Convert Plain Text to Pandas DataFrame Online

Paste a Plain Text table below, edit it if you need to, and get Pandas DataFrame back. The conversion runs in your browser, so nothing is uploaded, there is no size limit and there is no signup.

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Nothing here is uploaded. The parsing happens on this page.

Edit

Paste or upload something above and it will appear here.

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Header rowDetect numbers

The output will appear here once there is a table to convert.

What Plain Text is

Plain text here means columns lined up with spaces, the way a command line tool prints a table.

Terminal output, log files, fixed-width exports from older systems.

What Pandas DataFrame is

Python that builds a pandas DataFrame from a dictionary of columns.

Data analysis and notebooks.

What changes when you convert Plain Text to Pandas DataFrame

Two shapes are handled. If the same character positions are blank on every line, those gutters are treated as column boundaries, so values containing single spaces stay intact. Otherwise the lines are split on runs of whitespace.

The import line is included so the snippet runs as is. Turn type inference on and numeric columns arrive as numbers rather than strings, which is usually what you want in a DataFrame.

What carries over from Plain Text to Pandas DataFrame

pandas can express everything plain text holds about a table, so this conversion is about shape rather than loss.

pandas wants a type for each column, and plain text does not record one, so each column is typed from what its values look like. A column of digits that should stay text - a zip code, a phone number, a leading-zero id - is the usual thing to check afterwards.

pandas marks its header cells differently from its data cells, so the first row is written with that marker.

The identifier 007 comes out of the Pandas DataFrame as 007. Reading it as a number would have made it 7, and an id that changes value is worse than one that stays text.

The role "Analyst, data" survives with its comma, in one cell rather than split across two. That is the first thing to check in any converted table, and the usual place a Pandas DataFrame file goes wrong.

The quotes around Jonah "Jo" Pryce are escaped with a backslash in the Pandas DataFrame.

A worked example

Three rows of staff data, with an identifier that has a leading zero, a value containing a comma, a value containing quotes and one blank cell. Those are the four places formats disagree, so they are the four places to look.

Plain Text in

id   name              role           started     hours
007  Halima Yusuf      Analyst, data  2024-03-15  38.5
012  Jonah "Jo" Pryce  Engineer       2025-11-02
104  Wei Chen          Manager        2023-06-30  40

Pandas DataFrame out

import pandas as pd

staff = pd.DataFrame({
    "id": ["007", "012", "104"],
    "name": ["Halima Yusuf", "Jonah \"Jo\" Pryce", "Wei Chen"],
    "role": ["Analyst, data", "Engineer", "Manager"],
    "started": ["2024-03-15", "2025-11-02", "2023-06-30"],
    "hours": ["38.5", "", "40"],
})

Questions

How do I convert Plain Text to Pandas DataFrame?

Paste your Plain Text into the box above or drop the file onto it. Check the table in the grid, then copy or download the Pandas DataFrame from the output panel. It takes one step and the data never leaves your browser.

Is there a limit on file size?

No. The work happens on your own machine, so the limit is your machine's memory rather than an upload cap. A file with tens of thousands of rows converts in a second or two.

Is my data uploaded anywhere?

No. The parsing and generating are done by JavaScript running on this page. You can watch the network tab while you convert and see that nothing is sent.