DA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data_jobs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>job_title_short</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_location</th>\n",
       "      <th>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
       "      <th>job_work_from_home</th>\n",
       "      <th>search_location</th>\n",
       "      <th>job_posted_date</th>\n",
       "      <th>job_no_degree_mention</th>\n",
       "      <th>job_health_insurance</th>\n",
       "      <th>job_country</th>\n",
       "      <th>salary_rate</th>\n",
       "      <th>salary_year_avg</th>\n",
       "      <th>salary_hour_avg</th>\n",
       "      <th>company_name</th>\n",
       "      <th>job_skills</th>\n",
       "      <th>job_type_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>Senior Clinical Data Engineer / Principal Clin...</td>\n",
       "      <td>Watertown, CT</td>\n",
       "      <td>via Work Nearby</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Texas, United States</td>\n",
       "      <td>2023-06-16 13:44:15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boehringer Ingelheim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Guadalajara, Jalisco, Mexico</td>\n",
       "      <td>via BeBee México</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2023-01-14 13:18:07</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hewlett Packard Enterprise</td>\n",
       "      <td>['r', 'python', 'sql', 'nosql', 'power bi', 't...</td>\n",
       "      <td>{'analyst_tools': ['power bi', 'tableau'], 'pr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineer/Scientist/Analyst, Mid or Senior...</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2023-10-10 13:14:55</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALPHA Augmented Services</td>\n",
       "      <td>['python', 'sql', 'c#', 'azure', 'airflow', 'd...</td>\n",
       "      <td>{'analyst_tools': ['dax'], 'cloud': ['azure'],...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...</td>\n",
       "      <td>San Antonio, TX</td>\n",
       "      <td>via Diversity.com</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Texas, United States</td>\n",
       "      <td>2023-07-04 13:01:41</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southwest Research Institute</td>\n",
       "      <td>['python', 'c++', 'java', 'matlab', 'aws', 'te...</td>\n",
       "      <td>{'cloud': ['aws'], 'libraries': ['tensorflow',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineer- Sr Jobs</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>via Clearance Jobs</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>2023-08-07 14:29:36</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kristina Daniel</td>\n",
       "      <td>['bash', 'python', 'oracle', 'aws', 'ansible',...</td>\n",
       "      <td>{'cloud': ['oracle', 'aws'], 'other': ['ansibl...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785736</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>DevOps Engineer</td>\n",
       "      <td>Singapura</td>\n",
       "      <td>melalui Trabajo.org</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2023-03-13 06:16:16</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CAREERSTAR INTERNATIONAL PTE. LTD.</td>\n",
       "      <td>['bash', 'python', 'perl', 'linux', 'unix', 'k...</td>\n",
       "      <td>{'os': ['linux', 'unix'], 'other': ['kubernete...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785737</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>CRM Data Analyst</td>\n",
       "      <td>Bad Rodach, Jerman</td>\n",
       "      <td>melalui BeBee Deutschland</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2023-03-12 06:18:18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Germany</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HABA FAMILYGROUP</td>\n",
       "      <td>['sas', 'sas', 'sql', 'excel']</td>\n",
       "      <td>{'analyst_tools': ['sas', 'excel'], 'programmi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785738</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>Commercial Analyst - Start Now</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>melalui Ricebowl</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>2023-03-12 06:32:36</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lendlease Corporation</td>\n",
       "      <td>['powerpoint', 'excel']</td>\n",
       "      <td>{'analyst_tools': ['powerpoint', 'excel']}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785739</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Principal Associate, Data Engineer (Remote-Eli...</td>\n",
       "      <td>Newark, New Jersey, Amerika Serikat</td>\n",
       "      <td>melalui Recruit.net</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>2023-03-12 06:32:15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Capital One</td>\n",
       "      <td>['python', 'go', 'nosql', 'sql', 'mongo', 'she...</td>\n",
       "      <td>{'cloud': ['aws', 'snowflake', 'azure', 'redsh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785740</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>AWS System Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>melalui Trigyn</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-03-13 06:16:31</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trigyn</td>\n",
       "      <td>['aws', 'flow']</td>\n",
       "      <td>{'cloud': ['aws'], 'other': ['flow']}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>785741 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             job_title_short  \\\n",
       "0       Senior Data Engineer   \n",
       "1               Data Analyst   \n",
       "2              Data Engineer   \n",
       "3              Data Engineer   \n",
       "4              Data Engineer   \n",
       "...                      ...   \n",
       "785736     Software Engineer   \n",
       "785737          Data Analyst   \n",
       "785738      Business Analyst   \n",
       "785739         Data Engineer   \n",
       "785740     Software Engineer   \n",
       "\n",
       "                                                job_title  \\\n",
       "0       Senior Clinical Data Engineer / Principal Clin...   \n",
       "1                                            Data Analyst   \n",
       "2       Data Engineer/Scientist/Analyst, Mid or Senior...   \n",
       "3       LEAD ENGINEER - PRINCIPAL ANALYST - PRINCIPAL ...   \n",
       "4                                  Data Engineer- Sr Jobs   \n",
       "...                                                   ...   \n",
       "785736                                    DevOps Engineer   \n",
       "785737                                   CRM Data Analyst   \n",
       "785738                     Commercial Analyst - Start Now   \n",
       "785739  Principal Associate, Data Engineer (Remote-Eli...   \n",
       "785740                                 AWS System Analyst   \n",
       "\n",
       "                               job_location                    job_via  \\\n",
       "0                             Watertown, CT            via Work Nearby   \n",
       "1              Guadalajara, Jalisco, Mexico           via BeBee México   \n",
       "2                           Berlin, Germany               via LinkedIn   \n",
       "3                           San Antonio, TX          via Diversity.com   \n",
       "4                            Washington, DC         via Clearance Jobs   \n",
       "...                                     ...                        ...   \n",
       "785736                            Singapura        melalui Trabajo.org   \n",
       "785737                   Bad Rodach, Jerman  melalui BeBee Deutschland   \n",
       "785738                             Malaysia           melalui Ricebowl   \n",
       "785739  Newark, New Jersey, Amerika Serikat        melalui Recruit.net   \n",
       "785740                                India             melalui Trigyn   \n",
       "\n",
       "       job_schedule_type  job_work_from_home       search_location  \\\n",
       "0              Full-time               False  Texas, United States   \n",
       "1              Full-time               False                Mexico   \n",
       "2              Full-time               False               Germany   \n",
       "3              Full-time               False  Texas, United States   \n",
       "4              Full-time               False                 Sudan   \n",
       "...                  ...                 ...                   ...   \n",
       "785736   Pekerjaan tetap               False             Singapore   \n",
       "785737   Pekerjaan tetap               False               Germany   \n",
       "785738   Pekerjaan tetap               False              Malaysia   \n",
       "785739   Pekerjaan tetap               False                 Sudan   \n",
       "785740   Pekerjaan tetap               False                 India   \n",
       "\n",
       "            job_posted_date  job_no_degree_mention  job_health_insurance  \\\n",
       "0       2023-06-16 13:44:15                  False                 False   \n",
       "1       2023-01-14 13:18:07                  False                 False   \n",
       "2       2023-10-10 13:14:55                  False                 False   \n",
       "3       2023-07-04 13:01:41                   True                 False   \n",
       "4       2023-08-07 14:29:36                  False                 False   \n",
       "...                     ...                    ...                   ...   \n",
       "785736  2023-03-13 06:16:16                  False                 False   \n",
