Web Scraping Indeed for Key Data Science Job Skills

from bs4 import BeautifulSoup # For HTML parsing
import urllib2 # Website connections
import re # Regular expressions
from time import sleep # To prevent overwhelming the server between connections
from collections import Counter # Keep track of our term counts
from nltk.corpus import stopwords # Filter out stopwords, such as 'the', 'or', 'and'
import pandas as pd # For converting results to a dataframe and bar chart plots
%matplotlib inline

def text_cleaner(website):
    '''
    This function just cleans up the raw html so that I can look at it.
    Inputs: a URL to investigate
    Outputs: Cleaned text only
    '''
    try:
        site = urllib2.urlopen(website).read() # Connect to the job posting
    except: 
        return   # Need this in case the website isn't there anymore or some other weird connection problem 
    
    soup_obj = BeautifulSoup(site) # Get the html from the site
    
    for script in soup_obj(["script", "style"]):
        script.extract() # Remove these two elements from the BS4 object
    
    
    text = soup_obj.get_text() # Get the text from this
    
        
    
    lines = (line.strip() for line in text.splitlines()) # break into lines
    
        
        
    chunks = (phrase.strip() for line in lines for phrase in line.split("  ")) # break multi-headlines into a line each
    
    def chunk_space(chunk):
        chunk_out = chunk + ' ' # Need to fix spacing issue
        return chunk_out  
        
    
    text = ''.join(chunk_space(chunk) for chunk in chunks if chunk).encode('utf-8') # Get rid of all blank lines and ends of line
        
        
    # Now clean out all of the unicode junk (this line works great!!!)
        
    try:
        text = text.decode('unicode_escape').encode('ascii', 'ignore') # Need this as some websites aren't formatted
    except:                                                            # in a way that this works, can occasionally throw
        return                                                         # an exception
       
        
    text = re.sub("[^a-zA-Z.+3]"," ", text)  # Now get rid of any terms that aren't words (include 3 for d3.js)
                                                # Also include + for C++
        
       
    text = text.lower().split()  # Go to lower case and split them apart
        
        
    stop_words = set(stopwords.words("english")) # Filter out any stop words
    text = [w for w in text if not w in stop_words]
        
        
        
    text = list(set(text)) # Last, just get the set of these. Ignore counts (we are just looking at whether a term existed
                            # or not on the website)
        
    return text

sample = text_cleaner('http://www.indeed.com/viewjob?jk=5505e59f8e5a32a4&q=%22data+scientist%22&tk=19ftfgsmj19ti0l3&from=web&advn=1855944161169178&sjdu=QwrRXKrqZ3CNX5W-O9jEvWC1RT2wMYkGnZrqGdrncbKqQ7uwTLXzT1_ME9WQ4M-7om7mrHAlvyJT8cA_14IV5w&pub=pub-indeed')
sample[:20] # Just show the first 20 words

['professionally.',
 'code',
 'cj',
 'indeed',
 'competitive',
 'month',
 'label',
 'scientist',
 'frequency',
 'per',
 'human',
 'keywords',
 'follow',
 'alt',
 'viewthroughconversion',
 'find',
 'access',
 'style',
 'retirement',
 'candidate']

def skills_info(city = None, state = None):
 '''
 This function will take a desired city/state and look for all new job postings
 on Indeed.com. It will crawl all of the job postings and keep track of how many
 use a preset list of typical data science skills. The final percentage for each skill
 is then displayed at the end of the collation.
 Inputs: The location's city and state. These are optional. If no city/state is input,
 the function will assume a national search (this can take a while!!!).
 Input the city/state as strings, such as skills_info('Chicago', 'IL').
 Use a two letter abbreviation for the state.
 Output: A bar chart showing the most commonly desired skills in the job market for
 a data scientist.
 '''
 final_job = 'data+scientist' # searching for data scientist exact fit("data scientist" on Indeed search)
 # Make sure the city specified works properly if it has more than one word (such as San Francisco)
 if city is not None:
 final_city = city.split()
 final_city = '+'.join(word for word in final_city)
 final_site_list = ['http://www.indeed.com/jobs?q=%22', final_job, '%22&l=', final_city,
 '%2C+', state] # Join all of our strings together so that indeed will search correctly
 else:
 final_site_list = ['http://www.indeed.com/jobs?q="', final_job, '"']
 final_site = ''.join(final_site_list) # Merge the html address together into one string
 base_url = 'http://www.indeed.com'
 try:
 html = urllib2.urlopen(final_site).read() # Open up the front page of our search first
 except:
 'That city/state combination did not have any jobs. Exiting . . .' # In case the city is invalid
 return
 soup = BeautifulSoup(html) # Get the html from the first page
 # Now find out how many jobs there were
 num_jobs_area = soup.find(id = 'searchCount').string.encode('utf-8') # Now extract the total number of jobs found
 # The 'searchCount' object has this
 job_numbers = re.findall('d+', num_jobs_area) # Extract the total jobs found from the search result
 if len(job_numbers) > 3: # Have a total number of jobs greater than 1000
 total_num_jobs = (int(job_numbers[2])*1000) + int(job_numbers[3])
 else:
 total_num_jobs = int(job_numbers[2])
 city_title = city
 if city is None:
 city_title = 'Nationwide'
 print 'There were', total_num_jobs, 'jobs found,', city_title # Display how many jobs were found
 num_pages = total_num_jobs/10 # This will be how we know the number of times we need to iterate over each new
 # search result page
 job_descriptions = [] # Store all our descriptions in this list
 for i in xrange(1,num_pages+1): # Loop through all of our search result pages
 print 'Getting page', i
 start_num = str(i*10) # Assign the multiplier of 10 to view the pages we want
 current_page = ''.join([final_site, '&start=', start_num])
 # Now that we can view the correct 10 job returns, start collecting the text samples from each
 html_page = urllib2.urlopen(current_page).read() # Get the page
 page_obj = BeautifulSoup(html_page) # Locate all of the job links
 job_link_area = page_obj.find(id = 'resultsCol') # The center column on the page where the job postings exist
 job_URLS = [base_url + link.get('href') for link in job_link_area.find_all('a')] # Get the URLS for the jobs
 job_URLS = filter(lambda x:'clk' in x, job_URLS) # Now get just the job related URLS
 for j in xrange(0,len(job_URLS)):
 final_description = text_cleaner(job_URLS[j])
 if final_description: # So that we only append when the website was accessed correctly
 job_descriptions.append(final_description)
 sleep(1) # So that we don't be jerks. If you have a very fast internet connection you could hit the server a lot! 
 print 'Done with collecting the job postings!'
 print 'There were', len(job_descriptions), 'jobs successfully found.'
 doc_frequency = Counter() # This will create a full counter of our terms. 
 [doc_frequency.update(item) for item in job_descriptions] # List comp
 # Now we can just look at our final dict list inside doc_frequency
 # Obtain our key terms and store them in a dict. These are the key data science skills we are looking for
 prog_lang_dict = Counter({'R':doc_frequency['r'], 'Python':doc_frequency['python'],
 'Java':doc_frequency['java'], 'C++':doc_frequency['c++'],
 'Ruby':doc_frequency['ruby'],
 'Perl':doc_frequency['perl'], 'Matlab':doc_frequency['matlab'],
 'JavaScript':doc_frequency['javascript'], 'Scala': doc_frequency['scala']})
 analysis_tool_dict = Counter({'Excel':doc_frequency