libextract 0.0.11

Overview

Libextract provides two extractors out-of-the-box: api.articles and api.tabular

libextract.api.articles(document, encoding=’utf-8’, count=5)

Given an html document, and optionally the encoding and the number of predictions (count) to return (in descending rank), articles returns a list of HTML-nodes likely containing the articles of text of a given website.

The extraction algorithm is based of text length. Refer to rodricios.github.io/eatiht for an in-depth explanation.

libextract.api.tabular(document, encoding=’utf-8’, count=5)

Given an html document, and optionally the encoding, and the number of predictions (count) to return (in descending rank) tabular returns a list of HTML nodes likely containing “tabular” data (ie. table, and table-like elements).

Installation

pip install libextract

Usage

Extracting text-nodes from a wikipedia page:

from requests import get
from libextract.api import articles

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

Libextract uses Python’s de facto HT/XML processing library, lxml.

The predictions returned by both api.articles and api.tabular are lxml HtmlElement objects (along with the associated metric used to rank each prediction).

Therefore, you can access lxml’s methods for post-processing.

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

Tabular-data extraction is just as easy.

from libextract.api import tabular

height_data = get("http://en.wikipedia.org/wiki/Human_height")
tabs = tabular(height_data.content)

To convert HT/XML element to python dict (and, you know, use it with Pandas and stuff):

>>> from libextract import clean
>>> clean.to_dict(tabs[0])
{'Entity': ['Monaco',
  'Macau',
  'Japan',
  'Singapore',
  'San Marino',
  ...}

lxml
statscounter