Job+Info+Scraper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from urllib.request import urlopen\n",
    "from nltk.corpus import stopwords\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import bs4\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class job_scraper:\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.job = None\n",
    "        self.location = None\n",
    "        self.job_links = []\n",
    "        self.next_link = None\n",
    "        self.count = 0\n",
    "        self.json_list = []\n",
    "        self.jobs_parsed = 0\n",
    "        self.all_words = []\n",
    "        self.skills_dict = None\n",
    "        self.skill_frame = None\n",
    "        self.fig = None\n",
    "        self.short_words = []  # DEBUGGING\n",
    "        \n",
    "    \"\"\"Generates a url for the initial search based on job title with an optional location field\"\"\"\n",
    "    def url_gen(self, job, location=None):\n",
    "        self.job = job\n",
    "        self.location = location\n",
    "        job = job.replace(' ', '+')\n",
    "        url = 'https://www.indeed.com/jobs?q=' + job\n",
    "        if location:\n",
    "            try: \n",
    "                city, state = location.split(', ')\n",
    "                if (' ' in city) == True:\n",
    "                    city = city.replace(' ', '+')\n",
    "                url += '&l=' + city + '%2C' + state\n",
    "            except ValueError:    \n",
    "                if (' ' in location) == True:\n",
    "                    location = location.replace(' ', '+')\n",
    "                url += '&l=' + location\n",
    "        return url\n",
    "    \n",
    "    # MAYBE CHECK TO MAKE SURE THE LOCATION IS WHAT YOU SEARCHED\n",
    "    # REcursive method\n",
    "    '''A recusrive method that uses the initial url to search over a number of job links (num_links is 10 as default) and\n",
    "       creates a list of JSON objects, self.json_list, where a JSON object contains the company, job title, and job link \n",
    "       for a unique job listing'''\n",
    "    def job_search(self, url, num_links=10):\n",
    "        if (num_links==0) | (num_links%10!=0):\n",
    "            raise ValueError('Invalid number of jobs to search')     \n",
    "        try:\n",
    "            source = urlopen(url)\n",
    "        except urllib.error.HTTPError:\n",
    "            print('HTTP ERROR')\n",
    "        except urllib.error.URLError:\n",
    "            print('URL ERROR')\n",
    "        soup = bs4.BeautifulSoup(source, 'html.parser')        \n",
    "        if self.next_link == None:\n",
    "            next_link = 'https://www.indeed.com' + soup.find_all('span', attrs={'class':'pn'}).pop().findParent().get('href')\n",
    "            next_link_list = list(next_link)\n",
    "            self.next_link = ''.join(next_link_list[:-2])\n",
    "        jobs = soup.find_all('div', attrs={'data-tn-component': 'organicJob'})\n",
    "        for x in jobs:\n",
    "            self.job_links.append('https://www.indeed.com' + x.find('a', attrs={'class':'turnstileLink'}).get('href'))\n",
    "            job_json = {'Company':x.find('span', attrs={\"itemprop\":\"name\"}).text.strip(),\n",
    "                       'Job':x.find('a', attrs={'data-tn-element': \"jobTitle\"}).text.strip(),\n",
    "                       'Link':'https://www.indeed.com' + x.find('a', attrs={'class':'turnstileLink'}).get('href')}\n",
    "            self.json_list.append(job_json)        \n",
    "        self.count += 10\n",
    "        #print('Jobs Found: {0}'.format(self.count))\n",
    "        if self.count == num_links:\n",
    "            result = []\n",
    "            items_set = set()\n",
    "            for js in self.json_list:\n",
    "                if not js['Link'] in items_set:\n",
    "                    items_set.add(js['Link'])\n",
    "                    result.append(js)\n",
    "            self.json_list = result\n",
    "            self.job_links = set(self.job_links)\n",
