Text Mining-Online Job Posting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "import datetime as dt\n",
    "%matplotlib inline\n",
    "import re\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobpost</th>\n",
       "      <th>date</th>\n",
       "      <th>Title</th>\n",
       "      <th>Company</th>\n",
       "      <th>AnnouncementCode</th>\n",
       "      <th>Term</th>\n",
       "      <th>Eligibility</th>\n",
       "      <th>Audience</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>Duration</th>\n",
       "      <th>...</th>\n",
       "      <th>Salary</th>\n",
       "      <th>ApplicationP</th>\n",
       "      <th>OpeningDate</th>\n",
       "      <th>Deadline</th>\n",
       "      <th>Notes</th>\n",
       "      <th>AboutC</th>\n",
       "      <th>Attach</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>IT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMERIA Investment Consulting Company\\nJOB TITL...</td>\n",
       "      <td>Jan 5, 2004</td>\n",
       "      <td>Chief Financial Officer</td>\n",
       "      <td>AMERIA Investment Consulting Company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>To apply for this position, please submit a\\nc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26 January 2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>International Research &amp; Exchanges Board (IREX...</td>\n",
       "      <td>Jan 7, 2004</td>\n",
       "      <td>Full-time Community Connections Intern (paid i...</td>\n",
       "      <td>International Research &amp; Exchanges Board (IREX)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3 months</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Please submit a cover letter and resume to:\\nI...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12 January 2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The International Research &amp; Exchanges Board (...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caucasus Environmental NGO Network (CENN)\\nJOB...</td>\n",
       "      <td>Jan 7, 2004</td>\n",
       "      <td>Country Coordinator</td>\n",
       "      <td>Caucasus Environmental NGO Network (CENN)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Renewable annual contract\\nPOSITION</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Please send resume or CV toursula.kazarian@......</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 January 2004\\nSTART DATE:  February 2004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Caucasus Environmental NGO Network is a\\nn...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manoff Group\\nJOB TITLE:  BCC Specialist\\nPOSI...</td>\n",
       "      <td>Jan 7, 2004</td>\n",
       "      <td>BCC Specialist</td>\n",
       "      <td>Manoff Group</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Please send cover letter and resume to Amy\\nPe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23 January 2004\\nSTART DATE:  Immediate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yerevan Brandy Company\\nJOB TITLE:  Software D...</td>\n",
       "      <td>Jan 10, 2004</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>Yerevan Brandy Company</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Successful candidates should submit\\n- CV; \\n-...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20 January 2004, 18:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18996</th>\n",
       "      <td>Technolinguistics NGO\\n\\n\\nTITLE:  Senior Crea...</td>\n",
       "      <td>Dec 28, 2015</td>\n",
       "      <td>Senior Creative UX/ UI Designer</td>\n",
       "      <td>Technolinguistics NGO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>...</td>\n",
       "      <td>Competitive</td>\n",
       "      <td>To apply for this position, please send your\\n...</td>\n",
       "      <td>29 December 2015</td>\n",
       "      <td>28 January 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>As a company Technolinguistics has a mandate t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18997</th>\n",
       "      <td>\"Coca-Cola Hellenic Bottling Company Armenia\" ...</td>\n",
       "      <td>Dec 30, 2015</td>\n",
       "      <td>Category Development Manager</td>\n",
       "      <td>\"Coca-Cola Hellenic Bottling Company Armenia\" ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>All interested professionals.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ASAP</td>\n",
       "      <td>Long-term with a probation period of 3 months.</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All interested candidates are kindly requested...</td>\n",
       "      <td>30 December 2015</td>\n",
       "      <td>20 January 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18998</th>\n",
       "      <td>\"Coca-Cola Hellenic Bottling Company Armenia\" ...</td>\n",
       "      <td>Dec 30, 2015</td>\n",
       "      <td>Operational Marketing Manager</td>\n",
       "      <td>\"Coca-Cola Hellenic Bottling Company Armenia\" ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>All interested professionals.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ASAP</td>\n",
       "      <td>Long-term with a probation period of 3 months.</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All interested candidates are kindly requested...</td>\n",
       "      <td>30 December 2015</td>\n",
       "      <td>20 January 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18999</th>\n",
       "      <td>San Lazzaro   LLC\\n\\n\\nTITLE:  Head of Online ...</td>\n",
       "      <td>Dec 30, 2015</td>\n",
       "      <td>Head of Online Sales Department</td>\n",
       "      <td>San Lazzaro   LLC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Long-term</td>\n",
       "      <td>...</td>\n",
       "      <td>Highly competitive</td>\n",
       "      <td>Interested candidates can send their CVs to:\\n...</td>\n",
       "      <td>30 December 2015</td>\n",
       "      <td>29 January 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>San Lazzaro LLC works with several internation...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19000</th>\n",
       "      <td>\"Kamurj\" UCO CJSC\\n\\n\\nTITLE:  Lawyer in Legal...</td>\n",
       "      <td>Dec 30, 2015</td>\n",
       "      <td>Lawyer in Legal Department</td>\n",
       "      <td>\"Kamurj\" UCO CJSC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Indefinite</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All qualified applicants are encouraged to\\nsu...</td>\n",
       "      <td>30 December 2015</td>\n",
       "      <td>20 January 2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Kamurj\" UCO CJSC is providing micro and small...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19001 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 jobpost          date  \\\n",
       "0      AMERIA Investment Consulting Company\\nJOB TITL...   Jan 5, 2004   \n",
       "1      International Research & Exchanges Board (IREX...   Jan 7, 2004   \n",
       "2      Caucasus Environmental NGO Network (CENN)\\nJOB...   Jan 7, 2004   \n",
       "3      Manoff Group\\nJOB TITLE:  BCC Specialist\\nPOSI...   Jan 7, 2004   \n",
       "4      Yerevan Brandy Company\\nJOB TITLE:  Software D...  Jan 10, 2004   \n",
       "...                                                  ...           ...   \n",
       "18996  Technolinguistics NGO\\n\\n\\nTITLE:  Senior Crea...  Dec 28, 2015   \n",
       "18997  \"Coca-Cola Hellenic Bottling Company Armenia\" ...  Dec 30, 2015   \n",
       "18998  \"Coca-Cola Hellenic Bottling Company Armenia\" ...  Dec 30, 2015   \n",
       "18999  San Lazzaro   LLC\\n\\n\\nTITLE:  Head of Online ...  Dec 30, 2015   \n",
       "19000  \"Kamurj\" UCO CJSC\\n\\n\\nTITLE:  Lawyer in Legal...  Dec 30, 2015   \n",
       "\n",
       "                                                   Title  \\\n",
       "0                                Chief Financial Officer   \n",
       "1      Full-time Community Connections Intern (paid i...   \n",
       "2                                    Country Coordinator   \n",
       "3                                         BCC Specialist   \n",
       "4                                     Software Developer   \n",
       "...                                                  ...   \n",
       "18996                    Senior Creative UX/ UI Designer   \n",
       "18997                       Category Development Manager   \n",
       "18998                      Operational Marketing Manager   \n",
       "18999                    Head of Online Sales Department   \n",
       "19000                         Lawyer in Legal Department   \n",
       "\n",
       "                                                 Company AnnouncementCode  \\\n",
       "0                   AMERIA Investment Consulting Company              NaN   \n",
       "1        International Research & Exchanges Board (IREX)              NaN   \n",
       "2              Caucasus Environmental NGO Network (CENN)              NaN   \n",
       "3                                           Manoff Group              NaN   \n",
       "4                                 Yerevan Brandy Company              NaN   \n",
       "...                                                  ...              ...   \n",
       "18996                              Technolinguistics NGO              NaN   \n",
       "18997  \"Coca-Cola Hellenic Bottling Company Armenia\" ...              NaN   \n",
       "18998  \"Coca-Cola Hellenic Bottling Company Armenia\" ...              NaN   \n",
       "18999                                  San Lazzaro   LLC              NaN   \n",
       "19000                                  \"Kamurj\" UCO CJSC              NaN   \n",
       "\n",
       "            Term                    Eligibility Audience StartDate  \\\n",
       "0            NaN                            NaN      NaN       NaN   \n",
       "1            NaN                            NaN      NaN       NaN   \n",
       "2            NaN                            NaN      NaN       NaN   \n",
       "3            NaN                            NaN      NaN       NaN   \n",
       "4            NaN                            NaN      NaN       NaN   \n",
       "...          ...                            ...      ...       ...   \n",
       "18996  Full-time                            NaN      NaN       NaN   \n",
       "18997  Full-time  All interested professionals.      NaN      ASAP   \n",
       "18998  Full-time  All interested professionals.      NaN      ASAP   \n",
       "18999        NaN                            NaN      NaN       NaN   \n",
       "19000  Full-time                            NaN      NaN       NaN   \n",
       "\n",
       "                                             Duration  ...  \\\n",
       "0                                                 NaN  ...   \n",
       "1                                            3 months  ...   \n",
       "2                 Renewable annual contract\\nPOSITION  ...   \n",
       "3                                                 NaN  ...   \n",
       "4                                                 NaN  ...   \n",
       "...                                               ...  ...   \n",
       "18996                                       Long-term  ...   \n",
       "18997  Long-term with a probation period of 3 months.  ...   \n",
       "18998  Long-term with a probation period of 3 months.  ...   \n",
       "18999                                       Long-term  ...   \n",
       "19000                                      Indefinite  ...   \n",
       "\n",
       "                   Salary                                       ApplicationP  \\\n",
       "0                     NaN  To apply for this position, please submit a\\nc...   \n",
       "1                     NaN  Please submit a cover letter and resume to:\\nI...   \n",
       "2                     NaN  Please send resume or CV toursula.kazarian@......   \n",
       "3                     NaN  Please send cover letter and resume to Amy\\nPe...   \n",
       "4                     NaN  Successful candidates should submit\\n- CV; \\n-...   \n",
       "...                   ...                                                ...   \n",
       "18996         Competitive  To apply for this position, please send your\\n...   \n",
       "18997                 NaN  All interested candidates are kindly requested...   \n",
       "18998                 NaN  All interested candidates are kindly requested...   \n",
