job_posting_example.py

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

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['excel'],  'Tableau':doc_frequency['tableau'],
                        'D3.js':doc_frequency['d3.js'], 'SAS':doc_frequency['sas'],
                        'SPSS':doc_frequency['spss'], 'D3':doc_frequency['d3']})

    hadoop_dict = Counter({'Hadoop':doc_frequency['hadoop'], 'MapReduce':doc_frequency['mapreduce'],
                'Spark':doc_frequency['spark'], 'Pig':doc_frequency['pig'],
                'Hive':doc_frequency['hive'], 'Shark':doc_frequency['shark'],
                'Oozie':doc_frequency['oozie'], 'ZooKeeper':doc_frequency['zookeeper'],
                'Flume':doc_frequency['flume'], 'Mahout':doc_frequency['mahout']})

    database_dict = Counter({'SQL':doc_frequency['sql'], 'NoSQL':doc_frequency['nosql'],
                    'HBase':doc_frequency['hbase'], 'Cassandra':doc_frequency['cassandra'],
                    'MongoDB':doc_frequency['mongodb']})


    overall_total_skills = prog_lang_dict + analysis_tool_dict + hadoop_dict + database_dict # Combine our Counter objects



    final_frame = pd.DataFrame(overall_total_skills.items(), columns = ['Term', 'NumPostings']) # Convert these terms to a
                                                                                                # dataframe

    # Change the values to reflect a percentage of the postings

    final_frame.NumPostings = (final_frame.NumPostings)*100/len(job_descriptions) # Gives percentage of job postings
                                                                                    #  having that term

    # Sort the data for plotting purposes

    final_frame.sort(columns = 'NumPostings', ascending = False, inplace = True)

    # Get it ready for a bar plot

    final_plot = final_frame.plot(x = 'Term', kind = 'bar', legend = None,
                            title = 'Percentage of Data Scientist Job Ads with a Key Skill, ' + city_title)

    final_plot.set_ylabel('Percentage Appearing in Job Ads')
    fig = final_plot.get_figure() # Have to convert the pandas plot object to a matplotlib object


    return fig, final_frame # End of the function