Python tutorial

Convert Text File to CSV in Python

Q: How do I use the csv module to convert text to CSV?

Open the text file with csv.reader using the input delimiter, then write rows with csv.writer, whose default delimiter is a comma.

Q: How do I write CSV with pandas?

Load with pd.read_csv (set sep or use sep=None to auto-detect), then call df.to_csv("output.csv", index=False).

Q: How do I set the encoding?

Pass encoding="utf-8", or "utf-8-sig" when the file must open in Excel with a BOM.

Q: How do I convert a tab-delimited file?

Use delimiter="\t" with the csv module or sep="\t" with pandas.

Convert text files to CSV in Python with copy-paste examples for both the built-in csv module and pandas. Handle tab-, space- and custom-delimited input, keep UTF-8 encoding, and control quoting.

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Overview

Two ways to convert text to CSV in Python

Written by the TextToCSV team Updated June 2026 How we test

Python is ideal for repeatable text-to-CSV conversion. The standard library’s csv module is perfect for streaming a file line by line, while pandas is convenient when you also want to clean or reshape the data. Both are shown below with runnable code.

For a one-off conversion you do not need to write any code — the online converter handles it instantly — but a script wins when the job repeats or is part of a pipeline.

Using the csv module

The csv module reads and writes correctly quoted CSV without external dependencies. To convert a tab-separated text file to comma CSV:

Python — tab-delimited text to CSV

import csv

with open("input.txt", newline="", encoding="utf-8") as f_in:
    reader = csv.reader(f_in, delimiter="\t")   # input uses tabs
    rows = list(reader)

with open("output.csv", "w", newline="", encoding="utf-8") as f_out:
    writer = csv.writer(f_out)                  # default delimiter is a comma
    writer.writerows(rows)

If your text is separated by arbitrary whitespace (one or more spaces), split on whitespace instead of a fixed delimiter:

Python — whitespace-delimited text to CSV

import csv

with open("input.txt", encoding="utf-8") as f_in, \
     open("output.csv", "w", newline="", encoding="utf-8") as f_out:
    writer = csv.writer(f_out)
    for line in f_in:
        fields = line.split()          # split on any run of whitespace
        if fields:                     # skip blank lines
            writer.writerow(fields)

Using pandas

With pandas you can let it sniff the delimiter, then write CSV in one line:

Python — pandas, auto-detect the delimiter

import pandas as pd

# sep=None + engine="python" sniffs the delimiter automatically
df = pd.read_csv("input.txt", sep=None, engine="python")
df.to_csv("output.csv", index=False)

Or be explicit about the input delimiter and the output options:

Python — pandas, explicit delimiter and quoting

import csv
import pandas as pd

df = pd.read_csv("input.txt", sep="\t", encoding="utf-8")
df.to_csv(
    "output.csv",
    index=False,                 # do not write the row index
    quoting=csv.QUOTE_MINIMAL,   # quote only when needed
    encoding="utf-8",
)

See the dedicated pandas guide for read_csv parameters in depth.

Delimiters, quotes and encoding

Three settings cover almost every case:

Delimiter — pass delimiter (csv) or sep (pandas). Use "\t" for tabs, "|" for pipes, ";" for semicolons.
Quoting — csv.QUOTE_MINIMAL quotes only when needed; csv.QUOTE_ALL quotes every field.
Encoding — always pass encoding="utf-8". Use encoding="utf-8-sig" when writing a file that Excel must open with a BOM.

Python — write a BOM so Excel reads UTF-8 correctly

df.to_csv("for_excel.csv", index=False, encoding="utf-8-sig")

Good to know

Common errors and how to avoid them

The most frequent Python CSV mistakes are easy to prevent. Forgetting newline="" when opening a file for the csv module causes blank lines between rows on Windows, because the module and the OS both add carriage returns — always pass it. A UnicodeDecodeError on read means the source is not UTF-8; try encoding="latin-1" or detect the encoding first. With pandas, numbers that should keep leading zeros are converted unless you read them as strings with dtype=str. And if pandas guesses the wrong delimiter, set sep explicitly rather than relying on the sniffer.

To verify the result, read your own output back: list(csv.reader(open('output.csv', newline='', encoding='utf-8')))[:3] shows the first parsed rows, confirming columns split as intended. Building a tiny round-trip test like this into a script catches delimiter and quoting regressions before they reach real data.

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