Convert PDF to Pandas DataFrame Online

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

What PDF is

PDF describes a page: glyphs at coordinates, not rows and columns. A table in a PDF is a visual arrangement, not a data structure.

Reports, invoices, statements and anything sent to be read rather than processed.

What Pandas DataFrame is

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

Data analysis and notebooks.

What changes when you convert PDF to Pandas DataFrame

Every character in a text-based PDF carries its position on the page. Characters sharing a baseline are one row. Columns are found from the vertical strips no character ever occupies, which is why extraction works cleanly on a document laid out with spacing and badly on one laid out with ruled lines. A header repeated at the top of each page is detected and dropped rather than landing in the middle of the data.

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 PDF to Pandas DataFrame

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

pandas wants a type for each column, and PDF 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.

Anything visual in the PDF - weight, alignment, colour, column widths - has no counterpart in pandas and is dropped. The values are what survives.

This is the direction that recovers structure: PDF has no table in it to read, only an arrangement that looks like one, so the rows and columns are inferred rather than read.

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.

PDF 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 PDF to Pandas DataFrame?

Paste your PDF 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.

Can I get my table back out of the PDF?

Out of one made here, yes, because the text is real text. Out of a scanned document, no. A scan is an image of a page, and pulling data from it needs optical character recognition, which is a different job with a different failure rate.

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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