       "785737  2023-03-12 06:18:18                  False                 False   \n",
       "785738  2023-03-12 06:32:36                  False                 False   \n",
       "785739  2023-03-12 06:32:15                  False                 False   \n",
       "785740  2023-03-13 06:16:31                  False                 False   \n",
       "\n",
       "          job_country salary_rate  salary_year_avg  salary_hour_avg  \\\n",
       "0       United States         NaN              NaN              NaN   \n",
       "1              Mexico         NaN              NaN              NaN   \n",
       "2             Germany         NaN              NaN              NaN   \n",
       "3       United States         NaN              NaN              NaN   \n",
       "4               Sudan         NaN              NaN              NaN   \n",
       "...               ...         ...              ...              ...   \n",
       "785736      Singapore         NaN              NaN              NaN   \n",
       "785737        Germany         NaN              NaN              NaN   \n",
       "785738       Malaysia         NaN              NaN              NaN   \n",
       "785739          Sudan         NaN              NaN              NaN   \n",
       "785740          India         NaN              NaN              NaN   \n",
       "\n",
       "                              company_name  \\\n",
       "0                     Boehringer Ingelheim   \n",
       "1               Hewlett Packard Enterprise   \n",
       "2                 ALPHA Augmented Services   \n",
       "3             Southwest Research Institute   \n",
       "4                          Kristina Daniel   \n",
       "...                                    ...   \n",
       "785736  CAREERSTAR INTERNATIONAL PTE. LTD.   \n",
       "785737                    HABA FAMILYGROUP   \n",
       "785738               Lendlease Corporation   \n",
       "785739                         Capital One   \n",
       "785740                              Trigyn   \n",
       "\n",
       "                                               job_skills  \\\n",
       "0                                                     NaN   \n",
       "1       ['r', 'python', 'sql', 'nosql', 'power bi', 't...   \n",
       "2       ['python', 'sql', 'c#', 'azure', 'airflow', 'd...   \n",
       "3       ['python', 'c++', 'java', 'matlab', 'aws', 'te...   \n",
       "4       ['bash', 'python', 'oracle', 'aws', 'ansible',...   \n",
       "...                                                   ...   \n",
       "785736  ['bash', 'python', 'perl', 'linux', 'unix', 'k...   \n",
       "785737                     ['sas', 'sas', 'sql', 'excel']   \n",
       "785738                            ['powerpoint', 'excel']   \n",
       "785739  ['python', 'go', 'nosql', 'sql', 'mongo', 'she...   \n",
       "785740                                    ['aws', 'flow']   \n",
       "\n",
       "                                          job_type_skills  \n",
       "0                                                     NaN  \n",
       "1       {'analyst_tools': ['power bi', 'tableau'], 'pr...  \n",
       "2       {'analyst_tools': ['dax'], 'cloud': ['azure'],...  \n",
       "3       {'cloud': ['aws'], 'libraries': ['tensorflow',...  \n",
       "4       {'cloud': ['oracle', 'aws'], 'other': ['ansibl...  \n",
       "...                                                   ...  \n",
       "785736  {'os': ['linux', 'unix'], 'other': ['kubernete...  \n",
       "785737  {'analyst_tools': ['sas', 'excel'], 'programmi...  \n",
       "785738         {'analyst_tools': ['powerpoint', 'excel']}  \n",
       "785739  {'cloud': ['aws', 'snowflake', 'azure', 'redsh...  \n",
       "785740              {'cloud': ['aws'], 'other': ['flow']}  \n",
       "\n",
       "[785741 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_location</th>\n",
       "      <th>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
       "      <th>job_work_from_home</th>\n",
       "      <th>search_location</th>\n",
       "      <th>job_posted_date</th>\n",
       "      <th>job_no_degree_mention</th>\n",
       "      <th>job_health_insurance</th>\n",
       "      <th>job_country</th>\n",
       "      <th>salary_rate</th>\n",
       "      <th>salary_year_avg</th>\n",
       "      <th>salary_hour_avg</th>\n",
       "      <th>company_name</th>\n",
       "      <th>job_skills</th>\n",
       "      <th>job_type_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>Senior Clinical Data Engineer / Principal Clin...</td>\n",
       "      <td>Watertown, CT</td>\n",
       "      <td>via Work Nearby</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Texas, United States</td>\n",
       "      <td>2023-06-16 13:44:15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boehringer Ingelheim</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Guadalajara, Jalisco, Mexico</td>\n",
       "      <td>via BeBee México</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>2023-01-14 13:18:07</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hewlett Packard Enterprise</td>\n",
       "      <td>['r', 'python', 'sql', 'nosql', 'power bi', 't...</td>\n",
       "      <td>{'analyst_tools': ['power bi', 'tableau'], 'pr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        job_title_short                                          job_title  \\\n",
       "0  Senior Data Engineer  Senior Clinical Data Engineer / Principal Clin...   \n",
       "1          Data Analyst                                       Data Analyst   \n",
       "\n",
       "                   job_location           job_via job_schedule_type  \\\n",
       "0                 Watertown, CT   via Work Nearby         Full-time   \n",
       "1  Guadalajara, Jalisco, Mexico  via BeBee México         Full-time   \n",
       "\n",
       "   job_work_from_home       search_location      job_posted_date  \\\n",
       "0               False  Texas, United States  2023-06-16 13:44:15   \n",
       "1               False                Mexico  2023-01-14 13:18:07   \n",
       "\n",
       "   job_no_degree_mention  job_health_insurance    job_country salary_rate  \\\n",
       "0                  False                 False  United States         NaN   \n",
       "1                  False                 False         Mexico         NaN   \n",
       "\n",
       "   salary_year_avg  salary_hour_avg                company_name  \\\n",
       "0              NaN              NaN        Boehringer Ingelheim   \n",
       "1              NaN              NaN  Hewlett Packard Enterprise   \n",
       "\n",
       "                                          job_skills  \\\n",
       "0                                                NaN   \n",
       "1  ['r', 'python', 'sql', 'nosql', 'power bi', 't...   \n",
       "\n",
       "                                     job_type_skills  \n",
       "0                                                NaN  \n",
       "1  {'analyst_tools': ['power bi', 'tableau'], 'pr...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_location</th>\n",
       "      <th>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
       "      <th>job_work_from_home</th>\n",
       "      <th>search_location</th>\n",
       "      <th>job_posted_date</th>\n",
       "      <th>job_no_degree_mention</th>\n",
       "      <th>job_health_insurance</th>\n",
       "      <th>job_country</th>\n",
       "      <th>salary_rate</th>\n",
       "      <th>salary_year_avg</th>\n",
       "      <th>salary_hour_avg</th>\n",
       "      <th>company_name</th>\n",
       "      <th>job_skills</th>\n",
       "      <th>job_type_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>785739</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Principal Associate, Data Engineer (Remote-Eli...</td>\n",
       "      <td>Newark, New Jersey, Amerika Serikat</td>\n",
       "      <td>melalui Recruit.net</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>2023-03-12 06:32:15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Capital One</td>\n",
       "      <td>['python', 'go', 'nosql', 'sql', 'mongo', 'she...</td>\n",
       "      <td>{'cloud': ['aws', 'snowflake', 'azure', 'redsh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785740</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>AWS System Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>melalui Trigyn</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-03-13 06:16:31</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Trigyn</td>\n",
       "      <td>['aws', 'flow']</td>\n",
       "      <td>{'cloud': ['aws'], 'other': ['flow']}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_title_short                                          job_title  \\\n",
       "785739      Data Engineer  Principal Associate, Data Engineer (Remote-Eli...   \n",
       "785740  Software Engineer                                 AWS System Analyst   \n",
       "\n",
       "                               job_location              job_via  \\\n",
       "785739  Newark, New Jersey, Amerika Serikat  melalui Recruit.net   \n",
       "785740                                India       melalui Trigyn   \n",
       "\n",
       "       job_schedule_type  job_work_from_home search_location  \\\n",
       "785739   Pekerjaan tetap               False           Sudan   \n",
       "785740   Pekerjaan tetap               False           India   \n",
       "\n",
       "            job_posted_date  job_no_degree_mention  job_health_insurance  \\\n",
       "785739  2023-03-12 06:32:15                  False                 False   \n",