       "18999  Highly competitive  Interested candidates can send their CVs to:\\n...   \n",
       "19000                 NaN  All qualified applicants are encouraged to\\nsu...   \n",
       "\n",
       "            OpeningDate                                     Deadline Notes  \\\n",
       "0                   NaN                              26 January 2004   NaN   \n",
       "1                   NaN                              12 January 2004   NaN   \n",
       "2                   NaN  20 January 2004\\nSTART DATE:  February 2004   NaN   \n",
       "3                   NaN      23 January 2004\\nSTART DATE:  Immediate   NaN   \n",
       "4                   NaN                       20 January 2004, 18:00   NaN   \n",
       "...                 ...                                          ...   ...   \n",
       "18996  29 December 2015                              28 January 2016   NaN   \n",
       "18997  30 December 2015                              20 January 2016   NaN   \n",
       "18998  30 December 2015                              20 January 2016   NaN   \n",
       "18999  30 December 2015                              29 January 2016   NaN   \n",
       "19000  30 December 2015                              20 January 2016   NaN   \n",
       "\n",
       "                                                  AboutC Attach  Year Month  \\\n",
       "0                                                    NaN    NaN  2004     1   \n",
       "1      The International Research & Exchanges Board (...    NaN  2004     1   \n",
       "2      The Caucasus Environmental NGO Network is a\\nn...    NaN  2004     1   \n",
       "3                                                    NaN    NaN  2004     1   \n",
       "4                                                    NaN    NaN  2004     1   \n",
       "...                                                  ...    ...   ...   ...   \n",
       "18996  As a company Technolinguistics has a mandate t...    NaN  2015    12   \n",
       "18997                                                NaN    NaN  2015    12   \n",
       "18998                                                NaN    NaN  2015    12   \n",
       "18999  San Lazzaro LLC works with several internation...    NaN  2015    12   \n",
       "19000  \"Kamurj\" UCO CJSC is providing micro and small...    NaN  2015    12   \n",
       "\n",
       "          IT  \n",
       "0      False  \n",
       "1      False  \n",
       "2      False  \n",
       "3      False  \n",
       "4       True  \n",
       "...      ...  \n",
       "18996  False  \n",
       "18997  False  \n",
       "18998  False  \n",
       "18999  False  \n",
       "19000  False  \n",
       "\n",
       "[19001 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/Lenovo/Downloads/CaseStudy/6. Analyzing online Job Postings/data job posts.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19001 entries, 0 to 19000\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   jobpost           19001 non-null  object\n",
      " 1   date              19001 non-null  object\n",
      " 2   Title             18973 non-null  object\n",
      " 3   Company           18994 non-null  object\n",
      " 4   AnnouncementCode  1208 non-null   object\n",
      " 5   Term              7676 non-null   object\n",
      " 6   Eligibility       4930 non-null   object\n",
      " 7   Audience          640 non-null    object\n",
      " 8   StartDate         9675 non-null   object\n",
      " 9   Duration          10798 non-null  object\n",
      " 10  Location          18969 non-null  object\n",
      " 11  JobDescription    15109 non-null  object\n",
      " 12  JobRequirment     16479 non-null  object\n",
      " 13  RequiredQual      18517 non-null  object\n",
      " 14  Salary            9622 non-null   object\n",
      " 15  ApplicationP      18941 non-null  object\n",
      " 16  OpeningDate       18295 non-null  object\n",
      " 17  Deadline          18936 non-null  object\n",
      " 18  Notes             2211 non-null   object\n",
      " 19  AboutC            12470 non-null  object\n",
      " 20  Attach            1559 non-null   object\n",
      " 21  Year              19001 non-null  int64 \n",
      " 22  Month             19001 non-null  int64 \n",
      " 23  IT                19001 non-null  bool  \n",
      "dtypes: bool(1), int64(2), object(21)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['AnnouncementCode','Term','Eligibility','Audience','StartDate','Duration','Location','Salary','ApplicationP','OpeningDate','Deadline','Notes','AnnouncementCode','Term','Eligibility','Audience','StartDate','Duration','Location','Salary','ApplicationP','OpeningDate','Deadline','Notes','Attach','Year','Month'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['AboutC'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19001, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jobpost           False\n",
       "date              False\n",
       "Title             False\n",
       "Company           False\n",
       "JobDescription    False\n",
       "JobRequirment     False\n",
       "RequiredQual      False\n",
       "IT                False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date']= pd.to_datetime(df.date, errors = 'coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jobs which are demand in armenia "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Title.value_counts().head(25).plot(kind='bar')\n",
    "plt.figure(figsize=(100,100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No of job post opening per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2004.0     451\n",
       "2005.0     555\n",
       "2006.0     609\n",
       "2007.0     900\n",
       "2008.0    1064\n",
       "2009.0     706\n",
       "2010.0     893\n",
       "2011.0    1111\n",
       "2012.0    1494\n",
       "2013.0    1387\n",
       "2014.0    1404\n",
       "2015.0     917\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q=pd.DataFrame({'Year':pd.to_datetime(df['date']).dt.year,'Title':df['Title']})\n",
    "q=q.groupby('Year')['Title'].count()\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x2880 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q.plot(kind='bar')\n",
    "plt.figure(figsize=(40,40))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top job post 2004 -2015 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2640fc696d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tOW5zLtr6jHwWOBa4v6QPkvPw7oq6a9XUnfbDREn7DbseEQc01D10uGy8oqFuKxEUffrbAc8gt3c/joifVNC8D/A6MmRRpJ/6C/2r4RF1lwFeSd94ga9GpTdRWaE/vGjXPAA9BDiFjAraCXgjuevYq6HuGmQ0iYBzIqJa4XlJz6fv9YuIYyvptjXHbc5F9c9I0X0E8LSie0qtHXmfLsCpNXRdlOleiKS9I+Izi7s2ovbywCPIFemvmxr/Pt3nD7l8M+kDbxTxUKIS3kV+2CG/YD4QEf9soDksUeRm4A/RPCT0IxHx9sVdG0G3zTnufbEEcFatL5a2WERCzq01vrjKe6M3F2dHxIWNNcewop5DnhBvBKzQux7Ng+5XIFd7g7pNV9QbkDHajxzQrRHMfysLb8FvBi4A3hIRV42oe2FEbDpw7aKI2GS0kc7XeDZwEJmwJNLP+98R0XhbXlxhTwR6SRnbAOeQB6Hvi4iR0/YlbRIRFzUd44DmOcCmwKXkXDyq/HsNYK+IOLmB9rDX79JonuTRyhxL+iLwMCa6an4fEa9rMt6i/XzgI8D9yXmulZhyDdm56m9FczXgT8ANwKsjYiRXoaT3ArsARxfd/wKOjIgPNBlv41PZEU5FTyYN6pXAU4CvAR+poHsk8H7SiLy0PM9nKuieRW5jLiVjqfcHDqg0FwcA/01mUc4me06+l3yjnz6C3m6kz/hvpC+ydzsN+GmF8f4KeFjf/fWBX1Wai+OBtfrurwUcA6xOCftqoH1aGfv7gY0qjffwfi3yi/xQMvN2pEgK4DVkuOpt5f3Wu10NfLurc0yG2qrv/jLAFZXmua1oroPIXrC9+88APkm6b85toHslsELf/RUpoXqNxlt7AqbwH5lXfl7ad+2MCroX9euSUQ6nVhzvZX3XflZpLhZ6Q5D+PRghpKd8kWwD/IL8EuzdNgWWrTDeMwfua/BaA+3Lhmj34nIvqqD/ANI3fXYxhu9uqLeQMe5da2CoVwXmkivT9fpuq3d5jouxX2/gfXhYpTE3jplehO4Fi7o26utX/vYkYLW++6sBJzQd7ziiPno+oD+VrfT1ZMPcWrp/l/Qo4M/km74p/yyHaL9VtiT7X3IbVoN7lCUijyr3+0/gl9gnFZlW+wdye9sGV0j6IXAEOb5dgPN7vs9oFmHzM0knkDsjyEO/M0s41t8b6FLG9mfgs6XexdvInUuT7eivJX2JXFlD7oJ+Uw5yR/JzRtYiuZncGSHp/qS7bRVJq0Tz7N225ngN4EpJvey7zYFfKGusEM1qlLQVzfVXSW9n4uv3txJa1ySc7l/k5+Qn5GdkO+AsSZ+F0aPFxuGj3oEsnrQO8Dlyy79/RDQK85L0KtIv9Gjg68AqwHsi4ssNdTcntzOrkVvn2cBHI+LcJrpF+6HAZ0jDGqS/cB/yy+BxEXHWiLpbkHP7H2SCyizgtmju1xsWWdMjosF5QEm93YkM7RLpcjo6KrxBJf0H+UHcmYxJPrxoj3yAVuKGX8uCyIyzgC8C/wRWioh/NNB+DrkNfxDpM12P3D5vNOkfLl63lTnWImqT9IgGNUpajOZakwwr7H/9DiC/KNeNiN+NqDs0SqxHjBgtNg5DvVVEnL24ayPoPiQG8v+HXRtBd5eIOHJx17qEpAvIbvFHApsBe5C+5XeNdWBjohz8HUYe6lw/7vEsDkmXAE8lzxU2kbQtsFtE7DnmoS0StVCbZKaiFursjMNQDzvRXuhaJd15EdGodnRb4y06bUWqXBARm/VHCkj6eUQ06j4haW1ypb4VJQwL2DsirmuiW7Rb2QW0RcvRQL3X7xJgk4i4R9J5EfH4hrpt7bReAHwMOJ1cnT4J+J+IOGqyv5ui9meHXL6Z9Ccf10C3reizbcgaLdeQc7EO8NJoWMRt2nzUkp4IbAnMkfTmvl/NJt8wo+o+gpzsVQfiRGfT9wKMoLs98CzgwQNvltlAozjZPr5FRiM8E3gfmUFYI+j+9vKtfrGkj5JhRzVSbw8Fvkv6pgF2L9eGNYNYUj7PkF1AE0FJR0TECyRdxkSff42axoeSW+dPAdsCLy+6Nfi7pFXIRKXvSLqBOu+56nNceBeweW8VXYzgT1lw9tKEFci4/X6/+hXAKyVtGxFvGlH3O2QxqR2AvchIsRsbjhWyw8szopRwkLQhuZtrtGCczsPE5Um/8bJkOFqPW2iWxvpwcrJXI4vv9LiVrPY2KteT8czPZWL69a2kH7kGD4uIXSQ9LyK+Iem7ZDJGU15Chki9nhzrOuQbvClzIqLfZ/h1SaN+UBYiIn4naVZkl5pDJf28oWSvFdsODXWGsWJEnCJJ5RB3f0k/I413U55H+rr3Ib+8VyW/yBvTwhxDe7VJIL9Inholiagc4J5MLg4ua6C7RkQcUhLBziDLI9So971QnR1JjevsTJuh7puMr5c3di8leZWIuKWB7nHAcZKeGBG/qDRcIuISsiXSd3s+Jkn3A9aJiL9VeppWIlViQVH1f5IHJLW4SdLuLEhs2I38UNag+i4gIv5UTvEPiRE7/UxCa9FAEXFb390qpQoKbe20htUm+WEFXYAHk2PsFY9amaynfbekJoWf2oo+a6fOTtP4viW9kVvn2eSE/4p8s/xPBd05ZOusg8kkmq8BX6uge3oZ7+rAH8ukf7LSXLyKrFf7FLJY1Q1kVltT3a3Ibia/KbpXAVdV0F2XTKC5sYz1+/TFzzbUXo/c5s4mV6WfpC+5pqH2D4BVa2j1aW5O7hDXJt0gxwBbVNJuq7Z6m3O8U9H7FLBjxXl+JZnwcygZzXVV+dysDHysge4O5E7lUWRC1DzguRXGex+y+NUxZHGmfahQJnkch4kXR8RjJb2Y9Nu8nUwqaZoe+3My7G8e2a8MgIg4uqHuRZEn768iV9P7qUI6b5tI+hX5Bhmci1qr3xmFZl4rrt8Bz4l2yvbOOCQ9kCxTKzKipPORO7UZR8LLcsVn81/A5yPi35JqfFusFA2L1iyCZcsb5QXkoUk1SnLETqS7o7/NV1N/5M1Rof5GD0mfY5IEnBoGT9JWZHr+ekyci8ZRFMCJ5VaNckj0Pyw83sbNeGmptnrtOdbwWjVQoR6HpEdExK+0oPjVteXnAyQ9IBoWOioHnq9m4c/eSBFXQw6sJ9B0YTcOQ/1lMnTlEjIraj1ye9eUEyQ9KyJq+cZ6vI884DsrIs4vSSq/raR9HLm9nUe9QusAp0n6GLn96s/mGvXNfUGVUU3OIQzZBdQg8qB2Tvl3jZN9yCiEg8jayFXHS3vZeFXnOCLuu/hHjcybydo3nxj21GSceROOI3fgP6XO69fGgfV8OlHmVNKy0bw0ZK9N+7/Ig4IqVbbaRNLlEfGoFnRPG3I5Kq32kLRyTDzwqqF5bkQ8obKmSF/s68n3wzJkmNvnmu5aasToT6LdVjZe9Tnu0+5vQLsmcN+o0KS5LXou2Ja012NBA+gVyTo7jRpAT5uhlrR7RHx7IIZ6PtG8w0tVJL0tIj66qG1/pe3+waTRaBJmNG2UWPhDyEiddSVtTJY5fW0F7QPJePpauwAk7UPGwu/ZMxplR/Ql4EcR8akG2vuTB6rHDoz3r6Nqtk0bc1x09yPjsh8eERtKehCZBbpVE92ivQv5Wt0q6d1kgbH3R8OytZI+APy89g5c2YVmT7KQ1volMeqgiHjaYv50UqbT9dELA2pluyTpycOux+gZQT0fYZvb/q2Bl0m6mvzgNErEmIYvw0+TyTm9YjuXLGreR6C30tus71rTLe4ewHbR120kIq4qIYYnkxEKo9Kr6fA/fdeCLHM6EtOwOGhjjgF2BDYBLgSIiOsl1fqcvycijiwr9mcCHyddTk13BnsD7ywhfjV34K8jDz7PJQV/qyyu1YjpjKP+cvlZM663n/4PzArkZM1jxDdhlCJR0VdEpUbc9wDbV9Lp0eqXIUBEXKuJvQer+GcjYtsaOgMsF0NaQkXEjU2TECKiaXPcYbS6OGhpjqE0oO0FBaheA1pY8P56NvCliDiu7GYa0aJ//V8RcWfvM6JKDaCn/TCxBNp/ALiDbIi5MfCmiPh2E92I6M9KRNI6wEebaBad75IppneThn9VSZ+MiI811Y4FiT+9cpZN9dr+MrxW0pZAlMSJN1In5R2g10FmsPZCE1/yZG3CGrcQK0lKg7U+vjmq3rDFQW1amGNorwEtwP8W7acDHymRUlWyHksC2wZMnItGNTloqQH0OOOodyRD9PYBTouIjSs/j8gmAo9uqNNK3HfRfi55ql2lnKWGF7CZT9Otczkk+gz5oRHpPti7Rny2pIOAlci6GV8lywqcFxGvbKB5N31x0/2/IrtwjLyqLn7ZbUhD/UNyd3RWRIxcDkHS8Uwe4tWkrnMrc9yn3VYD2pWA/ySbHvy2hMo+Ohq0Oiu6ryLdH2sDF5Nx9r9oeuCulhpAjyWOuvx8FtkF4q8DW+mRGPDrLQM8lgwBbEpbcd+Q9a23YKCcZQO9XqrqVqQB+V65vws10ljzi/3FFXSGsWVEPKYkEx0g6RPkodfIRMTIxb6mwM7kbvCiiHi5ssznVxtqfrz8fD7Zkaa3y9yNDGltSvU5hvmHtkfWMs79RMTtyqJUW5NhsXdRJzx2bzK79JyI2FZZ3K3GTvR5wDcjotaOAhiPoT5emTl3B/DaEt86cjfoPvr9eneRXwKNalwX2or7Bvh3RPxF0jKSlomI0yR9ZFSx3pZZ0suAbWNBjZKDyNVvU35eDj6/Rxacb9x5pY87ys/bS9TAX4A2/MC1uCOy/OhdkmaTO6JGyTlRCuxLen9E9B/SHi+p6ZYc2pvj2cCPJf2VbMpwVET8XwXdCRElZBr5cuQXWNOIkn9GxD8lIek+kck1D2+oCVnE7dPl9Tqc3F00rnxYq8LVlImIfcmOJpsVQ3Ib+S3UVPcbZFGYeaRRPW/yv5iy7mcj4sER8axI/kBuHWswWM7yM9QpZ/kgJh4orlKuNSIiNgDeTfo4L5R0QomgqMEJklYj6xpfSH45Hj7pX4yXC8p4v0K+5y6k0nuOLAU83+hLeghZy6YprcxxRBxQ3HWvI99nZ0j6aVPdwo6k8butPNf11Dksv67MxfeBn0g6jizM1IiIeDlZ8e9I4EXA7yU13WmNpSjTLmQwPOSH/hhg0wq625D9As8gDd/VwJMr6O5NrhhExhBfSNabrTEXK5NxrcuS4V5vJMsvNtV9eZmLr5fb1WTx8pqv45rAN4G7W3iP3IfKRZTavJFpyI+pqPdMsgDY6eV2Ta33XJtzTLpr3kA2EL60kuZ55eeF5efKtbT7nuMp5JfB8hU1lyPLLh8D3NhUbxyHiZdG+sm2JjtkfBx4ZzTMmJI0D3hRDBTsjuYdXi6JiI0lPZNcMbwHODQqdHhpg3KYsQVZZaw3p+dGNndtqj2bXOG8EFifTPY4IiJG9n9rYrOHhYjmadNV0YLaE0OJ5skjy5D+7+PIgvkAv4qIkUsMtD3Hkl5DljadQzYL+F5E/LKJZp/2W8nIjO1Ie/EK4LsR8bkR9Vaf7PfRMGFJ0n+Sn49tyS/Z7wEnR9PM6zEY6l41ug+TJ7nf7V1rqLtQRbth10bVLW6J0yPi2Kbj1cLFbFTuVwm6l/SLiKjeibz4p79PGucqtb8l3UOeul/cu9T364iGadO1KeO9ggXdQAbH2zhNX9KZMdFH3VSv1TlWZjweHhEXL/bBo+lXiygpc3EdC1yMg3PR6JxB0uGkO+mkJl+uC+mOwVCfQBZZfzoZ7nYHub1pFJ4n6Wuksesv2L1spM+oie6hZPHyh5Cn/LNIgz3ySl3S98lt4jHkG/yPTcY4RP8A4FLgmKj4AktSRIQq1vooYZq7kn6948hd0EgdoKeDEuGwE1lM63Dg2GjQcXwRz/Ee8nPxPSaWZR1ptTcdc6yJtT7mkIlhjWt9FD/yBuXubyLi5skePwW9z5Bu0rPJM62zan5GynNUr/VRzc+zBL6blcjwow3K/QeSab5NdVsp2E0euG4KrFbur07GcTbVXZX0Jf+Y9Ku/lqwPUGOObwXuIVNjb6Ve4fknAr8E/ljubwx8sdKYVyYPX44jm+Y+ZbrekyOO9yFko4pzgSOAx1bUvnrIrUbjh1bmmCx8dTxpSCEPFM9uqLxvHs0AACAASURBVLk8eb7yd/Jc6GLgb2RDkEa+ZHIVvS3ZZORiMjHuIZXm4tXA+cDvy/0NgFMa69Z6czV44+wOnFhJa1bf/Vlkjeqa412fPAC9vKLmMmSc7E3Am8f5ekxhrOeS/Rcv6rtWZS7K6/Vsckd0EfDMcf9/pzDmjchY+GuAF4x7POOa42LsNPC+aHTgR5YX/g4l8KBcuy95gP3+SuNejcw6vhF4dcW5WH5gLi5rqjuOFPLlyWSXF5EZR0eTRVaacgrpTultQ1ckY4e3bCJaMqF2Jcf7GPJAo0lSSk93y6LzJHJ1s2NE/Kypbp/+c4Gen/P0iDihhm5UrvXRl+TzeLI28GciYjrqX49ECZt7IRlSei3p/vhgRNTIBeg9x3LAa+h7/YAvR4mLH0Gv7Tluo9bH84HHR8TtvQuRFfReC5xDHuovMWVsz2PB4Wcv6uzaSf9w6rRS62M6y5xuR75Znkn2KPseWeJzbiX9herLDru2BHqvJse7Nrm1PQI4LioU45F0DbmlOxw4lYHY6WgeOXAgmXX1nXJpNzLtfd+GukeRffE+T0aWvJGMh39hA817SH/6WeQbesIbMjrWLqtvvMeRiU+D421crrfE3S7Hgsa2LyHDIF81ol6rc1w7MqNoLjIQQNJlMWJpCEm3kZmNhwG/Y+G5aBoB81Hys70HGar4WuCXEdGoO9R0Gup7yI4KL4sFtYGvijqtlpB0NvCGnpGT9Dgy5Xuk6AdJdwK/AN7SW33UGq+k01nwBulFe/SIaF5v4FLSZ3pPuT+L3Io1jYCpXutD0ksn+320WKBoFJSV2yarx9E4DbkXErq4a0ug1/oc14zMKHqXkId+w+pLnNZgLr7Ool+/iOYRMK3U+phOQ70JuWXcmYzxPRx4b0SsV0l/86LZyy56ILBrjBjjW4zSLuRqdC1yRf2yiFinwnBbpRjqbaJECZTY0dObGmozPUi6ENglIn5f7j+UTMvuaux+1ciMonkNeSA+zFBHrQVeG6h+27fxtOJSNtncjQxzupgMcTq4gu5yZE0AkUkCI/n0huiuTX7J7EZGrRwbEe+sod0GknYDDiRdTCJ9ne+IiJHThYuf8w3k/EKWN/18RJzebLRmEElPI+taXEW+fusBL4+I08Y6sAHKedPBZMGyq8iD8fXIqKu9IqJxKdmZgrRQ2zeR5zeN277BmAz1/CfPbcJ2wAujebzz64DvRCkUpKw1u1tEfLH5SCc8z8PJ8bZV87kK5RB0c/IN0ygzUVnD+PPkSfyFRXNTMgLm9VG/ofBSj7Lucv+io2bz4ypIeh8ZCbVXlDhhZWeXLwB/iIiRDvxmImqx7RuM2VDXZBGHiY0zHmcqA1EfZ0QpSj+i1umkL/qSgeuPIVcMTxl5oGZSJO0fEfuPexzDkHQ5A5EZ5foqZPnQ6o2bu4qkixho+1auzyFTyBvZoWmvntciy6gvbqwcoC0/xvEsFkmnTOXaCLoHksWkfllub1Sm7I/KAwaNNEBEXEr67xsjaUNJp5QPP5Ieo2xm2kkkrSXpEEknlfuPlNS4AP8QGjUL6EfSRyXNlrRcmeub1Kz64T2DRhogMlOz0ytASStJeo+kr5T7G0jaoYHkItu+saAG/8jcmwz1j8mWQE+T9FQy/OakMY9pKJJWKAd8a0q6n6TVy20uFcqRkluw7SLiaxHxNTJe/dkN9CZLF6+SSk6WC30HmU3Z+xIYOexvGvg6+Z7rvV6/Ad7UwvM076qxgGdE9vvcgax3sSETe40uKTHw/p1/Iw8CGyNpC/U1ypV0X0lNG9tCngH8i8y2hZyPDzTQa7Xt2zgaB7RVF+DtZJv211CypMjIj0ZIOiUGWr0Pu7aE/Df5oX4QWcu492G8hfTv1WA1oFcbYtWGWutL+sGQ66Jhsfw+VoqI8zQxmaZGbe62WDMijpD0DoCIuEvZ+qs2NSM9andXWpWJ799+aq2ov8TEObhtyLVRWD8idi0H70TEHWo2GRtLGtZQRFTohzqOzMRWOjZEdts4hzQcu5I1OY5uMM4VyAiPNcvBZO9FnE3DVW9EfAb4jKQ3NEkKmIQPAxdJmhD10UBvssYOH5/kd0vCTZLWp3zAJe0M/KmSdhvcJmkNFox3C7JQU2MkrQrsT2atIukM4H0Vwt6qdleqlay2GNQfg1w+5zXs1p3Kgkm91299coU9EtFu27exFGWqWheA3L69lwwXO4sMIftDhXHuTRbD+RcZetQrjnMJGelQYy5eRyn2VO7fD3htJe0Hkv7N55E+5ml/rZdwvA8l05tvJ6srngXMHfe4JhnvpmQFtpvLz99QqXkAucA4oMzJQ8mwr2Mq6N6nvMdmlfsrA2uNey4XM+ZjyAzY5cptb+D7FXS3I4uh3Uhm8F5D5h6M/f88dLxjmPiqHRtIX9gZwMP6rjWuNNan9YYW5+LiIdcuaqD3TGDnIddfTIUKhdP0/liZvkI8XbyRxY32IXekGwGPIg+T2nxfLHRtBN0Lp3KtSzfg/mQi2w3A/wHfBe7fUHMZ4AXAGuTZzQ6kK2vs/99F3cZxmHiEpC8DqynrafyUPEgalZ2APwOnSfpKSRaoeQBzT8m8AjI+W1kYpga1I1UOIL+0BjmFjIHuLJI+JGm1iLgtsvjO/SQ1OdxpjYi4G3heRNwVEVdExOVRKbmqcEc5xwHmJ4jdMcnjJ0XSA5QlFVaUtImkTcttG9K911ki4oaIeGFE3D8i1oqIF0XEDQ017yF3xX+JiBMj4oQYErHRJcaVmVi1LkDRXJnMkNoNeCpZ0ObYiGjUfbvN+GxJHyP77R1E+sr2Aq6NiLeMqDdZIZvG3W76tGaTabzNiqFP1FxoTiVdGN1Nm/4geZg2WNy/UUGtor0xWc6zdwj8N7J8wUIhklPUeynwMvJsqL9q3q3A12PEQkRqsa2VpLdFxEclfY4hB5PRvJBU1eYMA9rVGweMo8PLPsCREXFdi8+xOlmnY9eoU+Bo4ygTVVa9l0Z2XW46zmXICJDeLuBksoDLSNEDkn4DPDIG+rMpU+t/GdlFvMl4NyMPgO9bxvt34BXRoGdin/alwOZRMvDKG/yCGvPcBuWgdpBo+n4beI7ZRXRYNMEoejtFxMgH7EP0rmZBUbF1yS8UkRFHf4wGlSYlPScijtciCkpFw0JSZexDZBu34no1GX22ekSsL2kD4KBoFiU2FkO9H+kf+ivpezoqIv5vWgexBNRe9Q7RXxFYN0pT3oZaB5IJKK+P0iqr7DQ+C9wUEW9vqH8p8LoodbPL9vyLNVbqkt5GHn4eSs7zK4AfRMRHm2rPFCS9Gbg5Ig4ZuP4G8gDw0w3170O6CufSF/EVDWtRSDqIfK1+WO5vDzy91mdkJiHpYrLu97m9HaIalGWdrzsO1wfMTz/elXzjXBcRTx/LQBZD7VXvgPZzgY+RrYUeIumxZBjWSNloJWzpA8CrgD+Uy+sChwDvaepHlXR2RGy1uGsN9Lenb54j4sc1dNtCWQNlI/riZJsYPWVW5qYxUMyoGNjzm34hSvoRGaUyj76GDxHxiYa682Kgh6ikCyJisya6RWdD4K0s/OVSo4nwo4BHMvH1+2ZDzXMj4gla0MR7WfLAttFrN5aEl8IN5CHgX8iT3U4SGbf5deDUGqveAfYjv31PL891sTI7cSSKy2NfZXPbh5XLv4uIkQ+iACT1/MTnlYPgw8hV766UsdcgIk6io9mkg5RV5Epk772vkuV7z2soG4NGulz8V8NkjB5rR8R/VtAZ5CZluv+3yffF7uTnugZHkrvZr9Kwm1A/ZWe/DWmofwhsT4aENjLUwBmS3kke3G5HNg4Yuc5Oj3EkvLyGBW1wjiJ7lf1yuscxVfpXvUDjVe8Ad0XEzXU+gwsohvmyipKDK679+p+uibCksyJia0m3DmiJNFyzm+i3yJYR8ZhySHuApE+QMb+NkLTWoCtQUpV6KsDPJT06Imq+NyAP8Pcjy5tCRh7VSv+/KyK+VEmrn53J5swXRcTLyxx/tYLu28kd7WXkTvyHNXTHsaJeD3hTRFw8hucehaqr3gEul/QiYFY5dHgj8PNK2tWIiG1blN+jPMd9F/fAjtHbpdwu6UHkCrJpm7aPASdKegtZThbgcWSX7BoZoFsDLysHaf9iwZdho215iZTYu3df0rqksfrYqJp9ESXHl3DYY+nLHKwQnXFH2S3fVQ5tb6BhOYTiJr00smpgk5DjhZg2Qy1pdjm9/mi5PyG0p0ZYTEu0suotvAF4F/kGPIws8vP+Np6oBmXV8SHgQRGxvaRHAk8cPPxaQo4EHqfm9VOmmxOU8fUfI41q0HDlFBHflHQjGfP+qKJ5BbBfcQs1ZfsKGkPRxI5ID2bB6npU5jGxTV1/8aigeY2ZC8rr95XyXP+goeuqGP5LJK0bEX9sOL4JTGcrrhMiYoeBkJ4ejcNi2kLSIWTCyL7kwecbySy0vcY6sEkYZvRqGEJlSc9DgXdFxMbloOSiJifayjq+3ydXYAsVV48KzWLbphz2rRAVWlBNB5Luz8QDtJGMirKq3Y7Ai8hSDseSIbFr1xjndFF2yLMjKzY21TqVbNhxHhPjsxu5SqdtRR0RO5Sfjbt4TzPVV72SPh0Rb5J0PAv7eIMMXfxyRJyzhLqtFZIqtFEx7oVkotKyZHx2p1HWb1ZEfKt3rRz27SHptoj47hiHNynlvOUT5HvhBtINeSUZuTIKN5AG6d3AWRERknasMdYe5T39WtJtE2SD7IMiYqRiUpKeSZYoOKp3LSKukfTicj7QNPmunc5PMf25+6dM5dq9+QY8rvx8yiJuO5EJKkuq22ohKdJPvwYL6rRsQXaPaaK5d/n53nG/LlMc70UMqUVCfsnMG/f4FjP2S8rrd1G5vy1wcAO9fYBzgcuBd5JtuarV2SnPcQQZXrptb7xkwtyoeucAc4ZcfwDwi3G/Rosc9zS+SVYgS49eQlbwWr3c5gJXjnsihoz30+Xn8cAPBm7HkS6ALSo8z/KkP3JCYR/gOQ00WykkRR5sVa0YRyk2RMeLA/WNd5EFxCb7XRduZKZnz2AvU/59XgXdh5K7zsvIsqlvBzasNOZLpnKtK69fWbycT/q87yRDCm9pqjudUR/TUSy/Jr2t7aJO29cEvkbGYY6EsijON8gSiwLWkfTSiDgzGvQ4pBSSikqNfiW9iTTMF5Er/l7T1V9H82JEV0q6BphTMh/nPy0VIhJaYDlJK0fJ/OxR/LVVWr+1dGgL8HdlP8Mzge9IuoEKzRki4irgg8AHJT2aPFA8iVxhN+UiSVtEcQMqu7uc3UBvBUnLxvAyCys20O3xedKddyRZW2UPoFHpBmAsro/Wyoa2OObqq97y9/OAh/fd35AK22fql0/9OBk2+FfS/fEhsjzk6pXm9wHkKm+9wdu4X/shY30raYTm9l2bC5wI/E+l5ziJLLNwSbm/LHBZBd2VyRKfywIvJQ/G1xj3nC5mzFeSpYyvKbd7yEiYyxhhBQwcSO6GVx6Yl0OAj1QYb2/XcmnftZ831R1X9bzqqZttMWzVC7w0Is6soL1QRbth10bRpYVCUpKWJ1cJW5K95p4I/D0iRt5V9GmvQGZTBvD7GPGwaDqQtBfZMWcVcry3AQdGpcQMSedHxObqqyioIVUcl1BzFlmpspOlGhaFshLdIomIP0z2+yF6bZdZOBN4Ohmq+WeyS9HLImLjJrrjasW1DfVTN9viE2RT0F/D/NoDh5H+2qbMK+F/PTfLi8lVdlN6jX77C0n9qILuimQEyarldj0NMyDLB+dDwMuBP5IrvrUl9cIAa9Z5rkJEHAQcVNwIiorlXgvV23xFxN2Sbpe0asyQMEJYYIhrhRRGS2UW+ngJ+R5+PXnYug4ZHNCIcVTPu4wFqZsb91I3I+I50zqQKdLWqrfo3Idsx7U1uVo/k6xGN3LvtqJbu3zqwWQI163kKf85wDkR8bcm4yzanyIjJvbpGbySKfZxMnts78n+/t6IsrbK50hX2+VkuYWdo2Gcr6QjyMOunzAxxnfk2s5lpf6NiNi9ydgm0R8aUth0d9gWkp5KfjZur6k7jhTy6qmbLdPKqrcY03mR6aZVkzqifiGpdcl+e78l+xleR9airsEOZITA/BVDRNyirAnzK/pSk5cGiuHrhWnWPLSF9KOfWEFnPmWlPkfS8jGkoFQF3k9+ufw0shrdtuRhZVd5Gbnb+gsZ8/0zMsa80aJmHIa6eupmy+xFrnrfSN+qt6lotJluWrmQVET8pzKHfiPSP/0W4FGS/krGnu43qcBi5Rfe1hUDMJ4avIuhfMluERHV67KU//fzIuJT5KFZTe1GxfYn4RrgbEk/YOJKvcYC5N8R8RdJy0haJiJOk/SRJoLlvbx2RFxbYXwTiIg9ynM8iCz89AVyN9DI1k6roS4T9OHIsLGDlPVxq6RutkGbq97CA4ErJFVNN6WFQlLFmF4u6e+kv/RmcjX8eCZW01tSfilpj8HD5JIB+KsGuq1RvmQ/QR6mtsHZkj5PpTZfkp5HGqYvlPvnku4UgLdFX5beiFxfbstQP7u0ekhhRISk71PnnGkC5X37JODRwE1kuN7PGuuOwUe9UJHxLiPpO8A7aq96i/ZThl2PiGENapdEd0Lx8nJtZL+6pDeSK+mtgH+Tcay/KD8vi2wWOupYH0yWB72DBYV4NicPLneMiP8dVbtNymHUpcAxw3YEDbVPG3I5YsRi+ZLOBl7YW0Equ5A8jQxLOzQqFcMqseQREf+ooVc0VybfG8uQbsdVge9ERKN615K+QPaLPL/5KCfo3gT8nqyhfVpEXFNDdxyuj3MkbV57glqk+qq3hKLtRZ46XwYcEgMB+A2pXT51Llk7fJ+I+FOF8c2nGOInlEOYjUj30kkRcUrN52mBN5OG7m5Jd1CxfnbULyu7/MA2/6xi6P5SDGEjSrjtt8hM456x2iMiGrluir/+uBJSeA8ZJluLbYG9lMlWt1Gv5OuakjYCnkwmAG1AnjG8pInuOFbUvyQTO/5AxQlqizZWvZK+R65Mf0aGJ/6hZnSDpJXIlN75nd6B93c5NtlMRBXbfEn6XUQ8bBG/+31ENMoglPRzMpTytHJ/G+BDEbFlE92i9QPgJbVDChcVn72kcdlDdGeTO8+nkC6QNcn+iXs00h2DoW5lgmrT5qpXfc0uSxzxeRGx6WL+zHSIct7yYuAhEfF+SesAD4yIxgfjWkSbr4h45Yh63wFOj4ivDFz/b2CbiGgURSHpkhhI6Bh2bUTt6iGFfdpbAxtExKGS5gCrRMTVDTUvJfNCzgLOjIjrmo4TxmOovzW4DRh2bdy0ueqVdGG/YR6830C3lfKpZmEkfYncjj81Iv5DWU/l5IjYvIL2pbGgzddjymHaMRHxjBH17k/W/P4XEzvH3Af4rxho/TWC/rFFtxfCujuwWUT8VxPdov3SYdebRrAoE+82I0s4bFiiNI6Mho2aJb0gIo4YuLZLRBzZRHccPuoJgerFD9XFw8VH9q16D6FuCOHGkm4p/xbZCPMWmvs5Wy8kZebzhIjYVNn4gIj4mzLFvgZV23xFxA3Aln3nAAAnRsSpzYY5n1eQdZiPYUEI68trCLcYUrgjsAnliysiri+HoU3ZlyzN2s87yCJNIzOdrbjeQdasXXHASN1J1pjtGvMTDCIL5FcTjohZ1cQm6s4rP88oRmPD8qv5CROS2khKWBr5d1lk9NK855Ar7BpUb/MFUAxzLePcr/s38sC6GtMQUnhnCdPrvX6NDlUlbQ88C3iwpM/2/Wo2FSoUjsP18eGIeMe0PukIKDuX9HxiIsPFbqfi6X5bqMVCUiaR9GJgV2BTcq53Bt7ddIs75Hk62+ZrMa62ppFRrYYUSnorWX50O+DD5K7guxHxuRH1ngA8gux3+d6+X91Khuk1ykwch6HeiizDeVsJDt8U+EzXDhNnMpLmAS+KgUJSMyl+fSYg6REsqKdySkRc2VBvoTZf5fqrgc61+ZL0uIiY11Jk1Pn9/n5Jn4+I15d/nxMRW4yq3ae5HX2RUdGgDVfvnEnSdyPiRU3HtpD+GAz1pWRRpseQPtVDgOdHxNAX2yw5w5JbmiS8mOGUA8R16HMhxojZg0XvIuDJMVCNr/hOT1+avmjbDins05rNxNfvryPqXE66qt7LxI7pPd1jRh0jjOcw8a7iG3oeuZI+ZFEnu2Zk2iqfagqS3k8W4Pk9C7b9AYyUPViYNWikASLiVmUHkk5Sdsn7k5XtlmWBe7BJsbVzJb16ESGFNUIg/5t0U9xBni2IfP1GHfNe5OdsNWCwEmiQB60jM44V9RlkbeSXk9k7N5KukEdP60Duxail8qlmAZJ+DTw6KlaMk3QlGdY2rM3X+RHxiFrPVRNJvyJrL88jewQCEA3SvKchpPC3ZHuzm5roDNF9ZTRvmbYQ41hR7wq8CHhlRPxZ0rrklsFUQO0XkjLJ5eTq6YaKmocAR0l6TZQaEcpiWl8ov+sqN0fESTUFpyGk8PdkcEBtvqWsjfPkcv8M4KBo2jlmulfUpn3UYiEpk0jajOxGfzm56gOaVz5Uy22+2kDSgcAscnvfPxcj++vbRtImZO/Ec5k45kZhhpK+CizHgrokLwHujohXNdKdLkMt6ayI2FrSrUwM5el8uNtMQ9KpZAW62uVTTUHSFcCXyfIC8+Onm0Q6DOi31earOqpc7W86UBZZO4uFX7+mGY+tpNNPm+sjIrYuP2vXqzULc8C4B7AUcFNEfHbxDxuNqFgqtG2ifrW/6eCuiHhzC7p3S1o/In4PIOmh9PntR2VcXchnAWsxMSzG2/SGqP3yqaYg6ZPklvkHzJDtflso+55+CHhQRGwv6ZHkQV1n/eqSPkhW8Dyeia/fSOF5fbpPI10qV5HegvWAl0epLDiy7hiiPt5AdgT5PxZsOcIxvs1ps5CUmUhb23212OarLSSdRBqnd0U2rF6WbF7d2UguScOq5DUNKexp34cF/S5/VSPaahyG+ndkQZtGHRrMwsjlU+8VSPpFRLTV5qs6vSxCTewodHFEPHbcY5suJG0OXBsRfy739wB2Ilft+zddqS/TfIhLzLVkvz1TnwmFpMY5kHs7klaV9ElJF5TbJyStWkn+ZEk7qWYlsHa5TdIaLChQtQUd/4xLWk7SGyUdVW6vb5hU9GWywBySngwcCHyTnIfGRefGsaI+hNwWnMhE35BjfhsykwtJzTQkHU2G5vWHYW0cEc+voH0rpc0XmTnX6ddP0qbA54BHkXMyB9g5Otq0GuqH0fVHdij7Md4YEfuX+413F+NIePljuS1fbqYS0VL5VDOU9SNip777B5QKb42ZaZFREXFhKczU88vOL6vbYTYfCJk7VdIlDfRmSVq27GSfBuzZ97vGdnbaDXVEOHTM3Bu4Q9LWEXEWzK93ccdi/mZKFJdHK22+atLvl42s2f44il9WUmO/bMvUDqM7DDhD2dj3DvJAH0kPo4IbaDoTXlqrXWvMdCNpY9IH2fNL/42s+d14u68W23zVRNKFwNMj4q/FL3s48AbgscB/RMTOYx3gJCwijO4VTVLUi2/+geRrdVu5tiHZi7FR2OZ0rqgX1ybKmJnELSUUbTZARNwiaeR2WQO02earJrP6Vs27AgdHxNHA0bXcQC1yFtk4YH4YXVPBGNKLNCJ+01QXpjczcX6bqOl6TmNa5Ghg04i4pe/aUdTp/9lmm6+atOqXbZlflNDV+TugskPoZDjrtE+mpB2A97Nw7dpOnmgb04+yq8tGwKqS+iM8ZgMrVHqazwLHAvcvGXQ7A++upF2TVv2ybSDpAcCDyd6tm5D2B/L1W2lsA1sM40p4eT5wWbh0n5lhKBte/BfwXDJ9vMetwOG1MgpVuc1XW7Tpl20DZZOSlwGbAeezwFDfCnw9GnZiaYtxGOrTgKdFRBe3csZMCUlPjIhftKhftc2XmYiknYo/fUYwDj/S24AfKju9OOHFzFR2LKVO7yA7Fm0MvCkivt1UWO20+TITWbscBN8KfIX0Te8bESePd1jDGUcK+QfJbLkVgPv23YyZSTyjHCTuAFwHbMiQpqYj8gIyoWabiNi23Gyk6/KK8vo9A7g/2RrwwPEOadGMY0W9ekQ8YwzPa0xNenUhngUcVmKJa2m30ebLTKT3Yj0LODQiLulybZVxGOqfSnpGV7cYxkyR45VNXe8AXltC6P5ZSfvDwEWSqrb5MhOYJ+lk4CHAO5QNhDt7bjaOw8RewZl/kdXeHJ5nZiTlwO+WiLhb0krA7F6Zy4a6rbb5MvPrfj8WuCoi/l6q/z24q4WkxlHrw/5oM+Mp9YZ7/+7/1TcryLfa5ssAsHX5+ZgOezzmM521PibN+HHokZlJSPpc390VyJjnC2vUt5DbfLVOqTnUYwXg8cC8rh7aTqeh7rUuWoEMNr+EdHs8Bjg3SvNbY2YipWnAt2r4kdtq82UWTalQ+NGI2G3cYxnGdNb62BZA0uHAnhFxWbn/KOCt0zUOY1ridrLIT2NiZnb1nulcRzY+6CTjiPp4RM9IA0TE5ZKWmt5q5t7BQLneZYBHAkdU0l6VbAD95HLpDOB9EdHJ+hkzkeK66n/9Hkvu8jvJOKI+DiPbRX2bnKjdyboAndxyGDOM0tGkx11kx/frKmm31ubLJKXmR4+7gGsi4uxxjWdxjMNQrwC8hgWrhTOBL9ZoqW5M25TKcGsNfqglPQm4vtcxpOFzLNRjr0bfPTO/ZOyciPjlwPWNgBsi4sbxjGxypj2FPCL+GRGfiogdI2JHsoKV63yYmcKnyfoQg9xRfleDOyTNP1yv2ebL8Dmy+e4gawOfmeaxTJmxFPcuPundyK4QVwOdLC1ozBDmDkuKiIgLJM2t9Bx7Ad8svmoobb4qaS/tPHpY4lBE/FjSJ8YxoKkwbYa61Kh9IWmg/wJ8j3S9+ITbzCQmaw6wYqXnaLPN19LOciP+bqxMp+vjV2RSwHMiYuuI+BzNuv4aMw7O4lDysAAABPNJREFUl/TqwYuSXgnMq/QcR0Ma6L5WX0dV0l7a+a2kZw1elLQ92ei2k0yn62MnckV9mqQfkR2Lu5+7acxE3gQcK+nFLDDMmwHLAzs2EZ6mNl9LO/sAJ0h6ARNfvyeSJWs7yTiiPlYmWxntRhZC/wZwrKvpmZmEpG1ZkCBxRUScWkFzWtp8Le1Iug/wIvpeP+C7EVGr+mF1pt1QT3hyaXVgF2BXp8cak7Td5svMPMbR4WU+EfHXiPiyjbQxE9hR0mxJy0k6RdJNknYf96DM+BiroTbGDKXNNl9mBmJDbUz3WKjN1zgHc29D0ixJjZsQTyc21MZ0j16br82AUyq3+VrqiYi7gTmSlh/3WKbKWA8TjTHDaavNl0kkfRnYlIyuua13PSI6Wc5iLCnkxphF03KbL5NcX27LAJ1vD+gVtTEdo802X2Yipft4RMQ/xj2WybChNqbj1GzzZZLSWepbwOrl0k3AHhFxxfhGtWh8mGhM96nW5svM52DgzRGxXkSsB7wF+MqYx7RI7KM2pmO02ebLzGfliJjfRDgiTi/lLTqJDbUx3ePjff+u2ubLzOcqSe8h3R+QLQGvHuN4JsU+amM6wnS0+TJJCX88ANiarOJ5JrB/RPxtrANbBDbUxnQESScA7xzsICNpM2C/iHjOeEZmxo1dH8Z0h+lo87VUI+nTEfGmgXOA+XQ1ssaG2pjuMB1tvpZ2ej7pj0/6qI5h14cxHUHSYcCpEfGVgeuvJCvq7TqekZlxY0NtTEeQtBZwLHAnQ9p8udZHPSRtBewPrEd6FkRmKD50nONaFDbUxnSMNtp8mYmU6oT7kF+I85tsR8RfxjaoSbChNsYsdUg6NyKeMO5xTBUbamPMUoekA4FZwDHAv3rXI+LCsQ1qEmyojTFLHZJOG3I5utq/1YbaGGM6jqvnGWOWOiStJekQSSeV+48sYZCdxIbaGLM08nXgx8CDyv3fAG8a22gWgw21MWZpZM2IOAK4ByAi7qIvTK9r2FAbY5ZGbpO0BqXeh6QtgJvHO6RF41ofxpilkTeTHcjXl3Q2MAfobE9KR30YY5ZKJC0LPJxMH/91RPx7zENaJHZ9GGOWGiRtLukBMN8v/Tjgg8AnJK0+6R+PERtqY8zSxJfJoldIejJwIPBN0j998BjHNSn2URtjliZmRcRfy793BQ6OiKOBoyVdPMZxTYpX1MaYpYlZxTcN8DSgvzJhZxeunR2YMca0wGHAGZJuAu4AfgbzGwt3NjzPUR/GmKWKEjP9QODkiLitXNsQWMXV84wxxoyEfdTGGNNxbKiNMabj2FCbGY+kNSRdXG5/lvS/5d//kPTF8phtJG3Z9zf7S3rr+EZtzNRx1IeZ8ZSGpI+FNMDAPyLi4wMP2wb4B/DzaR2cMRXwitrcaymr6BMkzQX2AvYpK+0nDTxufUk/kjRP0s8kPWIc4zVmUXhFbe71RMQ1kg6ib6Ut6Wl9DzkY2CsifivpCcAXgU72zjNLJzbUZqlG0irAlsCRknqX7zO+ERmzMDbUZmlnGeDvEfHYcQ/EmEVhH7VZWrgVuO/gxYi4Bbha0i4ASjae7sEZMxk21GZp4Xhgx2GHicCLgVdKugS4AnjetI/OmElwCrkxxnQcr6iNMabj2FAbY0zHsaE2xpiOY0NtjDEdx4baGGM6jg21McZ0HBtqY4zpODbUxhjTcf4f46Vq2WT2MbkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Year 2004\n",
    "df[df['date'].dt.year == 2004].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26416326e50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 2005\n",