    "            print('Number of Unique Jobs Found: {0}'.format(len(self.job_links)))\n",
    "            return        \n",
    "        self.job_search(self.next_link + str(self.count), num_links=num_links)\n",
    "    \n",
    "    '''Parses each unique job link found from the job_search method, filters out stopwords, generates a list of the set of \n",
    "       the words mentioned in each listing in order to prevent counting repeats, and then adds the words from the filtered \n",
    "       list of unique words to self.all_words. Not all links are successful, so self.jobs_parsed is stored for determining \n",
    "       percentages of postings mentioning skills later.'''\n",
    "    def page_parser(self):\n",
    "        texts = []\n",
    "        for link in self.job_links:\n",
    "            try:\n",
    "                source = urlopen(link)\n",
    "            except:\n",
    "                print('Link Failed: {0}'.format(link))\n",
    "                continue\n",
    "            soup = bs4.BeautifulSoup(source, 'html.parser')\n",
    "#             for script in soup([\"script\", \"style\"]):\n",
    "#                 script.extract() # Remove these two elements from the BS4 object\n",
    "            text = soup.get_text()\n",
    "            lines = (line.strip() for line in text.splitlines())\n",
    "            chunks = (phrase.strip() for line in lines for phrase in line.split(\"  \"))\n",
    "            def chunk_space(chunk):\n",
    "                chunk_out = chunk + ' '\n",
    "                return chunk_out  \n",
    "            text = ''.join(chunk_space(chunk) for chunk in chunks if chunk).encode('utf-8')\n",
    "            try:\n",
    "                text = text.decode('unicode_escape').encode('ascii', 'ignore')\n",
    "            except: \n",
    "                print('UnicodeDecodeError: {0}'.format(link))\n",
    "                continue            \n",
    "            text = text.decode('utf-8')\n",
    "            #text = re.sub(\"[^a-zA-Z.+3]\",\" \", text)  # Now get rid of any terms that aren't words (include 3 for d3.js)\n",
    "            text = text.lower().split()\n",
    "            stop_words = set(stopwords.words(\"english\"))\n",
    "            text = [word for word in text if not word in stop_words] # Filter out stop words\n",
    "            text = list(set(text))  # Get unique words per posting\n",
    "            texts.append(text)\n",
    "            if len(text)<30:\n",
    "                self.short_words.append(link)\n",
    "                print('Link Complete: {0} words | Link: {1}'.format(len(text), link))\n",
    "                print(text)\n",
    "            else:\n",
    "                print('Link Complete: {0} words'.format(len(text)))\n",
    "        for text in texts:\n",
    "            for words in text:\n",
    "                self.all_words.append(words)\n",
    "        self.jobs_parsed = len(texts)\n",
    "        print('Jobs Successfully Parsed: {0} | Total Words: {1}'.format(self.jobs_parsed, len(self.all_words)))\n",
    "    \n",
    "    '''Obtains frequency counts of skills and converts them to percents based on how many job postings mentioned each skill\n",
    "       and saves these percentages in self.skills_dict. This dictionary is then used to make a pandas dataframe,\n",
    "       self.skills_frame, where the skills are organized from those with greatest occurence probabilities to least. Using this\n",
    "       dataframe, self.fig is made which allows visualization of the data.'''\n",
    "    def skill_frequencies(self):\n",
    "        frequencies = Counter(self.all_words)        \n",
    "        skills_dict = {'R':frequencies['r'], 'Python':frequencies['python'],\n",
    "                     'Java':frequencies['java'], 'C++':frequencies['c++'],\n",
    "                     'C':frequencies['c'], 'C#':frequencies['c#'],\n",
    "                     'Ruby':frequencies['ruby'], 'Perl':frequencies['perl'],\n",
