Scrape the Names of Statistik Austrias Naming PDF obtained here: http://images.derstandard.at/2013/08/12/VN2p_2012.pdf

scrape-names.py

import scraperwiki
import itertools, re, csv
import lxml

# Configuration - file name and ranges for boys/girls here

filename="/home/mihi/Downloads/VN2p_2012.pdf"

# page numbers for boy and girl names
range_boys=[0,20]
range_girls=[20,43]

def split_element(e):
  return re.split("[ ]+",e.text.replace("*",""))

def take4(x):
  if (len(x)>5):
    return [x[0:4],x[4:]]
  else:
    return [x[0:4]]

def select(r,rng):
  return r.xpath('//page[@number>"%s" and @number<="%s"]/text[@left="64"]/b'%(rng[0],rng[1]))

# Open the file

f=open(filename)

# convert to XML
x=scraperwiki.pdftoxml(f.read())

# Parse XML
r=lxml.etree.fromstring(x)

# Close the File
f.close()

#Select Boys/ Girls

boys=select(r,range_boys)
girls=select(r,range_girls)

# split the lines  - so that the columns are split

boys=[split_element(i) for i in boys]
girls=[split_element(i) for i in girls]

#filter out empty elements

boys=[[i for i in itertools.ifilter(lambda x: x!="", j)] for j in boys]
girls=[[i for i in itertools.ifilter(lambda x: x!="", j)] for j in girls]

#make the two columns (4 elements each)

boys=reduce(lambda x,y: x+y,(take4(i) for i in boys),[])
girls=reduce(lambda x,y: x+y,(take4(i) for i in girls),[])

# append gender:

for x in boys:
  x.append("m")

for x in girls:
  x.append("f")

# put the two things together
names=boys+girls

# filter out extra headers etc
names=itertools.ifilter(lambda x: not x[0].isupper(),names)
names=itertools.ifilter(lambda x: not (x[0] in ["der","m","f"]) ,names)

# open file for writing as csv



f=open("names.csv","wb")
w=csv.writer(f)

# write header

w.writerow(["Name","Absolut","Prozent","Rank","Gender"])

# write names

for x in names:
  w.writerow([i.encode("utf-8") for i in x])

# close file
f.close()