       "785740  2023-03-13 06:16:31                  False                 False   \n",
       "\n",
       "       job_country salary_rate  salary_year_avg  salary_hour_avg company_name  \\\n",
       "785739       Sudan         NaN              NaN              NaN  Capital One   \n",
       "785740       India         NaN              NaN              NaN       Trigyn   \n",
       "\n",
       "                                               job_skills  \\\n",
       "785739  ['python', 'go', 'nosql', 'sql', 'mongo', 'she...   \n",
       "785740                                    ['aws', 'flow']   \n",
       "\n",
       "                                          job_type_skills  \n",
       "785739  {'cloud': ['aws', 'snowflake', 'azure', 'redsh...  \n",
       "785740              {'cloud': ['aws'], 'other': ['flow']}  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
       "      <th>job_work_from_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>via Work Nearby</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>via BeBee México</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>via Diversity.com</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             job_via job_schedule_type  job_work_from_home\n",
       "0    via Work Nearby         Full-time               False\n",
       "1   via BeBee México         Full-time               False\n",
       "2       via LinkedIn         Full-time               False\n",
       "3  via Diversity.com         Full-time               False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.iloc[0:4,3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   via Work Nearby\n",
       "1                  via BeBee México\n",
       "2                      via LinkedIn\n",
       "3                 via Diversity.com\n",
       "4                via Clearance Jobs\n",
       "                    ...            \n",
       "785736          melalui Trabajo.org\n",
       "785737    melalui BeBee Deutschland\n",
       "785738             melalui Ricebowl\n",
       "785739          melalui Recruit.net\n",
       "785740               melalui Trigyn\n",
       "Name: job_via, Length: 785741, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.loc[:,'job_via']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['job_posted_date']=pd.to_datetime(dataset['job_posted_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['job_skills'] = dataset['job_skills'].apply(lambda x: ast.literal_eval(x) if pd.notna(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_india = (dataset['job_country']=='India') & (dataset['job_title']=='Data Analyst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "785736    False\n",
       "785737    False\n",
       "785738    False\n",
       "785739    False\n",
       "785740    False\n",
       "Length: 785741, dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_india"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_india = dataset[(dataset['job_country']=='India') & (dataset['job_title']=='Data Analyst')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_location</th>\n",
       "      <th>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
       "      <th>job_work_from_home</th>\n",
       "      <th>search_location</th>\n",
       "      <th>job_posted_date</th>\n",
       "      <th>job_no_degree_mention</th>\n",
       "      <th>job_health_insurance</th>\n",
       "      <th>job_country</th>\n",
       "      <th>salary_rate</th>\n",
       "      <th>salary_year_avg</th>\n",
       "      <th>salary_hour_avg</th>\n",
       "      <th>company_name</th>\n",
       "      <th>job_skills</th>\n",
       "      <th>job_type_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Hyderabad, Telangana, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Contractor</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-11-28 13:12:07</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fusion Plus Solutions Inc</td>\n",
       "      <td>[sql]</td>\n",
       "      <td>{'programming': ['sql']}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>via BeBee India</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-06-20 13:11:10</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>QuEST Global Services Pte. Ltd</td>\n",
       "      <td>[python, sql, excel, ms access, powerpoint]</td>\n",
       "      <td>{'analyst_tools': ['excel', 'ms access', 'powe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Hyderabad, Telangana, India</td>\n",
       "      <td>via Shine</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-11-12 13:08:06</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HSBC</td>\n",
       "      <td>[sql, nosql, python, r, mysql, sql server, pos...</td>\n",
       "      <td>{'analyst_tools': ['qlik', 'tableau', 'power b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>via BeBee India</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-09-12 13:14:38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AOS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Pune, Maharashtra, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-07-04 13:13:00</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Infosys BPM</td>\n",
       "      <td>[sql, shell, r, bigquery, pandas, linux, excel...</td>\n",
       "      <td>{'analyst_tools': ['excel', 'tableau', 'qlik']...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784845</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Bengaluru, Karnataka, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-01-25 06:14:59</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Capgemini</td>\n",
       "      <td>[sql, visio]</td>\n",
       "      <td>{'analyst_tools': ['visio'], 'programming': ['...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785157</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Hyderabad, Telangana, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-01-31 06:11:12</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ARYAN TECHNOLOGY</td>\n",
       "      <td>[sql, javascript, sas, sas, excel, spss]</td>\n",
       "      <td>{'analyst_tools': ['sas', 'excel', 'spss'], 'p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785515</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Bengaluru, Karnataka, India</td>\n",
       "      <td>via Ai-Jobs.net</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-09-01 06:17:34</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Myntra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785548</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Bengaluru, Karnataka, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-01-19 06:14:17</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Artech L.L.C.</td>\n",
       "      <td>[sql, excel]</td>\n",
       "      <td>{'analyst_tools': ['excel'], 'programming': ['...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785628</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Gurugram, Haryana, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-01-09 06:08:12</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GUS Global Services India Pvt. Ltd.</td>\n",
       "      <td>[r, python, sql, sap, power bi, tableau, excel]</td>\n",
       "      <td>{'analyst_tools': ['sap', 'power bi', 'tableau...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1539 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       job_title_short     job_title                 job_location  \\\n",
       "159       Data Analyst  Data Analyst  Hyderabad, Telangana, India   \n",
       "194       Data Analyst  Data Analyst                        India   \n",
       "504       Data Analyst  Data Analyst  Hyderabad, Telangana, India   \n",
       "691       Data Analyst  Data Analyst                        India   \n",
       "1399      Data Analyst  Data Analyst     Pune, Maharashtra, India   \n",
       "...                ...           ...                          ...   \n",
       "784845    Data Analyst  Data Analyst  Bengaluru, Karnataka, India   \n",
       "785157    Data Analyst  Data Analyst  Hyderabad, Telangana, India   \n",
       "785515    Data Analyst  Data Analyst  Bengaluru, Karnataka, India   \n",
       "785548    Data Analyst  Data Analyst  Bengaluru, Karnataka, India   \n",
       "785628    Data Analyst  Data Analyst     Gurugram, Haryana, India   \n",
       "\n",
       "                job_via job_schedule_type  job_work_from_home search_location  \\\n",
       "159        via LinkedIn        Contractor               False           India   \n",
       "194     via BeBee India         Full-time               False           India   \n",
       "504           via Shine         Full-time               False           India   \n",
       "691     via BeBee India         Full-time               False           India   \n",
       "1399       via LinkedIn         Full-time               False           India   \n",
       "...                 ...               ...                 ...             ...   \n",
       "784845     via LinkedIn         Full-time               False           India   \n",
       "785157     via LinkedIn         Full-time               False           India   \n",
       "785515  via Ai-Jobs.net         Full-time               False           India   \n",
       "785548     via LinkedIn         Full-time               False           India   \n",
       "785628     via LinkedIn         Full-time               False           India   \n",
       "\n",
       "           job_posted_date  job_no_degree_mention  job_health_insurance  \\\n",
       "159    2023-11-28 13:12:07                  False                 False   \n",
       "194    2023-06-20 13:11:10                  False                 False   \n",