    "                     'MATLAB':frequencies['matlab'], 'JavaScript':frequencies['javascript'],\n",
    "                     'Scala':frequencies['scala'], 'Swift':frequencies['swift'],\n",
    "                     'Objective-C':frequencies['objective-c'], 'Visual Basic':frequencies['vb'],\n",
    "                     'Excel':frequencies['excel'],  'Tableau':frequencies['tableau'],\n",
    "                     'D3.js':frequencies['d3.js'], 'SAS':frequencies['sas'],\n",
    "                     'SPSS':frequencies['spss'], 'D3':frequencies['d3'],\n",
    "                     'Hadoop':frequencies['hadoop'], 'MapReduce':frequencies['mapreduce'],\n",
    "                     'Spark':frequencies['spark'], 'Pig':frequencies['pig'],\n",
    "                     'Hive':frequencies['hive'], 'Shark':frequencies['shark'],\n",
    "                     'Storm':frequencies['storm'], 'Oozie':frequencies['oozie'],\n",
    "                     'ZooKeeper':frequencies['zookeeper'], 'Splunk':frequencies['splunk'],\n",
    "                     'Flume':frequencies['flume'], 'Mahout':frequencies['mahout'],\n",
    "                     'SQL':frequencies['sql'], 'NoSQL':frequencies['nosql'],\n",
    "                     'HBase':frequencies['hbase'], 'Cassandra':frequencies['cassandra'],\n",
    "                     'MongoDB':frequencies['mongodb'], 'Angular':frequencies['angular']+frequencies['angularjs'],\n",
    "                     'OpenRefine':frequencies['openrefine'], 'Weka':frequencies['weka'],\n",
    "                     'KNIME':frequencies['knime'], 'RapidMiner':frequencies['rapidminer'],\n",
    "                     'WolframAlpha':frequencies['wolframalpha'], 'Mathematica':frequencies['mathematica'],\n",
    "                     'Node.js':frequencies['node.js'], 'Express.js':frequencies['express.js'],\n",
    "                     'Keras':frequencies['keras'], 'Theano':frequencies['theano'],\n",
    "                     'TensorFlow':frequencies['tensorflow'], 'Caffe':frequencies['caffe'],\n",
    "                     'SciKit-Learn':frequencies['scikit-learn']+frequencies['sklearn'],\n",
    "                     'Lua':frequencies['lua']} \n",
    "        # Normalizes the frequencies\n",
    "        for key in skills_dict.keys():\n",
    "            skills_dict[key] /= self.jobs_parsed\n",
    "        self.skills_dict = skills_dict\n",
    "        self.skills_frame = pd.DataFrame(list(skills_dict.items()), columns=['Skill', 'Percent']).sort_values('Percent', ascending=False)\n",
    "        plt.rcParams['figure.figsize']=(12,8)\n",
    "        fig = sns.barplot(x='Skill', y='Percent', data=self.skills_frame)\n",
    "        plt.ylabel('Percent')\n",
    "        if self.location:\n",
    "            plt.title('{0} Skills by Percent of Post Mentions in {1} ({2} listings)'.format(self.job, self.location, self.jobs_parsed))\n",
    "        else:\n",
    "            plt.title('{0} Skills by Percent of Post Mentions ({1} listings)'.format(self.job, self.jobs_parsed))\n",
    "        plt.xticks(rotation=90)\n",
    "        self.fig = fig.get_figure()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "LA = job_scraper()\n",
    "NY = job_scraper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "LA_url = LA.url_gen(job='Data Scientist', location='Los Angeles, CA')\n",
    "NY_url = NY.url_gen(job='Data Scientist', location='New York, NY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://www.indeed.com/jobs?q=Data+Scientist&l=Los+Angeles%2CCA'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LA_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://www.indeed.com/jobs?q=Data+Scientist&l=New+York%2CNY'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NY_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Jobs Found: 190\n"
     ]
    }
   ],
   "source": [
    "LA.job_search(LA_url, num_links=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Jobs Found: 189\n"
     ]
    }
   ],
   "source": [
