Question

I am trying to crawl wikipedia to get some data for text mining. I am using python’s urllib2 and Beautifulsoup. My question is that: is there an easy way of getting rid of the unnecessary tags(like links ‘a’s or ‘span’s) from the text I read.

for this scenario:

import urllib2
from BeautifulSoup import *
opener = urllib2.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0')]
infile = opener.open("http://en.wikipedia.org/w/index.php?title=data_mining&printable=yes")pool = BeautifulSoup(infile.read())
res=pool.findAll('div',attrs={'class' : 'mw-content-ltr'}) # to get to content directly
paragrapgs=res[0].findAll("p") #get all paragraphs

I get the paragraphs with lots of reference tags like:

paragrapgs[0] =

<p><b>Data mining</b> (the analysis step of the <b>knowledge discovery in databases</b> process,<sup id="cite_ref-Fayyad_0-0" class="reference"><a href="#cite_note-Fayyad-0"><span>[</span>1<span>]</span></a></sup> or KDD), a relatively young and interdisciplinary field of <a href="/wiki/Computer_science" title="Computer science">computer science</a><sup id="cite_ref-acm_1-0" class="reference"><a href="#cite_note-acm-1"><span>[</span>2<span>]</span></a></sup><sup id="cite_ref-brittanica_2-0" class="reference"><a href="#cite_note-brittanica-2"><span>[</span>3<span>]</span></a></sup> is the process of discovering new patterns from large <a href="/wiki/Data_set" title="Data set">data sets</a> involving methods at the intersection of <a href="/wiki/Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a>, <a href="/wiki/Machine_learning" title="Machine learning">machine learning</a>, <a href="/wiki/Statistics" title="Statistics">statistics</a> and <a href="/wiki/Database_system" title="Database system">database systems</a>.<sup id="cite_ref-acm_1-1" class="reference"><a href="#cite_note-acm-1"><span>[</span>2<span>]</span></a></sup> The goal of data mining is to extract knowledge from a data set in a human-understandable structure<sup id="cite_ref-acm_1-2" class="reference"><a href="#cite_note-acm-1"><span>[</span>2<span>]</span></a></sup> and involves database and <a href="/wiki/Data_management" title="Data management">data management</a>, <a href="/wiki/Data_Pre-processing" title="Data Pre-processing">data preprocessing</a>, <a href="/wiki/Statistical_model" title="Statistical model">model</a> and <a href="/wiki/Statistical_inference" title="Statistical inference">inference</a> considerations, interestingness metrics, <a href="/wiki/Computational_complexity_theory" title="Computational complexity theory">complexity</a> considerations, post-processing of found structure, <a href="/wiki/Data_visualization" title="Data visualization">visualization</a> and <a href="/wiki/Online_algorithm" title="Online algorithm">online updating</a>.<sup id="cite_ref-acm_1-3" class="reference"><a href="#cite_note-acm-1"><span>[</span>2<span>]</span></a></sup></p>

Any ideas how to remove them and have pure text?

Answer 1

This is how you could do it with lxml (and the lovely requests):

import requests
import lxml.html as lh
from BeautifulSoup import UnicodeDammit

URL = "http://en.wikipedia.org/w/index.php?title=data_mining&printable=yes"
HEADERS = {'User-agent': 'Mozilla/5.0'}

def lhget(*args, **kwargs):
    r = requests.get(*args, **kwargs)
    html = UnicodeDammit(r.content).unicode
    tree = lh.fromstring(html)
    return tree

def remove(el):
    el.getparent().remove(el)

tree = lhget(URL, headers=HEADERS)

el = tree.xpath("//div[@class='mw-content-ltr']/p")[0]

for ref in el.xpath("//sup[@class='reference']"):
    remove(ref)

print lh.tostring(el, pretty_print=True)

print el.text_content()