Metadata-Version: 2.1
Name: camelot-py
Version: 0.4.0
Summary: PDF Table Extraction for Humans.
Home-page: http://camelot-py.readthedocs.io/
Author: Vinayak Mehta
Author-email: vmehta94@gmail.com
License: MIT License
Description: <p align="center">
           <img src="https://raw.githubusercontent.com/socialcopsdev/camelot/master/docs/_static/camelot.png" width="200">
        </p>
        
        # Camelot: PDF Table Extraction for Humans
        
        [![Build Status](https://travis-ci.org/socialcopsdev/camelot.svg?branch=master)](https://travis-ci.org/socialcopsdev/camelot) [![Documentation Status](https://readthedocs.org/projects/camelot-py/badge/?version=master)](https://camelot-py.readthedocs.io/en/master/)
         [![codecov.io](https://codecov.io/github/socialcopsdev/camelot/badge.svg?branch=master&service=github)](https://codecov.io/github/socialcopsdev/camelot?branch=master)
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        **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/socialcopsdev/camelot/blob/master/docs/_static/pdf/foo.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
        >>> 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
        >>> 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%           |
        
        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 and HTML.
        
        See [comparison with other PDF table extraction libraries and tools](https://github.com/socialcopsdev/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.
        
        <pre>
        $ conda install -c conda-forge camelot-py
        </pre>
        
        ### 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:
        
        <pre>
        $ pip install camelot-py[cv]
        </pre>
        
        ### From the source code
        
        After [installing the dependencies](https://camelot-py.readthedocs.io/en/master/user/install.html#using-pip), clone the repo using:
        
        <pre>
        $ git clone https://www.github.com/socialcopsdev/camelot
        </pre>
        
        and install Camelot using pip:
        
        <pre>
        $ cd camelot
        $ pip install ".[cv]"
        </pre>
        
        ## 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:
        
        <pre>
        $ git clone https://www.github.com/socialcopsdev/camelot
        </pre>
        
        ### Setting up a development environment
        
        You can install the development dependencies easily, using pip:
        
        <pre>
        $ pip install camelot-py[dev]
        </pre>
        
        ### Testing
        
        After installation, you can run tests using:
        
        <pre>
        $ python setup.py test
        </pre>
        
        ## 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/socialcopsdev/camelot/blob/master/HISTORY.md).
        
        ## License
        
        This project is licensed under the MIT License, see the [LICENSE](https://github.com/socialcopsdev/camelot/blob/master/LICENSE) file for details.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: plot
Provides-Extra: all
Provides-Extra: cv
Provides-Extra: dev
