Skip to main content

Camelot: PDF Table Extraction for Humans

Build Status Documentation Status codecov.io image image image Gitter chat

Camelot is a Python library that makes it easy for anyone to extract tables from PDF files!

Note: You can also check out Excalibur, which is a web interface for Camelot!


Here's how you can extract tables from PDF files. Check out the PDF used in this example here.

>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html
>>> 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
>>> 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%

There's a command-line interface too!

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".)

Why Camelot?

  • You are in control.: Unlike other libraries and tools which either give a nice output or fail miserably (with no in-between), Camelot gives you the power to tweak table extraction. (This is important since everything in the real world, including PDF table extraction, is fuzzy.)
  • Bad tables can be discarded based on metrics like accuracy and whitespace, without ever having to manually look at each table.
  • Each table is a pandas DataFrame, which seamlessly integrates into ETL and data analysis workflows.
  • Export to multiple formats, including JSON, Excel and HTML.

See comparison with other PDF table extraction libraries and tools.

Installation

Using conda

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

$ conda install -c conda-forge camelot-py

Using pip

After installing the dependencies (tk and ghostscript), you can simply use pip to install Camelot:

$ pip install camelot-py[cv]

From the source code

After installing the dependencies, clone the repo using:

$ git clone https://www.github.com/socialcopsdev/camelot

and install Camelot using pip:

$ cd camelot
$ pip install ".[cv]"

Documentation

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

Development

The Contributor's Guide has detailed information about contributing code, documentation, tests and more. We've included some basic information in this README.

Source code

You can check the latest sources with:

$ git clone https://www.github.com/socialcopsdev/camelot

Setting up a development environment

You can install the development dependencies easily, using pip:

$ pip install camelot-py[dev]

Testing

After installation, you can run tests using:

$ python setup.py test

Versioning

Camelot uses Semantic Versioning. For the available versions, see the tags on this repository. For the changelog, you can check out HISTORY.md.

License

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

Metadata

Release files for camelot-py 0.3.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for camelot-py 0.3.2
File Size Uploaded Provenance Actions
camelot-py-0.3.2.tar.gz 26.9 kB Without provenance Details for camelot-py-0.3.2.tar.gz

Release files / camelot-py-0.3.2.tar.gz

Download URL camelot-py-0.3.2.tar.gz
Size 26.9 kB
Tags Source
SHA-256 checksum
How to use checksums
a7284b5d102bb76b63b8b92986a0bc1ff23b56d3ca0be503a79dca88a9d955cc
BLAKE2b-256 checksum
How to use checksums
8449b85e1d716feab926407a902df9b2ed8609b0728bf4013f70944d07ba9a8e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/1.12.1 pkginfo/1.4.2 requests/2.20.0 setuptools/40.4.3 requests-toolbelt/0.8.0 tqdm/4.28.1 CPython/3.6.6

Security for camelot-py 0.3.2

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page