NotImplementedError: File format not supported

[jatinchhabriya]

Describe the bug

NotImplementedError: File format not supported
I am facing this error where the expected result is the list of tables
However I am getting a Implementation exception instead
One of the maintainers of the package @bosd suggested using the fork branch pypdf-table-extraction main branch, however it is not clear how to import the package the way I earlier imported camelot-py[cv] as mentioned in the following steps to reproduce.

Steps to reproduce the bug

%pip install camelot-py[cv]
import camelot.io
from camelot.io import read_pdf

[bosd]

We're currently still updating the documentation. The rebranding has not been completed yet.
However the api has not been changed. So the package published on pypi is a direct compatible replacement of camelot.
https://pypi.org/project/pypdf-table-extraction/

You can install it with pip install pypdf-table-extraction

[jatinchhabriya]

@bosd Can you provide an implementable example starting with how to import and use the read_pdf functionality?

[bosd]

The example from the docs are still accurate.
The only thing to change afaik is the pip install command.

[jatinchhabriya]

@bosd So as I gather you have rebranded to pypdf-table-extraction, pip installing the package did not fix the NotImplemented Error. I don't see a confirmation from Kushal before the issue reported two weeks ago was closed out on the topic branch
Please also confirm the compatible versions of opencv, ghostscript, pandas @MartinThoma

[bosd]

Did you follow the quickstart as a test?
Example (from the readme):

>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
    'accuracy': 99.02,
    'whitespace': 12.24,
    'order': 1,
    'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite
>>> tables[0].df # get a pandas DataFrame!