SkillDemand.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('data_jobs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['job_posted_date'] = pd.to_datetime(dataset['job_posted_date'])\n",
    "dataset['job_skills'] = dataset['job_skills'].apply(lambda x: ast.literal_eval(x) if pd.notna(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ind = dataset[dataset['job_country']=='India']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>16</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Big Data Engineer</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>2023-06-15 13:28:03</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>Hexaware Technologies</td>\n",
       "      <td>[powershell, python, sql, mysql, sql server, d...</td>\n",
       "      <td>{'cloud': ['databricks', 'azure'], 'databases'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Pune, Maharashtra, India</td>\n",
       "      <td>via Indeed</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-04-26 13:20:12</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>Bitwise Solutions</td>\n",
       "      <td>[r, sql, python, scala, java, c++, hadoop, tab...</td>\n",
       "      <td>{'analyst_tools': ['tableau'], 'libraries': ['...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Pune, Maharashtra, India</td>\n",
       "      <td>via Exusia</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-03-26 13:16:20</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>Exusia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Quality and Governance Analyst 1</td>\n",
       "      <td>India</td>\n",
       "      <td>via Trabajo.org</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-07-23 13:24:57</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>Jones Lang LaSalle IP, Inc</td>\n",
       "      <td>[sql, python, databricks, power bi, excel, wor...</td>\n",
       "      <td>{'analyst_tools': ['power bi', 'excel', 'word'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>Senior Data Engineer(Python)-1563,1637&amp;1633</td>\n",
       "      <td>Chennai, Tamil Nadu, India</td>\n",
       "      <td>via HR Software For Growing Businesses | Fresh...</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-11-10 13:30:41</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>CESIT</td>\n",
       "      <td>[python, sql, aws, pyspark, docker, jira]</td>\n",
       "      <td>{'async': ['jira'], 'cloud': ['aws'], 'librari...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Director - Enterprise Architecture and Data En...</td>\n",
       "      <td>Chennai, Tamil Nadu, India</td>\n",
       "      <td>via Indeed</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-05-11 13:20:16</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>Ford Motor Company</td>\n",
       "      <td>[gcp, graphql, kubernetes]</td>\n",
       "      <td>{'cloud': ['gcp'], 'libraries': ['graphql'], '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>via Crimsoniteam.freshteam.com</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-07-31 13:35:48</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>Crimson Interactive Inc</td>\n",
       "      <td>[nosql, mongodb, mongodb, elasticsearch, mysql...</td>\n",
       "      <td>{'analyst_tools': ['tableau'], 'databases': ['...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Data Analytics work from home job/internship a...</td>\n",
       "      <td>Anywhere</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Part-time</td>\n",
       "      <td>True</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-03-07 13:37:56</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>Inoket Solutions Private Limited</td>\n",
       "      <td>[sql, excel, word, powerpoint, outlook, power bi]</td>\n",
       "      <td>{'analyst_tools': ['excel', 'word', 'powerpoin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineering Lead</td>\n",
       "      <td>New Delhi, Delhi, India</td>\n",
       "      <td>via LinkedIn</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-04-11 13:55:10</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>Axtria - Ingenious Insights</td>\n",
       "      <td>[python, sql, aws, azure, gcp, spark, tableau,...</td>\n",
       "      <td>{'analyst_tools': ['tableau', 'microstrategy',...</td>\n",
       "    </tr>\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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_title_short                                          job_title  \\\n",
       "16          Data Engineer                                  Big Data Engineer   \n",
       "19         Data Scientist                                     Data Scientist   \n",
       "29         Data Scientist                                     Data Scientist   \n",
       "38           Data Analyst              Data Quality and Governance Analyst 1   \n",
       "42   Senior Data Engineer        Senior Data Engineer(Python)-1563,1637&1633   \n",
       "80          Data Engineer  Director - Enterprise Architecture and Data En...   \n",
       "95          Data Engineer                                      Data Engineer   \n",
       "123          Data Analyst  Data Analytics work from home job/internship a...   \n",
       "149         Data Engineer                              Data Engineering Lead   \n",
       "159          Data Analyst                                       Data Analyst   \n",
       "\n",
       "                    job_location  \\\n",
       "16      Pune, Maharashtra, India   \n",
       "19      Pune, Maharashtra, India   \n",
       "29      Pune, Maharashtra, India   \n",
       "38                         India   \n",
       "42    Chennai, Tamil Nadu, India   \n",
       "80    Chennai, Tamil Nadu, India   \n",
       "95    Mumbai, Maharashtra, India   \n",
       "123                     Anywhere   \n",
       "149      New Delhi, Delhi, India   \n",
