Tablizer

Convert TSV to Pandas DataFrame Online

Paste a TSV 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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What TSV is

TSV is CSV with tabs instead of commas. Because tabs almost never appear inside a value, quoting is rarely needed and the files are easier to read by eye.

Bioinformatics, log processing, and anyone who has copied a block of cells out of a spreadsheet: the clipboard format is TSV.

What Pandas DataFrame is

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

Data analysis and notebooks.

What changes when you convert TSV to Pandas DataFrame

The tab is unambiguous, so there is no delimiter to guess. This is the most reliable of the delimited formats to read.

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 TSV to Pandas DataFrame

One property of the TSV has no home in pandas, and it is worth knowing which before you convert.

pandas wants a type for each column, and TSV 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.

TSV can be read one record at a time. pandas has to be complete before it means anything, so the whole table is held in memory while it is written and a file that gets cut off part way through is not partly usable - it is unusable.

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.

TSV 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 TSV to Pandas DataFrame?

Paste your TSV 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.

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