Job_posting_crawler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Job Posting Crawler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is code that will pull each job posting for a specific job title (currently just Data Scientist) in a specific location (or Nationally) and return / plot the percentage of the postings that have certain user-defined keywords that are passed to the code.  \n",
    "\n",
    "NOTE:  I got this idea from https://jessesw.com/Data-Science-Skills/.  Obviously, just using his code would be of no real benefit to me, as I wanted to use the idea to help better my skills with scraping data from HTML files.  So, I used his idea and developed my own code from scratch.  I also modified the overall process a bit to better fit my needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import urllib\n",
    "import re\n",
    "from time import sleep\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the city, state, job title, and keywords to be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "city = 'Seattle'\n",
    "state = 'WA'\n",
    "job_title = 'Data Scientist'\n",
    "# Create a list of key words that we want to search for\n",
    "keywords_input = ['R','Python','Perl','Hadoop','Hive','Pig','SQL',\\\n",
    "            'Scripting','SAS','Matlab','Oracle','Java','C','Unix',\\\n",
    "            'ML','Scikit','Scikitlearn','pandas','numpy','Octave',\\\n",
    "            'git','Statistics','Spark','Stata','Scala','Machine'] \n",
    "## I have 'machine' in the keywords, as all spaces are removed and i wanted to search\n",
    "## for 'machine learning'.  I need to modify the code to be able to search for multiple\n",
    "## words in sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function that will take the url and pull out the text of the main body as a list of strings.  Remove common words such as 'the', 'or', 'and', etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clean_the_html(url,keywords):\n",
    "    # First try to download the html file\n",
    "    html = urllib.urlopen(url)\n",
    "    \n",
    "    #print url\n",
    "    \n",
    "    # Open html in BeautifulSoup\n",
    "    soup = BeautifulSoup(html)\n",
    "        \n",
    "    # Extract everything within the <p> tags\n",
    "    text = soup.findAll('body')\n",
    "    word_list = ''\n",
    "    for line in text:\n",
    "        word_list = ' '.join([word_list,line.get_text(' ',strip=True).lower()])\n",
    "    \n",
    "    # Remove non text characters from list\n",
    "    word_list = re.sub('[^a-zA-Z+3]',' ', word_list)\n",
    "\n",
    "    list_of_words = word_list.encode('ascii','ignore').split()\n",
    "               \n",
    "    keywords_lower = [x.lower() for x in keywords]\n",
    "    \n",
    "    stop_words = set(stopwords.words(\"english\"))\n",
    "    \n",
    "    # Remove words from list\n",
    "    truncated_list = [w for w in list_of_words if w in keywords_lower]\n",
    "    \n",
    "    truncated_set = set(truncated_list)\n",
    "    truncated_list = list(truncated_set)\n",
    "        \n",
    "    return truncated_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to generate a list of urls for a given search (i.e., 'Data Scientist').  Each search result page has 10 non-sponsored links.  Search the first url for 'Jobs # to # of ###' in order to determine how many iterations to perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def gen_url_list(city,state,job_name):\n",
    "    base_url = 'http://www.indeed.com/'\n",
    "    \n",
    "    job_term = re.sub(' ','+',job_name.lower())\n",
    "    \n",
    "    search_url = ''.join([base_url,'jobs?q=',job_term,'&l=',city,'%2C+',state])\n",
    "    \n",
    "    html = urllib.urlopen(search_url)\n",
    "    \n",
    "    soup = BeautifulSoup(html)\n",
    "    \n",
    "    total_jobs = soup.find(id = 'searchCount').string.encode('utf-8')\n",
    "    job_nums = int([int(s) for s in total_jobs.split() if s.isdigit()][-1]/10)\n",
    "    print total_jobs\n",
    "\n",
    "    job_URLS = []\n",
    "    for i in range(job_nums):\n",
    "        if i % 10 == 0:\n",
    "            print i\n",
    "        if i > 10:\n",
    "            print \"Debug line to only pull first 100 postings\"\n",
    "            print \" \"\n",
    "            print \" \"\n",
    "            print \" \"\n",
    "            break\n",
    "        page_url = ''.join([base_url,job_term,'&1=',city,'%2C+',state,\\\n",
    "                            '&start=',str((i+1)*10)])\n",
    "        html = urllib.urlopen(search_url)\n",
    "        \n",
    "        soup = BeautifulSoup(html)\n",
    "        \n",
    "        job_link_area = soup.findAll('h2',{'class':'jobtitle'})\n",
    "\n",
    "        for link in job_link_area:\n",