       "159  Hyderabad, Telangana, India   \n",
       "\n",
       "                                               job_via job_schedule_type  \\\n",
       "16                                        via LinkedIn         Full-time   \n",
       "19                                          via Indeed         Full-time   \n",
       "29                                          via Exusia         Full-time   \n",
       "38                                     via Trabajo.org         Full-time   \n",
       "42   via HR Software For Growing Businesses | Fresh...         Full-time   \n",
       "80                                          via Indeed         Full-time   \n",
       "95                      via Crimsoniteam.freshteam.com         Full-time   \n",
       "123                                       via LinkedIn         Part-time   \n",
       "149                                       via LinkedIn         Full-time   \n",
       "159                                       via LinkedIn        Contractor   \n",
       "\n",
       "     job_work_from_home search_location     job_posted_date  \\\n",
       "16                False           India 2023-06-15 13:28:03   \n",
       "19                False           India 2023-04-26 13:20:12   \n",
       "29                False           India 2023-03-26 13:16:20   \n",
       "38                False           India 2023-07-23 13:24:57   \n",
       "42                False           India 2023-11-10 13:30:41   \n",
       "80                False           India 2023-05-11 13:20:16   \n",
       "95                False           India 2023-07-31 13:35:48   \n",
       "123                True           India 2023-03-07 13:37:56   \n",
       "149               False           India 2023-04-11 13:55:10   \n",
       "159               False           India 2023-11-28 13:12:07   \n",
       "\n",
       "     job_no_degree_mention  job_health_insurance job_country salary_rate  \\\n",
       "16                   False                 False       India         NaN   \n",
       "19                   False                 False       India         NaN   \n",
       "29                   False                 False       India         NaN   \n",
       "38                   False                 False       India         NaN   \n",
       "42                    True                 False       India         NaN   \n",
       "80                   False                 False       India         NaN   \n",
       "95                   False                 False       India         NaN   \n",
       "123                  False                 False       India         NaN   \n",
       "149                   True                 False       India         NaN   \n",
       "159                  False                 False       India         NaN   \n",
       "\n",
       "     salary_year_avg  salary_hour_avg                      company_name  \\\n",
       "16               NaN              NaN             Hexaware Technologies   \n",
       "19               NaN              NaN                 Bitwise Solutions   \n",
       "29               NaN              NaN                            Exusia   \n",
       "38               NaN              NaN        Jones Lang LaSalle IP, Inc   \n",
       "42               NaN              NaN                             CESIT   \n",
       "80               NaN              NaN                Ford Motor Company   \n",
       "95               NaN              NaN           Crimson Interactive Inc   \n",
       "123              NaN              NaN  Inoket Solutions Private Limited   \n",
       "149              NaN              NaN       Axtria - Ingenious Insights   \n",
       "159              NaN              NaN         Fusion Plus Solutions Inc   \n",
       "\n",
       "                                            job_skills  \\\n",
       "16   [powershell, python, sql, mysql, sql server, d...   \n",
       "19   [r, sql, python, scala, java, c++, hadoop, tab...   \n",
       "29                                                 NaN   \n",
       "38   [sql, python, databricks, power bi, excel, wor...   \n",
       "42           [python, sql, aws, pyspark, docker, jira]   \n",
       "80                          [gcp, graphql, kubernetes]   \n",
       "95   [nosql, mongodb, mongodb, elasticsearch, mysql...   \n",
       "123  [sql, excel, word, powerpoint, outlook, power bi]   \n",
       "149  [python, sql, aws, azure, gcp, spark, tableau,...   \n",
       "159                                              [sql]   \n",
       "\n",
       "                                       job_type_skills  \n",
       "16   {'cloud': ['databricks', 'azure'], 'databases'...  \n",
       "19   {'analyst_tools': ['tableau'], 'libraries': ['...  \n",
       "29                                                 NaN  \n",
       "38   {'analyst_tools': ['power bi', 'excel', 'word'...  \n",
       "42   {'async': ['jira'], 'cloud': ['aws'], 'librari...  \n",
       "80   {'cloud': ['gcp'], 'libraries': ['graphql'], '...  \n",
       "95   {'analyst_tools': ['tableau'], 'databases': ['...  \n",
       "123  {'analyst_tools': ['excel', 'word', 'powerpoin...  \n",
       "149  {'analyst_tools': ['tableau', 'microstrategy',...  \n",
       "159                           {'programming': ['sql']}  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ind.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_skills = df_ind.explode('job_skills')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_title_short</th>\n",
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       "      <th>job_location</th>\n",
       "      <th>job_via</th>\n",
       "      <th>job_schedule_type</th>\n",
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       "      <th>job_posted_date</th>\n",
