CSV to JSON Instantly

Key Features and Benefits

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  Automatic Delimiter Detection — The converter analyzes your input to determine whether it uses commas, tabs, semicolons, or pipes as the field separator. It does this by counting candidate delimiters across multiple lines and checking for consistency. A file where every line has exactly 7 commas is almost certainly comma-delimited. A file with inconsistent comma counts but consistent tab counts is tab-delimited. This detection works for the vast majority of CSV files without you needing to specify the delimiter manually.
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  Header Row Inference — Most CSV files use the first row as column headers, but some do not — especially machine-generated data exports and sensor logs. The converter examines the first row and compares its values against subsequent rows. If the first row contains only text while the data rows contain numbers and dates, it is almost certainly a header. If the first row looks structurally identical to the rest, the converter can generate synthetic headers like column_1, column_2, and so on. You can also manually toggle header mode when automatic detection gets it wrong.
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  Data Type Coercion — Values that look like integers become JSON numbers. Values that match true or false become JSON booleans. Empty fields become null. Quoted numbers stay as strings — because someone deliberately quoted them, probably to preserve leading zeros like zip codes ("07302") or phone numbers ("0044-20-1234"). This heuristic approach gets it right for the vast majority of data. For cases where it guesses wrong, you can override the type for specific columns.
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  Quoted Field and Escape Handling — Fields wrapped in double quotes can contain commas, newlines, and double quotes without breaking the parser. An escaped double quote inside a quoted field appears as two consecutive double quotes — that is the RFC 4180 convention. The converter handles all of this correctly, including the tricky case of a field that starts with a quote but is not actually a quoted field. Mishandling quotes is the number one cause of CSV parsing bugs, and it is the reason hand-written split-on-comma parsers break on real data.
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  Newlines Within Fields — A CSV field can legally contain a literal newline character as long as the field is quoted. Think of a product description or an address field that spans multiple lines. Naive parsers that split on newlines first and then parse each line break completely on these inputs. The converter uses a proper state machine that tracks whether it is inside a quoted field, so embedded newlines are treated as part of the field value rather than row separators.
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  Large File Streaming Support — For files with hundreds of thousands of rows, the converter processes data in chunks rather than loading the entire file into memory at once. This streaming approach means a 200 MB CSV file does not require 200 MB of RAM just to start parsing. The parser reads a buffer, extracts complete rows, emits JSON objects, and moves forward. You get results incrementally — the first few thousand rows appear almost immediately — instead of waiting for the entire file to finish processing.

How to Convert CSV to JSON

1. 1

   Load Your CSV Data

   Paste your CSV text into the input area or drag and drop a .csv file. The converter accepts files with any common encoding — UTF-8, UTF-16, Latin-1, Windows-1252. If your file contains non-ASCII characters like accented letters, CJK characters, or currency symbols, UTF-8 is the safest encoding. Files exported from older versions of Excel on Windows often use Windows-1252, which handles Western European characters but mangles everything else. When in doubt, open the file in a text editor first and check the encoding setting.

2. 2

   Verify Delimiter and Header Detection

   The converter auto-detects the delimiter and header row. Check that it got both right by looking at the preview. If your data shows up as a single column, the delimiter was detected incorrectly — switch it manually. If the first row of data appears as column headers, toggle off the header row option. Common pitfalls: files that use commas as decimal separators in numeric fields (like 3,14 for pi) can confuse comma detection. European CSV files from Excel almost always use semicolons precisely because of this conflict.

3. 3

   Configure Type Coercion

   Review the automatic type detection results. The converter shows you what type it inferred for each column — string, number, boolean, or null. Override any columns where the inference is wrong. Zip codes, phone numbers, and product codes that look like numbers should stay as strings to preserve leading zeros. Date strings can be left as strings or parsed into ISO 8601 format depending on your needs. If you are feeding the JSON into a typed system — a TypeScript interface, a database schema, a GraphQL resolver — matching the types correctly at this stage saves debugging later.

4. 4

   Choose Your JSON Output Structure

   The default output is an array of objects where each object represents one CSV row. But you might want a different structure. Some APIs expect an object with a data key wrapping the array. Others want a record-style format with separate arrays for headers and rows. The converter supports these variations. For database imports, the array-of-objects format is almost always what you want. For spreadsheet-style rendering, the header-plus-rows format maps more directly to the visual layout.

5. 5

   Export the JSON

   Copy the JSON output to your clipboard or download it as a .json file. The output uses UTF-8 encoding and includes proper escaping for all special characters — backslashes, double quotes, control characters, and Unicode escape sequences. If the output is large, you can choose between formatted JSON with indentation for readability or minified JSON for smaller file size. A 100,000-row CSV typically produces a JSON file 30-50% larger than the original CSV because of the repeated key names in every object. Keep this size increase in mind when working with large datasets.

Expert Tips for CSV to JSON Conversion

Related Tools

CSV to JSON conversion is rarely the end of the pipeline. After converting, you will often format the JSON for readability, validate it against a schema, or transform it further for a specific API. If your target system expects XML instead of JSON, the XML formatter handles that final step. And when you need to go the other direction — taking JSON data and preparing it for a spreadsheet import — the JSON to CSV converter completes the round trip. These tools work together to move data between the tabular and hierarchical worlds without writing custom code for each transformation.