SalaryAnalysis.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  \n",
    "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ind= dataset[(dataset['job_country'] == 'India')].dropna(subset=['salary_year_avg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>1209</th>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>Computer Vision Developer (R&amp;D)</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>2023-02-18 13:12:30</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bosch Group</td>\n",
       "      <td>[python, opencv, tensorflow, pytorch, keras, m...</td>\n",
       "      <td>{'libraries': ['opencv', 'tensorflow', 'pytorc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Data Engineer - SQL (Power BI)</td>\n",
       "      <td>India</td>\n",
       "      <td>via Ai-Jobs.net</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-10-31 13:09:54</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>156500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gartner</td>\n",
       "      <td>[java, t-sql, sql, azure, power bi, dax, notion]</td>\n",
       "      <td>{'analyst_tools': ['power bi', 'dax'], 'async'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2785</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Staff Software Engineer - Data Engineering (Ja...</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>2023-05-26 13:13:28</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zscaler</td>\n",
       "      <td>[java, nosql, neo4j, redshift, aws, spring, sp...</td>\n",
       "      <td>{'analyst_tools': ['excel'], 'cloud': ['redshi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3190</th>\n",
       "      <td>Senior Data Engineer</td>\n",
       "      <td>Senior Big Data DevOps &amp; Deployment Engineer</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>2023-07-07 13:14:21</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ServiceNow</td>\n",
       "      <td>[sql, perl, bash, groovy, python, shell, redis...</td>\n",
       "      <td>{'analyst_tools': ['tableau'], 'cloud': ['azur...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3930</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Lead Data Scientist</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>2023-12-12 13:27:33</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>114516.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boeing</td>\n",
       "      <td>[python, r, c++, tensorflow, tableau]</td>\n",
       "      <td>{'analyst_tools': ['tableau'], 'libraries': ['...</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>783733</th>\n",
       "      <td>Senior Data Scientist</td>\n",
       "      <td>Senior Data Scientist</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>2023-06-27 06:12:11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Publicis Groupe</td>\n",
       "      <td>[aws, spark, tableau, cognos, excel, powerpoin...</td>\n",
       "      <td>{'analyst_tools': ['tableau', 'cognos', 'excel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784033</th>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>Recruitment Data Analyst</td>\n",
       "      <td>Mumbai, Maharashtra, India</td>\n",
       "      <td>via Ai-Jobs.net</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-06-07 06:10:15</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>111175.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Framestore</td>\n",
       "      <td>[power bi, tableau]</td>\n",
       "      <td>{'analyst_tools': ['power bi', 'tableau']}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784768</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>SR. DATA SCIENTIST</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>2023-01-03 06:25:25</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>157500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Optiv</td>\n",
       "      <td>[r, python, sql, databricks, power bi]</td>\n",
       "      <td>{'analyst_tools': ['power bi'], 'cloud': ['dat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>785395</th>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>Senior Consultant - Tech Consulting -Azure Dat...</td>\n",
       "      <td>India</td>\n",
       "      <td>via Ai-Jobs.net</td>\n",
       "      <td>Full-time</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>2023-12-19 06:10:41</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>India</td>\n",
       "      <td>year</td>\n",
       "      <td>64800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EY</td>\n",
       "      <td>[sql, python, azure, databricks, spark, power ...</td>\n",
       "      <td>{'analyst_tools': ['power bi', 'tableau', 'qli...</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>582 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  job_title_short  \\\n",
       "1209    Machine Learning Engineer   \n",
       "2022                Data Engineer   \n",