       "504    2023-11-12 13:08:06                   True                 False   \n",
       "691    2023-09-12 13:14:38                   True                 False   \n",
       "1399   2023-07-04 13:13:00                   True                 False   \n",
       "...                    ...                    ...                   ...   \n",
       "784845 2023-01-25 06:14:59                   True                 False   \n",
       "785157 2023-01-31 06:11:12                  False                 False   \n",
       "785515 2023-09-01 06:17:34                  False                 False   \n",
       "785548 2023-01-19 06:14:17                  False                 False   \n",
       "785628 2023-01-09 06:08:12                  False                 False   \n",
       "\n",
       "       job_country salary_rate  salary_year_avg  salary_hour_avg  \\\n",
       "159          India         NaN              NaN              NaN   \n",
       "194          India         NaN              NaN              NaN   \n",
       "504          India         NaN              NaN              NaN   \n",
       "691          India         NaN              NaN              NaN   \n",
       "1399         India         NaN              NaN              NaN   \n",
       "...            ...         ...              ...              ...   \n",
       "784845       India         NaN              NaN              NaN   \n",
       "785157       India         NaN              NaN              NaN   \n",
       "785515       India        year         105000.0              NaN   \n",
       "785548       India         NaN              NaN              NaN   \n",
       "785628       India         NaN              NaN              NaN   \n",
       "\n",
       "                               company_name  \\\n",
       "159               Fusion Plus Solutions Inc   \n",
       "194          QuEST Global Services Pte. Ltd   \n",
       "504                                    HSBC   \n",
       "691                                     AOS   \n",
       "1399                            Infosys BPM   \n",
       "...                                     ...   \n",
       "784845                            Capgemini   \n",
       "785157                     ARYAN TECHNOLOGY   \n",
       "785515                               Myntra   \n",
       "785548                        Artech L.L.C.   \n",
       "785628  GUS Global Services India Pvt. Ltd.   \n",
       "\n",
       "                                               job_skills  \\\n",
       "159                                                 [sql]   \n",
       "194           [python, sql, excel, ms access, powerpoint]   \n",
       "504     [sql, nosql, python, r, mysql, sql server, pos...   \n",
       "691                                                   NaN   \n",
       "1399    [sql, shell, r, bigquery, pandas, linux, excel...   \n",
       "...                                                   ...   \n",
       "784845                                       [sql, visio]   \n",
       "785157           [sql, javascript, sas, sas, excel, spss]   \n",
       "785515                                                NaN   \n",
       "785548                                       [sql, excel]   \n",
       "785628    [r, python, sql, sap, power bi, tableau, excel]   \n",
       "\n",
       "                                          job_type_skills  \n",
       "159                              {'programming': ['sql']}  \n",
       "194     {'analyst_tools': ['excel', 'ms access', 'powe...  \n",
       "504     {'analyst_tools': ['qlik', 'tableau', 'power b...  \n",
       "691                                                   NaN  \n",
       "1399    {'analyst_tools': ['excel', 'tableau', 'qlik']...  \n",
       "...                                                   ...  \n",
       "784845  {'analyst_tools': ['visio'], 'programming': ['...  \n",
       "785157  {'analyst_tools': ['sas', 'excel', 'spss'], 'p...  \n",
       "785515                                                NaN  \n",
       "785548  {'analyst_tools': ['excel'], 'programming': ['...  \n",
       "785628  {'analyst_tools': ['sap', 'power bi', 'tableau...  \n",
       "\n",
       "[1539 rows x 17 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_india"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job_location\n",
       "India                                    531\n",
       "Hyderabad, Telangana, India              303\n",
       "Anywhere                                 202\n",
       "Bengaluru, Karnataka, India              100\n",
       "Maharashtra, India                        65\n",
       "Mumbai, Maharashtra, India                48\n",
       "Pune, Maharashtra, India                  38\n",
       "Gurugram, Haryana, India                  34\n",
       "Chennai, Tamil Nadu, India                32\n",
       "New Delhi, Delhi, India                   22\n",
       "Indore, Madhya Pradesh, India             22\n",
       "Secunderabad, Telangana, India            19\n",
       "Bhopal, Madhya Pradesh, India             16\n",
       "Nagpur, Maharashtra, India                15\n",
       "Noida, Uttar Pradesh, India               14\n",
       "Telangana, India                          10\n",
       "Raipur, Chhattisgarh, India                5\n",
       "Navi Mumbai, Maharashtra, India            5\n",
       "Madhya Pradesh, India                      4\n",
       "Jaipur, Rajasthan, India                   4\n",
       "West Bengal                                3\n",
       "Maharashtra                                3\n",
       "Ahmedabad, Gujarat, India                  3\n",
       "Surat, Gujarat, India                      2\n",
       "Pimpri-Chinchwad, Maharashtra, India       2\n",
       "Sagar, Madhya Pradesh, India               2\n",
       "Tirupati, Andhra Pradesh, India            2\n",
       "Karnataka                                  2\n",
       "Kolkata, West Bengal, India                2\n",
       "Warangal, Telangana, India                 1\n",
       "Visakhapatnam, Andhra Pradesh, India       1\n",
       "Thane, Maharashtra, India                  1\n",
       "Washim, Maharashtra, India                 1\n",
       "Thiruvananthapuram, Kerala, India          1\n",
       "Vadodara, Gujarat, India                   1\n",
       "Narmadapuram, Madhya Pradesh, India        1\n",
       "Chhattisgarh, India                        1\n",
       "Rajasthan, India                           1\n",
       "Guntakal, Andhra Pradesh, India            1\n",
       "Bilaspur, Chhattisgarh, India              1\n",
       "Lucknow, Uttar Pradesh, India              1\n",
       "Gwalior, Madhya Pradesh, India             1\n",
       "Bhilai, Chhattisgarh, India                1\n",
       "Karnataka, India                           1\n",
       "Udaipur, Rajasthan, India                  1\n",
       "Serilingampalle (M), Telangana, India      1\n",
       "Nellore, Andhra Pradesh, India             1\n",
       "Ranchi, Jharkhand, India                   1\n",
       "Nashik, Maharashtra, India                 1\n",
       "Nanakaramguda, Telangana, India            1\n",
       "Goregaon, Maharashtra, India               1\n",
       "Coimbatore, Tamil Nadu, India              1\n",
       "Jamshedpur, Jharkhand, India               1\n",
       "Jalgaon, Maharashtra, India                1\n",
       "Umaria, Madhya Pradesh, India              1\n",
       "Amravati, Maharashtra, India               1\n",
       "Borivali, Maharashtra, India               1\n",
       "Kochi, Kerala, India                       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_india['job_location'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job_location\n",
       "India                          531\n",
       "Hyderabad, Telangana, India    303\n",
       "Anywhere                       202\n",
       "Bengaluru, Karnataka, India    100\n",
       "Maharashtra, India              65\n",
       "Mumbai, Maharashtra, India      48\n",
       "Pune, Maharashtra, India        38\n",
       "Gurugram, Haryana, India        34\n",
       "Chennai, Tamil Nadu, India      32\n",
       "New Delhi, Delhi, India         22\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_india['job_location'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot = df_india['job_location'].value_counts().head(10).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyderabad, Telangana, India</th>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anywhere</th>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bengaluru, Karnataka, India</th>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maharashtra, India</th>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mumbai, Maharashtra, India</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pune, Maharashtra, India</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gurugram, Haryana, India</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chennai, Tamil Nadu, India</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Delhi, Delhi, India</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count\n",
       "job_location                      \n",
       "India                          531\n",
       "Hyderabad, Telangana, India    303\n",
       "Anywhere                       202\n",
       "Bengaluru, Karnataka, India    100\n",
       "Maharashtra, India              65\n",
       "Mumbai, Maharashtra, India      48\n",
       "Pune, Maharashtra, India        38\n",
       "Gurugram, Haryana, India        34\n",
       "Chennai, Tamil Nadu, India      32\n",