    "NY.job_search(NY_url, num_links=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
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      "Link Complete: 549 words\n",
      "Link Complete: 548 words\n",
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      "Link Complete: 8 words | Link: https://www.indeed.com/rc/clk?jk=192a3ea52b305250&fccid=ab0ef6192557a06f\n",
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      "Link Complete: 948 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=cb83c7896a8d0318&fccid=7455773bace145da\n",
      "Link Complete: 1251 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=fb7c07e821e0fd2f&fccid=79be1acede5a1870\n",
      "Link Complete: 2494 words\n",
      "Link Complete: 1232 words\n",
      "Link Complete: 1049 words\n",
      "Link Complete: 998 words\n",
      "Link Complete: 447 words\n",
      "Link Complete: 945 words\n",
      "Link Complete: 804 words\n",
      "Link Complete: 822 words\n",
      "Link Complete: 1223 words\n",
      "Link Complete: 545 words\n",
      "Link Complete: 574 words\n",
      "Link Complete: 2037 words\n",
      "Link Complete: 667 words\n",
      "Link Complete: 564 words\n",
      "Link Complete: 1368 words\n",
      "Link Complete: 45 words\n",
      "Jobs Successfully Parsed: 165 | Total Words: 144141\n"
     ]
    }
   ],
   "source": [
    "LA.page_parser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Link Complete: 304 words\n",
      "Link Complete: 965 words\n",
      "Link Complete: 504 words\n",
      "Link Complete: 807 words\n",
      "Link Complete: 4654 words\n",
      "Link Complete: 531 words\n",
      "Link Complete: 672 words\n",
      "Link Complete: 365 words\n",
      "Link Complete: 342 words\n",
      "Link Complete: 623 words\n",
      "Link Complete: 16 words | Link: https://www.indeed.com/rc/clk?jk=d862f9d88d33e634&fccid=ea25315ee9da22e5\n",
      "['font-family:', 'font-size:', '|', 'wait......................', '.', '#000000;}', '.content', 'ii', 'comcast', 'scientist', '{', 'color:', 'verdana;', '18px;', 'please', 'data']\n",
      "Link Complete: 421 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=4e8cc834dc42d068&fccid=26e916214f939c5b\n",
      "Link Complete: 921 words\n",
      "Link Complete: 494 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=a99aee5449d42c1f&fccid=38a1721a0daff480\n",
      "Link Complete: 600 words\n",
      "Link Complete: 273 words\n",
      "Link Complete: 502 words\n",
      "Link Complete: 341 words\n",
      "Link Complete: 1884 words\n",
      "Link Complete: 8 words | Link: https://www.indeed.com/rc/clk?jk=3d6396b83e93622e&fccid=bd976cc171c690e0\n",
      "['function', 'page.please', 'loading', \"location.replace('/applicants/jsp/shared/reloadframe.jsp?time=1499112483453');\", 'content', 'wait.', '}', 'forwardasreplace(){']\n",
      "Link Complete: 509 words\n",
      "Link Complete: 174 words\n",
      "Link Complete: 8 words | Link: https://www.indeed.com/rc/clk?jk=86c5b4dad7ff1422&fccid=bd976cc171c690e0\n",
      "['function', \"location.replace('/applicants/jsp/shared/reloadframe.jsp?time=1499112486859');\", 'page.please', 'loading', 'content', 'wait.', '}', 'forwardasreplace(){']\n",
      "Link Complete: 511 words\n",
      "Link Complete: 508 words\n",
      "Link Complete: 854 words\n",
      "Link Complete: 557 words\n",
      "Link Complete: 981 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=fbea37242f85207e&fccid=f924423dab22b92a\n",
      "Link Complete: 683 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=3f3b680c4629a6b8&fccid=5bd99dfa21c8a490\n",
      "Link Complete: 435 words\n",
      "Link Complete: 655 words\n",
      "Link Complete: 683 words\n",
      "Link Complete: 640 words\n",
      "Link Complete: 478 words\n",
      "Link Complete: 578 words\n",
      "Link Complete: 318 words\n",
      "Link Complete: 174 words\n",
      "Link Complete: 1225 words\n",
      "Link Complete: 878 words\n",
      "Link Complete: 564 words\n",
      "Link Complete: 2482 words\n",
      "Link Complete: 1984 words\n",
