Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/atlanhq/camelot
Camelot: PDF Table Extraction for Humans
https://github.com/atlanhq/camelot
extract for-humans pdf table
Last synced: 10 days ago
JSON representation
Camelot: PDF Table Extraction for Humans
- Host: GitHub
- URL: https://github.com/atlanhq/camelot
- Owner: atlanhq
- License: other
- Created: 2016-06-18T11:48:49.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-01-05T15:25:42.000Z (almost 2 years ago)
- Last Synced: 2024-05-23T00:16:27.252Z (6 months ago)
- Topics: extract, for-humans, pdf, table
- Language: Python
- Homepage: https://camelot-py.readthedocs.io
- Size: 16 MB
- Stars: 3,579
- Watchers: 84
- Forks: 349
- Open Issues: 112
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-python-machine-learning-resources - GitHub - 23% open · ⏱️ 15.10.2019): (数据读写与提取)
README
# Camelot: PDF Table Extraction for Humans
[](https://travis-ci.org/camelot-dev/camelot) [](https://camelot-py.readthedocs.io/en/master/)
[](https://codecov.io/github/camelot-dev/camelot?branch=master)
[](https://pypi.org/project/camelot-py/) [](https://pypi.org/project/camelot-py/) [](https://pypi.org/project/camelot-py/) [](https://gitter.im/camelot-dev/Lobby)
[](https://github.com/ambv/black)
**Camelot** is a Python library that makes it easy for *anyone* to extract tables from PDF files!
**Note:** You can also check out [Excalibur](https://github.com/camelot-dev/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](https://github.com/camelot-dev/camelot/blob/master/docs/_static/pdf/foo.pdf).
>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, 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_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% |
There's a [command-line interface](https://camelot-py.readthedocs.io/en/master/user/cli.html) too!
**Note:** Camelot only works with text-based PDFs and not scanned documents. (As Tabula [explains](https://github.com/tabulapdf/tabula#why-tabula), "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](https://gist.github.com/vinayak-mehta/e5949f7c2410a0e12f25d3682dc9e873).
- **Export** to multiple formats, including JSON, Excel, HTML and Sqlite.
See [comparison with other PDF table extraction libraries and tools](https://github.com/camelot-dev/camelot/wiki/Comparison-with-other-PDF-Table-Extraction-libraries-and-tools).
## Installation
### Using conda
The easiest way to install Camelot is to install it with [conda](https://conda.io/docs/), which is a package manager and environment management system for the [Anaconda](http://docs.continuum.io/anaconda/) distribution.
$ conda install -c conda-forge camelot-py
### Using pip
After [installing the dependencies](https://camelot-py.readthedocs.io/en/master/user/install-deps.html) ([tk](https://packages.ubuntu.com/trusty/python-tk) and [ghostscript](https://www.ghostscript.com/)), you can simply use pip to install Camelot:
$ pip install camelot-py[cv]
### From the source code
After [installing the dependencies](https://camelot-py.readthedocs.io/en/master/user/install.html#using-pip), clone the repo using:
$ git clone https://www.github.com/camelot-dev/camelot
and install Camelot using pip:
$ cd camelot
$ pip install ".[cv]"
## Documentation
Great documentation is available at [http://camelot-py.readthedocs.io/](http://camelot-py.readthedocs.io/).
## Development
The [Contributor's Guide](https://camelot-py.readthedocs.io/en/master/dev/contributing.html) 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/camelot-dev/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](https://semver.org/). For the available versions, see the tags on this repository. For the changelog, you can check out [HISTORY.md](https://github.com/camelot-dev/camelot/blob/master/HISTORY.md).
## License
This project is licensed under the MIT License, see the [LICENSE](https://github.com/camelot-dev/camelot/blob/master/LICENSE) file for details.