       "New Delhi, Delhi, India         22"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='ticks')\n",
    "sns.barplot(data=plot,x='count',y='job_location',hue='count',palette='dark:b_r',legend=False)\n",
    "sns.despine()\n",
    "plt.title('Counts of job location for data analyst jobs in india')\n",
    "plt.xlabel('Number of jobs')\n",
    "plt.ylabel('')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dict_column = {\n",
    "    'job_work_from_home': 'Work from Home Offered',\n",
    "    'job_no_degree_mention': 'Degree Requirement',\n",
    "}\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(11, 3.5))\n",
    "\n",
    "for i, (column, title) in enumerate(dict_column.items()):\n",
    "    ax[i].pie(df_india[column].value_counts(), labels=['False', 'True'], autopct='%1.1f%%', startangle=90)\n",
    "    ax[i].set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df_india['company_name'].value_counts().head(10).to_frame()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S&amp;P Global</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confidential</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virtusa</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recruin</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intellijobs</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AKS ProTalent</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5 Global Services Private Limited.</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indium Software</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wipro</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBS</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     count\n",
       "company_name                              \n",
       "S&P Global                              33\n",
       "Confidential                            21\n",
       "Virtusa                                 12\n",
       "Recruin                                 12\n",
       "intellijobs                             11\n",
       "AKS ProTalent                           10\n",
       "V5 Global Services Private Limited.      9\n",
       "Indium Software                          9\n",
       "Wipro                                    9\n",
       "UBS                                      9"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0/73oyUivuj/KU0oARESeU7lyZSIiInj06FGS/5GHhYUxatQolixZgp2dHfC0Hv35S+JXr141nJ1OOBsfGxtrOBP27Fnaf6t+/frUr1+fu3fvEh4eTkhICH379qVs2bLkzJkzUX87OzuuXbuWqP3q1asAqT4TndS4Z86cSdW4zx/ox8bGcvPmzVQ/5jI165A5c2bg6cFCwllHgJMnT3Ljxg2KFClC+/btcXFx4eeff8bZ2Rlzc3O2bdvGpk2bUhVHQiypXe/XITXrntINn89KiO369esUKFDA0J5wQJqgT58+nDhxgnnz5lG6dGmsrKyIjo42Kjd6XlxcHB07dsTS0pIff/yRYsWKYWFhwT///JPiU7QSauz/7fcCGA6Ehw4dSu3atQ1XTVIqi4Kn9/qMHDmSTZs2ceDAASpWrGhUrlSgQAHGjRtHbGwsf/75J6tXr2bp0qXkzp3bUJrzMhKuRiT3vT6/D92+fRtra2vD52vXrpEuXTrD36QqVapQpUoVoqOj2bVrFwsXLmT06NGUKlWKkiVLvnR8b4q9vT3m5uaJ1vv5/e95r7I/yv/oJmARkef4+flx69YtozrcBNevX2f27NnkzZvX8B+plZVVohcf7d27lwsXLhjqUBNKXZ59mser3qiWcPYsQY8ePejSpQsAtra2eHt707lzZ2JjY5M9CCxXrhz//e9/ja5QxMbGsm7dOlxdXbGysnql2MqVK8f58+eNnkYDsGbNGiwtLXFzczO07dixgydPnhg+b9q0iSdPnlChQoVUL+vAgQOJXsy2Zs0aHBwcjM4I/vLLL0Z9Jk2axIgRIzh58iS3bt2idevWFCpUyLBtf/vtN+B/Z+Wf3+b/Zr1fh9f5/bm7u2Ntbc3GjRuN2v/73/8afd63bx+1a9fGw8PDMP7z2+nZMg94ek/NqVOn8PHxwc3NDQsLiyTne15qv5fU2LdvHwULFsTHx8dw8H/58mUiIyNTHCdjxozUrVuXn3/+me3btxvO/gNs3LgRDw8Prl69Srp06XB3d2fo0KFkzpyZS5cuAS/eZ56XP39+HBwcEv0tOXfuHAcPHkxU0/5seVZcXBwbN26kZMmSWFtbExgYiI+PD/Hx8WTIkIEaNWoY7itK6olCaSl9+vS4u7vzn//8x+jMfUK5V3JSsz9K8nQFQETkOaVKlaJ79+5MnjyZEydO8Pnnn5MlSxaOHz/O3LlzuX//PrNmzcLMzAx7e3s6duxouKnWy8uLqKgopkyZQsGCBWncuDHwtN59zJgxfPvtt3To0IFLly4RHBz8SmU2mTNn5sCBA+zcuZNixYrh4eHBkCFDCAwMpGrVqty5c4fg4GDy5ctHkSJFkhyjS5cu/Pbbb7Ru3ZqOHTtiZWVFaGgo586dY/bs2a+87Ro3bsySJUvo0qUL3bp1I0+ePPz666+sXLmSLl26GM7Iw9Mzll27dsXX15fTp08zceJEKlWqZLjh8EXatm3LmjVraNu2LV26dCFLliysWrWKXbt2MXr0aMzNzSlSpAh16tRh/PjxPHz4kOLFixMeHs7mzZuZPHky+fPnx8bGhhkzZmBhYYGFhQWbNm0yPL4zOjrasM0BNm/eTNWqVRNd7XmZ9X4dXuf3lylTJjp37szkyZPJkCEDHh4ebNu2LVEC4Obmxtq1aylevDi5cuXiwIEDzJw5EzMzM8N2SjjA3rlzJ87OzpQsWRInJycWL15Mrly5yJw5M+Hh4SxYsAD43/Z9Xmq/l9Rwc3Nj2rRpzJo1i1KlSnHmzBlmzpxJTEzMC8fx8fHhyy+/xMbGhlq1ahnaS5cuTVxcnOFG3EyZMrFhwwbu3r1r6Jfwnf/888+ULFky0b0qzzM3N6dXr17079+fnj170qhRI27evElwcDB2dna0bdvWqP/kyZOJjY3F0dGRpUuXcurUKebNmwc8vdF93rx5BAQE0LBhQx4/fszs2bOxt7dP9klQaalXr1589dVXdOnShS+//JLTp08zffr0FOdJzf4oyVMCICKShG+++YZixYqxePFixowZw61bt8iVKxdVq1alU6dORqUAXbt2JXv27ISGhrJ8+XLs7e2pU6cOPXr0MNS+5s+fn8DAQKZPn07Hjh1xdnZmxIgRjBgx4qVja9myJYcPH6ZDhw6MGTOGZs2a8fjxY3744QeWLFmCtbU1FStWpG/fvlhaWiY5RqFChViyZAkTJ05kwIABmJmZ4ebmxsKFCylbtuyrbTSe1vouWrSICRMmMHXqVO7du0eBAgUYNWpUopKLL774gocPH+Lv74+VlRUNGjSgb9++qX5CiYODA0uXLmXChAmMGjWKx48fU6RIEaZNm2Z0Q+u4ceMIDg5m0aJF3Lx5k/z58zN58mTq1KkDPH2M63fffUf37t3JlCkTRYsWJTQ0lA4dOrB37148PT2pUKECn3zyCRMmTGDnzp3MmjXrldf7dXjd39/XX39NxowZWbBgAQsWLMDd3Z1+/foxdOhQQ5+xY8ca7bP58uVj2LBhrFmzxnCztY2NDW3btmXZsmVs3bqV33//nWnTpjFq1CgCAgKwsrKiYMGCTJ8+ndGjR7N3716jp/wksLW1TdX3ktp1u3nzJgsXLuT777/H0dGRzz77DDMzM2bOnMnt27cNZTPPK1WqFFmyZKFWrVpG5TY5cuRg9uzZTJkyhYEDBxIdHU2hQoUICgoyHGDXqlWL1atXExAQgI+Pj9G2TE7jxo3JlCkTM2fOxN/fHxsbG6pUqUKvXr0S1fyPGjWK7777jjNnzlC4cGFCQkIoX7488PQpOuPHj2fu3Ll06dIFMzMzypQpw8KFC5O98TktlS1blpCQECZOnEiXLl3InTs3o0ePTvIJTQlSsz9K8szidaeEiIj8C5UqVaJWrVpGj0EV+RD8+eefNG3alJUrV1KiRInXPv7SpUsZOnQoO3fufGfeCCymQVcARETklfz111/s37+fa9euGZ6TL/IhiIiIICIiglWrVuHh4fHaD/7j4+NZu3Yt69atI3PmzMlegRB5U5QAiIjIKwkLC2PlypVUqVLFcK+DyIfg5s2bzJs3j4IFCzJmzJjXPv6FCxcYMWIE1tbWDBo0KNHN0yJvmkqARERERERMiB4DKiIiIiJiQpQAiIiIiIiYECUAIiIiIiImRAmAiCTSqlUrWrVqldZhiIiIyBugpwCJSCLv2qviRURE5PXRFQAREREREROiBEBERERExIQoARAREREReUtiY2PTOgTdAyAiyevd5/84ceJEWochIiLyQXB2dmbC+O/SOgwlACKSvBMnTvDXX3+ndRgiIiLyGqkESERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRARERERMSEKAEQERERETEhSgBEREREREyIEgB576xdu5Yvv/wSd3d33N3dadKkCT/88EOifnfu3KFPnz6UK1eO6tWrs3z58kR9AgICcHFxMfopVaoUn332WZL9n/fXX38REBBAjRo1KFGiBBUqVKBDhw78/vvvRv18fX0JCAhI9Tq+bP83NYaIiIh8ePQiMHmvrFixgpEjRzJgwADKlStHfHw8O3fuZNSoUVy7do0uXboY+o4ZM4bIyEiWLFnCrl27GDx4MCVLlqRw4cJGY7q7uxMUFGT4/PDhQ1auXMmgQYOws7OjVq1aScayZs0aBgwYQN26dRk3bhxOTk7cuHGDNWvW0L59e8aMGUOjRo3eyHYQEREReVVKAOS9smTJEnx8fPjiiy8MbQUKFODSpUssXLjQKAE4cuQI5cuXp1ChQnz00UeMHDmS06dPJ0oALC0tcXBwMGrr0aMHGzZsYM2aNUkmAFFRUQwePJhWrVoZnWV3dHSkePHimJubM378eBo0aEC6dOle1+qLiIiI/GsqAZL3irm5Ofv37+f27dtG7R06dGDZsmVGbZUqVWLlypX8/fffjB8/HgcHBypWrJjqZaVLlw4rK6skpy1fvhwzMzO6d++e5PTOnTuzatWqZA/+T5w4QadOnahQoQJlypShW7duXLhwwajPgwcP6N27N6VKlaJKlSrMnz+f+Ph4w/Rff/2VZs2a4e7ujqurKz4+PuzYsSPV6yciIiKmSQmAvFc6dOjA33//TdWqVenYsSOzZs3izz//xNbWlvz58xv17dKlCzY2NjRu3JhDhw4RGhqKra3tC5dx7949Zs2axYkTJ6hTp06Sffbu3Yu7uzsZMmRIcrqtrS3Zs2dPctr58+f58ssvsbKyYsGCBcybN4/r16/TqlUr7t27Z+i3adMmsmTJwsqVK+nbty9TpkxhwYIFABw+fBh/f39q1arFmjVrWL58OdmyZaNPnz7ExMS8cB0BvLy8kv25ePFiqsYQERGR949KgOS9Urt2bZYtW8aiRYsIDw9n27ZtAOTLl4/Ro0dTpkwZAK5evUqPHj2Ij4/Hzs4OS0tLPvroI8PB8bNn9hMO5gHi4+OJjo42HEwnV/9/7do1SpQoYdS2fv16Bg4caNQWEhJC2bJljdqWLFlCxowZGT9+vCGOqVOn4unpyZo1a2jRogUAxYoVY9CgQQA4Oztz4sQJ5s6dS5s2bUiXLh2DBg2iZcuWhnFbt26Nn58f169fx9HR8SW2qoiIiJgSJQDy3nFzc2PcuHHEx8cTGRnJtm3bWLhwIR06dGDz5s1kyZKFr7/+GisrK9atW8fx48dp06YNI0eOpFChQowfP57t27eTOXNmAEqUKMH48eOBpyVGGTNmJFu2bCnGkCVLFm7dumXUVq1aNVatWgXA5cuX8fX1JTY2NtG8kZGRlChRwigJyZYtG/nz5+fYsWOGtoRk5tn1njFjBnfu3KFo0aLY2dkREhLCqVOnOH36NH///TdAkstMypYtW5Kd5uXllaoxRERE5P2jBEDeG5cuXSIkJISOHTuSM2dOzMzMDI/u9PLyom7duuzZs4f8+fNz5MgRli9fjp2dHWXLlmXIkCGGp/qUL1/ecPAPYG1tTd68eV8qljJlyrBs2TJiYmIMB/KZMmUiU6ZMACne+BsfH4+ZmVmi9tjYWCwtLQ2fzc2NK/Ti4uIwMzPD0tKSPXv24OfnR7Vq1Shbtiz16tUjOjoaf3//l1oPERERMT26B0DeG1ZWVixbtow1a9YkmmZjYwNA9uzZDXX5V69eNUxv2rQpNWrU4Pbt23h6ev7rWJo1a8aTJ0/4/vvvk5x+6dKlZOctXLgwf/75p1Gt/rVr1zhz5gzOzs6GtiNHjhjNt2/fPnLnzk2GDBmYM2cOFSpUIDg4mDZt2lCpUiVD3f6zNwqLiIiIPE9XAOS9kTVrVtq3b8/kyZO5d+8ederUwcbGhn/++Ydp06ZRoUIFQ719tWrVGDVqFNbW1jg5ObFmzRrCw8Nxc3Pju+++46OPPqJatWqvHEuePHkYM2YMAQEBnD59mmbNmvHxxx9z48YNNmzYwOLFi8mTJw9OTk6J5m3evDlLly6lT58+dO7cmZiYGAIDA8mSJQv16tUz9Nu/fz/jxo2jcePG7N+/nyVLljB06FDg6eNGf/nlF/bu3UuuXLmIiIhgypQpAKm+CVhERERMkxIAea/06NGDfPny8eOPP7J48WIePnyIo6MjdevW5euvvzb0mzhxIoGBgfTq1Yvo6GiKFSvGrFmzKFeuHP379yciIuJfJQAA3t7eFC5cmIULFzJ48GAuXbqEtbU1RYoUoV+/fjRu3Bhra+tE8+XJk4dFixYxfvx4w9OAKlWqxLhx44xKk5o2bcrp06f5/PPPyZo1K71796Zx48YAdOvWjWvXrtGpUycAChYsyOjRo+nbty9//vmn0ZUEERERkWeZxateQESek3ATsG1me/766+80jkZEROTDUKxYUVb9tDKtw9A9ACIiIiIipkQJgIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRAmAiIiIiIgJUQIgIiIiImJC9CIwEUmWXigmIiLy+rwr/68qARCRZE0Y/11ahyAiIvJBiY2NJV26dGkag0qARERERETekrQ++AclACIiIiIiJkUJgIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRAmAiIiIvNdi4+LSOgSR94reAyAiyRo5YQ5nzl1M6zBERJKVN48jg3q3S+swRN4rSgBEJFlnzl3k+MlzaR2GiIiIvEYqARIRERERMSFKAERERERETIgSABERERERE6IEQERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRA3htPnjxhwYIFNG7cGHd3dypUqEDbtm3ZuXPna13OkSNHqF+/PiVKlKB79+4EBATg6+ubbP+IiAhcXFyIiop6bTE8ePCAxYsXGz6/KIbn+fr6EhAQ8NriERERkQ+HXgQm74WYmBjatm3LxYsX6dq1K+7u7jx8+JCVK1fi5+fHmDFjaNSo0WtZ1rRp0zAzM+Pnn3/GxsaG9OnTExsb+1rGTq25c+cSFhZGy5YtARg4cOBbj0FEREQ+TEoA5L0wdepUjh49yrp168iVK5ehfeDAgTx48IDRo0dTs2ZNMmXK9K+XdefOHYoVK0a+fPn+9VivKj4+3uizra1tGkUiIiIiHxqVAMk77/HjxyxfvhwfHx+jg/8E3bt3Z/bs2VhbW3Pr1i2GDRtGtWrVcHNzo3nz5uzdu9fQNygoCF9fX0JCQqhatSqurq60bt2akydPAuDp6cnu3btZtWoVLi4uREREJCq/2bt3L02bNsXNzY1GjRpx7Ngxo3ji4+MJCQnBy8uLkiVL8tlnn7FmzRrD9ISSoW3bthlKjerVq8d///tfQ4zBwcGcP3/eUFr0fAy//vorzZo1w93dHVdXV3x8fNixY8fr2eAiIiLyQdMVAHnnnTt3jlu3blGqVKkkp+fIkYMcOXIQGxuLn58fjx8/JjAwEAcHB0JDQ2nTpg1Lly7F1dUVgAMHDpAhQwZmzZrF/fv36devH8OGDWPBggWsWLGCzp07kytXLgYOHIidnR0//fSTUSx+fn40atSIsWPH8s8//zB48GCjeCZNmsTatWsZPHgwzs7O7Nmzh6FDh3L37l1DSQ/AuHHjGDhwINmyZWPixIn06dOH3377DT8/Px48eMD69etZsWIFWbNmNRr/8OHD+Pv707dvX8aNG8f9+/eZNGkSffr0YevWrVhZWaVqu3p5eSU77eLFizg6OqZqHBEREXm/6AqAvPNu374NgJ2dXYr9wsPDOXLkCBMmTMDDwwNnZ2cGDx5M4cKFmTNnjqHfkydP+O677yhSpAhlypTB19eXffv2AZA1a1YsLS2xtrbGwcEh0cH0jz/+SPbs2RkyZAjOzs7Url2bb775xjD9wYMHzJ8/n379+lGjRg0+/vhjmjRpQps2bYxiAOjRowcVK1akcOHC9OjRg3v37hEZGUmmTJnImDEj6dKlw8HBgXTp0hnNly5dOgYNGoSfnx958uShSJEitG7dmuvXr3P9+vWX38AiIiJiUnQFQN55CWfAb926lWK/yMhIbG1tKVy4sKHNzMyMsmXLsn37dkNb9uzZsbe3N3y2tbXl8ePHqYolMjKSYsWKGR2Uly5d2vD7P//8w6NHj+jXrx/9+/c3tD958oSYmBgePnxoaCtQoIDhdxsbG4BUxVG0aFHs7OwICQnh1KlTnD59mr///hvgpW4U3rJlS7LTUro6ICIiIu83JQDyzsuTJw/Zs2fnwIED1K1bN9H006dPM3z4cEqXLo2ZmVmi6XFxcVhY/G9XT22JTHKev0H32bETpk2ePNnoAD+pZScVx/NjJ2XPnj34+flRrVo1ypYtS7169YiOjsbf3z/V6yAiIiKmSyVA8s4zNzfHx8eHsLAwLl++nGj67NmzOXjwIEWKFOHOnTtERkYaTd+3bx8FCxZ8LbEULVqUQ4cOERMTY2g7dOiQ4fcCBQpgYWHBhQsXyJs3r+Fn27ZtzJkzB3Pz1P2TSyqRSTBnzhwqVKhAcHAwbdq0oVKlSly8eBFIXQIhIiIipk0JgLwXOnXqRN68eWnWrBmrVq3i7NmzHDp0iIEDB7Jy5UpGjBhB9erVcXFxoXfv3kRERHDixAmGDRtGZGQkX3311WuJo3nz5kRHRzNgwABOnDjBf//7X4KDgw3TbW1tadasGZMnT2bVqlWcO3eOn376iXHjxpE9e/ZULydjxozcvn2bU6dOJSoLcnR05NixY+zdu5eoqChWrlzJlClTAIwSExEREZGkqARI3gsZMmQgNDSUuXPnEhISwoULF0ifPj3FixdnwYIFlC9fHoB58+YRGBhI165diYmJoXjx4syfPz/ZJwi9rJw5c7JgwQJGjx7N559/jqOjI9988w3Dhg0z9Onfvz9Zs2Zl6tSpXLlyhVy5ctGlSxc6duyY6uXUqlWLH3/8kYYNGxIaGmo0rVu3bly7do1OnToBULBgQUaPHk3fvn35888/cXZ2fi3rKiIiIh8ms3jVDIjIcxJuAi7gWoPjJ8+lcTQiIskrVCAPIZMHpXUYIu8VlQCJiIiIiJgQJQAiIiIiIiZECYCIiIiIiAlRAiAiIiIiYkKUAIiIiIiImBAlACIiIiIiJkQJgIiIiIiICdGLwEQkWXnzOKZ1CCIiKdLfKZGXpwRARJI1qHe7tA5BROSFYuPiSGeuogaR1NK/FhEREXmv6eBf5OXoX4yIiIiIiAlRAiAiIiIiYkKUAIiIiIiImBAlACIiIiIiJkQJgIiIiIiICVECICIi77W4uLi0DkFE5L2i9wCISLImz99M1KUbaR2GSLJy58pKjzY10zoMEZH3ihIAEUlW1KUbnIq6ltZhiIiIyGukEiAREREREROiBEBERERExIQoARARERERMSFKAERERERETIgSABERERERE6IEQERERETEhCgBEBERERExIUoARFLB19eXzz77LNnpgwcPxtPTExcXF8LCwpLtFx8fz08//cT169ffRJgiIiIiL6QEQCQVfHx8OHr0KMePH080LSYmho0bN9K4cWPCw8OpW7dusuPs2bOHgIAAoqOj32S4IiIiIslSAiCSCrVr18bW1pa1a9cmmrZlyxbu3r1LkyZNcHBwwNraOtlx4uPj32SYIiIiIi+kBEAkFaytralfvz4///xzooP41atXU6lSJRwdHY1KgAICAujSpQt+fn6ULl2amTNn0rp1awC8vLwICwsjLCwMFxcXo/EiIiJwcXEhKioKgNOnT9OuXTvKlCmDu7s77dq149ixY4b+x48fp3PnzlSoUIESJUpQs2ZNFixY8CY3h4iIiLzHlACIpJKPjw/nz59n3759hrbr16+zfft2mjZtmuQ8mzdv5pNPPmHlypXUr1+foKAgAJYvX55iqdCzevXqRY4cOVi5ciXLly/H3NycLl26ABAdHU3btm3JmDEjS5YsYd26dXh7ezN69Gj+/vvvFMf18vJK9ufixYupik1ERETePxZpHYDI+6JEiRIUKVKEtWvXUrZsWQDWrl1L5syZ8fT0THIeOzs72rdvb/iccFY/a9asKZYKPevs2bNUqlSJ3LlzY2FhwejRozl58iRxcXFER0fTunVrWrRogY2NDQBdunRh5syZHDt2jKJFi/6bVRYREZEPkBIAkZfg4+NDcHAwgwYNwtLSklWrVtGoUSMsLS2T7J83b95/vcyePXsyevRoli5dioeHB1WqVMHb2xtzc3OyZs1KixYtWL9+PUePHuXMmTOGM/9xcXEpjrtly5Zkp3l5ef3ruEVEROTdpBIgkZfQoEEDHjx4wG+//UZkZCR///03Pj4+yfZP7Vn+Z+8rePLkidG0li1b8ttvvzFgwAAyZMjAxIkTqVu3LteuXePatWs0bNiQZcuWkT17dpo1a5biY0hFREREdAVA5CXY29tTs2ZNNm7cSK5cuShdujTOzs6pnt/MzMzoc8KVg7t375I5c2YAzpw5Y5h+7do1pk2bRseOHWncuDGNGzfm8uXLVK1ald27d3P58mVu3brFpk2bDGMl3CCsJw6JiIhIUpQAiLwkHx8funfvjr29PZ06dXqpeTNmzAjA0aNHyZIlC6VKlcLc3JzJkyfTtm1bTpw4wdy5cw397e3t2bp1K2fPnqV3797Y2NiwYsUKLC0tKVGiBGZmZkRHR7NhwwbKli3LyZMnGTNmDPD0/QQiIiIiz1MJkMhLqlixIra2tly/fh1vb++Xmrdw4cJUq1aNHj16sGzZMvLkycPw4cPZtm0b3t7eTJ8+nQEDBhj6W1hYEBISgrm5OW3atKFevXrs2rWLWbNm8fHHH1OnTh3atWtHYGCg4ek/Pj4+lCtXjj///PN1r7qIiIh8AMziVScgIs9JuAnYvWZHTkVdS+NoRJKXP3d2xgd8mdZhiIi8V3QFQERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRARERERMSEKAEQERERETEhSgBEREREREyI3gQsIsnKnStrWocgkiLtoyIiL08JgIgkq0ebmmkdgsgLxcXFYW6uC9oiIqmlv5giIvJe08G/iMjL0V9NERERERETogRARERERMSEKAEQERERETEhSgBEREREREyIEgAREREREROiBEBERN5rcXFxaR2CiMh7Re8BEJFkTZ6/mahLN9I6DJFk5c6VVe+rEBF5SUoARCRZUZducCrqWlqHISIiIq+RSoBEREREREyIEgAREREREROiBEBERERExIQoARARERERMSFKAERERERETIgSABERERERE6IEQERERETEhOg9ACJJ8PX1Zffu3UZtlpaW5MiRAy8vL3r37o21tXUaRQcuLi6MGTOGxo0bp1kMIiIi8n5SAiCSDG9vbwYOHGj4/ODBA8LDwxkzZgyxsbEMHjw4zWILDw/H1tY2zZYvIiIi7y8lACLJsLa2xsHBwagtb968HD58mHXr1qVpAvB8XCIiIiKppQRA5CWlT58ec/Ont8/ExMQwZcoU1qxZw7179yhUqBDdunWjcuXKhv6HDx9mwoQJHDx4kAwZMvDpp58SEBBAxowZ8fX1JU+ePBw/fpxTp04xaNAgdu3axfnz51m0aJFhjKCgIH766Sd+/fVXwLgEKCAggNjYWLJnz86qVat48OABlSpVYtiwYUoUREREJBElACKp9OTJE8LDw1m9ejVffvklAP379+f48eOMGzeOXLly8d///pdOnToRHBxM9erViYqKwtfXF09PT5YtW8a9e/fo378/gwcPZvz48QCEhYUxbtw4ihQpQvbs2dm1a9dLx7ZhwwYaNGhAaGgoFy5coE+fPkyaNInRo0cnO4+Xl1ey0y5evIijo+NLxyEiIiLvPiUAIslYu3YtmzZtMnx++PAhH330Ee3ataNTp06cOXOGn3/+mRUrVuDq6gpA27ZtOXr0KHPmzKF69er8+OOP2NnZMXbsWCwtLQEYOXKk0Q3GRYsWpUGDBv8qVhsbG4YPH46lpSXOzs589tlnbNu27V+NKSIiIh8mJQAiyfD09KRPnz7ExcXxxx9/MGbMGD755BM6deqEhYUFf/31FwCtW7c2mu/x48dkzpwZgGPHjlG8eHHDwT9AuXLlKFeunOFz3rx5/3WsefPmNVqGra0tjx8/TnGeLVu2JDstpasDIiIi8n5TAiCSjEyZMhkOzvPnz0+uXLlo27Yt6dKlY+jQocTHxwOwePFiMmXKZDRvwj0CFhYWmJmZpbicpB4nmjB2gidPnqQ4hpWVVcorIyIiIvL/6UVgIqnk4eFB27ZtWbp0Kb/99huFChUC4MqVK+TNm9fwExYWxsqVKwEoWLAgf/31F7GxsYZxNm/eTNWqVYmOjk5yOZaWlty9e9eo7cyZM29orURERMTUKAEQeQndu3cnX758DBkyhI8++ogaNWowZMgQtmzZwrlz55gzZw4zZ84kT548ALRo0YKbN28yZMgQTpw4wd69exk/fjyVKlUiQ4YMSS6jdOnSHDt2jLCwMKKioggNDVU9v4iIiLw2KgESeQnp06dnxIgRtG7dmkmTJhl+hgwZwu3bt8mTJw8jRoygSZMmAOTMmZO5c+cyfvx4Pv/8czJnzkzdunXp1atXssto0KABf//9N4GBgcTExFC1alW6d+/OwoUL39ZqioiIyAfMLP75YmMRMXkJNwG71+zIqahraRyNSPLy587O+IAv0zoMEZH3ikqARERERERMiBIAERERERETogRARERERMSEKAEQERERETEhSgBEREREREyIEgAREREREROiBEBERERExIToRWAikqzcubKmdQgiKdI+KiLy8pQAiEiyerSpmdYhiLxQXFwc5ua6oC0iklr6iykiIu81HfyLiLwc/dUUERERETEhSgBEREREREyIEgAREREREROiBEBERERExIQoARARERERMSFKAERE5J0QFxeX1iGIiJgEvQdARJI1ZeGvnL98M63DEBPglDML3Vt7pnUYIiImQQmAiCTr/OWbnIq6ntZhiIiIyGukEiAREREREROiBEBERERExIQoARARERERMSFKAERERERETIgSABERERERE6IEQERERETEhCgBEBERERExIUoAxGS4uLgQFhaW6v7Hjx9n69atqe4fFBSEp+f/XmT07PKenRYVFYWLiwsRERGpGjciIgIXFxeioqJSHYuIiIhIcpQAiMkIDw+nbt26qe7/9ddfc+jQodeybD8/P1asWAGAo6Mj4eHhuLu7v5axRURERF6G3gQsJsPBwSHNlp0pUyYyZcoEQLp06dI0FhERETFtugIgJuPZkpyAgAD69u1LYGAgFStWpGTJknTu3JmrV68C4Onpyfnz5wkODsbX1xeAu3fv8u233+Lh4UGZMmVo3bp1qq8QpFQCFBsby/z586lduzaurq7Url2bH3/8MdEY//3vf6lVqxZubm60bduWc+fOGaadPn2adu3aUaZMGdzd3WnXrh3Hjh179Y0lIiIiHyxdARCTtWHDBho0aEBoaCgXLlygT58+TJo0idGjR7NixQo+//xz6taty9dff018fDwdOnTA0tKSmTNnYmNjw+rVq2nevDk//vgjxYoVe+U4xo4dy+rVq/n2229xdXXl999/Z/jw4Tx69MiQfADMmTOHESNGkDNnTiZOnEjz5s3ZvHkzGTJkoFevXri4uLBy5UqePHlCYGAgXbp0YfPmzcku18vLK9lpFy9exNHR8ZXXSURERN5dSgDEZNnY2DB8+HAsLS1xdnbms88+Y9u2bQBkzZqVdOnSkTFjRuzt7dm5cycHDhxg586dZM2aFYBevXqxf/9+Fi5cyNixY18phnv37rF06VICAgJo0KABAPny5ePcuXPMmDGDVq1aGfoOGjSIKlWqAPDdd99RrVo1fv75Z5o2bcrZs2epVKkSuXPnxsLCgtGjR3Py5Eni4uIwN9eFPhEREfkfJQBisvLmzYulpaXhs62tLY8fP06y75EjR4DEZ81jYmJ49OjRK8dw8uRJHj9+TJkyZYzay5Yty7x587h+/bpRW4LMmTOTL18+IiMjAejZsyejR49m6dKleHh4UKVKFby9vVM8+N+yZUuy01K6OiAiIiLvNyUAYrKsrKxS3TcuLg4bG5skHyP6MuM8Lz4+HgAzM7NEywOwsPjfP9F06dIZ9YmNjTUsu2XLltSpU4dt27axc+dOJk6cSFBQEKtWrSJ79uyvHJ+IiIh8eFQbIJIKhQsX5t69e8TExJA3b17DT0hISIpn0l+kQIECWFhYsHfvXqP2vXv34uDggJ2dnaHt8OHDht9v3LjB6dOnKVSoENeuXWP48OE8fvyYxo0bM27cONasWcPVq1fZvXv3K8cmIiIiHyYlACLJyJQpE6dPn+batWtUqVKFokWL0qNHD3bu3MmZM2cIDAxk5cqVODs7v/IybG1t+eKLL5g6dSpr167lzJkzLF68mCVLluDn52d0ZWDw4MHs3LmTv//+m549e+Lo6EjdunWxt7dn69atDBo0iL///ptz586xZMkSLC0tKVGixOvYFCIiIvIBUQmQSDJ8fX0JDAzk+PHjrFmzhrlz5zJu3Dh69uxJdHQ0zs7OBAUFUbFixX+1nIEDB5IlSxYmTJjAtWvXyJs3L4MHD+aLL74w6te5c2f69+/PjRs3qFChArNnzzaUAIWEhBAYGEibNm2Ijo6maNGizJo1i48//vhfxSYiIiIfHrP4hCJkEXkrzpw5Q61atVi6dCmlS5dO63CSlHATcJk6nTkVdf0FvUX+vfy5s/Fd3yZpHYaIiElQCZDIW3Tu3Dm2bt0KQK5cudI2GBERETFJKgESeYu+++47duzYgZ+fHx999FFahyMiIiImSAmAyFsUFBSU1iGIiIiIiVMJkIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRDcBi0iynHJmSesQxERoXxMReXuUAIhIsrq39kzrEMSExMXFYW6uC9MiIm+a/tKKiMg7QQf/IiJvh/7aioiIiIiYECUAIiIiIiImRAmAiIiIiIgJUQIgIiIiImJClACIiIiIiJgQJQAiIvJaxcXFpXUIIiKSAr0HQESSFbT4N85fvpXWYch7xCmnPV1bVk3rMEREJAVKAEQkWecv3+LU+RtpHYaIiIi8RioBEhERERExIUoARERERERMiBIAERERERETogRARERERMSEKAEQERERETEhSgBEREREREyIEgAREREREROiBEDeinv37lGyZEk++eQTYmJijKYFBATg6+ubaJ6NGzdSvHhxAgICDG8WPXXqFL169aJixYqUKFECT09Phg0bxrVr15JddlRUFC4uLkY/xYoVo3LlyvTs2ZOLFy++8nolNfbzP0FBQSmOERQUhKen5yvHkJQLFy6wbt261zqmiIiIfBj0IjB5K9atW0e2bNm4du0amzdvpl69ein237RpE71796ZJkyYMGzYMMzMzrl27RvPmzalatSohISFkyZKFU6dOMW7cOHx9fVm9ejVWVlbJjhkUFIS7uzsAcXFxnDt3joEDB/L111+zevVqzMzMXnq9HB0dCQ8PN3yeO3cu69evZ8WKFYa2jBkzvvS4/1a/fv1wcnJ64XYWERER06MEQN6KlStXUrlyZS5fvswPP/yQ4oHpf/7zH3r16kXz5s0ZOHCg4cB848aNPHnyhMDAQEObk5MTH330Ed7e3mzfvh0vL69kx7Wzs8PBwcHwOWfOnHTp0oU+ffpw7NgxihQp8tLrlS5dOqMxM2bMmKhNRERE5F2iEiB5406cOMEff/xBpUqVqFOnDrt37+bEiRNJ9v3Pf/5Dz549ad26NYMGDTI6K29mZsb9+/eJiIgwmqdAgQKsW7cODw+Pl44tXbp0AFhZWRnKeaZNm0alSpXw9PTkzp073Lp1i2HDhlGtWjXc3Nxo3rw5e/fuTfUyjh8/TufOnalQoQIlSpSgZs2aLFiwINn+d+/e5dtvv8XDw4MyZcrQunVrDh06ZJgeFBSEr68vISEhVK1aFVdXV1q3bs3JkycB8PX1Zffu3fz000+vvbRIRERE3n+6AiBv3IoVK8iYMSNVq1blyZMnWFlZsXTpUgYNGmTUb/PmzfTq1YsSJUrQr1+/ROPUq1ePuXPn8tVXX1GkSBE8PDwoV64cHh4eFCxY8KViiouL49ixY0yfPp2iRYuSL18+Lly4AMCaNWtYsGAB0dHRZMqUiaZNm/L48WMCAwNxcHAgNDSUNm3asHTpUlxdXVNcTnR0NG3btsXDw4MlS5ZgYWHBypUrGT16NOXLl6do0aJG/ePj4+nQoQOWlpbMnDkTGxsbVq9eTfPmzfnxxx8pVqwYAAcOHCBDhgzMmjWL+/fv069fP4YNG8aCBQsICgqiU6dO5MqVi8GDBycbW0pXSy5evIijo2NqN6eIiIi8R3QFQN6oJ0+esHbtWmrUqEGGDBmwtbWlWrVqrF69mujoaEO/48eP07NnT8qXL8/BgwfZtGlTorHs7e0JCwujS5cuxMXFMX/+fPz9/alUqRLff//9C2Pp0KED7u7uuLu74+rqSpMmTciSJQtTpkzB3Px//xRatGhBwYIFcXV1JTw8nCNHjjBhwgQ8PDxwdnZm8ODBFC5cmDlz5rxwmdHR0bRu3ZqhQ4fi7OxM3rx56dKlCwDHjh1L1H/Xrl0cOHCAKVOmULJkSZydnenVqxelSpVi4cKFRtv1u+++o0iRIpQpUwZfX1/27dtn2E6WlpZYW1uTNWvWF8YoIiIipkVXAOSN2rZtG1evXqVu3bqGtrp167J582bWrVuHj48PADdv3qRv3760b9+ejh07MmjQIEqUKIGTk5PReHZ2dnTt2pWuXbty/fp1du3axbJly5g6dSpZsmShRYsWycYycuRISpYsCYCFhQXZsmXD2to6Ub+8efMafo+MjMTW1pbChQsb2szMzChbtizbt29/4fpnzZqVFi1asH79eo4ePcqZM2f4+++/AQxPNnrWkSNHgMRn52NiYnj06JHhc/bs2bG3tzd8trW15fHjxy+M51lbtmxJdlpKVwdERETk/aYEQN6osLAwALp165Zo2g8//GBIAEqXLk379u0BGD16NPXr16d3796EhoZiYfF0Nw0JCSF37tx4e3sDkC1bNurVq0fdunX58ssv2bZtW4oJQM6cOY0O7pPzbFIQHx+f5NOB4uLiDHGl5Nq1a3zxxRdkyZIFLy8vKlasiKurK9WqVUuyf1xcHDY2Nobt9qxnn3CU0tOORERERFKiBEDemBs3brBt2zYaN25M27ZtjaYtWLCAFStWGM54P3swnT17dkaOHIm/vz9BQUH07NkTgD/++IM1a9ZQs2ZNo/5mZmZkypSJbNmyvfZ1cHFx4c6dO0RGRhpdBdi3b1+q7jtYu3Ytt27dYtOmTVhaWgL/K/2Jj49P1L9w4cLcu3ePmJgYChUqZGgfNGgQRYoUoVWrVv92lURERMTE6R4AeWNWr17NkydPaN++PYULFzb66dSpE+nSpWPp0qVJzvvpp5/SpEkTZs2axa5duwDw9/cnKiqKdu3aER4ezvnz5zlw4ABjx47l4MGDiZKM16FSpUq4uLjQu3dvIiIiOHHiBMOGDSMyMpKvvvrqhfPnypWL6OhoNmzYwIULFwgPD6dXr14AiV6IBlClShWKFi1Kjx492LlzJ2fOnCEwMJCVK1fi7Oyc6rgzZcrE+fPnuXTpUupXVkREREyCEgB5Y8LCwvjkk0+SPHDNkycPNWvWZN26ddy7dy/J+QcOHIiTkxN9+/blxo0bFC1alOXLl5MtWzb69+9P7dq16dSpE1FRUfzwww9GZ8xfFwsLC+bNm0fRokXp2rUrTZo0ITIykvnz51OqVKkXzl+nTh3atWtHYGAg3t7ejB49Gh8fH8qVK8eff/6ZqH+6dOmYO3cubm5u9OzZk4YNGxIREUFQUBAVK1ZMddzNmjUjMjKShg0bEhsb+zKrLCIiIh84s/ik6hBExKQl3ARcrl53Tp2/kcbRyPskv1NWxvZqmNZhiIhICnQFQERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRARERERMSEKAEQERERETEhSgBEREREREyIRVoHICLvLqec9mkdgrxntM+IiLz7lACISLK6tqya1iHIeyguLg5zc11gFhF5V+kvtIiIvFY6+BcRebfpr7SIiIiIiAlRAiAiIiIiYkKUAIiIiIiImBAlACIiIiIiJkQJgIiIiIiICVECICIiicTFxad1CCIi8oboPQAikqzvl/7O+Su30zoMecucctjh37xSWochIiJviBIAEUnW+Su3OX3+ZlqHISIiIq+RSoBEREREREyIEgAREREREROiBEBERERExIQoARARERERMSFKAERERERETIgSABERERERE6IEQERERETEhKQ6AfD19eWzzz5LdvrgwYPx9PQkPv7p2yM9PT1xcXEx+unTp0+Ky4iPjycsLAxfX188PDwoUaIEn376KcOHD+fy5cuGflFRUbi4uBAREZGq2F+2/6uOcffuXQIDA/Hy8qJEiRJ4eHjQpUsX/vrrr1debmq5uLgQFhb2xpeTlLCwsETfdcJ3FxgYSExMTLLzRkRE4OLiQlRU1GuJ5cGDByxevPhfjeHr60tAQECy04OCgvD09Hzl8RO2V4ILFy6wbt26Vx4vqTFFREREkpPqF4H5+Pjwf//3fxw/fpxChQoZTYuJiWHjxo20bt0aMzMz7t27x4ULF5g5cybFixc39LO2tk52/NjYWPz9/dm/fz+dOnVi8ODBZMqUiePHjzNt2jSaNGnCqlWryJ49+yus5tvxzTff8OjRI0aOHMnHH3/M9evXmTNnDi1btmT58uUULFjwjS07PDwcW1vbNzZ+amNI8PjxYw4cOMCAAQN4/PgxgwYNSnIed3d3wsPDyZo162uJYe7cuYSFhdGyZcvXMl5S/Pz8/tX4devWpUqVKobP/fr1w8nJiXr16r2O8ERERERSlOorALVr18bW1pa1a9cmmrZlyxbu3r1LkyZNAIiMjCQ+Pp7SpUvj4OBg+EnpAHXevHls376defPm4efnR6FChfjoo4+oVq0a8+fPx9LSkrlz577CKr4dkZGR7Nmzh6FDh1KxYkWcnJxwc3NjwoQJ2NnZsXz58je6fAcHhxQTrLfh2e/6o48+ol69ejRs2JA1a9YkO4+VlRUODg6kS5futcSQcAXqTcqUKdO/Slisra1xcHB4jRGJiIiIpF6qEwBra2vq16/Pzz//nOgga/Xq1VSqVAlHR0cAjh07hoODA5kzZ07V2PHx8SxevJiGDRsaXTFIkCFDBkJDQ+nRo0eyY6xatYqGDRvi5uaGp6cnM2bMIC4uzqjPwYMHadiwIa6urjRt2pQjR44YpsXExDBhwgQ+/fRTSpQoQYUKFejVqxc3b95M1TqYmz/dlNu2bTPaPhYWFixatIiOHTsa2k6cOEGHDh1wd3encuXK9O7dm6tXrxqm+/r6MmDAAJo2bUrZsmVZtWoVLi4unDhxwmiZbdu2pW/fvkDiEqCff/6Zzz77DDc3N7y8vJg3b55h2t27d/n222/x8PCgTJkytG7dmkOHDhmmR0dHM3DgQCpVqoSrqyuNGjXiP//5T6q2w/PSpUuHlZUV8LRMxdPTk1GjRlG2bFk6depkVAIUFBRE5cqVjb63R48eUaZMGUMC9euvv9KsWTPc3d1xdXXFx8eHHTt2AE9Lc4KDgzl//rxRWdHKlSvx9vbGzc0Nb29vFixYkGjfeBnPlgAllIZt27aNxo0b4+rqSoMGDTh48CDLly+nRo0alC5dmt69e/Po0SPDdkgo1/H19WX37t389NNPhjFjYmIYN24cVapUwd3dnS+++MLo6grA5s2badCgAW5ubrRq1YoLFy688vqIiIiIaXmpm4B9fHw4f/48+/btM7Rdv36d7du307RpU0NbZGQkGTNmpGvXrlSuXJmGDRsyf/78ZA+6oqKiuHDhAp988kmyy3ZycjIcSD5v/vz5fPvtt3z55ZesWbOGnj17MmfOHL777jujfrNnz6ZTp06GA+oWLVoY7i347rvv+Pnnnxk1ahSbNm0iMDCQ33//nenTp6dq2xQsWBBPT0+mTJlC9erVGTBgAGFhYVy+fJk8efKQLVs2AC5fvkyLFi3IkycPK1asYMaMGdy7d49mzZrx4MEDw3hhYWG0bt2apUuXUq1aNXLnzm10Jv3KlSvs2rWLzz//PFEsGzdupG/fvtSrV481a9bQu3dvJk+ezPLly4mPj6dDhw6cPn2amTNn8uOPP1KqVCmaN29uuFdhypQpHDt2jFmzZrF+/XqqVq1Kz549X6pOPyYmhq1bt7J69Wrq1KljaD9//jyXL1/mp59+onfv3kbzfP7551y7do1du3YZ2rZs2cKTJ0/w9vbm8OHD+Pv7U6tWLdasWcPy5cvJli0bffr0ISYmBj8/P/z8/MiVKxfh4eE4OjqybNkyAgMD8ff3Z926dfTo0YOQkBDGjx+f6nVJjeHDh9OnTx9WrVqFtbU1HTt2ZMOGDcyYMYOxY8eyadOmJK8CBQUF4e7ujre3NytWrACgf//+bN++nXHjxvHTTz/h7e1Np06d2Lp1KwD79++na9eu1KpVi9WrV/PZZ58xa9asl47Zy8sr2Z+LFy/+q+0hIiIi765U3wMAUKJECYoUKcLatWspW7YsAGvXriVz5sxGN0UeP36cu3fvUrduXbp06cLevXsZP348t2/fpnv37onGvXbtGkCisoqEM8QJPvroo0Q3S8bHxxMSEkKrVq0Mddn58uXj1q1bhgO/BF26dKFu3boADB06lB07drBkyRJ69uyJq6srtWrVonz58sDThKNy5cocO3Ys1dsnODiYlStXsnbtWtasWcPKlSsxMzPD29ubESNGYGNjw9KlS8mRIweDBw82zDd58mQ8PDzYuHEjjRs3BqBo0aI0aNDA0Ofzzz/np59+omfPnsDTM/w5c+bEw8MjURzz58/H29vbcNUhX7583L9/nwwZMrBr1y4OHDjAzp07Ddu7V69e7N+/n4ULFzJ27FjOnj2LjY0NH3/8Mba2tnTv3p2yZctiZ2eX4vq7u7sbfo+OjiZ9+vTUrVuXXr16GfXr3LkzefLkATD6fnPnzk25cuVYu3atIRlcs2YNNWvWxMbGhnTp0jFo0CCj+vvWrVvj5+fH9evXcXR0JGPGjKRLl85QYjNt2jS+/vpr6tevD0CePHm4d+8ew4YNo3v37qRPnz7FdUqttm3bGmJu1KgRw4cPZ8iQIeTNmxcXFxeKFStGZGRkovns7e2xtLTE2tqarFmzcubMGX7++WdWrFiBq6urYeyjR48yZ84cqlevTmhoKKVLl6Zr164A5M+fn8jISBYuXPha1kVEREQ+bC+VAMDTqwDBwcEMGjQIS0tLVq1aRaNGjbC0tDT0mTdvHo8ePcLGxgZ4Wp5y//59pk+fTteuXQ3lMgmyZMkCwK1bt4zahw0bxsOHDwFYtGgRv/76a6J4bty4wbVr1yhTpoxRe7ly5Xj8+DEnT540nH1PSFrgaWlOsWLFOH78OACfffYZO3fuZOLEiZw+fZoTJ05w8uRJo3leJF26dHzxxRd88cUXPHjwgH379rFhwwZ++ukn4uPjmTx5Mn/99RcnTpwwOliGp6Uuz5b45M2b12h6o0aNCA4OZv/+/ZQuXdpw5vf5bQlPS7C8vb2N2hKu0MyePRt4evb3WTExMYYSlQ4dOtCpUycqVqyIu7s7lSpVol69ei+8yXjVqlUAmJmZkT59erJnz55kbX++fPmSHaNx48aMHDmSoUOHcv/+fcLDw5k5cybwNCmys7MjJCSEU6dOcfr0af7++2/g6U3kz7tx4waXLl1iypQpBAcHG9rj4uJ49OgRUVFRODs7p7hOqZU/f37D7xkyZAAwJDkA6dOnT/FpSAkSrsK0bt3aqP3x48eGkrrIyEgqVapkNN3d3f2lE4AtW7YkO+35/UNEREQ+HC+dADRo0IDvvvuO3377jTx58vD3338zYcIEoz6WlpZGCQFA4cKFefDgAbdv3zYc8CfIkycPDg4O7N692+hJKDlz5jT8ntzZ5+Ru+kw4ILSw+N8qPn8wGhsbazgDPHToUNavX0+jRo2oXr0633zzDXPmzDF6/GhKNm/ezMmTJ/n6668ByJgxI1WqVKFKlSpkyZKFJUuWAE8PPj08PBgyZEiiMZ49wH7+ht7cuXNTvnx51q5di62tLUePHmXy5MlJxmJhYYGZmVmS0+Li4rCxsUnykaEJJVbu7u5s27aN33//nZ07d7JixQqCgoKYPXs2FStWTHYbPJ+0JCelm5Vr167N8OHD+fXXX7l+/TrZsmUzLHPPnj34+flRrVo1ypYtS7169YiOjja6yvP8usLTkpqkyssS7ll5HZ7dzxIklZy9SML+vHjxYjJlypTseM/v98//exMRERFJzksfodjb21OzZk02btzI2rVrKV26tNFZ1Li4ODw9PRPVzh86dIjs2bMnOviHpwfmrVu3ZtWqVRw9ejTJ5SZXk5wtWzayZctmdF8CwN69e7G0tOTjjz82tB0+fNjwe0xMDIcPH6ZQoULcvHmTpUuXMnToUAYMGEDjxo0pWrQoJ0+eTPVTZS5evEhQUFCScdrY2BiuQhQqVIgTJ07g6OhI3rx5yZs3L3Z2dowePTrJEpFnNW7cmM2bN7N27Vrc3d2Nzjo/y9nZ2eimXoDRo0fTuXNnChcuzL1794iJiTEsP2/evISEhBjOCE+dOpV9+/bh5eXFoEGD2LRpE3ny5GHTpk2p2hb/RsaMGalTpw6bNm1i3bp1Rlc55syZQ4UKFQgODqZNmzZUqlTJsL0TvqdnE5+EfePs2bNG63rkyJFkk6e0lvCI3StXrhjFHBYWxsqVK4GnV0L2799vNN/z37eIiIhIcl7pTcA+Pj5s3bqVjRs34uPjYzyguTm1a9dm9uzZbNiwgbNnz7Js2TJmz56dZP1/gvbt21OjRg1atGjBjBkzOHr0KFFRUfz666/4+fmxcuXKJOvdzczM8PPzIzQ0lMWLF3PmzBnWrl1LcHAwX375pdFZ9QkTJvDLL7/wzz//EBAQQExMDC1btsTW1hZbW1u2bNnCmTNnOHbsGN9++y1HjhxJVdkGPD04//jjj/H19WXNmjWcO3eOo0ePsnjxYmbNmmU4S92iRQvu3r1Lr169+Pvvvzl69Ci9e/fmzz//TPR+hefVrl2bBw8eEBoamuTNvwk6duzI+vXrWbhwIWfPnmXdunX88MMP1KxZkypVqlC0aFF69OjBzp07OXPmDIGBgaxcudKQyJ05c4YhQ4awc+dOzp8/z8aNG7lw4UKisqU3pUmTJmzdupWDBw/SqFEjQ7ujoyPHjh1j7969REVFsXLlSqZMmQJg+J4yZszI7du3OXXqFE+ePKF9+/YsWrSIRYsWcfbsWX755ReGDRuGlZVVsjeVw9ObtX/77bdEP2/iMaOZMmXi/PnzXLp0iUKFClGjRg2GDBnCli1bOHfuHHPmzGHmzJmGkiI/Pz+OHj1KYGAgp06dYs2aNYlefnb//n2jJ0uJiIiIJHjpEiCAihUrYmtry/Xr1xPVmgP07t2bzJkzM2HCBC5dukTu3LkZOHAgX3zxRbJjmpubM3nyZDZs2MDKlStZuHAhd+7cIXv27JQtW5bQ0FDKlSuX5Lzt27fHysqKBQsWMGbMGHLlykWHDh1o166dUb+uXbsyfvx4oqKicHNzY968edjb2wNPn3wzduxYGjRogJ2dneExoDNmzDB6Ok9ybGxsWLJkCdOnT+f777/n4sWLpEuXjqJFizJu3Dg+/fRT4Gm5U2hoKBMmTKBFixakS5eOUqVKsWDBAsNVguRkyJABb29v1q5dm+R2T+Dp6cmIESMICQnhu+++w8nJiQEDBhiShrlz5zJu3Dh69uxJdHQ0zs7OBAUFGUpthg0bRmBgIH379uXWrVs4OTnRp0+fFN8E/TqVLVuWHDlykCVLFgoUKGBo79atG9euXaNTp07A0ycvjR49mr59+/Lnn3/i7OxMrVq1+PHHH2nYsCGhoaH4+fmRPn16Fi1aRGBgINmyZaNx48aGm6mTs2PHDsPjRZ/17KNjX5dmzZrRr18/GjZsyM6dO5k0aRKTJk1iyJAh3L59mzx58jBixAjDezaKFi1KSEgI48aNIzQ0lEKFCtGpUyejJxvNnTuX4ODgl7qJXUREREyDWfzbeHOSiLxXEm4CrtCwN6fPp+5dGPLhyOeUhdHd66Z1GCIi8oa8UgmQiIiIiIi8n5QAiIiIiIiYECUAIiIiIiImRAmAiIiIiIgJUQIgIiIiImJClACIiIiIiJgQJQAiIiIiIibklV4EJiKmwSmHXVqHIGlA37uIyIdNCYCIJMu/eaW0DkHSSFxcPObmZmkdhoiIvAEqARIRkUR08C8i8uFSAiAiIiIiYkKUAIiIiIiImBAlACIiIiIiJkQJgIiIiIiICVECICIiIiJiQpQAiIhIInFx8WkdgoiIvCF6D4CIJOv7pb9z/srttA5D3jKnHHZ6B4SIyAdMCYCIJOv8lducPn8zrcMQERGR10glQCIiIiIiJkQJgIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRAmAiIiIiIgJUQIgIiIiImJClADISwkICMDX1/dfjREWFoaLi4vhs6enJ0FBQf82tJcWHh5O69atKVOmDCVLlqRBgwbMmjWLx48fv9Q4q1atokqVKri6urJw4UJu3rzJ8uXL31DUIiIiIv+OXgQmaW7FihWkT5/+rS5zx44ddOrUiW7dujFkyBAsLS3Zv38/Y8aM4dSpU4wZMybVY40aNQpPT0+6detG5syZGT16NFFRUTRt2vQNroGIiIjIq1ECIGkua9asb32ZP/zwA5UrV6Zjx46Gto8//piHDx8ydOhQ+vfvT+bMmVM11p07dyhfvjxOTk4AxMfHv5GYRURERF4HlQDJv+Lp6cmsWbPo2rUr7u7uVKhQgdGjR/PkyRNDn82bN9OgQQPc3Nxo1aoVFy5cSDRGQglQUFAQnp6eRtOTKhlatGgRXbt2pWTJklStWpXly5dz4MABGjVqRMmSJWnWrBlnz55NNm4zMzOOHTvGpUuXjNo/++wz1q1bR8aMGQGIjY1l/vz51K5dG1dXV2rXrs2PP/4IQFRUlCGuAQMG4OLiQkBAAD/99BO7d+/GxcWFMWPG0KBBA8P4t2/fpmjRogwePNjQtm3bNtzc3Hjw4AF37txhyJAhVKtWjeLFi1OpUiWGDBnCw4cPAYiIiMDFxYWQkBAqVKjA559/TmxsLJcvX6Znz56ULVuWChUq0KlTJ06fPp3ylyciIiImSVcA5F8LCgqib9++9O7dm/DwcEaOHEmxYsVo1KgR+/fvp2vXrvj7+1O/fn327t3LiBEj/vUyJ0yYwIABA/i///s/QkJCGDp0KM7OzgwYMIBMmTLRo0cPxo8fz9SpU5Ocv02bNnz11Vd4eXlRpkwZypcvT7ly5ShTpgzOzs6GfmPHjmX16tV8++23uLq68vvvvzN8+HAePXpEixYtCA8Pp3LlygwYMIC6detibW3Nw4cPuXTpEkFBQZw8eZL58+dz9epVHBwc2LVrF/Hx8URERBiWsXXrVipWrEjGjBn55ptvuHTpElOnTiVbtmwcPHiQ/v37U6BAAb766iujeZYtW0Z0dDSPHj3C19eXIkWKEBoairm5OfPmzeOLL75g7dq15MyZM8lt4OXllez2vXjxIo6Oji/7tYiIiMh7QFcA5F+rUqUKrVu3Jl++fLRq1YoiRYqwf/9+AEJDQyldujRdu3Ylf/78NG3alC+//PJfL7Nq1ap88cUX5MmTh9atW/PkyRN8fX3x8PDA1dUVb29vIiMjk53f3d2dn376iSZNmnDmzBmCgoJo3bo1NWrU4JdffgHg3r17LF26lG7dutGgQQPy5ctHy5YtadWqFTNmzMDc3BwHBwcAbG1tcXBwwNbWFmtraywtLXFwcKBMmTLY2dnx+++/A0/vPahZsyanT5/m8uXLwNMrAJ9++ikAlSpVYsyYMZQsWZLcuXNTv359ihcvzrFjx4zi9/PzI1++fBQtWpR169Zx8+ZNJkyYQJEiRShcuDCjRo3CxsbGcLVCREREJIGuAMi/9uwZc3h6MJzwJJ3IyEgqVapkNN3d3Z2FCxf+q2Xmz5/f8Lu1tTUAuXPnNrSlT5+emJiYF8Y9fPhwAE6fPk14eDgLFy6ke/fuhIWF8ejRIx4/fkyZMmWM5itbtizz5s3j+vXrZM+ePcVlWFhYUKVKFXbs2EGjRo3YuXMnAwcO5M8//2Tnzp0UL16cixcvUqNGDQBatGjBr7/+yurVqzl79iyRkZGcO3eOfPnyGY377Oe//vqLe/fuUb58eaM+jx494sSJE8nGtmXLlmSnpXR1QERERN5vSgDkX7OyskrU9uyNsM/fFGtpaZnieM/3f/Z+ggQWFol3XXPz1F3QevDgAZMmTaJJkyYUKVIEeHpAnS9fPurXr0/VqlUJDw+nbNmywNP7BZ4VFxeXbAxJ8fT0ZMyYMURFRXH+/HnKli2Lh4cHERERXL16lZIlS5I9e3bi4+Pp1KkTx44do0GDBtSuXZtevXrx7bffJhrz2acmxcXFkT9/fqZPn56oX8K9DCIiIiIJVAIkb1TRokUN5UAJDh06lGx/S0tL7t27Z5QEnDlz5rXGZG1tzZo1a/jhhx8STcuQIQMWFhZky5aNAgUKYGFhwd69e4367N27FwcHB+zs7JIc//mEoWrVqty6dYuFCxfi6upKpkyZqFixIhEREWzdutVwtv2vv/5i27ZtTJ06lT59+tCwYUM+/vhjzp49m+KThQoXLsyFCxewtbUlb9685M2bFycnJyZMmMCePXtedvOIiIjIB04JgLxRfn5+HD16lMDAQE6dOsWaNWtYvHhxsv1Lly7NnTt3mDVrFlFRUaxdu5awsLDXGpO5uTl9+vRh6dKlDBkyhD///JOoqCh27NiBv78/uXLlok6dOtja2vLFF18wdepU1q5dy5kzZ1i8eDFLlizBz88v0YF+gowZM3LlyhXOnTsHPC2JKlOmDEuXLsXDwwOAihUrcv78efbv32+o/8+ePTsWFhZs2LCBc+fOcejQIXr06MHVq1dTLGdq2LAhdnZ2dOnShYMHD3LixAn69+/Ptm3bKFSo0GvddiIiIvL+UwIgb1TRokUJCQkhIiKChg0bMn/+fDp16pRs//Lly9OzZ09CQ0OpW7cuq1atol+/fq89rqZNmzJjxgzOnj1Lhw4dqFOnDgMGDCBv3ryEhoYa7isYOHAgrVq1YsKECdSrV48lS5YwePBg/Pz8kh27UaNGREdHU79+fa5cuQI8LQOKiYkxJAA5c+bE2dmZfPnyGe5nyJkzJ2PHjuXXX3+lbt26dO/enZw5c9KmTRsOHTqU7FUAW1tbQkNDyZYtG+3bt8fHx4fz588zZ84cJQAiIiKSiFm83lokIs9JKEuq0LA3p8/fTONo5G3L55SF0d3rpnUYIiLyhugKgIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRAmAiIiIiIgJUQIgIiIiImJClACIiIiIiJgQi7QOQETeXU457NI6BEkD+t5FRD5sSgBEJFn+zSuldQiSRuLi4jE3N0vrMERE5A1QCZCIiCSig38RkQ+XEgAREREREROiBEBERERExIQoARARERERMSFKAERERERETIgSABERERERE6IEQEREEomLi0/rEERE5A3RewBEJFnfL/2d81dup3UY8pY55bDTOyBERD5gSgBEJFnnr9zm9PmbaR2GiIiIvEYqARIRERERMSFKAERERERETIgSABERERERE6IEQERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRA5DXw9/fniy++SNTevHlzXFxc2L17t1H7xo0bcXFxoU2bNvj6+r6tMEVERESUAIi8Dp988gl//fUXDx8+NLTdvXuXP//8E0dHR3777Tej/nv37qVAgQIEBQURFBT0tsMVERERE6YEQOQ1qFixIo8fP+bQoUOGth07dpA5c2aaNm3K9u3bjfrv2bOHSpUqYWtri729/VuOVkREREyZEgCR16BAgQLkypWL/fv3G9q2b9/OJ598QpUqVTh69ChXrlwB4M6dO0RGRlK5cmUCAgIMJUARERG4uLiwZcsWatWqRalSpWjTpg0nTpwwjOnr68uAAQNo2rQpZcuWZdWqVQCsWrWKhg0b4ubmhqenJzNmzCAuLu7tbQARERF5b1ikdQAiH4qKFSty4MABw+fw8HB69OhBiRIlsLe3Z/v27TRp0oR9+/aRLl06ypcvz8aNGxONM2rUKIYMGUKuXLkYN24crVu3ZuPGjdja2gIQFhbGuHHjKFKkCNmzZ2f+/PlMmDCBgIAAKlWqxKFDhxg+fDi3bt0iICAg2Xi9vLySnXbx4kUcHR3/xdYQERGRd5WuAIi8JgkJQHx8PP/88w+XLl2iUqVKmJubU7FiRUMZ0J49e3B3dydjxoxJjhMQEEC1atVwcXFh/Pjx3L9/n3Xr1hmmFy1alAYNGlCoUCHs7e0JCQmhVatWtGzZknz58tGgQQO6detGaGgod+/efSvrLiIiIu8PXQEQeU0qVqzIrVu3OHnyJOHh4RQpUgQHBwcAKleuzMSJE4GnNwB7enomO0758uUNv9vb25MvXz4iIyMNbXnz5jX8fuPGDa5du0aZMmWMxihXrhyPHz/m5MmTlCxZMsnlbNmyJdkYUro6ICIiIu83XQEQeU1y5MhBwYIFOXDgAOHh4VSuXNkwrXLlyly/fp3Dhw/z119/UalSpWTHsbAwzsvj4uIwN//fP1Vra2vD7/Hx8UmOERsbm+RYIiIiIkoARF6jhDKgffv2GSUAuXLlomDBgvzwww9kypSJ4sWLJzvGs08SunHjBmfOnEm2f7Zs2ciWLRv79u0zat+7dy+WlpZ8/PHH/3KNRERE5EOjBEDkNapYsSLr168HoHTp0kbTKleuzLp16/jkk0+Mzug/b9iwYezZs4ejR4/Sp08fHBwcqFOnTpJ9zczM8PPzIzQ0lMWLF3PmzBnWrl1LcHAwX375peHGYREREZEEqg8QeY0qVKhATEwMVapUwcrKymha5cqVmT9/forlPwBNmzalT58+3LlzBw8PDxYuXEiGDBmS7d++fXusrKxYsGABY8aMIVeuXHTo0IF27dq9lnUSERGRD4tZfHJFxCLyVkVERNC6dWu2bNlC7ty50zSWhJuAKzTszenzN9M0Fnn78jllYXT3umkdhoiIvCEqARIRERERMSFKAERERERETIjuARB5R1SoUIFjx46ldRgiIiLygdMVABERERERE6IEQERERETEhCgBEBERERExIUoARERERERMiG4CFpFkOeWwS+sQJA3oexcR+bApARCRZPk3T/mtxfLhiouLx9zcLK3DEBGRN0AlQCIikogO/kVEPlxKAERERERETIgSABERERERE6IEQERERETEhCgBEBERERExIUoARERERERMiBIAERFJJC4uPq1DEBGRN0TvARCRZH2/9HfOX7md1mHIW+aUw07vgBAR+YApARCRZJ2/cpvT52+mdRgiIiLyGqkESERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRARERERMSEKAEQERERETEhSgBEREREREyI3gMg8pb4+vri5OTE2LFjE00LCgrip59+4tdff8XX15fdu3cbpllYWJAjRw7q1atHt27dsLKyMkw7deoUQUFB7Ny5k7t375IjRw6qVauGv78/2bNnfyvrJSIiIu8XJQAi7yBvb28GDhwIQExMDJGRkQwaNIjY2Fj69esHwLVr12jevDlVq1YlJCSELFmycOrUKcaNG4evry+rV682ShZEREREQAmAyDvJ2toaBwcHw2cnJyd8fX2ZN2+eIQHYuHEjT548ITAwEDMzM0O/jz76CG9vb7Zv346Xl1eaxC8iIiLvLt0DIPKeyJAhg9FnMzMz7t+/T0REhFF7gQIFWLduHR4eHm8zPBEREXlP6AqAyHvgxIkTLFmyhC+//NLQVq9ePebOnctXX31FkSJF8PDwoFy5cnh4eFCwYMEXjpnS1YGLFy/i6Oj4WmIXERGRd4sSAJF30Nq1a9m0aRMAjx8/5vHjx+TJk4eWLVsa+tjb2xMWFsbChQv5z3/+w/z585k/fz7W1tZ07NgRf3//tApfRERE3mFKAETeEgsLC+Li4pKcFhcXh4XF//45enp60qdPHwCePHnCxYsXmTZtGj4+PqxevZqsWbMCYGdnR9euXenatSvXr19n165dLFu2jKlTp5IlSxZatGiRbDxbtmxJdpruHRAREflw6R4AkbfEzs6OO3fuJDnt1q1b2NnZGT5nypSJvHnzkjdvXpydnalcuTITJ07kypUrbNiwAYCQkBDD7wDZsmWjXr16LFiwgJIlS7Jt27Y3u0IiIiLyXlICIPKWuLq6cujQIR49emTUHh8fz969e3F1dU3VOAlXEf744w+mTZvGkydPjKabmZmRKVMmsmXL9noCFxERkQ+KEgCRt6RJkyaYmZnh7+/Pvn37OH/+PPv376d3795cvHiRtm3bGvo+fPiQq1evGn4OHz7MwIEDyZgxI7Vq1QLA39+fqKgo2rVrR3h4OOfPn+fAgQOMHTuWgwcPGo0nIiIikkD3AIi8Jfb29ixbtowpU6bQo0cPbt68iZ2dHeXKlWPZsmXkyZPH0HfDhg2G8h4zMzMyZ86Mq6sr8+fPJ2fOnAAULVqU5cuXM23aNPr378/NmzfJlCkT5cqV44cffqBQoUJpsp4iIiLybjOLj4+PT+sgROTdknATcIWGvTl9/mYaRyNvWz6nLIzuXjetwxARkTdEJUAiIiIiIiZECYCIiIiIiAlRAiAiIiIiYkKUAIiIiIiImBAlACIiIiIiJkQJgIiIiIiICVECICIiIiJiQvQiMBFJllMOu7QOQdKAvncRkQ+bEgARSZZ/80ppHYKkkbi4eMzNzdI6DBEReQNUAiQiIono4F9E5MNlFh8fH5/WQYjIu8XV1ZXY2FgcHR3TOhQRERF5CY6OjoSGhqbYRyVAIpLI48eP0bmBpF28eBFAyVEytH2Sp22TMm2f5GnbpEzb5+UpARCRRJycnADYsmVLGkfy7vHy8gK0bZKj7ZM8bZuUafskT9smZdo+L0/3AIiIiIiImBAlACIiIiIiJkQJgIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRC8CExERERExIboCICIiIiJiQpQAiIiIiIiYECUAIiIiIiImRAmAiIiIiIgJUQIgIiIiImJClACIiEFcXBxTp06lSpUqlCxZEj8/P86cOZPWYb0zzp8/j4uLS6Kf5cuXp3VoaWratGn4+voatf3999+0atWKUqVKUb16debMmZNG0aWtpLZN//79E+1DVatWTaMI375bt24xePBgqlatSunSpWnevDl79+41TDflfedF28bU953r16/Tt29fPDw8cHd3p2PHjvzzzz+G6aa877wsi7QOQETeHdOmTeOHH35gzJgx5MyZk3HjxtGhQwd+/vlnrKys0jq8NHfs2DHSp0/PL7/8gpmZmaHd1tY2DaNKW/Pnz2fq1KmUK1fO0Hbz5k3atm3Lp59+yrBhwzh48CDDhg3D3t6eJk2apGG0b1dS2wae7kedOnWiVatWhrZ06dK97fDSTK9evbh+/ToTJ04ka9asLFmyhHbt2hEWFkbWrFlNet9Jads4Ozub/L7zzTffYG5uTkhICBkzZmTKlCm0adOGzZs38/DhQ5Ped16WEgARASAmJoa5c+fSt29fqlWrBsCkSZOoUqUKmzdvpl69emkcYdqLjIwkf/785MiRI61DSXOXL19m4MCB7Nu3j/z58xtN+/HHH7GysmLo0KFYWFjg7OzMmTNnCAkJMYn/iFPaNrGxsfzzzz907twZBweHNIow7Zw5c4bff/+dpUuXUrp0aQAGDhzIb7/9xs8//4y1tbXJ7jsv2jZdunQx6X3n5s2b5M6dm2+++YZChQoB0LlzZz777DOOHz/Ozp07TXbfeRUqARIRAI4ePcr9+/fx8PAwtGXOnJlixYqxZ8+eNIzs3XHs2DEKFiyY1mG8E44cOYKdnR1r1qyhZMmSRtP27t1LuXLlsLD43zkmDw8PTp06xfXr1992qG9dStvm9OnTPHr0CGdn5zSKLm1lyZKFWbNmUaJECUObmZkZ8fHx3L5926T3nRdtG+07WZg4caLh4P/atWvMmTOHXLlyUbBgQZPed16FrgCICACXLl0CwNHR0ag9R44cXLx4MS1CeudERkbi4OBAixYtOH36NHnz5qVz585UqVIlrUN76zw9PfH09Exy2qVLlyhcuLBRW8JVkwsXLpAtW7Y3Hl9aSmnbREZGYmZmxoIFC/jtt98wNzenWrVq9OjRwyRKyTJnzmy4wphgw4YNnD17lsqVKzNp0iST3XdetG1Mfd951rfffmu40jh9+nQyZsxo8n93XpauAIgIANHR0QCJav3Tp0/Po0eP0iKkd0pMTAynT5/m3r179OjRg1mzZuHq6kqHDh3YuXNnWof3Tnn48GGS+xFg8vvS8ePHMTc3x8nJiRkzZtCvXz+2bdtG586diYuLS+vw3rp9+/YxYMAAvLy88PT01L7zjOe3jfad//nqq69YuXIlDRs2xN/fnyNHjmjfeUm6AiAiAFhbWwNPD3QTfoenfzgzZMiQVmG9M6ysrNizZw8WFhaG/2RKlCjBiRMnmDNnDhUrVkzjCN8d1tbWxMTEGLUl/AecMWPGtAjpndG1a1fatGlD5syZAShcuDAODg58+eWXHDp0KFHJ0Ifsl19+oU+fPpQsWZKJEycC2ncSJLVttO/8T0Ip5ogRIzh48CChoaHad16SrgCICPC/0p8rV64YtV+5coVcuXKlRUjvnIwZMyY6w1S4cGEuX76cRhG9m3LlypXkfgSQM2fOtAjpnWFmZmY4gEuQULaQUIZnCkJDQ+natStVq1YlJCTEcNJB+07y28bU953r16/z888/Exsba2gzNzfH2dnZ8P+Uqe87L0MJgIgAUKRIEWxsbIiIiDC03blzh7/++ouyZcumYWTvhqNHj+Lu7m70TG6Aw4cP68bg55QrV459+/YZ/Ue9c+dO8ufPb/J1uL1796Zdu3ZGbYcOHQIwmf1oyZIljBgxgpYtWzJ58mSjpNrU952Uto2p7ztXrlyhd+/e7N6929D2+PFj/vrrL5ydnU1+33lZSgBEBHha4tKqVSvGjx/Pli1bOHr0KD179iRXrlzUrFkzrcNLc4ULF6ZQoUIMGzaMvXv3cuLECcaMGcPBgwfp1KlTWof3TmnSpAn37t1j4MCB/PPPP4SFhbFgwQK+/vrrtA4tzdWvX5/ff/+d6dOnc/bsWbZt28aAAQOoX7++STzd5dSpU4wePZqaNWvy9ddfc/36da5evcrVq1e5e/euSe87L9o2pr7vFClShMqVKxv+BkdGRtKvXz/u3LlDmzZtTHrfeRVm8fHx8WkdhIi8G2JjY5k4cSJhYWE8fPiQcuXKMXjwYHLnzp3Wob0Tbty4wfjx4/ntt9+4c+cOxYoVo0+fPiZ/hSQgIIDz58+zaNEiQ9uff/7JqFGj+Ouvv3BwcMDPz8/o5UWmIqlts2nTJmbMmMHJkyextbWlQYMG9OjRw3DD4odsxowZTJo0Kclpn3/+OWPHjjXZfSc128aU9x2Au3fvMmHCBH755Rfu3r1L2bJlCQgIMDwa1FT3nVehBEBERERExISoBEhERERExIQoARARERERMSFKAERERERETIgSABERERERE6IEQERERETEhCgBEBERERExIUoARERERERMiBIAERERERETogRARETkOb6+vhQrVoxDhw4lOd3T05OAgIC3EktQUBAuLi5vZVkva/z48VSoUIFSpUqxatWqRNMjIiJwcXEhIiIi1WO+yjwi8nKUAIiIiCQhNjaW/v37ExMTk9ahvJMiIyMJCQmhVq1azJ49m6pVqybqU7x4cZYtW0bx4sXTIEIRSY4SABERkSTY2tpy/Phxvv/++7QO5Z1069YtAOrVq0fZsmXJmjVroj42NjaUKlUKGxubtxydiKRECYCIiEgSihYtSqNGjZg9ezaHDx9OsW9SJUFhYWG4uLgQFRUFPC3lqVOnDr/88gv169fH1dWVzz77jAMHDnDw4EGaNm2Km5sb9evXZ+fOnYmW8csvv1C7dm1cXV1p2rRpoj63bt1i8ODBfPLJJ7i6uvLFF18k6uPi4kJwcDBNmjShTJkyTJs2Ldl1Wr9+PY0bN8bd3Z1KlSoxePBgbt++bVgXX19fAL766is8PT2THCOpcp5Dhw7Rrl07KlSoQOnSpenUqRPHjx9PNO8///xDixYtcHV1pWbNmixatMho+o4dO/jyyy9xd3enXLlydO7cmZMnTya7PiLyP0oAREREkjFw4ECyZs362kqBLl26xJgxY+jUqROTJ0/m9u3bdOvWjV69evHFF18wceJE4uLi6NmzJw8fPjSad8CAAbRu3ZqgoCAyZcpEhw4d+OeffwB49OgRX331FVu2bKFnz54EBweTK1cu2rdvnygJmD59OrVr12bixIl4eXklGee0adPo2bMnJUuWZOrUqfj7+7Np0yZ8fX15+PAhTZs2ZfDgwQAMHjyY4ODgVK3/rl27aN68OXFxcYwaNYqRI0dy8eJFmjVrxokTJ4z6jhkzhpIlSzJt2jSqVKnCyJEj+fHHHwE4d+4c33zzDcWLF2f69OmMHDmSkydP0rFjR+Li4lIVi4gps0jrAERERN5VmTNnZtiwYXzzzTd8//339OzZ81+NFx0dzZAhQwz18idOnGDChAmMGjUKHx8f4Om9B926dePUqVMULVrUMO+QIUOoV68eABUrVsTLy4vp06czYcIEVq9ezdGjR/nxxx8pWbIkAFWrVsXX15fx48ezcuVKwzhubm507Ngx2Rhv377N9OnTadq0KUOGDDG0Fy5cmJYtWxIWFkaLFi0oWLAgAAULFqRYsWKpWv8JEyaQJ08eZs+eTbp06QCoXLkyNWvWJCgoiMmTJxv6Nm7cmH79+gFQpUoVLl++zPfff4+Pjw9//vknDx8+5OuvvyZnzpwAODo6smXLFh48eKCSI5EX0BUAERGRFHh6etKwYUNmz57NkSNH/vV4pUuXNvyePXt2AEqVKmVos7e3B+DOnTuGtnTp0lGrVi3D5/Tp01O1alV27NgBwM6dO3FwcKB48eI8efKEJ0+eEBsbS40aNTh8+LChdAeeHsin5ODBg8TExNCgQQOj9rJly+Lk5PTKT+d58OABhw4dom7duoaDf3iaZNWoUSPRuHXr1jX6XLNmTS5dusTJkycpWbIk6dOnx8fHhzFjxrBjxw6KFClCz549dfAvkgq6AiAiIvICgwYNYufOnQQEBBidTX8VSR2gWltbpziPvb09lpaWRm3ZsmUzJAm3bt3i6tWryT5t5+rVq9jZ2QH/SzqSk5AsJNUve/bs3L17N8X5k3P37l3i4+NTPa6Dg4PR52zZshniK1iwIKGhocyaNYsff/yR+fPnkzlzZlq0aEH37t0xN9f5TZGUKAEQERF5ATs7O4YOHYq/vz/Tp09Psk9sbKzR5wcPHry25SccPJuZmRnarl27Znjyjq2tLfny5WP8+PFJzp87d+5ULyshUbh27RrOzs5G065evUqePHleNnxDjGZmZly7di3RtKtXrxqufCR49qpFQjzwv0TAzc2N4OBgYmJi2LdvH8uWLWPGjBm4uLgkunogIsaUIouIiKTCp59+Sv369Zk1axY3btwwmmZjY8OlS5eM2vbv3//alh0TE8OuXbsMn+/fv8/WrVupUKECAOXLl+fixYtky5YNV1dXw8/OnTuN6u1To2TJklhZWbF27Vqj9r1793LhwgWjEqaXkTFjRkqUKMH69euNkqW7d++ydetWypQpY9R/+/btRp/XrVuHo6MjefPmZf78+Xh6ehITE4OVlRUVK1ZkxIgRAFy8ePGV4hMxJboCICIikkrffvstu3btSnQWu0aNGsycOZMZM2ZQqlQptm7dmuSjPF+VpaUlAwYMoFevXtjY2DBr1iwePnxI586dgac3zIaGhtK2bVs6deqEo6MjO3bsICQkhFatWiUqH0qJvb09HTt2JDg4GEtLS7y8vIiKimLKlCkULFiQxo0bv/J69O7dm3bt2tG+fXtatWrF48ePmTVrFjExMXTp0sWo76JFi8iUKRPFihVj3bp1bN++ne+++w4zMzM8PDwYP348/v7+tGrVinTp0vHDDz9gZWVFjRo1Xjk+EVOhBEBERCSV7O3tGTp0aKKD1a+//pobN24wd+5cHj9+TPXq1Rk1ahTffPPNa1munZ0dffv2Zfz48Vy9epWSJUsSGhpKgQIFgKdn1xcvXsyECRMYN24cd+/excnJid69e+Pn5/fSy+vatSvZs2cnNDSU5cuXY29vT506dejRowcZMmR45fWoWLEi8+bNY+rUqfTq1QsrKyvKli1LYGAghQoVMuo7fPhw5s6dy+TJk8mTJw8TJ040PAWpSJEizJgxg++//55evXoRGxtLiRIlmDt3rmGbiEjyzOLj4+PTOggRERH58Pz222906NCB5cuX4+bmltbhiMj/pysAIiIi8tpt3ryZtWvXYm5u/lI3IYvIm6cEQERERF6rmJgYhg4dSlxcHL169TI8rUhE3g0qARIRERERMSF6DKiIiIiIiAlRAiAiIiIiYkKUAIiIiIiImBAlACIiIiIiJkQJgIiIiIiICVECICIiIiJiQpQAiIiIiIiYECUAIiIiIiIm5P8BrrMuMyXo/TUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='ticks')\n",
    "sns.barplot(data=df,x='count',y='company_name',hue='count',palette='dark:b_r',legend=False)\n",
    "sns.despine()\n",
    "plt.title('Counts of job location for data analyst jobs in india')\n",
    "plt.xlabel('Number of jobs')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}