      "Link Complete: 743 words\n",
      "Link Complete: 1960 words\n",
      "Link Complete: 1229 words\n",
      "Link Complete: 1896 words\n",
      "Link Complete: 329 words\n",
      "Link Complete: 429 words\n",
      "Link Complete: 1205 words\n",
      "Link Complete: 663 words\n",
      "Link Complete: 5011 words\n",
      "Link Complete: 2059 words\n",
      "Link Complete: 2490 words\n",
      "Link Complete: 1900 words\n",
      "Link Complete: 555 words\n",
      "Link Complete: 767 words\n",
      "Link Complete: 663 words\n",
      "Link Complete: 1225 words\n",
      "Link Complete: 931 words\n",
      "Link Complete: 706 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=408d040ef779019d&fccid=2b58e9f8bedadb3c\n",
      "Link Complete: 960 words\n",
      "Link Complete: 601 words\n",
      "Link Complete: 1159 words\n",
      "Link Complete: 2079 words\n",
      "Link Complete: 273 words\n",
      "Link Complete: 1071 words\n",
      "Link Complete: 2482 words\n",
      "Link Complete: 4658 words\n",
      "Link Complete: 1014 words\n",
      "Link Complete: 2089 words\n",
      "Link Complete: 66 words\n",
      "Link Complete: 452 words\n",
      "Link Complete: 627 words\n",
      "Link Complete: 4658 words\n",
      "Link Complete: 337 words\n",
      "Link Complete: 448 words\n",
      "Link Complete: 1373 words\n",
      "Link Complete: 2010 words\n",
      "Link Complete: 550 words\n",
      "Link Complete: 1223 words\n",
      "Link Complete: 164 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=3afd681c4573bcd5&fccid=49edf7ec660670a1\n",
      "Link Complete: 11 words | Link: https://www.indeed.com/rc/clk?jk=dd57a3a63266c971&fccid=f770da67b3b51c62\n",
      "['|', 'new', 'york', 'careerstry{typekit.load();}catch(e){}', '-', 'scientist', 'support', 'client', 'bloomberg', 'data', 'engineering']\n",
      "Link Complete: 1637 words\n",
      "Link Complete: 2047 words\n",
      "Link Complete: 509 words\n",
      "Link Complete: 574 words\n",
      "Link Complete: 1605 words\n",
      "Link Complete: 982 words\n",
      "Link Complete: 11 words | Link: https://www.indeed.com/rc/clk?jk=f61749934c624b4f&fccid=c80edb3c814b6a1e\n",
      "['error.', 'process', 'unexpected', 'oracle', 'requestunable', 'request', 'bad', 'access', 'due', 'requestbad', 'manager']\n",
      "Link Complete: 839 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=aebac25a5ae8c9e0&fccid=b85c5070c3d3d8c8\n",
      "Link Complete: 1376 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=18ab3a0c6849f268&fccid=f34c8d83f77dbba4\n",
      "Link Complete: 954 words\n",
      "Link Complete: 189 words\n",
      "Link Complete: 1951 words\n",
      "Link Complete: 600 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=559fd2fbb9751928&fccid=d08ed43f33fe2ef4\n",
      "Link Complete: 491 words\n",
      "Link Complete: 515 words\n",
      "Link Complete: 1178 words\n",
      "Link Complete: 1938 words\n",
      "Link Complete: 550 words\n",
      "Link Complete: 452 words\n",
      "Link Complete: 1390 words\n",
      "Link Complete: 1481 words\n",
      "Link Complete: 300 words\n",
      "Link Complete: 560 words\n",
      "Link Complete: 898 words\n",
      "Link Complete: 857 words\n",
      "Link Complete: 495 words\n",
      "Link Complete: 272 words\n",
      "Link Complete: 1308 words\n",
      "Link Complete: 887 words\n",
      "Link Complete: 2490 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=bb58d9043ddc929a&fccid=84a584dd774f70f9\n",
      "Link Complete: 2051 words\n",
      "Link Complete: 1277 words\n",
      "Link Complete: 988 words\n",
      "Link Complete: 2490 words\n",
      "Link Complete: 501 words\n",
      "Link Complete: 174 words\n",
      "Link Complete: 831 words\n",
      "Link Complete: 466 words\n",
      "Link Complete: 645 words\n",
      "Link Complete: 412 words\n",
      "Link Complete: 536 words\n",
      "Link Complete: 1275 words\n",
      "Link Complete: 572 words\n",
      "Link Complete: 2010 words\n",
