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scraping_indeed.py
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scraping_indeed.py
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# -*- coding: utf-8 -*-
"""
Created on Fri May 18 16:41:12 2018
@author: andreas
"""
import requests
from bs4 import BeautifulSoup
from newspaper import Article
import re
import time
import pandas as pd
import numpy as np
import sys
import json
from slimit import ast
from slimit.parser import Parser
from slimit.visitors import nodevisitor
import pickle
from itertools import combinations
from nltk.util import ngrams
import string
import matplotlib.pyplot as plt
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF
from random import randint, shuffle
def get_job_links(url_listings):
page = requests.get(url_listings)
soup = BeautifulSoup(page.content)
found = 0
n = -1
# parse all the script instances from the content
# this is javascript so we need to weed it out from html
while found == 0:
n = n + 1
script = soup.findAll('script')[n].string
if script!=None:
if "jobmap" in script:
found = 1
#thanks to StackExchange I got the following to isolate the
#relevant fields which contain job descriptions
script = soup.findAll('script')[11].string
data = script.split("jobmap = ", 1)[-1].rsplit(';', 1)[0]
job_urls = []
#parse the fields and get the urls
parser = Parser()
tree = parser.parse(data)
for node in nodevisitor.visit(tree):
if isinstance(node, ast.Assign):
value = getattr(node.left, 'value', '')
if value=="jk":
jobID = getattr(node.right, 'value', '')
jobID = re.sub('\'', '', jobID)
new = "https://www.indeed.co.uk/viewjob?jk="+ jobID + "&q"
job_urls.append(new)
return job_urls
def get_job_description(job_url):
try:
job_page = Article(url = job_url)
job_page.download()
job_page.parse()
job_text = job_page.text
except ArticleException:
print("url issues during text retrieval! no txt saved")
job_text=[]
except Exception:
print("error during text retrieval! no txt saved")
job_text=[]
return job_text
root_url = "https://www.indeed.co.uk/jobs?q=\"data+scientist\"&start="
job_docs = pd.Series()
n_scraps = range(0,110)
for scrap in n_scraps:
ind = scrap * 10
url_listing = root_url + str(ind)
found_links = get_job_links(url_listing)
adict = []
for link in range(0,len(found_links)):
try:
txt = get_job_description(found_links[link])
except Exception:
txt = []
print("error")
if txt != []:
adict.append(txt)
job_docs = pd.concat([job_docs,pd.Series(adict)],axis=0)
time_int = int(round(abs(np.random.normal(loc=18.0, scale=6.0, size=None)) + (np.random.rand() * 10)))
time.sleep(time_int)
print('another scrap for the heap...!')
output = open('data4.pkl', 'wb')
pickle.dump(job_docs, output)
output.close()