Spreadsheet formats & CSV

How To Convert Tab Delimited To CSV?

Parse tab-separated records using a tab delimiter, then write them with a comma delimiter and normal CSV quoting.

By Uddhav Gopani, FounderPublished 27 September 2026Updated 29 September 2026

Parse tab-separated records using a tab delimiter, then write them with a comma delimiter and normal CSV quoting. This preserves commas within values instead of turning them into accidental column boundaries.

Set up the conversion

Inspect the source contract first. A TSV exporter may support quotes around multiline fields, or it may forbid tabs and newlines inside values entirely. The example assumes the standard csv module’s quoted-field rules with a tab separator. It writes a separate UTF-8 file and leaves the source intact.

A reproducible conversion example

import csv

with open("input.tsv", encoding="utf-8-sig", newline="") as source, \
     open("output.csv", "w", encoding="utf-8", newline="") as target:
    reader = csv.reader(source, delimiter="\t")
    writer = csv.writer(target, delimiter=",", lineterminator="\n")
    width = None
    for record in reader:
        if width is None:
            width = len(record)
        if len(record) != width:
            raise ValueError("Inconsistent number of fields")
        writer.writerow(record)

The code is an illustrative conversion core. Run it in an isolated working folder with the named test files, install only the dependencies used by the example, and handle its reported errors before applying it to a larger dataset. It does not contact a remote conversion service.

Check the expected result

Make a three-column test with a text id, a name containing a comma and an empty note. The comma-containing name must be quoted in the output. Parse the output again and compare its fields with the parsed TSV records. An empty final column must still exist; do not strip trailing tabs before parsing. Add a tab inside a quoted value only if the producer and recipient both permit it.

Format-specific limits

Tab characters copied from a web page may be spaces rather than separators. Do not split on arbitrary whitespace because names and addresses often contain spaces. For a source with no header, agree on a column count rather than treating the first row as field names. If a row fails width validation, keep its record number and correct the source before retrying.

Verify the file contract before using the result

Write down the delimiter, encoding, header names, field order and treatment of empty values. Keep the original file unchanged and write the converted output to a different path. Conversion is successful when a parser for the destination format recovers the intended values; a familiar filename extension or a successful save message is not enough.

Start with a small fixture that contains the cases your real data can contain. Include a leading-zero identifier, a quoted comma, an embedded quotation mark, a blank field and a non-ASCII character. Add a multiline field only if both formats and the receiving system support it. Verify the fixture first, then run a bounded sample from the real input. Record parsed row counts and check the first and last records so truncation or an accidental duplicate header is visible.

Keep identifiers separate from quantities. An account-like or product identifier may contain digits while still being text; converting it to a floating-point number can remove leading zeros or precision. Dates also need an agreed format. When a conversion produces an unexpected blank, investigate whether it came from an empty source cell, a missing field, an invalid value or an automatic missing-value rule. Those conditions can require different downstream handling.

Do not resolve malformed records by silently skipping them unless that behavior is an explicit requirement and the skipped records are reported. Use a failure message that identifies the record or field, retain enough context to correct the input, and avoid exposing sensitive contents in public logs. For production automation, write to a temporary destination and promote it only after validation so a failed run cannot replace a previously good export.

Where the browser tool fits

Convert Text to CSV helps with already extracted plain text. It does not replace a workbook reader, a JSON parser, an XML schema validator or a Parquet engine. Use it only when its input structure and delimiter choices match your actual data. For structured source files, perform the format-aware conversion above and check the result with an independent reader.

Primary reference

Official format and API documentation