pypdf-table-extraction

pypdf_table_extraction (Camelot): PDF Table Extraction for Humans

pypdf_table_extraction Formerly known as Camelot is a Python library that can help you extract tables from PDFs!

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Here's how you can extract tables from PDFs. You can check out the quickstart notebook.

Or follow the example below. You can check out the PDF used in this example here.

>>> import pypdf_table_extraction
>>> tables = pypdf_table_extraction.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!

Cycle Name	KI (1/km)	Distance (mi)	Percent Fuel Savings
			Improved Speed	Decreased Accel	Eliminate Stops	Decreased Idle
2012_2	3.30	1.3	5.9%	9.5%	29.2%	17.4%
2145_1	0.68	11.2	2.4%	0.1%	9.5%	2.7%
4234_1	0.59	58.7	8.5%	1.3%	8.5%	3.3%
2032_2	0.17	57.8	21.7%	0.3%	2.7%	1.2%
4171_1	0.07	173.9	58.1%	1.6%	2.1%	0.5%

pypdf_table_extraction also comes packaged with a command-line interface!

Refer to the QuickStart Guide to quickly get started with pypdf_table_extraction, extract tables from PDFs and explore some basic options.

Tip: Visit the parser-comparison-notebook to get an overview of all the packed parsers and their features.

Note: pypdf_table_extraction only works with text-based PDFs and not scanned documents. (As Tabula explains, "If you can click and drag to select text in your table in a PDF viewer, then your PDF is text-based".)

You can check out some frequently asked questions here.

Why pypdf_table_extraction?

• Configurability: pypdf_table_extraction gives you control over the table extraction process with tweakable settings.
• Metrics: You can discard bad tables based on metrics like accuracy and whitespace, without having to manually look at each table.
• Output: Each table is extracted into a pandas DataFrame, which seamlessly integrates into ETL and data analysis workflows. You can also export tables to multiple formats, which include CSV, JSON, Excel, HTML, Markdown, and Sqlite.

See comparison with similar libraries and tools.

Installation

Using conda

The easiest way to install pypdf_table_extraction is with conda, which is a package manager and environment management system for the Anaconda distribution.

conda install -c conda-forge pypdf-table-extraction

Using pip

After installing the dependencies (tk and ghostscript), you can also just use pip to install pypdf_table_extraction:

pip install pypdf-table-extraction

From the source code

After installing the dependencies, clone the repo using:

git clone https://github.com/py-pdf/pypdf_table_extraction.git

and install using pip:

cd pypdf_table_extraction
pip install "."

Documentation

The documentation is available at http://pypdf-table-extraction.readthedocs.io/.

Wrappers

• camelot-php provides a PHP wrapper on Camelot.

Related projects

• camelot-sharp provides a C sharp implementation of pypdf_table_extraction (Camelot).

Contributing

The Contributor's Guide has detailed information about contributing issues, documentation, code, and tests.

Versioning

pypdf_table_extraction uses Semantic Versioning. For the available versions, see the tags on this repository. For the changelog, you can check out the releases page.

License

This project is licensed under the MIT License, see the LICENSE file for details.