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Camelot: PDF Table Extraction for Humans

Camelot is a Python library that can help you extract tables from PDFs.

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Extract tables from PDFs in just a few lines of code:

Try it yourself in our interactive quickstart notebook.

Or check out a simple example using this pdf.

<pre> >>> import camelot >>> tables = camelot.read_pdf('foo.pdf') >>> tables &lt;TableList n=1&gt; >>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite >>> tables[0] &lt;Table shape=(7, 7)&gt; >>> 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! </pre>

| 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% |

Camelot also comes packaged with a command-line interface!

Refer to the QuickStart Guide to quickly get started with Camelot, 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: Camelot 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 Camelot?

• Configurability: Camelot 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 Camelot is with conda, which is a package manager and environment management system for the Anaconda distribution.

conda install -c conda-forge camelot-py

Using pip

You can also use pip to install Camelot:

pip install "camelot-py"

Note that additional dependencies may be required if you want to use the non-default backend ghostscript.

From the source code

git clone https://github.com/camelot-dev/camelot.git

and install using pip:

cd camelot
pip install "."

Note that additional dependencies may be required if you want to use the non-default backend ghostscript.

Documentation

The documentation is available at http://camelot-py.readthedocs.io/.

Wrappers

• camelot-php provides a PHP wrapper on Camelot.

Related projects

• camelot-sharp provides a C sharp implementation of Camelot.

Contributing

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

Versioning

Camelot 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.

The documentation theme is licensed under a seperate BSD-like License, see the LICENSE file for details.