       "      <th>job_no_degree_mention</th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Big Data Engineer</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>2023-06-15 13:28:03</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>Hexaware Technologies</td>\n",
       "      <td>powershell</td>\n",
       "      <td>{'cloud': ['databricks', 'azure'], 'databases'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Big Data Engineer</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>2023-06-15 13:28:03</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>Hexaware Technologies</td>\n",
       "      <td>python</td>\n",
       "      <td>{'cloud': ['databricks', 'azure'], 'databases'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Big Data Engineer</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>2023-06-15 13:28:03</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>Hexaware Technologies</td>\n",
       "      <td>sql</td>\n",
       "      <td>{'cloud': ['databricks', 'azure'], 'databases'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Big Data Engineer</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>2023-06-15 13:28:03</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>Hexaware Technologies</td>\n",
       "      <td>mysql</td>\n",
       "      <td>{'cloud': ['databricks', 'azure'], 'databases'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Big Data Engineer</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>2023-06-15 13:28:03</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>Hexaware Technologies</td>\n",
       "      <td>sql server</td>\n",
       "      <td>{'cloud': ['databricks', 'azure'], 'databases'...</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>785716</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Application Support Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>melalui BeBee India</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>CompuGroup Medical</td>\n",
       "      <td>c#</td>\n",
       "      <td>{'programming': ['c#', 'sql']}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785716</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>Application Support Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>melalui BeBee India</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>CompuGroup Medical</td>\n",
       "      <td>sql</td>\n",
       "      <td>{'programming': ['c#', 'sql']}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785718</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>Senior Performance QA Analyst</td>\n",
       "      <td>India</td>\n",
       "      <td>melalui BeBee India</td>\n",
       "      <td>Pekerjaan tetap</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-03-13 06:16:28</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>Diebold Nixdorf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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</td>\n",
       "      <td>{'cloud': ['aws'], 'other': ['flow']}</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>flow</td>\n",
       "      <td>{'cloud': ['aws'], 'other': ['flow']}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>297707 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_title_short                      job_title  \\\n",
       "16          Data Engineer              Big Data Engineer   \n",
       "16          Data Engineer              Big Data Engineer   \n",
       "16          Data Engineer              Big Data Engineer   \n",
       "16          Data Engineer              Big Data Engineer   \n",
       "16          Data Engineer              Big Data Engineer   \n",
       "...                   ...                            ...   \n",
       "785716  Software Engineer    Application Support Analyst   \n",
       "785716  Software Engineer    Application Support Analyst   \n",
       "785718   Business Analyst  Senior Performance QA Analyst   \n",
       "785740  Software Engineer             AWS System Analyst   \n",
       "785740  Software Engineer             AWS System Analyst   \n",
       "\n",
       "                    job_location              job_via job_schedule_type  \\\n",
       "16      Pune, Maharashtra, India         via LinkedIn         Full-time   \n",
       "16      Pune, Maharashtra, India         via LinkedIn         Full-time   \n",
       "16      Pune, Maharashtra, India         via LinkedIn         Full-time   \n",
       "16      Pune, Maharashtra, India         via LinkedIn         Full-time   \n",
       "16      Pune, Maharashtra, India         via LinkedIn         Full-time   \n",
       "...                          ...                  ...               ...   \n",
       "785716                     India  melalui BeBee India   Pekerjaan tetap   \n",
       "785716                     India  melalui BeBee India   Pekerjaan tetap   \n",
       "785718                     India  melalui BeBee India   Pekerjaan tetap   \n",
       "785740                     India       melalui Trigyn   Pekerjaan tetap   \n",
       "785740                     India       melalui Trigyn   Pekerjaan tetap   \n",
       "\n",
       "        job_work_from_home search_location     job_posted_date  \\\n",
       "16                   False           India 2023-06-15 13:28:03   \n",
       "16                   False           India 2023-06-15 13:28:03   \n",
       "16                   False           India 2023-06-15 13:28:03   \n",
       "16                   False           India 2023-06-15 13:28:03   \n",
       "16                   False           India 2023-06-15 13:28:03   \n",
       "...                    ...             ...                 ...   \n",
       "785716               False           India 2023-03-13 06:16:31   \n",
       "785716               False           India 2023-03-13 06:16:31   \n",
       "785718               False           India 2023-03-13 06:16:28   \n",
       "785740               False           India 2023-03-13 06:16:31   \n",
