"
2,Jane Smith,jane@example.com,25,456 Oak Ave,Los Angeles,90210,"[""coding"",""hiking""]"

Without Flattening

id,name,email,age,address,hobbies
1,John Doe,john@example.com,30,"{""street"":""123 Main St"",""city"":""New York"",""zipCode"":""10001""}","[""reading"",""swimming""]"
2,Jane Smith,jane@example.com,25,"{""street"":""456 Oak Ave"",""city"":""Los Angeles"",""zipCode"":""90210""}","[""coding"",""hiking""]"

Keyboard Shortcuts

• Ctrl+S (Cmd+S): Save CSV file
• Ctrl+F (Cmd+F): Format JSON input
• Ctrl+L (Cmd+L): Load sample data
• Ctrl+K (Cmd+K): Clear all data

Data Analysis Features

Statistics Panel

• Total Records: Number of data rows processed
• Total Columns: Number of columns in output
• Processing Time: Conversion time in milliseconds
• File Size: Size of generated CSV data
• Null Values: Count of null/undefined values
• Nested Objects: Count of nested object structures
• Arrays: Count of array fields

Schema Detection

Automatically detects and displays:

• Field names and data types
• Required vs optional fields
• Sample values for each field
• Data type icons (string 📝, number 🔢, boolean ✅, object 📦)

Error Handling

Common Errors

• Invalid JSON: Displays line and column of syntax errors
• File Too Large: 10MB file size limit
• Conversion Errors: Detailed error messages for processing issues

Validation

• Real-time JSON syntax validation
• Error highlighting with line numbers
• Graceful handling of malformed data

Browser Compatibility

• Chrome 60+
• Firefox 55+
• Safari 12+
• Edge 79+

File Size Limits

• Single File: 10MB maximum
• Batch Upload: No limit on number of files
• Memory Usage: Optimized for large datasets

Tips & Best Practices

Performance Optimization

• Use "Top 10 Row" option for large datasets preview
• Enable "Flatten Nested Objects" for complex JSON structures
• Use batch processing for multiple related files

Data Quality

• Validate JSON before conversion
• Check for consistent data types across records
• Review schema detection results for data understanding

Export Recommendations

• Use CSV for general spreadsheet applications
• Use TSV for tab-delimited requirements
• Use Excel format for Microsoft Office compatibility

Troubleshooting

Common Issues

JSON Not Converting

• Check JSON syntax validity
• Ensure proper quote escaping
• Verify bracket/brace matching

Missing Data in Output

• Enable "Flatten Nested Objects" for nested data
• Check if data is in array format
• Verify field names are consistent

Performance Issues

• Enable "Top 10 Row" for large datasets
• Use batch processing for multiple files
• Clear browser cache if memory issues occur

Support

For technical support or feature requests, please contact our support team or visit our documentation portal.

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Last updated: 2024