CSV to JSON

Paste your CSV data into the input field and click the Convert to JSON button. The tool will use the first row as property names and convert each subsequent row into a JSON object, creating an array of objects as output.

Your CSV should have headers in the first row (column names) and data in subsequent rows. For example: 'name,age\nJohn,30\nJane,25'. The headers become JSON object keys, and each data row becomes an object in the JSON array.

Yes! Simply copy your data from Excel, Google Sheets, or any spreadsheet application and paste it into the tool. Most spreadsheet applications export to CSV format when you copy cells.

The converter intelligently detects and preserves data types. Numbers remain as numbers in JSON, and text is converted to strings. Boolean values (true/false) and null values are also properly recognized and converted.

The tool properly parses CSV files following standard CSV conventions, including handling quoted fields that contain commas, newlines, or other special characters. This ensures accurate conversion even with complex data.

Yes, the tool generates properly formatted, valid JSON that conforms to all JSON specifications. You can use the output directly in JavaScript, APIs, databases, or any application that accepts JSON data.

Empty cells in CSV are typically converted to empty strings ("") in JSON, while truly missing values might become null depending on the converter's settings. If a row has fewer columns than the header row, the missing values can be handled as null, undefined, or omitted properties. Conversely, if a data row has more columns than headers, extra values may be ignored or assigned to generic property names. For best results, ensure your CSV has consistent column counts and use explicit empty strings or null indicators for missing data.

While standard CSV uses commas, many datasets use semicolons (common in European locales where comma is the decimal separator), tabs (TSV format), pipes (|), or other delimiters. Advanced CSV parsers can detect or accept custom delimiter specifications. If your CSV uses non-standard delimiters, you may need to pre-process it or use a converter that supports delimiter configuration. Tab-separated values (TSV) are especially common for large datasets as tabs rarely appear in data, reducing escaping needs.

Standard CSV is flat and cannot naturally represent hierarchical data. To convert hierarchical data from CSV to JSON, you need to either: use dot notation in headers (like 'user.name', 'user.address.city') that the converter expands into nested objects, include parent-child ID relationships that you manually restructure after conversion, or use multiple CSV files with foreign key relationships. For truly nested data, consider using JSON or XML as the source format. Some advanced converters support pivot operations to create nested structures from flat CSV data.

Converting large CSV files (10MB+) in the browser can be memory-intensive. Browser-based tools may struggle with files over 50MB due to JavaScript memory limitations. For large datasets: use streaming parsers that process chunks rather than loading the entire file into memory, consider server-side conversion for files over 100MB, watch for browser performance warnings, and optimize by removing unnecessary columns first. Node.js tools like 'csv-parser' or Python's 'pandas' library handle large files more efficiently than browser-based converters. Always test with a small sample before processing huge datasets.