       "785740               False           India 2023-03-13 06:16:31   \n",
       "\n",
       "        job_no_degree_mention  job_health_insurance job_country salary_rate  \\\n",
       "16                      False                 False       India         NaN   \n",
       "16                      False                 False       India         NaN   \n",
       "16                      False                 False       India         NaN   \n",
       "16                      False                 False       India         NaN   \n",
       "16                      False                 False       India         NaN   \n",
       "...                       ...                   ...         ...         ...   \n",
       "785716                  False                 False       India         NaN   \n",
       "785716                  False                 False       India         NaN   \n",
       "785718                  False                 False       India         NaN   \n",
       "785740                  False                 False       India         NaN   \n",
       "785740                  False                 False       India         NaN   \n",
       "\n",
       "        salary_year_avg  salary_hour_avg           company_name  job_skills  \\\n",
       "16                  NaN              NaN  Hexaware Technologies  powershell   \n",
       "16                  NaN              NaN  Hexaware Technologies      python   \n",
       "16                  NaN              NaN  Hexaware Technologies         sql   \n",
       "16                  NaN              NaN  Hexaware Technologies       mysql   \n",
       "16                  NaN              NaN  Hexaware Technologies  sql server   \n",
       "...                 ...              ...                    ...         ...   \n",
       "785716              NaN              NaN     CompuGroup Medical          c#   \n",
       "785716              NaN              NaN     CompuGroup Medical         sql   \n",
       "785718              NaN              NaN        Diebold Nixdorf         NaN   \n",
       "785740              NaN              NaN                 Trigyn         aws   \n",
       "785740              NaN              NaN                 Trigyn        flow   \n",
       "\n",
       "                                          job_type_skills  \n",
       "16      {'cloud': ['databricks', 'azure'], 'databases'...  \n",
       "16      {'cloud': ['databricks', 'azure'], 'databases'...  \n",
       "16      {'cloud': ['databricks', 'azure'], 'databases'...  \n",
       "16      {'cloud': ['databricks', 'azure'], 'databases'...  \n",
       "16      {'cloud': ['databricks', 'azure'], 'databases'...  \n",
       "...                                                   ...  \n",
       "785716                     {'programming': ['c#', 'sql']}  \n",
       "785716                     {'programming': ['c#', 'sql']}  \n",
       "785718                                                NaN  \n",
       "785740              {'cloud': ['aws'], 'other': ['flow']}  \n",
       "785740              {'cloud': ['aws'], 'other': ['flow']}  \n",
       "\n",
       "[297707 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>job_skills</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>powershell</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>python</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>sql</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>mysql</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>sql server</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785716</th>\n",
       "      <td>Application Support Analyst</td>\n",
       "      <td>c#</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785716</th>\n",
       "      <td>Application Support Analyst</td>\n",
       "      <td>sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785718</th>\n",
       "      <td>Senior Performance QA Analyst</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785740</th>\n",
       "      <td>AWS System Analyst</td>\n",
       "      <td>aws</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785740</th>\n",
       "      <td>AWS System Analyst</td>\n",
       "      <td>flow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>297707 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            job_title  job_skills\n",
       "16                  Big Data Engineer  powershell\n",
       "16                  Big Data Engineer      python\n",
       "16                  Big Data Engineer         sql\n",
       "16                  Big Data Engineer       mysql\n",
       "16                  Big Data Engineer  sql server\n",
       "...                               ...         ...\n",
       "785716    Application Support Analyst          c#\n",
       "785716    Application Support Analyst         sql\n",
       "785718  Senior Performance QA Analyst         NaN\n",
       "785740             AWS System Analyst         aws\n",
       "785740             AWS System Analyst        flow\n",
       "\n",
       "[297707 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skills[['job_title', 'job_skills']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_skills_count = df_skills.groupby(['job_skills', 'job_title_short']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">airflow</th>\n",
       "      <th>Business Analyst</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud Engineer</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Analyst</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Engineer</th>\n",
       "      <td>2990</td>\n",
       "    </tr>\n",