    "            match_href = re.search('<a\\shref=\"(.+?)\"',str(link))\n",
    "            if match_href:\n",
    "                job_URLS.append([base_url + match_href.group(1)])\n",
    "\n",
    "    return job_URLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a list of all of the URLs of job postings, pull the information from each site, clean the data, and populate the keyword list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def job_posting_analysis(url_list,keywords):\n",
    "    job_skills = []\n",
    "    count = 0\n",
    "    for url in url_list:\n",
    "        count += 1\n",
    "        if count % 10 == 1:\n",
    "            print count\n",
    "        \n",
    "        posting_keywords = clean_the_html(url[0],keywords)\n",
    "        if posting_keywords:\n",
    "            job_skills.append(posting_keywords)\n",
    "        sleep(0.5)\n",
    "        \n",
    "    return job_skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the various functions are defined, run the code.  \n",
    "    First: run gen_url_list for the specified city, state, and jobtitle in order to generate\n",
    "    the list of job posting links\n",
    "    \n",
    "Second: run job_posting_analysis to pull out the job_skills listed for each job posting.\n",
    "    job_skills will be the skills that match skills found in the 'keywords_input' variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crawl indeed.com for Seattle, WA Data Scientist postings and generate a list of all of the job posting links\n",
      "Jobs 1 to 10 of 721\n",
      "0\n",
      "10\n",
      "Debug line to only pull first 100 postings\n",
      " \n",
      " \n",
      " \n",
      "given the job posting links, pull out the keywords for each posting that is found in the provided keywords_input variable\n",
      "1\n",
      "11\n",
      "21\n",
      "31\n",
      "41\n",
      "51\n",
      "61\n",
      "71\n",
      "81\n",
      "91\n",
      "101\n"
     ]
    }
   ],
   "source": [
    "print 'Crawl indeed.com for ' + city + ', ' + state + ' ' + job_title + \\\n",
    "' postings and generate a list of all of the job posting links'\n",
    "\n",
    "url_list = gen_url_list(city,state,job_title)\n",
    "\n",
    "print \"given the job posting links, pull out the keywords for each posting \\\n",
    "that is found in the provided keywords_input variable\"\n",
    "\n",
    "job_skills = job_posting_analysis(url_list,keywords_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the list of keywords in the job postings, as well as the keywords that we defined previously, calculate the number of postings in which each keyword appears.  Then plot the data on a bar graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('pig', 66), ('unix', 22), ('sas', 44), ('statistics', 88), ('java', 44), ('hadoop', 66), ('machine', 88), ('matlab', 55), ('stata', 11), ('scikitlearn', 11), ('numpy', 11), ('pandas', 11), ('python', 88), ('git', 11), ('octave', 22), ('sql', 99), ('spark', 22), ('c', 44), ('ml', 11), ('hive', 66), ('r', 99), ('oracle', 22), ('scripting', 55)]\n"
     ]
    }
   ],
   "source": [
    "skill_frequency = Counter() # This will create a full counter of our terms. \n",
    "[skill_frequency.update(item) for item in job_skills] # List comp\n",
    "print skill_frequency.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1092ad990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_to_plot = pd.DataFrame(skill_frequency.items(),columns = ['Skill','Occurances'])\n",
    "\n",
    "data_to_plot.Occurances = (data_to_plot.Occurances)*100/len(job_skills)\n",
    "\n",
    "data_to_plot.sort(columns = 'Occurances',ascending = False,inplace = True)\n",
    "\n",
    "frame = data_to_plot.plot(x='Skill',kind='bar',legend=None,\\\n",
    "                  title='Percentage of Data Scientist Job Ads with each Skill, '\\\n",
    "                  + city + ', ' + state)\n",
    "\n",
    "fig = frame.get_figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filename = '_'.join([city,state,'skills'])\n",
    "filename = ''.join([filename,'.pdf'])\n",
    "pp = PdfPages(filename)\n",
    "pp.savefig(fig)\n",
    "pp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'text_cleaner' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-36-b51da3e90319>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext_cleaner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'http://www.indeed.com/rc/clk?jk=f54117307d3f6805'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'text_cleaner' is not defined"
     ]
    }
   ],
   "source": [
    "sample = text_cleaner('http://www.indeed.com/rc/clk?jk=f54117307d3f6805')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['operations',\n",
       " 'knowledgeable',\n",
       " 'hands',\n",