    "df[df['date'].dt.year == 2005].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x264162f08b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2006].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2641616e7c0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1cHqFuBcQZZyv7jt2/Zifi1ZeJ8BlRC2m2ueilbgDf2Ml4k2scayxmYUCfIi4mljD9uq2V3PDq+Ti97ZvrBBn0Ea2PwT8E8B2r3+2ls0cV9x7AmcC6wEvbRhzbdsnA/dDlEMA5jWM2XM48aTc3PbjykejAcziCbYPIfqVsf0noMYsgfscr6bnE1ebH6Nen+e7iET7ZwDbc4huwqZW9sJ3HjW6GNs8F629Tmz/euBQledyW3EBJD0XmAOcVR5vPTD2t0xGPojZp61Ee4Wkk4BvAP/oHXTDkXvg3nKbbABJG/XHr2B5ScsTCfx/bP9TUtP+rrslPYT5bX4iccdTw68rxur3z9I102vzLMobUENzyyDe/sCTy99YvkJciIT4l4EZfzX6Ku8oz7PeudiHuDNrqs1z0dbr5NdljYDLtL9DgRr5o624Pe8i3ty/D/HmLmnoN/dxSuBtJdrVgXuAp/cdG3rkvs87iXfRR0n6CrATDeeXD/gMcAtwDXBhGext1AdO9EWeDmwk6QfALGCfhjF7bga+L+nbLPj/13T61ceBrwPrlGmQ+wD/0TAmwL7E7KeDbP9O0npArYUy10t6MTCz9PkeSsyKauoQYnbIppJ+A/ySGB9oqs1z0dbr5NXAx4BHAL8hiuodstjfWPa4twLnVIrbU/XNfWwGMSUdN8Fh237FBMfHQrmafSJxS3ip7bZ27uj9veXccFaOpOWInZEE/NT2Pyu17Z0THbf97gqxNwV2Lw/Pa+lOrZoycPc25l80nA28z/bfG8Z9tO1fSloFmGF7bu9Ywya3arJfJ+NM0rHAucSMrb2JN/flbb96qHjjksDbIulBwEHA5sCDesdrvDGUGS47E++gF9v+eoWY+9v+8sAc8Ac0uaIt7R30F2Ig8/Zh47ZN0rbMP88/cIOFR5Iutr2zpLkseOVTZXbSwN+qOhVtEYulrrT9+CHjtXYuyv/ZIjX5PyzxNySulJ9ItP0S4AjbNzeMOwv4N2ADFpxNVeVCsvab+9h0obSYaL9EzOJ4BrGA5SVU6NOS9EngX4ATyqFXSXpqGXBrYpXyueoiguIgYAfg/PJ4F+BSYBNJ77E99PLp8sR/Mwv//w29SKjEfQfwAuBUIrEcJ+kUDzl1zvbO5XMb5xeA0of6OWBVoPFUtHIHsjmwxsCb8Or0netl1fK5WFwJhEaLx4qvErOT9iqPX0S8Fp/QMO43iZXb36Pi4GWfDW2/jUjijY3NFbikU4hE+2L6Eq3tpoWnrra9jcqy7jIweHaFxHIDsEUZve/N1b7O9uZN4rZJ0reAV9r+fXm8DjFN75XAhba3aBD7HGLK2BuJfsQDgD/Y/n8N23wjsE3vCqUMiF1l+7FDxltrcd93hcVHki4j+upPdykFIen6Yc+vpOcTg9nPY8Fdr+YCJ9oeqn99Ms5FWyRdZvsJA8cutf3EhnHn2N66WesWG/9iYhbVF4Cv2v5zk3hjcwUO/IvtF0h6vu3jJX2VuL1oqtfH+2dJWwC/I26PmvopMbWvt9jhUdRZog+0diu3QS95F7cDm9j+o6SmfeEPsX2spMNsXwBcIOmChjEhBnIfRJlGCKwI/KJBvCuJK8BFbcrddIl3BLJ/PTBQNfTVnO1vAt+UtIPtSxo3br7WzsUiuuvmB28+OeF8SUcCJxJt3Rf4du9NqcGbzxmSnm37zIbtm1DpstoEOJCYuHE5cJzt7w4Tb5wSeFuJ9hhJaxIzF04nbmvfXiHuQ4Aby38AwHbAJb05nbaf1zB+G7dyF0k6gyiGBDGIcmEZFGt0JcD8/7/bJD2HqCfxyIYxIWa03KCoK2LgacDFkj4Oy74E2XaN+dhL0tZUtDslnUullZgtn4vnLu5P03wW2L7l86sGjr+CZm8+hwFvlfQP4jldfWzE9s8k/QdwBTHLahvFu/1bl/WNbZy6UF5J9HM+jri9WBV4u+3PNIy70Ch9jZF7Rc2SRSpXoU3iV7+VK0+SvYmpXAIuBk51hSeBpD2IN5xHAZ8g+mffZftbDeNOuPS4xw3qi5Q39o1ZsM/+wmHj9cVdmxhgeypxns8BDrPdpDgb5Y7mTcBnanTNDMRu5VykBZU33QOB5xD1UI61fZWkhwOX2F5/meKNUQJvK9FWHbkfiLMOceUNcHnNmRyS3gf8sK1budok7WT7B0s6NmTsFYBNysMqUx/LBcNhxF3CHGI2wyVNx0baJOlHtrdTX5nlGm/0bZ4LtVR/p4xlvQZ4cjn0feKNrdFzYxGzZ/4C/KrpFN4S/0Lgs8DXHKtS+7/30mWeSOCKa/ybfBADU4PHrmwQb1PiavMXRJ2V3sfLgRsqtPeFRP/38cAXiUUV+1Q8H3OJFYd/L183rg1DvDB/BPyVKBk6r2nMJfz/LXRsiLi7lPN8AXBhOc9PrhD3OuJqc07f8+WkSudiFlGm9Rjg872PCnG/A2zUO6/EQOl3xvxctFJ/h5jlczwxm2U34DjgcxXiXlpeG1eWj3vLa+Zm4OkV4u9OlERofG5tj74PvK0pUsRilT2AB7Ngf9xcYnCwqbcB27lcdZdBx+8BX6sQG7cztet/iOlWpwCzgZcRUyGHpnbrl0NMR3u6S1W/MgB0AtD0Durvtv8uCUkr2v6JpMc0bWzR1lS0tlZiVj8Xmr/obG3bJyuW6mP7Pkk1zsl2trfqe3yepGsqxL2FWJF6A0C5Y3gT8F6i3/6chvEPAD4l6U7iOXIRsYbkT8MEG3kCp6VE6/ZG7ntmeMEukzupvEWdpOfRd4voCqVqbf9c0kzH9lHHSWq6xHsFYrxiORacu34XdZbpL+++kqyOAaAadTpulfRgonTDdyX9iRh4rWFlN5w+OUhRn+Q1tp+qvpWYlcK3cS4uJ8rStlV/Z56kjWz/osTdkDpvlpv2kjeA7R9L2sb2zaqwm53tlwGUPu99iLnsD2fIXDxOfeCtJNqWpuOh2GB2S+Yv5NmXKJ9a5YUr6QNE//pXyqH9iC6lIxf9W0uMeSExsPY5YpbPbcDLB65kho29vivXLy+xPk+8+Ht9gy8BlrN9YNPYfX/jKcRWc2fZvrdCvFbGLySd55b76GudC81ff7EtMai9BXA9pf6O7UZTbiXtTnSb3EwMFK8PHGj7/MX+4pLjngT8kZieCPG6XpuoBHqx7e0W9btLGX9/4EnEZI07iIkEFw2b+8YpgbeVaH9I3KZcSd87tO1Tm8QtsftndFzoCkvp+2JfC2zt2Bi2dwV2tRuUaFUUxPo9cdV8BPFC/aTtn1do71eJBTzziHO9BnC07UZFkSStSHQd7Ew5z0Sbh6poJ2l123dpEYtYXGchz1xiRW3VqWiqvCdmm+dC0q1Ar+zDDGL+vohzMs919phckfl1fX4y7HNiIOZKxBaBvefbxcAnibGolW3/tWH8O4hxuU8D59u+pVG8MUrgrSTaNqbjTYaSwHfpvYjKi+z7TRJ4m3rnWdJLiP7p/0fcMYxVeyWdYXsPSb9k/iKWBz7brrKQpw2qXPCtzXMh6TZile+E/Q4essiZ2l8g1DpJmxNdozsTb8g/tT1Urf9x6APvqd5vWFRdWaWFC/888C3qTvg/Crha0vkl9pOBRvsWKrbOehdxu9l/l1MjaVWtXy7pOhZTZnPYNwbbe5TP1RexSNq0DABOWMjJDQs41ew2KvFaOxfAbbbfs+QfW2atLBCSdLLtFy7qeVfrQkTS6sQK7vWJ3oY1aFDffpyuwNvqN2zldnYySFqX6AcXcJnt3zWM9xOi62TwLqfRApMS+1DiqvsaYpHCesCXbT9pyHiLXdDghvs1StqLKE37l/L4wcQdzzcaxDzGse/jRP2wbtp/rYkr8B3u5msl2jgXD8xV7wJJ69q+bVHPu6bPt76/cy3RLXMx0e16a6N4Y5TAO5doJe0MbGz7OMXqu9UqvJhau4rTBAWA2qQK9ctLnPWJ8/y90ke5XNMZGBN1rY170pF0KTFroTdw/iLgdU3/T9s4F5LWqjGesJj4bS0QWgX4m+37y5TVTYm59lXq5tc2Ngm8LZKePNFxN1wmrNjAYDaxD+QmZVrQKbZ3ahi3tau4MrNlJnGb2b9rTpM3hdbql5f4/wYcDKxleyPFDjeftr37En51SXGvHbwtlnSd7cc1iVvitFJ3faI3YNWpwNfauWiLpO8Qs1DeZnsrxUYlVzdts6QriVkiaxKLeq4A7rFdY759b7JGtbLLY9MH3laiJSbh9zyI2I/uSprXI94L2Aa4CsD2byU1Xnxj++Dy5bM8UORdUTO9id6Lf3b/n6TZuWizfjnEDJTtid3CsX2TpIdWiHuFpKOJK1oDryOeFzW0VXe9rQp8bZ6LtrS1QEi275F0EPAJ2x+SdHWFuD1fIcou70Ff2eVhg41NAqelRGt7gUEPSY8CPtQkZnGvbfcG6sqtV00/JBZCLOnYUrO9a6MWTRzzM+Vz463TFuEftu9VWURRrrRq3Da+jqhKeVJ5fA519tqEGJR6rBeuu/4EYhrksAm8rQp8bZ6LtrS1QEiK1cUvId6IoW6erFp2eWwSeIuJdtCtxKKCpk6W9BngweU2/xVEkZpGJD2M2FB1JUnbMH8a1urAyhXiP4eFb98azxaQ9CHgfcDfiE1styIG2L7cMPQFkt5KnI+nEXN0G1U4BHBsdXakpFWbzu2dwAauXHddsThqf1coDjao5XPRljfQzgbdhxOzvb5u+4YycNxocdCAqmWXx7YPXHHJdW2FPq1PMP+KbQawNXCL7f0bNpGSUJ5OJNmzPWRR9oGYBxAFt2YT/W89c4EvNJnnKunTxJvArsRqzH2IKooHLfYXly52bx74XsRUwiOIhQqNVnmWxHUQfeeZKFrU6Imrvm3PbDfe9mwg9ieJWTj9dddvJe4yzxj2TkjSJbZ3aNq+CeK2di7apJY26C6xq+5n2he3atnlsUngbSVaLVhP+r4Ss0aJ0yOIQctG04AWE39vV1gtOhCzt61c7/OqwGm2n77EX15y7Btsby7ps0SN8bMkXVMhge8FnOkKq+wG4lbd9mwgdit11yW9m9j16bSmsQbitnYu2qIoXHUSUTWxyQ5Ng3F3AI5lEt/MJB1u+6PD/O7YdKGw4NXmfcAJNRKtY3u2BepJN41ZrA6cLalXN+FrA7fNTX1fsevMA7veA+9xsznbvfrD95RZM3cCtRZxfEsxz/xvwL+X0fahdtoe8Dzgo4o6LicSdzqNpyZC3W3PBuKaqEpZpTJln9cTg8bzJP2NilNt2zoXLXoeMSZwsqT7iWR+su3/axj3o8QG6L2dta5Z1ASLil5f/u4yq1o9rwnHzionEAOX1xDVzBqTtAtwEzHC/kngZzX+Q2y/27GB8SFENbELJH2vadw+JxKj03sTV0d/YP4g07DOUCzS+C9i9swtzC/a04ijyNYOwOxyK3s38PwKcQ8kSt6eQmx4/QtJn2sal4FtzyS9kTrbniHpXyXdJOkvku6SNFdS48JetlezPcP28rZXL49rrJNo7Vy0xfavbH/IsTHLi4nCco3WYPTF/vXAobbfzIYvc+hKhcWbftBe4f4ribnavceb0GCjiAniP4wYxf8B0WdfK+5CbQSuqBh/RWCNivFeQCxkgpjBcBqwbcX4yxPLqE8jdrtvGm9tYkrX74k3xy8TMwRqtPXnxCyUKv/2vrgC9ie2GoToR91+nM9Fmx/EUvQ3l9f45cAbKsT8GlHf/iqi6NsbgRNb/nf837C/O0594FcCL/ZA4X433PpsEYsUFjo2RNzXELdws4j/9JNs/7hJzIH4Hya6lU4uh/YBNrf9ziFitV4AqK9ffWeijsuHiU1am64SfCax4nBXYtusk4BzXKkbpQ2SfuCGC7oWEfdTxBTF3Ww/VrGP5TluWOK0i0q//fLEndlJtm+uFLet/UwXV0NpJdudrwfeVqJtpZ60YlXjibbnNImzmPi90gL3E+2fyfwSovYy3DqXPsI55QMWvGWzG5bsLX+jV//5KGLF4VdVYWm6pBOJbp7vuOJApiauK3JEjUQg6WPEndk3WHDFa6M3SpX9XbXgnpg1BopbOxdtUSk5Mep2jNrY9IETq8GOlbRL+fgsdVaDvQa4ATiU2Lj1x8QKqEYcfb6rSjoQYomspGpV3Ty/v3M5R5/njCtJoIEAACAASURBVHJsmH7PvYGfMb+f8P22DywfjZN38ZsyL/6FwJmKWs2Nn1+2XwRcTSxvRtJKqrDiFfgqcXezLjGGcQrza4w0tTpwDzH18bnlY48Kcf+pqAvfW7wyiwaV7Pq0eS7a8qeSL74DIGkzxerJoUnaVdJpkm4oH18rY2jjazL6qpayH2hFYjT2NODrxDziFSvEXQWY2fd4JhU2FQXeSSwo+Vl5/HDgBxXPR/X+znIuXkzs2Xgx8JSK7V2Z2DR64/J4XeBpFeL+G7Gp7C/K442BcyvEvWyCY5fWOh9tfBB3j6cTc8rfT8yoeuE0PRdVN0smKmj+EjiQWIS2NbE472bg2aP+9y6y3aNuQN8JbCvRXkrM6ew9XpUoW9s07pySZK/uO1ZzEPNTxMyZG8vjNYEfNYw5szxRv0Rc1T6jxf/L/YFvVzrPKwyc5xq7mn8AOJIYCFufGAx7O7AWUThrmJgn9339wYHvnVPp3G5KzHx6LZUGSts4F21/9F4LA8+LOQ3ifR/YaoLjWwIXjPrfu6iPcZoHfi4xcNBbyrsSMYCwY8O4D3Lf8mDbf5XUeEk67ddCeYJLfyeA7T+V+ezLTNKuxJ6a2xM7pX/M9hWL/61l/hsrAM8mrvCfCZxKbBvVVFu1UNqoK7Jx39dPI+qj98waIt4CJH3JsXPLTyY41kRbNVbaVLsWysNsL7Srve1rFbVsxtI4JfC2Eu3dkrZ1KZkq6fHMX9DSRCu1UPrU7O88l1jBdzHRVfUySS/rfdP2ocM2UlFOYD9i8cP5xNX99q63e8wFaqcWShu70CzujaXGm87m/Q/K86PRLC1o7Vy07fXUrYWyuGXz1ZfU1zJOCbytRHs4cIqk35bH6zL/imNotj9cEspdRD2Gd7hCLZQ+HyfGAh4q6f3Ek3PYCnFVt+IacDZR22Fnl80syiyMWo4kaqFcR1whnknU7RiKpO2AX7vsblTeyPYm1iC8y802IVhZUYBsBgsWIxNxRzlsm98C9N7E7mL+LKJ7gWMaxG3zXLTK9lWSnkK9WigbSTp9guNiPO9AgPGaRrgdMV1sgURru/FMFMVejf27VzcuelNWNPZumX/msh1VTZI2BXYn2n2u7bFbHVeS1IuIN5ibif/Dd9he7JZoy/g3ZgHYHrpucl+sq4CnOioDPplo7+uIQavH2h76Kk4Tb8LxADcs5yvpKNuN9kUdiNfauWhT6Tp5MTEeALFq9KtN3nDKm8EiOUq/jp2xSeDQWqI9BPiK7T+Xx2sC+9n+5JDxViCuevYkEtYMYuDn68Crbd/btM3l7zwRuMFl67AydW4z25fViN8GxabJ+xFXcXOIkpxDXSEqOr3fSQzW9a5i5xFF9ocuf9s/b1rS/xKrOt9VHi+0tdi4kfQIFt6UeqhNT7p4LiQ9FjiPuPO7mnhebEOMOezm6TY3fNSjqL0PYmT9wX2P1wT+vULchUam6Ru5HiLee4hlx6v1HVsN+CLw3orn42rKG2x5PAO4atT/T0vZ9hlEn/hxDWIcAXwXeHTfsQ2JF+4RDeJeTyzkghgMfHL/90Z97pbQ9g8Q9WvOJMYBvkVUEJw254JY9bzQ1EniouHUUbdvsj/G5gp8onf8Siv5riWmB/UGA2cS0/02X/xvLjLe9cQg3T0Dx1cl5s5WKcG5iPPReGVqV5TZN0+zfcfA8VnElLyhnheS3kbMlrmDqNm9rW1L+hfgeLewBL4WST8FtnSlFaldPBeSfmr7Mcv6valqnFZizpDm17MsiXaoaXMDziZmjOwuaTdihdlZDeLdP5i8IWbNUGemQc/Nkg6VtHz5OIzoshmapE0knVvehJC0paRx3Tpr+cHkDQ/0gy8/bFDb7yd2c/kCMfDaX4P+dcPGnSQ30+DfPqij52JSZotIWr3Sit9WjdMslF6i/TSRCF9NrLZq6v8Ru5q/hvnFaZpM93PpR5+oBGSNZc09ryZmovwHcT7OJf4dTXyW2BWmt4/ltZK+SmyFNm4WN5bQaJzB9qUTHPtZk5j9yoXIS4ANbb9H0nrEPOOmJZLvAeZIOpcFa6wMPQ207XPRgodKev0Ex0Wdufazid3uV4uH+jPwCleYTNGGcepCmUEkqF4VsKuBdW0fUvnv7EwMYg4VV9ItRKKeKIHb9vhOOZJ+ZHs7LVgMqdpgVTm3G9s+rnR1rOoytXCIWPOY+IpKxJqBaleitamlqoFacHepBzhq6U8LkhZbjdMNN9cuXa6H2L6oPN4Z+OS4dl2OzRW47fslXUoMVO1LLOOtsqWYpK2J2RH7EvUOhq4KZ3uDGm1aEkU53U8B69jeQtKWwPNsN7lavkPSRsxfHLQPcFvz1j7wwppNzCI6jrjV/zKxrdgysz2zRrtGpNoq2n7TKVEvStMEvRTm9pJ3+XsXKyqDjqWRJ/CSqF5EJNg7KbvOuPmc2YniqmncSdRGd8chxBTITSX9hngza7y5c7EXMZ3rKgDbv+1CH2JLqlYNlHQdixlfGfbqUNI5rrAf6lQgadvy5eWKFdYnEOd8X6JOylgaeQInpi9dBDzX9s8BFBsGj2vcybKy7cu14D6FjTYxcNR3fqqibssMlznmlbRdG6YqTVxg/y/EJhpvcLNa2DVX0UKdUrQTadxnPIV8ZOBxf1fNePQzT2AcEvjexJXy+ZLOIlaDDb9HXPtxJ0v17g5J/wl8yAsuanqD7RozUdquDVPb0cSq368Sz4sXEZsw/BT4PLHF31Bsf0Wxw1RvFe2ebrCK1vavhv3dJVhDi9mtyRV2auqKDt2ZL2CcBjFXIVY37gfsBhxPrOQ7Zxzj9sV/KPCg3mM33xW7F3dDortjR+BPRHfHS5q8mCeaV6+yy0ujxs6P9TRiEwMRu8dXqQ0jaX1icPR7klYiFp80unuQdJkHtnuTdKntJ6rhLjddWUUr6U6iNvyiBuRrbfZRnaJC4H8CD7f9LEmbATvYPnYc47am1oqgmh/EAOargPPGNS7wPGK3+7uJ5Ho/8aKtfS5WIaY0LUck8CaxrqVvkwyiwFKVNhMrJx/Zwr+/rQ0dLiE2BJhRPl5I2cSABnWly+93YhXtOLZpGdpedUOHtuO29TFOC3keYPuPtj9je7cxjvteYg/BnznKce5O7EzfSFlA8BZJ/1OuaO8BDiB2On9hw/BfBs6VdJCkVxBL1WvNbFgdOFvSRZIOUb0ayocQM1nuArB9E/DQCnFfArwUuJ3Yjf2lwP7lCv+1DWPL5dUPMcOKBt2Vko6RtFcLg8Jd6lIctLbtkymDw45NrueNcdxWjEMfeFf90/adkmZImmH7fEkfrBD3S0SXySXE1eebiRWpe7rhBsq2P1RmNPT6Zt9r++yG7e3Ffjfw7jLdcV+ijvettp/aMHQrGzo4Bimfu4hvX9ww/M2SDiWmgULUMG8yKPp5YpOM10u6l1iMdpYn2IBgGdWagTQKtTd0aDtuK8amD7w2SSu64i7mE8T/HtG3fhSwNnElt53tRjsISbrO9uPK1zMpdSpcd8ZIayQ9DHgBMSi4mhsugJD0IeDPwMuI5d3/DvzY9tsaxp1FvEFuwIKV/Rr3+5ZxkY8TYy69VbSH2769QuyHEOMMzwIeR3TXnFWuGpc11kQzcSDe3O1l3zx70pRpf58AtiCKcs0C9rF9bcO4jyf+76rGbctUTuBXORZT1NhyaqL4qxAbTswgbsfXIMrW3tkw7gKDijUGGSVdbHvnCV6w1V6okl5DXHnPIirGnWT7xxXiCnglfYOjwOfc8Ikr6YfENNMr6btFtl1l8VhtZaXyPoOJuiScZzrqmkwL5cLmUCKBV9nQQdLhRBfo1eVQrY0iWjWVE/j1wH8B7yAWxCzADadIlTnlp9i+tUmcCeL2LyHv7eRyDw2SraQN3Wxe89L8jQ8AJzbt5hmIOYOoHFmlwuNA7NbqXbd1dS/pIttPata6qUHS923vUjHeh4kZX5sSg/0/JBL6JR7jnYmmcgLfmbgyfiGxd14/V3gxvbPE/iMxx/xrtn/fJGZbJF1p+/GSzrW9e+XYq9u+S9JaE32/6ZNf0leAt7jS9My+uO8Dfmj7zJpxS+xWru4lvZ246zuJvjox45xg2lIWSK3BwufiqoZxVyBKQuwI7FA+/mx7syZx2zJlE3iPpIPc4hzOvkG7vYEag3bVKWpyfIPoivjvwe/bPrpB7DNs7yHpl0T3TP/MBrthcS9J5wHbAZez4Av1eQ3jziWmaP4D+Cd1u5Naubov53hQ43PcRZp4+zo3nWEmaQ0iae9UPj+YmEbY5r6yQ5sOs1BOVNS8Xs/2wZI2Bh5j+4xK8W8HfkfUW6kxva0NLyIGXJcj5pRXY3uP8rmtnc1bKV5ku806LWdIenbtq/sWz3HnuPLKSUnHAJsDc4HLiC6Uo23/qebfqW06JPDPE7eyvdkhtwKnAI0S+ASDdv9WY9CuJc+0/cEyM2fo/SQXZ6LumRpdNq68maykTW3/RPOLFw3+vUa34MVhwFslVb26V+wZ+xrgyeXQ94HPjPMgW5skPYdIuv0roYd9fq8HrEgszvsNkSf+3LSNbZsOXShX2J6tBWtgN1oqXWJUH7RrS++Wvuay+b7YDwJWBs4n6of0ulBWB75j+7EN4z+RmG3wWGI+/Ezg7mGToaRjyp1YK7fgbZL0OaJMb2/x1UuBebZfObpWjYZi45eVgV2BzxEFwy63fVCDmCLeEHYsH1sQY1yX2F5sHfJRmQ5X4PeW1XW9ifkb0bebybBsH1nitVILpbIbFRtRzFIUrO/pXRk2mav9KuBw4OHEnU4vgd8F/G+DuD3/Q3QBnUIMLr2MWE4/FNu9XY2eZfvv/d8rb0ZVKAqFbcyCz42hdo/vs93Ahcd5kpou5umqHW1vqdgn9t2SPkKDOv8QLwTgesUuPH8pH3sA27NgdcKxMR0S+DuJPTAfVWY07AS8vGlQSc8lKto9nOgHXx+4kXgHHyu29ysLbM4marjUjP0x4GOSXmf7EzVj9/2Nn0uaaXsecFyZ5dHUD4HBu5GJji0zSa8kulEeCcwhSi5cQizsaWKepI1s/6L8nQ0Z42XeLftb+XyPpIcTY1BDjxGUlbM7Evnhn5QphEQX7HXNmtqeKZ/AbX9X0lXEi0jAYZ5gs9whvK/E/J7tbSTtSlQ8HEu2fyfpCcC/EHcjvxi8Am0Y/xOStgA2Y8Grzi82DH1Pmdo1p6zKvI2YPTKU8kb2CGAlSduwYJfPyg3b2nMYMXPmUtu7StqUOoOxbyLKI99MtHt9omzvdHSGpAcTaz2uIp7TTcoXb0CMZR1hu8ouVZNhyvaBtz1Y1de3fg2wjWNLuMttb98kbhsU9UP+EzgQ+D9i9egjia3P3lZjEKzMi9+FSOBnEku9L7a9T8O46xPFplYgKh6uQexR+PMh4x1A3IHNJjZv6LkLOL7pAq/yN3p7j84htlf7R42phZJWLF/2Vgn+BMAtlozognJeHmR7bGuWtGUqX4G/ntgkeXCnDYh366a3s3+WtCqxYOMrkm6n4Y45LfovYvrghp5fo3p14MPl47AKf2MfYCvgatsHKqoRfq5C3I2AP9i+iwpXsY59JY+XtHfThTWLcWu5OvwG8F1JfyI2j2jqkjII/cA4Rrm7rDow3QWSLgIuJF5/P5iOyRum8BV42yStDPyduBLan7gF/8o4roqTdBOwiQf+s0tNiZ/YHnpQsC/W5ba3V+xEsysxn/Z6243GBCR9keiqupN4sV5EXNk3mp9bulLeT8uF+yU9hbhrOMv2vUPG6HX7fBl4MQt2+3za9qY12tolpf9/Z+BJxPPjH8BFtru0bWJjU/kK/AGSdmThuhRD9c1q4gpuvRfUOyT9guiWOHeY+C3xYPIuB+ep7GFZwRXlqvOzxGyUvxKrJxux/TKAMlC1DzGz5eE0f+4eVz56VQ1/RizLbrqjywL1WyrNY38G0e3zSGLgvGcu8NYK8TvH9s2S/gbcWz52JaaaTitT/gpc0peI2/A5zB+xt+1DW/hbM4m5o19xCwWYhiXpG8Bpg29akvYHXlhhWbqI3Xh+XR5vAKzuCiU4SxufRJROvYOo1X2R7Usaxu31U/evD6iyBF7t1W9ps9unU8qF0h3EnqYXEbso3T/aVk2+6XAFPpvYj7D1d6oyze0aSa1Mp2vgEOA0xS48VxJ3ENsRlQ73ahrctsubxOPL41uaxuzzUeAXwKeB8yvGbrNw/7rADZKq1m+xfWrl1Ydd9nGiC2U/YBtiA5ELe1Msp4vpcAV+CnBol6YGtUXSbsSLX8RemNW6eST9L/AF2z+qFbMv9ubE8vGdicUxP3XDGu9qaUOAEvspEx1v2p3SxurDrisTCQ4E3kjcBc4ccZMm1ZRN4JK+RVxdrQZsTfTHPjDdqunVUFqQpB8DmwC/Iq46a6zy7M2W2Ql4CtGVsjZwWa9vvEHcDYh6Fw8U7ge2buMNqJay6nDLvs+rEl1jTx912yZbWXm5M7AqseDmIqJrrdW69+NmKnehfHjUDZhmntVS3Iv7Pv7H9TbQOBV4nu0bACQ9mRggfVzTwAMD3SsQ9UuGrt/Sp+rqw467FPiQx7QG/2SZygn8N8A6thfYKb68UH8zmiZNae8b7NYoA8hNt7N7nxfeRuwFtk9pGPfVwDdKSYRtiYVOz24YE1i4VK2kPYl6Gk1NtPqwxlz7Lnr14HNALWxYMu6mchfKGcBbB/s0Jc0G3ml7UTuSpyFo4b08ZxKF8BvtZDIYd1HHhoy9A/AZYj7/c2z/oWnMxfytS20/sWK8abn6UC1Xv+yaqXwFvsFEA1K2ryj9n6kCSW8h5iKvJOmu3mFibu4xDeI+i7gifoSkj/d9a3UarHjtGxvpWZmYfXKspCpjI5L+te/hDGIm1NBXSgPxBr/XeH/Xjmm7+mWnTOUEvrjSoCtNWiumONtHAUdJOsr2WyqG/iNRq+R5xAu1Zy5RE2VYkzE20n93dx9wC/D8SvEGmYZlVLvG9qMlvWOaTp9cwFTuQjkBOM/2ZweOHwQ83fa+o2nZ1CRpJ2Ixxd1l8c22wMds/2rIeFfZ3lbSV22/uHJbZwJnewz3L02LpxY3J+miqXwFfjjwdUkvYf4V3GxiVkDjxStpIZ8CtpK0FfBmYkn6F4npf8NYoVQOfMJEXQhNug1KCYF7JK3RRh+youzt+4hZI2cRRb4Ot/3lhnEPI5b/zyVKFmwLHGn7nGYt7pQ2NyfpnCl7Bd6jqNPdW9Z+g+3zRtmeqarvivkdwG9sH9vkKknSzsBLgBcCpw9827Yb1cGWdDJRBOm7LLhasnGJhb6rxL2IzaSPIFaRNt3G7xrbW0l6BrG69u3AcdPtSlSL2Zxk2Du+rprKV+AA2D6fGLFO7ZpbBjT3B55cuimWHzaY7YuBixV116tWCCy+XT7a0Pt3Pxs4wfYfo1xMY70gzyYS9zWqFLhLbP+OuNtbgVg8BrE6d9pt7jzlr8DT5ChXRS8GfmT7IknrAbsMFtAaIu4KxJzt3k7sFxAlVMf2xarY8HpPogtle+DBwBm2n9Aw7nFEWdlHE90yM4Hv2358sxZ3TylX8EVigFjAo4AD3Hzf0U7JBJ7GmlraiV3SxsBRLLwF3IZN4vbFXxO4q/S3r0xUZ/xdw5gziLIQN9v+cynG9Yga9Vu6RlF3/sW2f1oeb0Lc7UyrN7Mp34WS2iXpYts7a+E66b1BpabLx9vaif04YsPr/yaKQx3I/C6KRiS9gNjAYZ6k/yAGG98HNErgjm37fg9sptgmbzpbvpe8AWz/TNLQXXZdNd2fBKkh2zuXz6st6WeH1NZO7CvZPleSysDXuxTbdL2zQuy32z6lDMQ+g5h7/imgaRfKB4F9gR/TV9ue2FpsurlC0rHAl8rj/tlm00Ym8FRNGbhchwV3Pmq6qcFEO7Ef2DAmwN9Ll8RNkl5L1Md5aIW4MD+5Pgf4lO1vSnpXhbh7Ao/xNN/EuHgNMRPnUOJ5cSHwyZG2aASyDzxVIel1xNXr74HezihV5uWWuh8P7MReI4FJ2g64kRhgfC+xb+WHbF9aIfYZxBvCU4lNLv5G1O1uOo3wO8ALbP+1aRunijLIvTkxdfX2UbdnsmUCT1VI+jnwBNt3Voq3HfDr3sCfpJcBexP1xt/lMdw8uqcMWj6TKOZ1k6R1gS1sf7dh3FOJ2SfnsmBt++rbA46rsqnFJ2zfIGkNohb4PGAt4I22TxhpAydZJvBUhaTzgafZHrrQ1EC8q4CnljnUTwZOBF5HzMJ4rO19how7uChoATWKWQ38vVWIlb/72X5Ow1gHTHTc9vETHZ+KJN1ge/Py9eHEVNU9yzTW77jsbzpdZB94quVm4PuSvs2CV4dHL/pXFmtm31X2vsAxjg19T5U0p0E7dwB+DZwAXEalmSf9ym39s4l58c8kNo/4dNO40ylRL8a9fV8/DTgFYnHPNFzTlAk8VfN/5WOF8tHUTEnLlSv63YGD+77X5Hn7MOKFvx+RYL9NzB++oUFMACT14j6DWP37JWB72zUGXVufu94Rf5a0BzHGsBNwEECZVjntqoxmAk9V2H535ZAnEDuN30EMAl4EIOlfaLB7vO15RIGps8rg6H7EncN7bH+iYZvPLu3c2fYvS3s/1jBmv9bmrnfIq4gd6R9GFAjrza3fnfZKI4yt7ANPjUj6qO3DJ9goAWjWpyzpicC6wDm27y7HNgFWtX1Vg7grElP89gM2IIplfd52o632JG0DvIjYLf5mot/+HbbXbxK3L/6Vth8v6TrbjyvHLrL9pBrxU/dkAk+NSHq87StLbYqF2L5gstu0OJKOJ6pTfgc40fb1Lf2dnYg3iL2BOcDXbQ+9Q1GJ+QPgScDXgPOIboQP2H5Mw+amjsoEnqYVSfczv3xsG0v/B//eDKLP/UVN+8InmLu+OvBfNeaup27KBJ6qKANL7yVWSi5HSwlxuiqrXD9g+02jbss4kPTo3jjD4o5NdTNG3YA0ZXwUOAB4iO3Vba+WybuOMhtnHvD46Vj/exFOneDY1ya9FSOWs1BSLb8Grnfe0rXhcqKi4dXANyWdwoK7CE2bTY0lbUosnV9jYKu91Vn8RuZTUibwVMubgTMlXUCdhTydJenDxI45jeeWD1gLuBPYjei/F9NvV/rHAHsQ4wDP7Ts+F/i3kbRohDKBp1reD/yVuAqqsZCny34CHFMWlxxHLBRqsnnyQyW9Hrie+Ym7Z1rd8dj+JnEXsoPtS0bdnlHLBJ5qWcv200fdiHFg+3PA5yQ9hlhsc22ZAvjZskfrspoJrMrEi3amVQLvc6ekc4F1bG8haUvgebbfN+qGTaachZKqKPtAnmf7nFG3ZRyUWSN7EAn8UcDJwM7A3bZftIyxrvI023l+SUpX3ZuAz/QKWEm63vYWo23Z5Mor8FTLIcCbJf0D+CfTeBqhpKOJ/tnzgP+0fXn51gcl/XTRv7nokNUaN3WsbPvygUk5VSphdkkm8FRFi1uqddH1wH/YvmeC720/RLzdG7ZnKrpD0kaULiRJ+wC3jbZJky+7UFIjkhZ7a9+kZklXSTrX9u5LOpaGV/ZGPQbYEfgT8Etgf9u3jLJdky2vwFNTHymfHwTMBq4hbvm3JOpt7zyidk06SQ8CVgbWlrQm87s+VgcePrKGTUG2bwaeWjbMmGF77qjbNAqZwFMjtncFkHQicLDt68rjLYA3jrJtI/Aq4HAiWfffedwF/O9IWjTFlK31JjoOgO0vTmqDRiy7UFIVkubY3npJx6YDSa+rUFs8TUDSROdVxKDxI2xPq4vSTOCpCkknEMu7v0wMLO1P1O3eb6QNm0SSdrN93sAS7wdMpyXvk6HUhXkJ8P+AHwPvt33taFs1uabVu1Vq1YHAa4DDyuMLgU+Orjkj8RRi6uBzJ/jedFvy3pqywvXlwBuIcZZ9bA8zPbPz8go8tULSzsRO7IeMui1p6pB0CHGRcC5RXvdXI27SSGUCT9VI2prYhWZfYlrXadOpL7jUK1mk6VjYq7ayIcftwB+YeEOOLUfSsBHJLpTUSNmj8kVE4r4TOIm4MNh1pA0bjVzM1L5Hj7oB4ySvwFMj5YroIuAg2z8vx262veFoW5bS1JdX4KmpvYkr8PMlnUXsxD6ta3eUBT0HERsPPLDJgO1XjKxR04CkY2wfPOp2TKbcUi01YvvrtvcFNgW+DxwBrCPpU5Kma3nZLwEPA54BXAA8kthwIDUk6aGSPirpDElHSeovlvaZkTVsRLILJVUnaS3gBcC+tncbdXsmm6SrbW8j6VrbW0paHjh7Op6L2spd3pXENNU9gNVsv3ykjRqhTOApVSbpctvbS7oQ+Hfgd8DlOS7Q3ODq3uleKz37wFOq75hSzOrtwOnEbjrvGG2TpgwNFAqb2f/Y9h9H1rIRyCvwlFJnSLoFuJ9FbC833e5yMoGnVJmkCa+2bb9nstuSprachZJSfXf3fcwDngVsMMoGTRWS1pe0Rt/jXSV9TNIRklYYZdtGIa/AU2qZpBWB020/Y9Rt6TpJlwF72f5tKd3wPeAoYgORf9p+5UgbOMlyEDOl9q0MTKu+2RatZPu35ev9gc/b/oikGcCcEbZrJDKBp1SZpOuYX2hpJjALyP7vOvoHL3cD3gJg+/6BHeqnhUzgKdW3R9/X9wG/t33fqBozxZwn6WRiB/o1ifrrSFoXuHeUDRuFTOAp1bcxsFn5+grbvxllY6aYw4lyxesCO9v+Zzn+MOCtI2vViOQgZkqVSHoU8E2i7smVxO3+tsDfgOcDL7X9udG1cOqarhuI5BV4SvX8L/Bx21/oP1h2Ur+kPMwEXkmZhfJi4IXEBiKnjrZFky+vwFOqRNLPbG+yiO/dCmxr+/ZJbtaUsogNRN5oe/2RNmxE8go8pXomXBhXprj9LZN3FT8hNhB5bt8GIkeMtkmjkysxU6rnW5I+K2mV3oHy9aeBM0fXrCllb6K64/nlXO/ONN5AJLtQUqqk1P0+Cng58CtiLvj6KnGH+AAABAlJREFUwPHAW21Pu2lubSlvjHsSXSm7Eef467bPGWnDJlkm8JQqk7QS8C/EleHPbd8z4iZNadN5A5FM4Cml1FHZB55SSh2VCTylSiTlrK40qbILJaVKJF0B3AqcBZxl+5bRtihNdZnAU6pI0vrEBg7PBB4BXAx8B7jA9j9G2bY09WQCT6klZVrhk4hkvgvwB9vPGWmj0pSSCTyliiTNBI63vf8E33tEViZMNeUgZkoV2Z4HzJpof8ZM3qm2HDVPqb5bgB9IOp3Y2BgA20ePrEVpSsoEnlJ9vy0fM4DVRtyWNIVlH3hKLZG0iu27l/yTKQ0n+8BTqkzSDpJ+DNxYHm8l6ZMjblaagjKBp1TfR4FnEBsOYPsa4MkjbVGakjKBp9QC278eODRvJA1JU1oOYqZU368l7Qi4TCc8lNKdklJNOYiZUmWS1gY+BjyVqAl+DnCo7T+OtGFpyskEnlJlknay/YMlHUupqUzgKVUm6Srb2y7pWEpNZR94SpVI2gHYkVhK//q+b60OzBxNq9JUlgk8pXpWAFYlXlf9KzDvAvYZSYvSlJZdKClVJml9278qX88AVrV914iblaagnAeeUn1HSVpd0irAj4GfSnrTqBuVpp5M4CnVt1m54t4TOBNYD3jpaJuUpqJM4CnVt3zZjWdP4Ju2/wlkX2WqLhN4SvV9hqgJvgpwYdknM/vAU3U5iJnSJJC0nO37Rt2ONLXkFXhKlUlaR9Kxkr5THm8GHDDiZqUpKBN4SvV9ATgbeHh5/DPg8JG1Jk1ZmcBTqkRSb2Hc2rZPBu4HKF0nWU42VZcJPKV6Li+f75b0EMrME0lPBP4yslalKSuX0qdUj8rn1wOnAxtJ+gEwi1xKn1qQs1BSqkTSrcDR5eEMYEUiqf8DmGf76EX9bkrDyCvwlOqZSRSz0sDxlUfQljQN5BV4SpVkze802XIQM6V6Bq+8U2pVXoGnVImktXLfyzSZMoGnlFJHZRdKSil1VCbwlFLqqEzgaUqT9BBJc8rH7yT9pnz9V0mfLD+zi6Qd+37nXZLeOLpWp7R0ch54mtJs3wlsDZGYgb/a/vDAj+0C/BX44aQ2LqWG8go8TUvlqvsMSRsArwaOKFfmTxr4uY0knSXpSkkXSdp0FO1NaSJ5BZ6mNdu3SPo0fVfmknbv+5FjgFfbvknSE4BPAruNoKkpLSQTeEqLIGlVYEfgFOmBNTorjq5FKS0oE3hKizYD+LPtrUfdkJQmkn3gKcFcYLXBg7bvAn4p6QUACltNduNSWpRM4CnBt4C9JhrEBF4CHCTpGuAG4PmT3rqUFiGX0qeUUkflFXhKKXVUJvCUUuqoTOAppdRRmcBTSqmjMoGnlFJHZQJPKaWOygSeUkod9f8BwEycN5YKUxsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2007].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2640fba3730>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2008].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2641037f130>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2009].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26410406a90>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2010].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26410476790>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2011].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x264104fedf0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2012].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x264104f6b80>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2013].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x264105034c0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2014].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2641051dbb0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['date'].dt.year == 2015].groupby('Title')['Title'].count().sort_values(ascending=False).head(15).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['RequiredQual']\n",
    "y=df['IT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26410485910>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding desired characteristics and skillset using Topic modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## finding how there are desired characteristics changing over time.\n",
    "   ### analysing the changes after 5-6 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 13124 entries, 0 to 19000\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   jobpost         13124 non-null  object        \n",
      " 1   date            11491 non-null  datetime64[ns]\n",
      " 2   Title           13124 non-null  object        \n",
      " 3   Company         13124 non-null  object        \n",
      " 4   JobDescription  13124 non-null  object        \n",
      " 5   JobRequirment   13124 non-null  object        \n",
      " 6   RequiredQual    13124 non-null  object        \n",
      " 7   IT              13124 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](1), object(6)\n",
      "memory usage: 833.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=pd.DataFrame(df.loc[(df['date'].dt.year >= 2004) & (df['date'].dt.year <2010),'JobDescription'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus =[]\n",
    "for i in range(0,len(c)):\n",
    "    review = re.sub(r'\\W', ' ', str(c.JobDescription[i]))\n",
    "    review = review.lower()\n",
    "    review = re.sub(r'^br$', ' ', review)\n",
    "    review = re.sub(r'^[a-z]\\s+', '', review)\n",
    "    review = re.sub(r'\\s+', ' ', review)\n",
    "    corpus.append(review)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ameria investment consulting company is seeking a chief financial officer this position manages the company s fiscal and administrative functions provides highly responsible and technically complex staff assistance to the executive director the work performed requires a high level of technical proficiency in financial management and investment management as well as management supervisory and administrative skills '"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4285"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatize = WordNetLemmatizer()\n",
    "for i in range(len(corpus)):\n",
    "    words =nltk.word_tokenize(corpus[i])\n",
    "    words=[lemmatize.lemmatize(word) for word in words]\n",
    "    corpus[i]= ' '.join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4285"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(corpus)):\n",
    "    words = nltk.word_tokenize(corpus[i])\n",
    "    words = [word for word in words if word not in stopwords.words('english')]\n",
    "    corpus[i] = ' '.join(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4285"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ameria investment consulting company seeking chief financial officer position manages company fiscal administrative function provides highly responsible technically complex staff assistance executive director work performed requires high level technical proficiency financial management investment management well management supervisory administrative skill'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = ['position','looking','candidate','company','seeking','armenia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=TfidfVectorizer(max_df=.6,stop_words=stop_words)\n",
    "tf_df = tf.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4285, 5632)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(tf_df).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "lsa = TruncatedSVD(n_components=4)\n",
    "lsa.fit(tf_df)\n",
    "concept_words={}\n",
    "names=tf.get_feature_names()\n",
    "for i ,j in enumerate(lsa.components_):\n",
    "    component_terms = zip(names,j)\n",
    "    sortedTerms = sorted(component_terms,key=lambda x:x[1],reverse=True)\n",
    "    sortedTerms = sortedTerms[:20]\n",
    "    concept_words[\"Concept \"+str(i)] = sortedTerms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Concept 0': [('project', 0.2571734442228109),\n",
       "  ('development', 0.19025388415349218),\n",
       "  ('software', 0.18941918669770227),\n",
       "  ('responsible', 0.16177233278274608),\n",
       "  ('manager', 0.15772346590477954),\n",
       "  ('work', 0.1551774585590202),\n",
       "  ('incumbent', 0.1476409184241651),\n",
       "  ('developer', 0.13497664480103835),\n",
       "  ('team', 0.12652625313492194),\n",
       "  ('office', 0.1260136621473861),\n",
       "  ('service', 0.1252564369650346),\n",
       "  ('system', 0.1230637111354616),\n",
       "  ('product', 0.12179786873654737),\n",
       "  ('motivated', 0.11962763146115052),\n",
       "  ('support', 0.11212870320320789),\n",
       "  ('highly', 0.10919814635078098),\n",
       "  ('supervision', 0.10853824235268411),\n",
       "  ('sale', 0.10731742666927985),\n",
       "  ('qualified', 0.1060738398776411),\n",
       "  ('program', 0.10491216418232972)],\n",
       " 'Concept 1': [('software', 0.3419161805091463),\n",
       "  ('developer', 0.2820060536820413),\n",
       "  ('java', 0.20200207535602596),\n",
       "  ('long', 0.19873814325345152),\n",
       "  ('term', 0.1922273660105525),\n",
       "  ('product', 0.18231200896966232),\n",
       "  ('entail', 0.15227876730684492),\n",
       "  ('actively', 0.1502068283744329),\n",
       "  ('complex', 0.14385716595866477),\n",
       "  ('project', 0.14146859936530135),\n",
       "  ('epam', 0.11945243975906376),\n",