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       "      <th>Data Scientist</th>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine Learning Engineer</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Data Analyst</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Data Engineer</th>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Senior Data Scientist</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Software Engineer</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         0\n",
       "job_skills job_title_short                \n",
       "airflow    Business Analyst             19\n",
       "           Cloud Engineer               29\n",
       "           Data Analyst                 89\n",
       "           Data Engineer              2990\n",
       "           Data Scientist              327\n",
       "           Machine Learning Engineer    72\n",
       "           Senior Data Analyst          29\n",
       "           Senior Data Engineer        929\n",
       "           Senior Data Scientist        72\n",
       "           Software Engineer            96"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skills_count.head(10).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_skills_count = df_skills_count.reset_index(name='skill_count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>airflow</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2990</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>airflow</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Senior Data Analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>zoom</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1689 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     job_skills        job_title_short  skill_count\n",
       "0       airflow       Business Analyst           19\n",
       "1       airflow         Cloud Engineer           29\n",
       "2       airflow           Data Analyst           89\n",
       "3       airflow          Data Engineer         2990\n",
       "4       airflow         Data Scientist          327\n",
       "...         ...                    ...          ...\n",
       "1684       zoom         Data Scientist           32\n",
       "1685       zoom    Senior Data Analyst            1\n",
       "1686       zoom   Senior Data Engineer           14\n",
       "1687       zoom  Senior Data Scientist            4\n",
       "1688       zoom      Software Engineer           11\n",
       "\n",
       "[1689 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skills_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_skills_count.sort_values(by='skill_count', ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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_skills</th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>skill_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>sql</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>12993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>python</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>11568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>python</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>9248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>spark</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>7157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>aws</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>6993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>esquisse</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>node.js</td>\n",
       "      <td>Senior Data Analyst</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>753</th>\n",
       "      <td>matlab</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>fastapi</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>delphi</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1689 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     job_skills      job_title_short  skill_count\n",
       "1374        sql        Data Engineer        12993\n",
       "1088     python        Data Engineer        11568\n",
       "1089     python       Data Scientist         9248\n",
       "1327      spark        Data Engineer         7157\n",
       "103         aws        Data Engineer         6993\n",
       "...         ...                  ...          ...\n",
       "389    esquisse       Data Scientist            1\n",
       "887     node.js  Senior Data Analyst            1\n",
       "753      matlab       Cloud Engineer            1\n",
       "411     fastapi       Cloud Engineer            1\n",
       "318      delphi        Data Engineer            1\n",
       "\n",
       "[1689 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skills_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_titles = df_skills_count['job_title_short'].unique().tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Engineer',\n",
       " 'Data Scientist',\n",
       " 'Data Analyst',\n",
       " 'Senior Data Engineer',\n",
       " 'Senior Data Scientist',\n",
       " 'Software Engineer',\n",
       " 'Business Analyst',\n",
       " 'Senior Data Analyst',\n",
       " 'Machine Learning Engineer',\n",
       " 'Cloud Engineer']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_titles = sorted(job_titles[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Analyst', 'Data Engineer', 'Data Scientist']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(len(job_titles), 1)\n",
    "\n",
    "sns.set_theme(style='ticks')\n",
    "\n",
    "for i, job_title in enumerate(job_titles):\n",
    "    df_plot = df_skills_count[df_skills_count['job_title_short'] == job_title].head(5)[::-1]\n",
    "    sns.barplot(data=df_plot, x='skill_count', y='job_skills', ax=ax[i], hue='skill_count', palette='dark:b_r')\n",
    "    ax[i].set_title(job_title)\n",
    "    ax[i].invert_yaxis()\n",
    "    ax[i].set_ylabel('')\n",
    "    ax[i].set_xlabel('')\n",
    "    ax[i].get_legend().remove()\n",
    "    ax[i].set_xlim(0, 45000) # make the scales the same\n",
    "\n",
    "fig.suptitle('Counts of Skills Requested in India Job Postings', fontsize=15)\n",
