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Extract data from websites using basic statistical magic
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README.rst

Libextract: extract data from websites

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Libextract is a statistics-enabled data extraction library that works on HTML and XML documents and written in Python. Originating from eatiht, the extraction algorithm works by making one simple assumption: data appear as collections of repetitive elements. You can read about the reasoning here.

Overview

libextract.api.extract(document, encoding='utf-8', count=5)
Given an html document, and optionally the encoding, return a list of nodes likely containing data (5 by default).

Installation

pip install libextract

Usage

Due to our simple definition of "data", we open up a single interfaceable method. Post-processing is up to you.

from requests import get
from libextract.api import extract

r = get('http://en.wikipedia.org/wiki/Information_extraction')
textnodes = list(extract(r.content))

Using lxml's built-in methods for post-processing:

>> print(textnodes[0].text_content())
Information extraction (IE) is the task of automatically extracting structured information...

The extraction algo is agnostic to article text as it is with tabular data:

height_data = get("http://en.wikipedia.org/wiki/Human_height")
tabs = list(extract(height_data.content))
>> [elem.text_content() for elem in tabs[0].iter('th')]
['Country/Region',
 'Average male height',
 'Average female height',
 ...]

Dependencies

lxml
statscounter

Disclaimer

This project is still in its infancy; and advice and suggestions as to what this library could and should be would be greatly appreciated

:)

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