       "  ('web', 0.11263480537293452),\n",
       "  ('net', 0.11040726540739612),\n",
       "  ('solution', 0.10880318811369262),\n",
       "  ('engineer', 0.10274849044456359),\n",
       "  ('system', 0.10154724865255124),\n",
       "  ('development', 0.09928396026772518),\n",
       "  ('working', 0.09871918541894155),\n",
       "  ('microsoft', 0.09459831762301305),\n",
       "  ('engaged', 0.09071486833835982)],\n",
       " 'Concept 2': [('software', 0.2955450175614464),\n",
       "  ('highly', 0.2687726954829565),\n",
       "  ('motivated', 0.24343591028019362),\n",
       "  ('qualified', 0.22142963629746962),\n",
       "  ('web', 0.17655137299371962),\n",
       "  ('practical', 0.14744247871694427),\n",
       "  ('experience', 0.14552560675929857),\n",
       "  ('deep', 0.1425749551562827),\n",
       "  ('person', 0.1358102786171248),\n",
       "  ('programming', 0.13129445768489267),\n",
       "  ('knowledge', 0.12986136041325058),\n",
       "  ('llc', 0.12390603382667643),\n",
       "  ('experienced', 0.11850293481715497),\n",
       "  ('fulfill', 0.10978924948720357),\n",
       "  ('engineer', 0.10178825558446157),\n",
       "  ('development', 0.09255033565034249),\n",
       "  ('cjsc', 0.08991544576663456),\n",
       "  ('design', 0.0898462603029145),\n",
       "  ('professional', 0.08720772394844908),\n",
       "  ('accountant', 0.08557812684933737)],\n",
       " 'Concept 3': [('software', 0.2734826387022914),\n",
       "  ('quality', 0.21569436921352306),\n",
       "  ('design', 0.15728533671626113),\n",
       "  ('primary', 0.15044226289215895),\n",
       "  ('objective', 0.14736897833502596),\n",
       "  ('engineer', 0.14388046077405633),\n",
       "  ('development', 0.13454401594726026),\n",
       "  ('required', 0.12877434302210897),\n",
       "  ('member', 0.12406984732019465),\n",
       "  ('produce', 0.12125675839220883),\n",
       "  ('implementation', 0.12121892382725989),\n",
       "  ('conjunction', 0.11696205976809533),\n",
       "  ('high', 0.10424139658102806),\n",
       "  ('test', 0.10309283924293233),\n",
       "  ('team', 0.10278728319829344),\n",
       "  ('process', 0.09266556915150335),\n",
       "  ('technical', 0.09148339433789135),\n",
       "  ('testing', 0.08776442319531803),\n",
       "  ('timely', 0.08707118263473947),\n",
       "  ('program', 0.07835951967935799)]}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concept_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=pd.DataFrame(df.loc[(df['date'].dt.year >= 2010) & (df['date'].dt.year <2016),'JobDescription'])\n",
    "c.reset_index(inplace=True)\n",
    "corpus =[]\n",
    "for i in range(0,len(c)):\n",
    "    review = re.sub(r'\\W', ' ', str(c.JobDescription[i]))\n",
    "    review = review.lower()\n",
    "    review = re.sub(r'^br$', ' ', review)\n",
    "    review = re.sub(r'^[a-z]\\s+', '', review)\n",
    "    review = re.sub(r'\\s+', ' ', review)\n",
    "    corpus.append(review)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=TfidfVectorizer(max_df=.6,stop_words=stopwords.words('english'))\n",
    "tf_df = tf.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "lsa = TruncatedSVD(n_components=4)\n",
    "lsa.fit(tf_df)\n",
    "concept_words={}\n",
    "names=tf.get_feature_names()\n",
    "for i ,j in enumerate(lsa.components_):\n",
    "    component_terms = zip(names,j)\n",
    "    sortedTerms = sorted(component_terms,key=lambda x:x[1],reverse=True)\n",
    "    sortedTerms = sortedTerms[:20]\n",
    "    concept_words[\"Concept \"+str(i)] = sortedTerms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Concept 0': [('looking', 0.21524819972770054),\n",
       "  ('development', 0.2071058574803331),\n",
       "  ('company', 0.20539901427185792),\n",
       "  ('software', 0.20021592573620725),\n",
       "  ('responsible', 0.19664501692240388),\n",
       "  ('incumbent', 0.1839141076988513),\n",
       "  ('position', 0.1654912042499906),\n",
       "  ('team', 0.15408346174397647),\n",
       "  ('seeking', 0.14802291576523),\n",
       "  ('llc', 0.1467573801521887),\n",
       "  ('manager', 0.13592107398031886),\n",
       "  ('developer', 0.13277782772088104),\n",
       "  ('work', 0.12733183409183885),\n",
       "  ('project', 0.12576736270228844),\n",
       "  ('candidate', 0.12214421217288188),\n",
       "  ('motivated', 0.12049595108171614),\n",
       "  ('applications', 0.11445245721790372),\n",
       "  ('sales', 0.11265801893551884),\n",
       "  ('web', 0.10824595500165639),\n",
       "  ('cjsc', 0.10795821915036145)],\n",
       " 'Concept 1': [('software', 0.34982115566840233),\n",
       "  ('developer', 0.2594965563160414),\n",
       "  ('web', 0.16684979511821832),\n",
       "  ('applications', 0.15100112743110453),\n",
       "  ('senior', 0.14731013028193418),\n",
       "  ('looking', 0.13793465466010035),\n",
       "  ('llc', 0.12038306428061797),\n",
       "  ('development', 0.11870714095781254),\n",
       "  ('engineer', 0.11230714128433616),\n",
       "  ('team', 0.10909275620014022),\n",
       "  ('long', 0.09926212489493061),\n",
       "  ('term', 0.09402112189153387),\n",
       "  ('java', 0.08986750569448583),\n",
       "  ('projects', 0.0863319186421588),\n",
       "  ('global', 0.08446418066021945),\n",
       "  ('motivated', 0.08306722990564568),\n",
       "  ('design', 0.07995714424511334),\n",
       "  ('engaged', 0.079448129870985),\n",
       "  ('net', 0.07618627354608727),\n",
       "  ('experienced', 0.07404276650058919)],\n",
       " 'Concept 2': [('looking', 0.28250954068718653),\n",
       "  ('position', 0.2800466017021337),\n",
       "  ('llc', 0.2190314401394992),\n",
       "  ('motivated', 0.20380952286508935),\n",
       "  ('qualified', 0.20136939196797676),\n",
       "  ('cjsc', 0.16710582542833985),\n",
       "  ('fulfill', 0.1546843996662326),\n",
       "  ('candidate', 0.1465292425271635),\n",
       "  ('seeking', 0.1348493341169932),\n",
       "  ('specialist', 0.12066559229673422),\n",
       "  ('manager', 0.11868105273888221),\n",
       "  ('professional', 0.11503830597908302),\n",
       "  ('experienced', 0.10850748139087278),\n",
       "  ('accountant', 0.1010181343683924),\n",
       "  ('highly', 0.10065659895916888),\n",
       "  ('fill', 0.09790194258467556),\n",
       "  ('sales', 0.0878308102183575),\n",
       "  ('person', 0.07687221526375201),\n",
       "  ('haypost', 0.07524009725606425),\n",
       "  ('bank', 0.07346076964315482)],\n",
       " 'Concept 3': [('company', 0.3496588833592547),\n",
       "  ('president', 0.29365387634781903),\n",
       "  ('supervision', 0.23677990159127904),\n",
       "  ('direct', 0.2325621617264666),\n",
       "  ('accounting', 0.22819991323524336),\n",
       "  ('covering', 0.22672558950672572),\n",
       "  ('desk', 0.2245561181587426),\n",
       "  ('front', 0.2223436911965864),\n",
       "  ('primarily', 0.19981859878436145),\n",
       "  ('candidate', 0.13500649310693957),\n",
       "  ('work', 0.12807886083883627),\n",
       "  ('accountant', 0.1036464891031448),\n",
       "  ('routine', 0.09955871806511975),\n",
       "  ('contracted', 0.09536716319962982),\n",
       "  ('developer', 0.08897190895317933),\n",
       "  ('association', 0.0886001965977213),\n",
       "  ('selected', 0.0862227033733125),\n",
       "  ('perform', 0.08470038345704423),\n",
       "  ('duties', 0.0846732505685853),\n",
       "  ('software', 0.06089233314957588)]}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concept_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IT job classifier and identying the factors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['Title'].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus =[]\n",
    "for i in range(0,len(x.Title)):\n",
    "    review = re.sub(r'\\W', ' ', str(x.Title[i]))\n",
    "    review = review.lower()\n",
    "    review = re.sub(r'^br$', ' ', review)\n",
    "    review = re.sub(r'^[a-z]\\s+', '', review)\n",
    "    review = re.sub(r'\\s+', ' ', review)\n",
    "    corpus.append(review)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13124"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['IT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(max_df=.8)\n",
    "data = vectorizer.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata =pd.DataFrame(data,columns=vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13124, 2285)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2285\n",
       "dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.isna().any().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata['It']=df['IT'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainx,testx,trainy,testy = train_test_split(mydata[mydata.columns.difference(['It'])],mydata['It'],test_size=0.3,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid={'n_estimators':[100,200,300,400,500]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainy = trainy.astype('int64').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gs = GridSearchCV(RandomForestClassifier(),param_grid,cv=5,n_jobs=-1)\n",
    "#gs.fit(trainx,trainy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy']})"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid={'criterion':['gini','entropy']}\n",
    "gs = GridSearchCV(RandomForestClassifier(),param_grid,cv=5,n_jobs=-1)\n",
    "gs.fit(trainx,trainy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini'}"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_jobs=-1, oob_score=True, random_state=123)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(100,n_jobs=-1,oob_score=True,random_state=123)\n",
    "rf.fit(trainx,trainy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</table>\n",
       "<p>9186 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual  predicted\n",
       "0          0          0\n",
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       "...      ...        ...\n",
       "9181       0          0\n",
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       "\n",
       "[9186 rows x 2 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_pred=pd.DataFrame({'actual':trainy,'predicted':rf.predict(trainx)})\n",
    "train_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "testy=testy.astype('int64').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred=pd.DataFrame({'actual':testy,'predicted':rf.predict(testx)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3934</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3935</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3936</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3937</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3938 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual  predicted\n",
       "0          0          0\n",
       "1          1          1\n",
       "2          1          1\n",
       "3          0          0\n",
       "4          0          0\n",
       "...      ...        ...\n",
       "3933       0          0\n",
       "3934       0          0\n",
       "3935       0          0\n",
       "3936       0          0\n",
       "3937       0          0\n",
       "\n",
       "[3938 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics as metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9890807516505841"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(test_pred.actual,test_pred.predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      3093\n",
      "           1       0.99      0.96      0.97       845\n",
      "\n",
      "    accuracy                           0.99      3938\n",
      "   macro avg       0.99      0.98      0.98      3938\n",
      "weighted avg       0.99      0.99      0.99      3938\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(test_pred.actual,test_pred.predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3086,    7],\n",
       "       [  36,  809]], dtype=int64)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(test_pred.actual,test_pred.predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "indices = np.argsort(rf.feature_importances_)[::-1]\n",
    "feature_rank = pd.DataFrame( columns = ['rank', 'feature', 'importance'] )\n",
    "for f in range(trainx.shape[1]):\n",
    "  feature_rank.loc[f] = [f+1,\n",
    "                         trainx.columns[indices[f]],\n",
    "                         rf.feature_importances_[indices[f]]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x2160 with 0 Axes>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x2160 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot( y = 'feature', x = 'importance', data = feature_rank[:20] )\n",