       " 'insight',\n",
       " 'results',\n",
       " 'feature',\n",
       " 'performance',\n",
       " 'scientist',\n",
       " 'including',\n",
       " 'solutions',\n",
       " 'statistical',\n",
       " 'innovative',\n",
       " 'jobpowered',\n",
       " 'decisions',\n",
       " 'networks',\n",
       " 'relationships',\n",
       " 'web',\n",
       " 'group',\n",
       " 'knowledge',\n",
       " 'candidate']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def skills_info(city = None, state = None):\n",
    "    '''\n",
    "    This function will take a desired city/state and look for all new job postings\n",
    "    on Indeed.com. It will crawl all of the job postings and keep track of how many\n",
    "    use a preset list of typical data science skills. The final percentage for each skill\n",
    "    is then displayed at the end of the collation. \n",
    "\n",
    "    Inputs: The location's city and state. These are optional. If no city/state is input, \n",
    "    the function will assume a national search (this can take a while!!!).\n",
    "    Input the city/state as strings, such as skills_info('Chicago', 'IL').\n",
    "    Use a two letter abbreviation for the state.\n",
    "\n",
    "    Output: A bar chart showing the most commonly desired skills in the job market for \n",
    "    a data scientist. \n",
    "    '''\n",
    "\n",
    "    final_job = 'data+scientist' # searching for data scientist exact fit(\"data scientist\" on Indeed search)\n",
    "\n",
    "    # Make sure the city specified works properly if it has more than one word (such as San Francisco)\n",
    "    if city is not None:\n",
    "        final_city = city.split() \n",
    "        final_city = '+'.join(word for word in final_city)\n",
    "        final_site_list = ['http://www.indeed.com/jobs?q=%22', final_job, '%22&l=', final_city,\n",
    "                    '%2C+', state] # Join all of our strings together so that indeed will search correctly\n",
    "    else:\n",
    "        final_site_list = ['http://www.indeed.com/jobs?q=\"', final_job, '\"']\n",
    "\n",
    "    final_site = ''.join(final_site_list) # Merge the html address together into one string\n",
    "\n",
    "\n",
    "    base_url = 'http://www.indeed.com'\n",
    "\n",
    "\n",
    "    try:\n",
    "        html = urllib2.urlopen(final_site).read() # Open up the front page of our search first\n",
    "    except:\n",
    "        'That city/state combination did not have any jobs. Exiting . . .' # In case the city is invalid\n",
    "        return\n",
    "    soup = BeautifulSoup(html) # Get the html from the first page\n",
    "\n",
    "    # Now find out how many jobs there were\n",
    "\n",
    "    num_jobs_area = soup.find(id = 'searchCount').string.encode('utf-8') # Now extract the total number of jobs found\n",
    "                                                                        # The 'searchCount' object has this\n",
    "\n",
    "    job_numbers = re.findall('\\d+', num_jobs_area) # Extract the total jobs found from the search result\n",
    "\n",
    "\n",
    "    if len(job_numbers) > 3: # Have a total number of jobs greater than 1000\n",
    "        total_num_jobs = (int(job_numbers[2])*1000) + int(job_numbers[3])\n",
    "    else:\n",
    "        total_num_jobs = int(job_numbers[2]) \n",
    "\n",
    "    city_title = city\n",
    "    if city is None:\n",
    "        city_title = 'Nationwide'\n",
    "\n",
    "    print 'There were', total_num_jobs, 'jobs found,', city_title # Display how many jobs were found\n",
    "\n",
    "    num_pages = total_num_jobs/10 # This will be how we know the number of times we need to iterate over each new\n",
    "                                      # search result page\n",
    "    job_descriptions = [] # Store all our descriptions in this list\n",
    "\n",
    "    for i in xrange(1,num_pages+1): # Loop through all of our search result pages\n",
    "        print 'Getting page', i\n",
    "        start_num = str(i*10) # Assign the multiplier of 10 to view the pages we want\n",
    "        current_page = ''.join([final_site, '&start=', start_num])\n",
    "        # Now that we can view the correct 10 job returns, start collecting the text samples from each\n",
    "\n",
    "        html_page = urllib2.urlopen(current_page).read() # Get the page\n",
    "\n",
    "        page_obj = BeautifulSoup(html_page) # Locate all of the job links\n",
    "        job_link_area = page_obj.find(id = 'resultsCol') # The center column on the page where the job postings exist\n",
    "\n",
    "        job_URLS = [base_url + link.get('href') for link in job_link_area.find_all('a')] # Get the URLS for the jobs\n",
    "\n",
    "        job_URLS = filter(lambda x:'clk' in x, job_URLS) # Now get just the job related URLS\n",
