# Camelot: PDF Table Parsing for Humans Camelot is a Python 2.7 library and command-line tool for getting tables out of PDF files. ## Usage
>>> import camelot
>>> tables = camelot.read_pdf("foo.pdf")
>>> tables
<TableList n=2>
>>> tables.export("foo.csv", f="csv", compress=True) # json, excel, html
>>> tables[0]
<Table shape=(3,4)>
>>> tables[0].to_csv("foo.csv") # to_json, to_excel, to_html
>>> tables[0].parsing_report
{
"accuracy": 96,
"whitespace": 80,
"order": 1,
"page": 1
}
>>> df = tables[0].df
Camelot comes with a CLI where you can specify page numbers, output format, output directory etc. By default, the output files are placed in the same directory as the PDF.
Camelot: PDF parsing made simpler!
usage:
camelot [options] <method> [<args>...]
options:
-h, --help Show this screen.
-v, --version Show version.
-V, --verbose Verbose.
-p, --pages <pageno> Comma-separated list of page numbers.
Example: -p 1,3-6,10 [default: 1]
-P, --parallel Parallelize the parsing process.
-f, --format <format> Output format. (csv,tsv,html,json,xlsx) [default: csv]
-l, --log Log to file.
-o, --output <directory> Output directory.
-M, --cmargin <cmargin> Char margin. Chars closer than cmargin are
grouped together to form a word. [default: 2.0]
-L, --lmargin <lmargin> Line margin. Lines closer than lmargin are
grouped together to form a textbox. [default: 0.5]
-W, --wmargin <wmargin> Word margin. Insert blank spaces between chars
if distance between words is greater than word
margin. [default: 0.1]
-J, --split_text Split text lines if they span across multiple cells.
-K, --flag_size Flag substring if its size differs from the whole string.
Useful for super and subscripts.
-X, --print-stats List stats on the parsing process.
-Y, --save-stats Save stats to a file.
-Z, --plot <dist> Plot distributions. (page,all,rc)
camelot methods:
lattice Looks for lines between data.
stream Looks for spaces between data.
See 'camelot <method> -h' for more information on a specific method.
## Dependencies
Currently, camelot works under Python 2.7.
The required dependencies include [numpy](http://www.numpy.org/), [OpenCV](http://opencv.org/) and [ghostscript](https://www.ghostscript.com/).
## Installation
Make sure you have the most updated versions for `pip` and `setuptools`. You can update them by
pip install -U pip setuptools### Installing dependencies numpy can be install using `pip`. OpenCV and ghostscript can be installed using your system's default package manager. #### Linux * Arch Linux
sudo pacman -S opencv tk ghostscript* Ubuntu
sudo apt-get install python-opencv python-tk ghostscript#### OS X
brew install homebrew/science/opencv ghostscriptFinally, `cd` into the project directory and install by
make install## Development ### Code You can check the latest sources with the command:
git clone https://github.com/socialcopsdev/camelot.git### Contributing See [Contributing doc](). ### Testing
make test## License BSD License