    "\n",
    "plt.figure(figsize=(100,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given a job title, find the 5 top jobs that are of a similar nature, based on the job post.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
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       "<p>13124 rows × 2286 columns</p>\n",
       "</div>"
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       "\n",
       "       young  youth  zang  zend  zend2  zonal  zone  zooplankton  zvartnots  \\\n",
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       "[13124 rows x 2286 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>jobpost</th>\n",
       "      <th>date</th>\n",
       "      <th>Title</th>\n",
       "      <th>Company</th>\n",
       "      <th>JobDescription</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMERIA Investment Consulting Company\\nJOB TITL...</td>\n",
       "      <td>2004-01-05</td>\n",
       "      <td>Chief Financial Officer</td>\n",
       "      <td>AMERIA Investment Consulting Company</td>\n",
       "      <td>AMERIA Investment Consulting Company is seekin...</td>\n",
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       "      <td>To perform this job successfully, an\\nindividu...</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caucasus Environmental NGO Network (CENN)\\nJOB...</td>\n",
       "      <td>2004-01-07</td>\n",
       "      <td>Country Coordinator</td>\n",
       "      <td>Caucasus Environmental NGO Network (CENN)</td>\n",
       "      <td>Public outreach and strengthening of a growing...</td>\n",
       "      <td>- Working with the Country Director to provide...</td>\n",
       "      <td>- Degree in environmentally related field, or ...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manoff Group\\nJOB TITLE:  BCC Specialist\\nPOSI...</td>\n",
       "      <td>2004-01-07</td>\n",
       "      <td>BCC Specialist</td>\n",
       "      <td>Manoff Group</td>\n",
       "      <td>The LEAD (Local Enhancement and Development fo...</td>\n",
       "      <td>- Identify gaps in knowledge and overseeing in...</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Food Security Regional Cooperation and Stabili...</td>\n",
       "      <td>2004-01-14</td>\n",
       "      <td>Community Development, Capacity Building and C...</td>\n",
       "      <td>Food Security Regional Cooperation and Stabili...</td>\n",
       "      <td>Food Security Regional Cooperation and Stabili...</td>\n",
       "      <td>- Assist the Tavush Marz communities and commu...</td>\n",
       "      <td>- Higher Education and/or professional experie...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>United Nations Development Programme, Armenia\\...</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>Country Economist (NOB)</td>\n",
       "      <td>United Nations Development Programme, Armenia</td>\n",
       "      <td>The United Nations Development Programme in Ar...</td>\n",
       "      <td>The incumbent under direct supervision of UNDP...</td>\n",
       "      <td>- Minimum Masters Degree in Economics;\\n- Mini...</td>\n",
       "      <td>False</td>\n",
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       "      <th>18992</th>\n",
       "      <td>Macadamian AR CJSC\\n\\n\\nTITLE:  .NET Developer...</td>\n",
       "      <td>2015-12-25</td>\n",
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       "      <td>The incumbent will develop software applicatio...</td>\n",
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       "      <th>18995</th>\n",
       "      <td>\"Transport PIU\" State Institution of the RA Mi...</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>Deputy Director</td>\n",
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       "    <tr>\n",
       "      <th>18996</th>\n",
       "      <td>Technolinguistics NGO\\n\\n\\nTITLE:  Senior Crea...</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>Senior Creative UX/ UI Designer</td>\n",
       "      <td>Technolinguistics NGO</td>\n",
       "      <td>A tech startup of Technolinguistics based in N...</td>\n",
       "      <td>- Work closely with product and business teams...</td>\n",
       "      <td>- At least 5 years of experience in Interface/...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18999</th>\n",
       "      <td>San Lazzaro   LLC\\n\\n\\nTITLE:  Head of Online ...</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>Head of Online Sales Department</td>\n",
       "      <td>San Lazzaro   LLC</td>\n",
       "      <td>San Lazzaro LLC is looking for a well-experien...</td>\n",
       "      <td>- Handle the project activites of the online s...</td>\n",
       "      <td>- At least 1 year of experience in online sale...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19000</th>\n",
       "      <td>\"Kamurj\" UCO CJSC\\n\\n\\nTITLE:  Lawyer in Legal...</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>Lawyer in Legal Department</td>\n",
       "      <td>\"Kamurj\" UCO CJSC</td>\n",
       "      <td>\"Kamurj\" UCO CJSC is looking for a Lawyer in L...</td>\n",
       "      <td>- Properly provide internal legal services of ...</td>\n",
       "      <td>- Higher legal education; Master's degree is a...</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13124 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 jobpost       date  \\\n",
       "0      AMERIA Investment Consulting Company\\nJOB TITL... 2004-01-05   \n",
       "2      Caucasus Environmental NGO Network (CENN)\\nJOB... 2004-01-07   \n",
       "3      Manoff Group\\nJOB TITLE:  BCC Specialist\\nPOSI... 2004-01-07   \n",
       "13     Food Security Regional Cooperation and Stabili... 2004-01-14   \n",
       "17     United Nations Development Programme, Armenia\\... 2004-01-15   \n",
       "...                                                  ...        ...   \n",
       "18992  Macadamian AR CJSC\\n\\n\\nTITLE:  .NET Developer... 2015-12-25   \n",
       "18995  \"Transport PIU\" State Institution of the RA Mi... 2015-12-24   \n",
       "18996  Technolinguistics NGO\\n\\n\\nTITLE:  Senior Crea... 2015-12-28   \n",
       "18999  San Lazzaro   LLC\\n\\n\\nTITLE:  Head of Online ... 2015-12-30   \n",
       "19000  \"Kamurj\" UCO CJSC\\n\\n\\nTITLE:  Lawyer in Legal... 2015-12-30   \n",
       "\n",
       "                                                   Title  \\\n",
       "0                                Chief Financial Officer   \n",
       "2                                    Country Coordinator   \n",
       "3                                         BCC Specialist   \n",
       "13     Community Development, Capacity Building and C...   \n",
       "17                               Country Economist (NOB)   \n",
       "...                                                  ...   \n",
       "18992                                     .NET Developer   \n",
       "18995                                    Deputy Director   \n",
       "18996                    Senior Creative UX/ UI Designer   \n",
       "18999                    Head of Online Sales Department   \n",
       "19000                         Lawyer in Legal Department   \n",
       "\n",
       "                                                 Company  \\\n",
       "0                   AMERIA Investment Consulting Company   \n",
       "2              Caucasus Environmental NGO Network (CENN)   \n",
       "3                                           Manoff Group   \n",
       "13     Food Security Regional Cooperation and Stabili...   \n",
       "17         United Nations Development Programme, Armenia   \n",
       "...                                                  ...   \n",
       "18992                                 Macadamian AR CJSC   \n",
       "18995  \"Transport PIU\" State Institution of the RA Mi...   \n",
       "18996                              Technolinguistics NGO   \n",
       "18999                                  San Lazzaro   LLC   \n",
       "19000                                  \"Kamurj\" UCO CJSC   \n",
       "\n",
       "                                          JobDescription  \\\n",
       "0      AMERIA Investment Consulting Company is seekin...   \n",
       "2      Public outreach and strengthening of a growing...   \n",
       "3      The LEAD (Local Enhancement and Development fo...   \n",
       "13     Food Security Regional Cooperation and Stabili...   \n",
       "17     The United Nations Development Programme in Ar...   \n",
       "...                                                  ...   \n",
       "18992  The incumbent will develop software applicatio...   \n",
       "18995  The incumbent will be responsible for supporti...   \n",
       "18996  A tech startup of Technolinguistics based in N...   \n",
       "18999  San Lazzaro LLC is looking for a well-experien...   \n",
       "19000  \"Kamurj\" UCO CJSC is looking for a Lawyer in L...   \n",
       "\n",
       "                                           JobRequirment  \\\n",
       "0      - Supervises financial management and administ...   \n",
       "2      - Working with the Country Director to provide...   \n",
       "3      - Identify gaps in knowledge and overseeing in...   \n",
       "13     - Assist the Tavush Marz communities and commu...   \n",
       "17     The incumbent under direct supervision of UNDP...   \n",
       "...                                                  ...   \n",
       "18992  - Participate in all the steps of the software...   \n",
       "18995  - Support the Director in organizing the activ...   \n",
       "18996  - Work closely with product and business teams...   \n",
       "18999  - Handle the project activites of the online s...   \n",
       "19000  - Properly provide internal legal services of ...   \n",
       "\n",
       "                                            RequiredQual     IT  \n",
       "0      To perform this job successfully, an\\nindividu...  False  \n",
       "2      - Degree in environmentally related field, or ...  False  \n",
       "3      - Advanced degree in public health, social sci...  False  \n",
       "13     - Higher Education and/or professional experie...  False  \n",
       "17     - Minimum Masters Degree in Economics;\\n- Mini...  False  \n",
       "...                                                  ...    ...  \n",
       "18992  - 2 - 5 years of experience in software develo...   True  \n",
       "18995  - University degree in Civil Engineering, Econ...  False  \n",
       "18996  - At least 5 years of experience in Interface/...  False  \n",
       "18999  - At least 1 year of experience in online sale...  False  \n",
       "19000  - Higher legal education; Master's degree is a...  False  \n",
       "\n",
       "[13124 rows x 8 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors =[]\n",
    "for i in range(1,21):\n",
    "    km=KMeans(i)\n",
    "    km=km.fit(mydata)\n",
    "    errors.append(km.inertia_)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[15042.040679735432,\n",
       " 12562.490532200249,\n",
       " 12295.994677039871,\n",
       " 11980.227038736779,\n",
       " 11781.165440572146,\n",
       " 11644.806447293531,\n",
       " 11465.698142747342,\n",
       " 11289.779385714757,\n",
       " 11168.152776479224,\n",
       " 11106.608673727731,\n",
       " 10912.888837376988,\n",
       " 10818.543276484923,\n",
       " 10698.970922750286,\n",
       " 10622.81761533194,\n",
       " 10569.702375565006,\n",
       " 10448.276911289418,\n",
       " 10469.981328843807,\n",
       " 10250.726870931996,\n",
       " 10288.763879913888,\n",
       " 10162.20953034635]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26456e9bdf0>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(errors).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "score=[]\n",
    "for i in range(2,21):\n",
    "    km=KMeans(i)\n",
    "    km=km.fit(mydata)\n",
    "    score.append(metrics.silhouette_score(mydata,km.labels_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26422b6aca0>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(score).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km_5=KMeans(5)\n",
    "km_5=km_5.fit(mydata)\n",
    "km_5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata['clusters']=km_5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    7118\n",
       "0    2803\n",
       "3    1730\n",
       "2     988\n",
       "4     485\n",
       "Name: clusters, dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata['clusters'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clusters'] =km_5.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobpost</th>\n",
