Supported input
Paste a header row followed by one or more delimited data rows.
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CSV to JSON
Convert UTF-8 CSV, TSV, semicolon or pipe-delimited rows to a formatted JSON array with header controls, delimiter detection and empty-value handling.
Detect comma, semicolon, tab or pipe delimiters Use the first row as property names Generate column names when no header exists Parse quoted fields and embedded line breaks
Tips
Confirm the delimiter and test a short representative sample first.
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INPUT Delimited CSV rows *
Paste a header row followed by one or more delimited data rows. Example: name,role
Ada,Developer
Lin,Designer
Reading file…
CSV preview
JSON output
JSON output ready to review and copy.
About the tool
What CSV to JSON does
CSV to JSON converts delimited table rows into a formatted JSON array of objects. It supports comma, semicolon, tab and pipe delimiters, including quoted fields that contain delimiters or line breaks.
With headers enabled, the first row supplies JSON property names. Without headers, the converter generates column_1, column_2 and so on. Every CSV value remains a string unless an empty field is explicitly converted to null; numeric-looking IDs and dates are therefore not silently retyped.
How to use
- Add UTF-8 data. Paste the complete table or upload a supported text file.
- Choose the delimiter. Use automatic detection or explicitly select comma, semicolon, tab or pipe.
- Set header behavior. Enable headers when the first row contains unique property names; disable it to generate numbered columns.
- Choose value rules. Decide whether surrounding whitespace is trimmed and whether empty fields become null.
- Convert and inspect. Check the detected delimiter, row count, column count and several representative records.
Built for the task
Why use CSV to JSON?
Focused controls, predictable output, and a workflow designed around this exact transformation.
01
Real CSV parsing
Quoted delimiters and embedded line breaks are parsed as fields instead of being split naively.
02
Delimiter detection
Detect common separators automatically or select one explicitly for predictable imports.
03
Header controls
Use unique source headers or create stable numbered property names.
04
Formatted JSON output
Receive a readable array with conversion statistics and a downloadable JSON file.
Useful answers
Questions about CSV to JSON
Practical details about input, output, privacy, limits, and the best way to use this tool.
01 Which delimiters can be detected?
The converter supports comma, semicolon, tab and pipe. Choose one explicitly when automatic detection is ambiguous.
02 Are numbers converted to JSON numbers?
No. CSV has no universal data-type system, so non-empty fields remain JSON strings. This preserves values such as leading-zero identifiers.
03 What happens to empty fields?
They become empty strings by default. You can choose to output JSON null values instead.
04 Can quoted fields contain line breaks?
Yes. Properly quoted multiline fields are read as one logical CSV record.
05 What header errors are rejected?
When headers are enabled, empty or duplicate column names are rejected because they cannot map safely to unique object properties.
06 What happens when a row is short?
Missing trailing cells are padded with empty strings or null according to the selected empty-value setting.
07 What happens when a row has extra cells?
The conversion stops with the physical row number, expected column count and actual column count.
08 Why can another importer behave differently?
CSV dialects vary in delimiter, escape and encoding rules. Test the output with the destination application.
Learn JSON
Read the complete JSON Guide
Learn JSON objects, arrays, value types, validation, formatting, parsing, API workflows, and common syntax errors.
- Objects, arrays, and values
- Validation and formatting
- API and parsing workflows
Read guide
Practical explanations and examples
Headers and JSON property names
Headers must be present, non-empty and unique when the first row is used for property names. If the file has no header, disable the option and the first row becomes data under generated names such as column_1. This avoids silently treating real data as labels.
How rows are parsed
The parser reads logical CSV records, so a newline inside a properly quoted field does not create an extra JSON object. A row with missing trailing fields is padded; a row wider than the header is rejected because its extra values have no property name.
Delimiter detection
Automatic mode compares comma, semicolon, tab and pipe against the first line. Explicit selection is safer when a file contains only one column, unusual punctuation or a delimiter that appears frequently inside unquoted text.
Value types and empty fields
CSV values remain strings. This prevents automatic changes to phone numbers, SKUs, postal codes and identifiers with leading zeroes. Empty cells can remain empty strings or become null, but the converter does not guess booleans, numbers or dates.
Validate the workflow
Use CSV Validator to locate row-width, quote or header issues before converting. Use JSON Formatter for a dedicated formatting pass, or JSON to CSV to create a delimited export from compatible JSON data.
CSV conventions and privacy
RFC 4180 documents a common CSV format, but real applications use several dialects. Follow the destination importer when it specifies a delimiter or encoding. Remove confidential records and credentials from production exports before using any online utility.