    "\n",
    "\n",
    "        for j in xrange(0,len(job_URLS)):\n",
    "            final_description = text_cleaner(job_URLS[j])\n",
    "            if final_description: # So that we only append when the website was accessed correctly\n",
    "                job_descriptions.append(final_description)\n",
    "            sleep(1) # So that we don't be jerks. If you have a very fast internet connection you could hit the server a lot! \n",
    "\n",
    "    print 'Done with collecting the job postings!'    \n",
    "    print 'There were', len(job_descriptions), 'jobs successfully found.'\n",
    "\n",
    "\n",
    "    doc_frequency = Counter() # This will create a full counter of our terms. \n",
    "    [doc_frequency.update(item) for item in job_descriptions] # List comp\n",
    "\n",
    "    # Now we can just look at our final dict list inside doc_frequency\n",
    "\n",
    "    # Obtain our key terms and store them in a dict. These are the key data science skills we are looking for\n",
    "\n",
    "    prog_lang_dict = Counter({'R':doc_frequency['r'], 'Python':doc_frequency['python'],\n",
    "                    'Java':doc_frequency['java'], 'C++':doc_frequency['c++'],\n",
    "                    'Ruby':doc_frequency['ruby'],\n",
    "                    'Perl':doc_frequency['perl'], 'Matlab':doc_frequency['matlab'],\n",
    "                    'JavaScript':doc_frequency['javascript'], 'Scala': doc_frequency['scala']})\n",
    "\n",
    "    analysis_tool_dict = Counter({'Excel':doc_frequency['excel'],  'Tableau':doc_frequency['tableau'],\n",
    "                        'D3.js':doc_frequency['d3.js'], 'SAS':doc_frequency['sas'],\n",
    "                        'SPSS':doc_frequency['spss'], 'D3':doc_frequency['d3']})  \n",
    "\n",
    "    hadoop_dict = Counter({'Hadoop':doc_frequency['hadoop'], 'MapReduce':doc_frequency['mapreduce'],\n",
    "                'Spark':doc_frequency['spark'], 'Pig':doc_frequency['pig'],\n",
    "                'Hive':doc_frequency['hive'], 'Shark':doc_frequency['shark'],\n",
    "                'Oozie':doc_frequency['oozie'], 'ZooKeeper':doc_frequency['zookeeper'],\n",
    "                'Flume':doc_frequency['flume'], 'Mahout':doc_frequency['mahout']})\n",
    "\n",
    "    database_dict = Counter({'SQL':doc_frequency['sql'], 'NoSQL':doc_frequency['nosql'],\n",
    "                    'HBase':doc_frequency['hbase'], 'Cassandra':doc_frequency['cassandra'],\n",
    "                    'MongoDB':doc_frequency['mongodb']})\n",
    "\n",
    "\n",
    "    overall_total_skills = prog_lang_dict + analysis_tool_dict + hadoop_dict + database_dict # Combine our Counter objects\n",
    "\n",
    "\n",
    "\n",
    "    final_frame = pd.DataFrame(overall_total_skills.items(), columns = ['Term', 'NumPostings']) # Convert these terms to a \n",
    "                                                                                                # dataframe \n",
    "\n",
    "    # Change the values to reflect a percentage of the postings \n",
    "\n",
    "    final_frame.NumPostings = (final_frame.NumPostings)*100/len(job_descriptions) # Gives percentage of job postings \n",
    "                                                                                    #  having that term \n",
    "\n",
    "    # Sort the data for plotting purposes\n",
    "\n",
    "    final_frame.sort(columns = 'NumPostings', ascending = False, inplace = True)\n",
    "\n",
    "    # Get it ready for a bar plot\n",
    "\n",
    "    final_plot = final_frame.plot(x = 'Term', kind = 'bar', legend = None, \n",
    "                            title = 'Percentage of Data Scientist Job Ads with a Key Skill, ' + city_title)\n",
    "\n",
    "    final_plot.set_ylabel('Percentage Appearing in Job Ads')\n",
    "    fig = final_plot.get_figure() # Have to convert the pandas plot object to a matplotlib object\n",
    "\n",
    "\n",
    "    return fig, final_frame # End of the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were 100 jobs found, Seattle\n",
      "Getting page 1\n",
      "Getting page 2\n",
      "Getting page 3\n",
      "Getting page 4\n",
      "Getting page 5\n",
      "Getting page 6\n",
      "Getting page 7\n",
      "Getting page 8\n",
      "Getting page 9\n",
      "Getting page 10\n",
      "Done with collecting the job postings!\n",
      "There were 109 jobs successfully found.\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b14d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, seattle_info = skills_info(city = 'Seattle', state = 'WA') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pp = PdfPages('Seattle_skills.pdf')\n",
    "pp.savefig(fig)\n",
    "pp.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}