Tool Overview

This tool turns CSV into JSON. You paste or upload CSV and get a JSON array back.

CSV is common in spreadsheets and data export. JSON is common in APIs and apps. Moving data from CSV to JSON by hand is slow and easy to get wrong. This tool does the conversion for you so you get correct JSON without writing code.

It is for developers, analysts, and anyone who needs to feed CSV into an API or app that expects JSON. You can use it with basic knowledge of CSV and JSON.

Background and Concepts

CSV uses commas and newlines. JSON uses braces and brackets. CSV is flat rows and columns; JSON can be an array of objects. A related operation involves converting JSON to CSV as part of a similar workflow.

CSV is common in Excel, Google Sheets, and data export. JSON is common in web APIs and configs. When one system sends CSV and another expects JSON, you need a conversion step.

Converting by hand is tedious. You must decide how to split rows and columns, whether the first row is headers, how to handle commas and quotes inside values, and how to type numbers and booleans. This tool uses a standard parser: you choose options; the tool parses the CSV and builds a JSON array where each row is an object and the first row (if you use it) is the keys.

Key Features

• Paste or upload. You can paste CSV into the input box or upload a .csv or .txt file. The tool converts as you type (after a short delay). Uploaded files are limited by size so the page stays responsive.
• Delimiter. You choose how columns are split. Auto-detect lets the parser guess (comma, semicolon, or tab). Or you set comma, semicolon, tab, or pipe. This ensures the right columns are read even when your CSV uses a different separator.
• Header row. You choose whether the first row is headers. When enabled, the first row becomes the keys of each object and each following row becomes one object. When disabled, each row becomes an array of values and the result is an array of arrays.
• Smart type casting. When enabled, the parser tries to turn values into numbers or booleans where it makes sense. When disabled, every value stays a string. This helps when you need real numbers in JSON or when you want to keep leading zeros.
• Skip empty lines. When enabled, blank lines are ignored. When disabled, they are kept. This avoids extra empty rows in the JSON.
• Automatic structure. With header row on, the result is an array of objects. Each object has one property per column; the property name is the header and the value is the cell. Headers and values are trimmed of extra spaces.
• Errors and warnings. The parser reports issues (e.g. wrong number of columns in a row). If the row count is limited for performance, a warning is shown so you know the data was truncated.
• Record and column count. After conversion you see how many records (rows) and columns were produced. You can copy the JSON or download it as a file.
• Copy and download. You can copy the JSON to the clipboard or download it as converted_data.json. Use clear to remove input and output and start over.
• Optional AI analysis. After conversion you can request a data summary, a TypeScript interface for the structure, and suggestions. This is optional and not required for conversion.

Common Use Cases

API integration. You have a CSV export and need to send the data to an API that expects JSON. Paste or upload the CSV, set header row and delimiter, and use the generated JSON in your request or script. For adjacent tasks, converting CSV to XML addresses a complementary step.

Data processing. Moving data from a spreadsheet or export into a JSON-based pipeline or database. Convert once or repeatedly as data changes.

Testing and mocking. Creating JSON payloads from sample CSV for tests, mocks, or documentation.

Config and imports. Some tools take JSON config or import. If your source is CSV, convert it to the expected JSON shape. When working with related formats, converting JSON to YAML can be a useful part of the process.

Learning. Seeing how the same table looks as CSV vs JSON helps understand both formats.

How to Use This Tool (Step-by-Step)

1. Open the CSV to JSON converter page.
2. Paste your CSV into the input area or use the upload button to choose a .csv or .txt file. The file must be within the allowed size.
3. Open settings if you need to change options. Set delimiter to auto-detect or pick comma, semicolon, tab, or pipe. Check or uncheck "Has Header Row" depending on whether the first row is column names. Check or uncheck "Smart Type Casting" to convert numbers and booleans or keep everything as strings. Check or uncheck "Skip Empty Lines" to ignore or keep blank lines.
4. Wait a moment. The tool converts automatically after you type or change options. The JSON appears in the output area.
5. If you see an error, check that the CSV is valid and the delimiter matches your data. Fix extra commas or unclosed quotes and try again.
6. Check the record and column count at the bottom. Use copy to put the JSON in the clipboard or download to save it as a file. Use clear to start over.
7. Optionally, use AI Analysis to get a summary, TypeScript interface, and suggestions for the converted data. This works only when conversion succeeded.

Calculations and Logic

The tool does not do numeric calculations. It transforms structure and text.

The input string is parsed as CSV with a standard parser. You set the delimiter (or leave it to auto-detect), whether the first row is headers, whether to skip empty lines, and whether to cast types. The parser splits the input into rows and columns, respecting quoted fields and escaped quotes. If "Has Header Row" is on, the first row becomes the list of keys and each following row becomes one object with those keys; values are trimmed and optionally type-cast. If "Has Header Row" is off, each row becomes an array of values and the result is an array of arrays. The number of rows is limited; excess rows are dropped and a warning is added. Parser errors (e.g. inconsistent columns) are collected and shown. The result is turned into a JSON string with indentation. Output length is limited; if the result would be too large, you get an error and are asked to reduce input size. In some workflows, converting YAML to JSON is a relevant follow-up operation.

Tips, Limitations and Best Practices

Valid CSV. Use a consistent delimiter and quote values that contain commas or newlines. Check that every row has the same number of columns if you expect clean objects.

Header row. Turn "Has Header Row" on when the first line is column names. Turn it off when the first line is data so every row becomes an array of values.

Delimiter. If columns are wrong, set the delimiter explicitly. Many European CSV files use semicolon instead of comma. For related processing needs, converting JSON to XML handles a complementary task.

Smart type casting. Use it when you want numbers and booleans in JSON. Turn it off when you need to keep leading zeros or exact string values.

Empty cells. Empty CSV cells become empty strings or null in the JSON depending on the parser. The tool trims spaces from headers and values.

Size limits. Input and output have maximum sizes. Row count is also limited. Very large CSV may be truncated with a warning. Split data or process in chunks if needed.

Privacy. Do not paste sensitive or secret data if you use AI Analysis; that data may be sent for analysis.