       "2785                Data Engineer   \n",
       "3190         Senior Data Engineer   \n",
       "3930               Data Scientist   \n",
       "...                           ...   \n",
       "783733      Senior Data Scientist   \n",
       "784033               Data Analyst   \n",
       "784768             Data Scientist   \n",
       "785395              Data Engineer   \n",
       "785515               Data Analyst   \n",
       "\n",
       "                                                job_title  \\\n",
       "1209                      Computer Vision Developer (R&D)   \n",
       "2022                       Data Engineer - SQL (Power BI)   \n",
       "2785    Staff Software Engineer - Data Engineering (Ja...   \n",
       "3190         Senior Big Data DevOps & Deployment Engineer   \n",
       "3930                                  Lead Data Scientist   \n",
       "...                                                   ...   \n",
       "783733                              Senior Data Scientist   \n",
       "784033                           Recruitment Data Analyst   \n",
       "784768                                 SR. DATA SCIENTIST   \n",
       "785395  Senior Consultant - Tech Consulting -Azure Dat...   \n",
       "785515                                       Data Analyst   \n",
       "\n",
       "                       job_location          job_via job_schedule_type  \\\n",
       "1209    Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "2022                          India  via Ai-Jobs.net         Full-time   \n",
       "2785    Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "3190    Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "3930    Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "...                             ...              ...               ...   \n",
       "783733  Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "784033   Mumbai, Maharashtra, India  via Ai-Jobs.net         Full-time   \n",
       "784768  Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "785395                        India  via Ai-Jobs.net         Full-time   \n",
       "785515  Bengaluru, Karnataka, India  via Ai-Jobs.net         Full-time   \n",
       "\n",
       "        job_work_from_home search_location     job_posted_date  \\\n",
       "1209                 False           India 2023-02-18 13:12:30   \n",
       "2022                 False           India 2023-10-31 13:09:54   \n",
       "2785                 False           India 2023-05-26 13:13:28   \n",
       "3190                 False           India 2023-07-07 13:14:21   \n",
       "3930                 False           India 2023-12-12 13:27:33   \n",
       "...                    ...             ...                 ...   \n",
       "783733               False           India 2023-06-27 06:12:11   \n",
       "784033               False           India 2023-06-07 06:10:15   \n",
       "784768               False           India 2023-01-03 06:25:25   \n",
       "785395               False           India 2023-12-19 06:10:41   \n",
       "785515               False           India 2023-09-01 06:17:34   \n",
       "\n",
       "        job_no_degree_mention  job_health_insurance job_country salary_rate  \\\n",
       "1209                    False                 False       India        year   \n",
       "2022                    False                 False       India        year   \n",
       "2785                    False                 False       India        year   \n",
       "3190                    False                 False       India        year   \n",
       "3930                    False                 False       India        year   \n",
       "...                       ...                   ...         ...         ...   \n",
       "783733                  False                 False       India        year   \n",
       "784033                  False                 False       India        year   \n",
       "784768                  False                 False       India        year   \n",
       "785395                  False                 False       India        year   \n",
       "785515                  False                 False       India        year   \n",
       "\n",
       "        salary_year_avg  salary_hour_avg     company_name  \\\n",
       "1209            79200.0              NaN      Bosch Group   \n",
       "2022           156500.0              NaN          Gartner   \n",
       "2785           147500.0              NaN          Zscaler   \n",
       "3190            79200.0              NaN       ServiceNow   \n",
       "3930           114516.0              NaN           Boeing   \n",
       "...                 ...              ...              ...   \n",
       "783733         157500.0              NaN  Publicis Groupe   \n",
