How to Convert CSV to JSON Online
Transform CSV data into JSON format instantly with our free CSV to JSON converter. Handles headers, custom delimiters, and nested structures.
Steps
Paste your CSV data
Copy your CSV content and paste it into the input area. The first row is assumed to be the header row containing column names — these become the JSON keys. If your CSV has no header row, toggle the 'No Headers' option.
Set the delimiter
Most CSV files use a comma as the delimiter, but some use semicolons, tabs, or pipes. Check your file and select the correct delimiter. Tab-separated values (TSV) can be converted by selecting the tab delimiter option.
Choose output structure
Select 'Array of Objects' to produce a JSON array where each row becomes an object with header keys. Select 'Array of Arrays' if you want nested arrays without property names. Array of Objects is the most common format for use in JavaScript and APIs.
Handle data types (optional)
Toggle 'Auto-detect Types' to have the converter parse numbers as numbers, booleans as true/false, and null/empty values as JSON null rather than empty strings. This produces cleaner JSON for use in applications that need typed data.
Copy the JSON output
The converted JSON appears in the output panel. Copy it and use it in your API, database import, JavaScript application, or any system that consumes JSON. You can also download it as a .json file.
CSV vs JSON: Choosing the Right Format
CSV (Comma-Separated Values) and JSON serve different strengths. CSV is compact, universally supported by spreadsheet applications (Excel, Google Sheets), and ideal for tabular data with consistent columns. It is the standard format for data exports from databases, analytics platforms, and business applications. JSON is more flexible, supports nested data structures, arrays within objects, mixed types, and null values explicitly. JSON is the de facto standard for web APIs and JavaScript applications. The need to convert between formats arises frequently: you receive data in CSV (from a spreadsheet export) but need JSON for your API, or you have an API response in JSON but want to analyse it in Excel. This converter handles the CSV → JSON direction; the reverse is equally common.
Understanding CSV Format Variations
Despite its name, CSV (Comma-Separated Values) does not always use commas. European locales often use semicolons because commas are the decimal separator there: '1.234,56 EUR' would be ambiguous in a comma-delimited file. Some systems export tab-separated values (TSV), pipe-delimited values, or space-delimited files. The RFC 4180 standard defines the canonical CSV format but is widely ignored — real-world CSV files vary in quoting style, line ending (CRLF vs LF), encoding (UTF-8 vs Latin-1), and handling of null values. When your CSV does not import correctly, the first things to check are: the delimiter character, the file encoding, and whether the first row is a header. Our converter lets you specify all of these explicitly.
Frequently Asked Questions
If a row has fewer columns than the header, the missing fields are set to null in the output. If a row has more columns than the header, the extra values are either ignored or added with auto-generated keys depending on your settings. Well-formed CSV should have consistent column counts — inconsistencies indicate a data quality issue worth fixing at the source.
RFC 4180 compliant CSV wraps fields that contain the delimiter character in double quotes: 'London, UK' becomes '"London, UK"' in CSV. The converter handles these quoted fields correctly, treating the content as a single value regardless of internal commas or newlines.
Yes, but the reverse conversion is only straightforward for flat (non-nested) JSON arrays of objects. If your JSON has nested objects or arrays, you need to decide how to flatten them into columns. Most CSV converters handle flat structures automatically and require manual specification for nested structures.
This browser-based tool works well for CSV files up to a few thousand rows. For large files (hundreds of thousands of rows), use a server-side solution: Python's pandas library, the csvtojson npm package, or database import tools (most SQL databases have CSV import utilities). Processing large files in the browser can exhaust available memory.