      "Link Complete: 629 words\n",
      "Link Complete: 490 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=872101a59dc079f9&fccid=b85c5070c3d3d8c8\n",
      "Link Complete: 538 words\n",
      "Link Complete: 427 words\n",
      "Link Complete: 571 words\n",
      "Link Complete: 629 words\n",
      "Link Complete: 803 words\n",
      "Link Complete: 271 words\n",
      "Link Complete: 310 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=bc5d5dcd2410d7c3&fccid=efa27d5c17dc27ba\n",
      "Link Complete: 1138 words\n",
      "Link Complete: 679 words\n",
      "Link Complete: 172 words\n",
      "Link Complete: 1153 words\n",
      "Link Complete: 554 words\n",
      "Link Complete: 4658 words\n",
      "Link Complete: 339 words\n",
      "Link Complete: 760 words\n",
      "Link Complete: 864 words\n",
      "Link Complete: 1225 words\n",
      "Link Complete: 480 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=b48bf7b1a3e62896&fccid=11b98e51ac2e6bae\n",
      "Link Complete: 438 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=289026c5ecbf4d35&fccid=d3be9723a73f2641\n",
      "Link Complete: 11039 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=bca14174684fb4b1&fccid=5bcd1ef0a7f4fb99\n",
      "Link Complete: 10 words | Link: https://www.indeed.com/rc/clk?jk=7d5bd86800cb6ae5&fccid=f770da67b3b51c62\n",
      "['engineer', '|', 'york', 'careerstry{typekit.load();}catch(e){}', '-', 'learning', 'kyc', 'machine', 'bloomberg', 'new']\n",
      "Link Complete: 917 words\n",
      "Link Complete: 590 words\n",
      "Link Complete: 174 words\n",
      "Link Complete: 412 words\n",
      "Link Complete: 273 words\n",
      "Link Complete: 18 words | Link: https://www.indeed.com/rc/clk?jk=3f8059077ee10db4&fccid=c36431f1d989663b\n",
      "['code', 'site,', 'problem', 'http://www.nyc.gov/careersand', 'time.', 'process', 'accessing', 'later', '001-10', 'request', 'trouble', 'mention', 'still', 'report', 'unable', 'try', 'error', 'please']\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=316922f8afb579b8&fccid=5bcd1ef0a7f4fb99\n",
      "Link Complete: 600 words\n",
      "Link Complete: 975 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=17bffadc29f36e10&fccid=4a9b8c61a3e17d38\n",
      "Link Complete: 1109 words\n",
      "Link Complete: 1036 words\n",
      "Link Complete: 275 words\n",
      "Link Complete: 501 words\n",
      "Link Complete: 959 words\n",
      "Link Complete: 603 words\n",
      "Link Complete: 3431 words\n",
      "Link Complete: 468 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=81e50490c3e0204a&fccid=d9a36a1771c05d30\n",
      "Link Complete: 8 words | Link: https://www.indeed.com/rc/clk?jk=9af2e5b50f297050&fccid=bd976cc171c690e0\n",
      "['function', 'page.please', 'loading', 'content', 'wait.', \"location.replace('/applicants/jsp/shared/reloadframe.jsp?time=1499112800500');\", '}', 'forwardasreplace(){']\n",
      "Link Complete: 662 words\n",
      "Link Complete: 5011 words\n",
      "Link Complete: 911 words\n",
      "Link Complete: 480 words\n",
      "Link Failed: https://www.indeed.com/rc/clk?jk=7881dc493c5ca891&fccid=38a1721a0daff480\n",
      "Jobs Successfully Parsed: 170 | Total Words: 175326\n"
     ]
    }
   ],
   "source": [
    "NY.page_parser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Xy+Xc0S7PwoULddlllznXTcuWLfXWW285X5/H+umnn9SuXbtKt1tSUqKvv/5aPXv21IgR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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x44348f1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LA.skill_frequencies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LXpXzbz/Exy0BAACAL9BDDQAAAFggUAMAAAAWfDbkw+12a9SoUfrxxx8VEBCg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\ngDOoOTg4ODg4ODg4OGrA/wEQpk/LEbVGnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x44368399e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NY.skill_frequencies()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow]",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}