       "784033         111175.0              NaN       Framestore   \n",
       "784768         157500.0              NaN            Optiv   \n",
       "785395          64800.0              NaN               EY   \n",
       "785515         105000.0              NaN           Myntra   \n",
       "\n",
       "                                               job_skills  \\\n",
       "1209    [python, opencv, tensorflow, pytorch, keras, m...   \n",
       "2022     [java, t-sql, sql, azure, power bi, dax, notion]   \n",
       "2785    [java, nosql, neo4j, redshift, aws, spring, sp...   \n",
       "3190    [sql, perl, bash, groovy, python, shell, redis...   \n",
       "3930                [python, r, c++, tensorflow, tableau]   \n",
       "...                                                   ...   \n",
       "783733  [aws, spark, tableau, cognos, excel, powerpoin...   \n",
       "784033                                [power bi, tableau]   \n",
       "784768             [r, python, sql, databricks, power bi]   \n",
       "785395  [sql, python, azure, databricks, spark, power ...   \n",
       "785515                                                NaN   \n",
       "\n",
       "                                          job_type_skills  \n",
       "1209    {'libraries': ['opencv', 'tensorflow', 'pytorc...  \n",
       "2022    {'analyst_tools': ['power bi', 'dax'], 'async'...  \n",
       "2785    {'analyst_tools': ['excel'], 'cloud': ['redshi...  \n",
       "3190    {'analyst_tools': ['tableau'], 'cloud': ['azur...  \n",
       "3930    {'analyst_tools': ['tableau'], 'libraries': ['...  \n",
       "...                                                   ...  \n",
       "783733  {'analyst_tools': ['tableau', 'cognos', 'excel...  \n",
       "784033         {'analyst_tools': ['power bi', 'tableau']}  \n",
       "784768  {'analyst_tools': ['power bi'], 'cloud': ['dat...  \n",
       "785395  {'analyst_tools': ['power bi', 'tableau', 'qli...  \n",
       "785515                                                NaN  \n",
       "\n",
       "[582 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_titles = df_ind['job_title_short'].value_counts().index[:6].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data Engineer',\n",
       " 'Data Analyst',\n",
       " 'Data Scientist',\n",
       " 'Machine Learning Engineer',\n",
       " 'Senior Data Engineer',\n",
       " 'Software Engineer']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ind_top6 = df_ind[df_ind['job_title_short'].isin(job_titles)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_order = df_ind_top6.groupby('job_title_short')['salary_year_avg'].median().sort_values(ascending=False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df_ind_top6, x='salary_year_avg', y='job_title_short', order=job_order)\n",
    "sns.set_theme(style='ticks')\n",
    "sns.despine()\n",
    "\n",
    "# this is all the same\n",
    "plt.title('Salary Distributions of Data Jobs in India')\n",
    "plt.xlabel('Yearly Salary (USD)')\n",
    "plt.ylabel('')\n",
    "plt.xlim(0, 600000) \n",
    "ticks_x = plt.FuncFormatter(lambda y, pos: f'${int(y/1000)}K')\n",
    "plt.gca().xaxis.set_major_formatter(ticks_x)\n",
    "plt.show()"
   ]
  },
  {
   "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>salary_year_avg</th>\n",
       "      <th>job_skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>89000.0</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>89000.0</td>\n",
       "      <td>r</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>89000.0</td>\n",
       "      <td>alteryx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>89000.0</td>\n",
       "      <td>tableau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>90250.0</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     salary_year_avg job_skills\n",
       "109          89000.0     python\n",
       "109          89000.0          r\n",
       "109          89000.0    alteryx\n",
       "109          89000.0    tableau\n",
       "180          90250.0      excel"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DA_India = dataset[(dataset['job_title_short'] == 'Data Analyst')& (dataset['job_country'] == 'India')].copy()\n",
    "\n",
    "# Drop NaN values from the 'salary_year_avg' column for accurate visualization\n",
    "df_DA_India = df_DA_India.dropna(subset=['salary_year_avg'])\n",
    "\n",
    "df_DA_India = df_DA_India.explode('job_skills')\n",
    "\n",
    "df_DA_India[['salary_year_avg', 'job_skills']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_skills</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dplyr</th>\n",
       "      <td>2</td>\n",
       "      <td>196250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitbucket</th>\n",
       "      <td>3</td>\n",
       "      <td>189000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gitlab</th>\n",
       "      <td>3</td>\n",
