Awesome
<p align="center"> <img src="https://github.com/py-pdf/pypdf_table_extraction/blob/main/docs/_static/pypdf-table-extraction.png" width="200"> </p>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!
Here's how you can extract tables from PDFs. You can 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, 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.
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[base]
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 camelot
pip install ".[base]"
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.