    "fig.tight_layout(h_pad=0.5) # fix the overlap\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>jobs_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>19062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>13279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>6121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>4353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>2486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Senior Data Analyst</td>\n",
       "      <td>1083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             job_title_short  jobs_total\n",
       "0              Data Engineer       19062\n",
       "1             Data Scientist       13279\n",
       "2               Data Analyst        6121\n",
       "3       Senior Data Engineer        4353\n",
       "4      Senior Data Scientist        2486\n",
       "5           Business Analyst        1887\n",
       "6          Software Engineer        1795\n",
       "7        Senior Data Analyst        1083\n",
       "8  Machine Learning Engineer         671\n",
       "9             Cloud Engineer         351"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_job_title_count = df_ind['job_title_short'].value_counts().reset_index(name='jobs_total')\n",
    "\n",
    "df_job_title_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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_skills</th>\n",
       "      <th>job_title_short</th>\n",
       "      <th>skill_count</th>\n",
       "      <th>jobs_total</th>\n",
       "      <th>skill_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sql</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>12993</td>\n",
       "      <td>19062</td>\n",
       "      <td>68.161788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>python</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>11568</td>\n",
       "      <td>19062</td>\n",
       "      <td>60.686182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>python</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>9248</td>\n",
       "      <td>13279</td>\n",
       "      <td>69.643798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>spark</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>7157</td>\n",
       "      <td>19062</td>\n",
       "      <td>37.545903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aws</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>6993</td>\n",
       "      <td>19062</td>\n",
       "      <td>36.685552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>esquisse</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>1</td>\n",
       "      <td>13279</td>\n",
       "      <td>0.007531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>node.js</td>\n",
       "      <td>Senior Data Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>1083</td>\n",
       "      <td>0.092336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>matlab</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>0.284900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>fastapi</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>0.284900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>delphi</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>1</td>\n",
       "      <td>19062</td>\n",
       "      <td>0.005246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1689 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     job_skills      job_title_short  skill_count  jobs_total  skill_percent\n",
       "0           sql        Data Engineer        12993       19062      68.161788\n",
       "1        python        Data Engineer        11568       19062      60.686182\n",
       "2        python       Data Scientist         9248       13279      69.643798\n",
       "3         spark        Data Engineer         7157       19062      37.545903\n",
       "4           aws        Data Engineer         6993       19062      36.685552\n",
       "...         ...                  ...          ...         ...            ...\n",
       "1684   esquisse       Data Scientist            1       13279       0.007531\n",
       "1685    node.js  Senior Data Analyst            1        1083       0.092336\n",
       "1686     matlab       Cloud Engineer            1         351       0.284900\n",
       "1687    fastapi       Cloud Engineer            1         351       0.284900\n",
       "1688     delphi        Data Engineer            1       19062       0.005246\n",
       "\n",
       "[1689 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_skills_perc = pd.merge(df_skills_count, df_job_title_count, on='job_title_short', how='left')\n",
    "\n",
    "df_skills_perc['skill_percent'] = (df_skills_perc['skill_count'] / df_skills_perc['jobs_total']) * 100\n",
    "\n",
    "df_skills_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(len(job_titles), 1)\n",
    "\n",
    "\n",
    "for i, job_title in enumerate(job_titles):\n",
    "    df_plot = df_skills_perc[df_skills_perc['job_title_short'] == job_title].head(5)\n",
    "    sns.barplot(data=df_plot, x='skill_percent', y='job_skills', ax=ax[i], hue='skill_count', palette='dark:b_r')\n",
    "    ax[i].set_title(job_title)\n",
    "    ax[i].set_ylabel('')\n",
    "    ax[i].set_xlabel('')\n",
    "    ax[i].get_legend().remove()\n",
    "    ax[i].set_xlim(0, 78)\n",
    "    # remove the x-axis tick labels for better readability\n",
    "    if i != len(job_titles) - 1:\n",
    "        ax[i].set_xticks([])\n",
    "\n",
    "    # label the percentage on the bars\n",
    "    for n, v in enumerate(df_plot['skill_percent']):\n",
    "        ax[i].text(v + 1, n, f'{v:.0f}%', va='center')\n",
    "\n",
    "fig.suptitle('Likelihood of Skills Requested in Indian Job Postings', fontsize=15)\n",
    "fig.tight_layout(h_pad=.8)\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
}