       "      <td>186000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solidity</th>\n",
       "      <td>1</td>\n",
       "      <td>179000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hugging face</th>\n",
       "      <td>1</td>\n",
       "      <td>175000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>couchbase</th>\n",
       "      <td>1</td>\n",
       "      <td>160515.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ansible</th>\n",
       "      <td>1</td>\n",
       "      <td>159640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mxnet</th>\n",
       "      <td>2</td>\n",
       "      <td>149000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cassandra</th>\n",
       "      <td>6</td>\n",
       "      <td>148250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vmware</th>\n",
       "      <td>1</td>\n",
       "      <td>147500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count    median\n",
       "job_skills                   \n",
       "dplyr             2  196250.0\n",
       "bitbucket         3  189000.0\n",
       "gitlab            3  186000.0\n",
       "solidity          1  179000.0\n",
       "hugging face      1  175000.0\n",
       "couchbase         1  160515.0\n",
       "ansible           1  159640.0\n",
       "mxnet             2  149000.0\n",
       "cassandra         6  148250.0\n",
       "vmware            1  147500.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DA_top_pay = df_DA_India.groupby('job_skills')['salary_year_avg'].agg(['count', 'median']).sort_values(by='median', ascending=False)\n",
    "\n",
    "df_DA_top_pay = df_DA_top_pay.head(10)\n",
    "\n",
    "df_DA_top_pay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_skills</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>1431</td>\n",
       "      <td>97500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tableau</th>\n",
       "      <td>1364</td>\n",
       "      <td>92875.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>893</td>\n",
       "      <td>92500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sql server</th>\n",
       "      <td>286</td>\n",
       "      <td>92500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sql</th>\n",
       "      <td>2508</td>\n",
       "      <td>91000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sas</th>\n",
       "      <td>926</td>\n",
       "      <td>90000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>power bi</th>\n",
       "      <td>838</td>\n",
       "      <td>90000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powerpoint</th>\n",
       "      <td>462</td>\n",
       "      <td>85000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excel</th>\n",
       "      <td>1808</td>\n",
       "      <td>84392.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word</th>\n",
       "      <td>461</td>\n",
       "      <td>81194.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count    median\n",
       "job_skills                 \n",
       "python       1431  97500.00\n",
       "tableau      1364  92875.00\n",
       "r             893  92500.00\n",
       "sql server    286  92500.00\n",
       "sql          2508  91000.00\n",
       "sas           926  90000.00\n",
       "power bi      838  90000.00\n",
       "powerpoint    462  85000.00\n",
       "excel        1808  84392.00\n",
       "word          461  81194.75"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DA_skills = df_DA_India.groupby('job_skills')['salary_year_avg'].agg(['count', 'median']).sort_values(by='count', ascending=False)\n",
    "\n",
    "df_DA_skills = df_DA_skills.head(10).sort_values(by='median', ascending=False)\n",
    "\n",
    "df_DA_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1)  \n",
    "\n",
    "# Top 10 Highest Paid Skills for Data Analysts\n",
    "sns.barplot(data=df_DA_top_pay, x='median', y=df_DA_top_pay.index, hue='median', ax=ax[0], palette='dark:b_r')\n",
    "ax[0].legend().remove()\n",
    "ax[0].set_title('Highest Paid Skills for Data Analysts in India')\n",
    "ax[0].set_ylabel('')\n",
    "ax[0].set_xlabel('')\n",
    "ax[0].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'₹{int(x/1000)}K'))\n",
    "\n",
    "\n",
    "# Top 10 Most In-Demand Skills for Data Analysts')\n",
    "sns.barplot(data=df_DA_skills, x='median', y=df_DA_skills.index, hue='median', ax=ax[1], palette='light:b')\n",
    "ax[1].legend().remove()\n",
    "ax[1].set_title('Most In-Demand Skills for Data Analysts in India')\n",
    "ax[1].set_ylabel('')\n",
    "ax[1].set_xlabel('Median Salary')\n",
    "ax[1].set_xlim(ax[0].get_xlim())  # Set the same x-axis limits as the first plot\n",
    "ax[1].xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'₹{int(x/1000)}K'))\n",
    "\n",
    "sns.set_theme(style='ticks')\n",
    "plt.tight_layout()\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
}