Pandas to CSV Converter

How to convert a Pandas DataFrame to CSV

In a script, the whole job is one method call. df.to_csv() takes the output path as its first argument and writes the DataFrame to disk in CSV form; the rest of the arguments decide what the file looks like.

import pandas as pd

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 28],
    "City": ["Delhi", "Mumbai", "Chandigarh"],
    "Score": [91.5, 87.25, 94.75],
})

df.to_csv("output.csv", index=False)

That call produces exactly this file:

Name,Age,City,Score
Alice,25,Delhi,91.5
Bob,30,Mumbai,87.25
Charlie,28,Chandigarh,94.75

In the converter above, the same result takes four steps, and every one of them is optional except the first.

1. Paste your data. Comma, semicolon, tab or pipe separated rows, space-aligned columns as print(df) shows them, or apd.DataFrame({…}) literal copied out of a notebook.
2. Set the options that matter. index=False is the one most exports need. Header, delimiter, encoding, missing values, float format and date format are all here too, each labelled with the parameter it maps to.
3. Check the preview. It shows the values as they will be written — same order, same missing-value text, same float formatting — so you can catch a wrong delimiter or an unwanted index column before anything is saved.
4. Download the CSV, or copy the code. The download is a real file assembled in the browser. The snippet is thedf.to_csv(...) call that produces the identical file in your own script.

Pandas to_csv() options

Every control in the tool writes exactly one of these arguments, in pandas' own spelling. The examples are literal fragments you can paste into a call.

Common Pandas to CSV examples

These are the exports people reach for most often. Each one is a real call — set the matching options in the tool and it generates the same line.

Save a DataFrame to CSV without the index

df.to_csv("output.csv", index=False)

Write a CSV with no header row

df.to_csv("output.csv", header=False, index=False)

Write a semicolon-separated file

df.to_csv("output.csv", sep=";", index=False)

Append rows to a file that already exists

df.to_csv("output.csv", mode="a", header=False, index=False)

Write floats rounded to two decimal places

df.to_csv("output.csv", index=False, float_format="%.2f")

Write missing values as NULL

df.to_csv("output.csv", index=False, na_rep="NULL")

Export only two columns, in a chosen order

df.to_csv("output.csv", index=False, columns=["Name", "Score"])

Write a CSV that Excel reads correctly

df.to_csv("output.csv", index=False, encoding="utf-8-sig", lineterminator="\r\n")

Get the CSV as a string

csv_text = df.to_csv(index=False)
print(csv_text.splitlines()[0])  # Name,Age,City,Score

Write dates in a chosen format

df.to_csv("output.csv", index=False, date_format="%Y-%m-%d")

read_csv() vs to_csv()

The two methods run in opposite directions, and mixing them up is the most common source of confusion. to_csv() writes a DataFrame out to a CSV file;read_csv() reads a CSV file back into a DataFrame.

df.to_csv("output.csv", index=False)      # DataFrame → CSV file
back = pd.read_csv("output.csv")          # CSV file → DataFrame

This converter only covers the writing direction. For the reading direction, thepandas read_csv() documentation is the reference to use.

Common mistakes when exporting to CSV

• Forgetting index=False. The index becomes an unnamed first column, and anyone reading the file back gets a column they did not ask for.
• Appending with the header still on. mode="a" writes rows, not structure — so the column names land in the middle of the file unless you also pass header=False.
• Expecting float_format to round your data. It formats the text that is written. The DataFrame is untouched, and reading the file back gives you the rounded values, not the originals.
• Opening a plain UTF-8 file in Excel on Windows. Accented characters show up as mojibake until you write it with encoding="utf-8-sig".
• Treating na_rep as a real missing-value marker. The file just says NULL. On the way back in,read_csv(..., na_values=["NULL"]) is what turns it into NaN again.
• Writing with a custom separator and reading with the default. read_csv() assumes a comma unless you tell it otherwise.
• Being surprised by the trailing newline. pandas ends the file with a line terminator after the last row, and this converter does the same.