       "      <th>date</th>\n",
       "      <th>Title</th>\n",
       "      <th>Company</th>\n",
       "      <th>JobDescription</th>\n",
       "      <th>JobRequirment</th>\n",
       "      <th>RequiredQual</th>\n",
       "      <th>IT</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMERIA Investment Consulting Company\\nJOB TITL...</td>\n",
       "      <td>2004-01-05</td>\n",
       "      <td>Chief Financial Officer</td>\n",
       "      <td>AMERIA Investment Consulting Company</td>\n",
       "      <td>AMERIA Investment Consulting Company is seekin...</td>\n",
       "      <td>- Supervises financial management and administ...</td>\n",
       "      <td>To perform this job successfully, an\\nindividu...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Caucasus Environmental NGO Network (CENN)\\nJOB...</td>\n",
       "      <td>2004-01-07</td>\n",
       "      <td>Country Coordinator</td>\n",
       "      <td>Caucasus Environmental NGO Network (CENN)</td>\n",
       "      <td>Public outreach and strengthening of a growing...</td>\n",
       "      <td>- Working with the Country Director to provide...</td>\n",
       "      <td>- Degree in environmentally related field, or ...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manoff Group\\nJOB TITLE:  BCC Specialist\\nPOSI...</td>\n",
       "      <td>2004-01-07</td>\n",
       "      <td>BCC Specialist</td>\n",
       "      <td>Manoff Group</td>\n",
       "      <td>The LEAD (Local Enhancement and Development fo...</td>\n",
       "      <td>- Identify gaps in knowledge and overseeing in...</td>\n",
       "      <td>- Advanced degree in public health, social sci...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Food Security Regional Cooperation and Stabili...</td>\n",
       "      <td>2004-01-14</td>\n",
       "      <td>Community Development, Capacity Building and C...</td>\n",
       "      <td>Food Security Regional Cooperation and Stabili...</td>\n",
       "      <td>Food Security Regional Cooperation and Stabili...</td>\n",
       "      <td>- Assist the Tavush Marz communities and commu...</td>\n",
       "      <td>- Higher Education and/or professional experie...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>United Nations Development Programme, Armenia\\...</td>\n",
       "      <td>2004-01-15</td>\n",
       "      <td>Country Economist (NOB)</td>\n",
       "      <td>United Nations Development Programme, Armenia</td>\n",
       "      <td>The United Nations Development Programme in Ar...</td>\n",
       "      <td>The incumbent under direct supervision of UNDP...</td>\n",
       "      <td>- Minimum Masters Degree in Economics;\\n- Mini...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18992</th>\n",
       "      <td>Macadamian AR CJSC\\n\\n\\nTITLE:  .NET Developer...</td>\n",
       "      <td>2015-12-25</td>\n",
       "      <td>.NET Developer</td>\n",
       "      <td>Macadamian AR CJSC</td>\n",
       "      <td>The incumbent will develop software applicatio...</td>\n",
       "      <td>- Participate in all the steps of the software...</td>\n",
       "      <td>- 2 - 5 years of experience in software develo...</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18995</th>\n",
       "      <td>\"Transport PIU\" State Institution of the RA Mi...</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>Deputy Director</td>\n",
       "      <td>\"Transport PIU\" State Institution of the RA Mi...</td>\n",
       "      <td>The incumbent will be responsible for supporti...</td>\n",
       "      <td>- Support the Director in organizing the activ...</td>\n",
       "      <td>- University degree in Civil Engineering, Econ...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18996</th>\n",
       "      <td>Technolinguistics NGO\\n\\n\\nTITLE:  Senior Crea...</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>Senior Creative UX/ UI Designer</td>\n",
       "      <td>Technolinguistics NGO</td>\n",
       "      <td>A tech startup of Technolinguistics based in N...</td>\n",
       "      <td>- Work closely with product and business teams...</td>\n",
       "      <td>- At least 5 years of experience in Interface/...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18999</th>\n",
       "      <td>San Lazzaro   LLC\\n\\n\\nTITLE:  Head of Online ...</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>Head of Online Sales Department</td>\n",
       "      <td>San Lazzaro   LLC</td>\n",
       "      <td>San Lazzaro LLC is looking for a well-experien...</td>\n",
       "      <td>- Handle the project activites of the online s...</td>\n",
       "      <td>- At least 1 year of experience in online sale...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19000</th>\n",
       "      <td>\"Kamurj\" UCO CJSC\\n\\n\\nTITLE:  Lawyer in Legal...</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>Lawyer in Legal Department</td>\n",
       "      <td>\"Kamurj\" UCO CJSC</td>\n",
       "      <td>\"Kamurj\" UCO CJSC is looking for a Lawyer in L...</td>\n",
       "      <td>- Properly provide internal legal services of ...</td>\n",
       "      <td>- Higher legal education; Master's degree is a...</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13124 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 jobpost       date  \\\n",
       "0      AMERIA Investment Consulting Company\\nJOB TITL... 2004-01-05   \n",
       "2      Caucasus Environmental NGO Network (CENN)\\nJOB... 2004-01-07   \n",
       "3      Manoff Group\\nJOB TITLE:  BCC Specialist\\nPOSI... 2004-01-07   \n",
       "13     Food Security Regional Cooperation and Stabili... 2004-01-14   \n",
       "17     United Nations Development Programme, Armenia\\... 2004-01-15   \n",
       "...                                                  ...        ...   \n",
       "18992  Macadamian AR CJSC\\n\\n\\nTITLE:  .NET Developer... 2015-12-25   \n",
       "18995  \"Transport PIU\" State Institution of the RA Mi... 2015-12-24   \n",
       "18996  Technolinguistics NGO\\n\\n\\nTITLE:  Senior Crea... 2015-12-28   \n",
       "18999  San Lazzaro   LLC\\n\\n\\nTITLE:  Head of Online ... 2015-12-30   \n",
       "19000  \"Kamurj\" UCO CJSC\\n\\n\\nTITLE:  Lawyer in Legal... 2015-12-30   \n",
       "\n",
       "                                                   Title  \\\n",
       "0                                Chief Financial Officer   \n",
       "2                                    Country Coordinator   \n",
       "3                                         BCC Specialist   \n",
       "13     Community Development, Capacity Building and C...   \n",
       "17                               Country Economist (NOB)   \n",
       "...                                                  ...   \n",
       "18992                                     .NET Developer   \n",
       "18995                                    Deputy Director   \n",
       "18996                    Senior Creative UX/ UI Designer   \n",
       "18999                    Head of Online Sales Department   \n",
       "19000                         Lawyer in Legal Department   \n",
       "\n",
       "                                                 Company  \\\n",
       "0                   AMERIA Investment Consulting Company   \n",
       "2              Caucasus Environmental NGO Network (CENN)   \n",
       "3                                           Manoff Group   \n",
       "13     Food Security Regional Cooperation and Stabili...   \n",
       "17         United Nations Development Programme, Armenia   \n",
       "...                                                  ...   \n",
       "18992                                 Macadamian AR CJSC   \n",
       "18995  \"Transport PIU\" State Institution of the RA Mi...   \n",
       "18996                              Technolinguistics NGO   \n",
       "18999                                  San Lazzaro   LLC   \n",
       "19000                                  \"Kamurj\" UCO CJSC   \n",
       "\n",
       "                                          JobDescription  \\\n",
       "0      AMERIA Investment Consulting Company is seekin...   \n",
       "2      Public outreach and strengthening of a growing...   \n",
       "3      The LEAD (Local Enhancement and Development fo...   \n",
       "13     Food Security Regional Cooperation and Stabili...   \n",
       "17     The United Nations Development Programme in Ar...   \n",
       "...                                                  ...   \n",
       "18992  The incumbent will develop software applicatio...   \n",
       "18995  The incumbent will be responsible for supporti...   \n",
       "18996  A tech startup of Technolinguistics based in N...   \n",
       "18999  San Lazzaro LLC is looking for a well-experien...   \n",
       "19000  \"Kamurj\" UCO CJSC is looking for a Lawyer in L...   \n",
       "\n",
       "                                           JobRequirment  \\\n",
       "0      - Supervises financial management and administ...   \n",
       "2      - Working with the Country Director to provide...   \n",
       "3      - Identify gaps in knowledge and overseeing in...   \n",
       "13     - Assist the Tavush Marz communities and commu...   \n",
       "17     The incumbent under direct supervision of UNDP...   \n",
       "...                                                  ...   \n",
       "18992  - Participate in all the steps of the software...   \n",
       "18995  - Support the Director in organizing the activ...   \n",
       "18996  - Work closely with product and business teams...   \n",
       "18999  - Handle the project activites of the online s...   \n",
       "19000  - Properly provide internal legal services of ...   \n",
       "\n",
       "                                            RequiredQual     IT  clusters  \n",
       "0      To perform this job successfully, an\\nindividu...  False         1  \n",
       "2      - Degree in environmentally related field, or ...  False         1  \n",
       "3      - Advanced degree in public health, social sci...  False         1  \n",
       "13     - Higher Education and/or professional experie...  False         1  \n",
       "17     - Minimum Masters Degree in Economics;\\n- Mini...  False         1  \n",
       "...                                                  ...    ...       ...  \n",
       "18992  - 2 - 5 years of experience in software develo...   True         0  \n",
       "18995  - University degree in Civil Engineering, Econ...  False         1  \n",
       "18996  - At least 5 years of experience in Interface/...  False         1  \n",
       "18999  - At least 1 year of experience in online sale...  False         1  \n",
       "19000  - Higher legal education; Master's degree is a...  False         1  \n",
       "\n",
       "[13124 rows x 9 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter  Job Title :.NET Developer\n"
     ]
    }
   ],
   "source": [
    "h=[]\n",
    "h=input('Enter  Job Title :')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myfunc(h):\n",
    "    u=df['Title'].apply(lambda x : 1 if x in h else 0)\n",
    "    return list(u)\n",
    "df2['id']=myfunc(h)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobpost</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3764</th>\n",
       "      <td>Toto International Bookmakers\\nTITLE:  .NET De...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4093</th>\n",
       "      <td>Damaris AM Ltd.\\nTITLE:  Developer\\nANNOUNCEME...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4283</th>\n",
       "      <td>IOGenic\\nTITLE:  .NET Developer\\nANNOUNCEMENT ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>Energize Global Services CJSC\\nTITLE:  Develop...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10239</th>\n",
       "      <td>Firmplace Corporation, Yerevan branch\\nTITLE: ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 jobpost  clusters\n",
       "3764   Toto International Bookmakers\\nTITLE:  .NET De...         0\n",
       "4093   Damaris AM Ltd.\\nTITLE:  Developer\\nANNOUNCEME...         0\n",
       "4283   IOGenic\\nTITLE:  .NET Developer\\nANNOUNCEMENT ...         0\n",
       "6610   Energize Global Services CJSC\\nTITLE:  Develop...         0\n",
       "10239  Firmplace Corporation, Yerevan branch\\nTITLE: ...         0"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[df2['id'] ==1,['jobpost','clusters']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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