OptimalSkills.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "dataset = pd.read_csv('data_jobs.csv')\n",
    "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": [
    {
     "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>11412</th>\n",
       "      <td>50400.0</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11412</th>\n",
       "      <td>50400.0</td>\n",
       "      <td>sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11412</th>\n",
       "      <td>50400.0</td>\n",
       "      <td>aws</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11412</th>\n",
       "      <td>50400.0</td>\n",
       "      <td>azure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11412</th>\n",
       "      <td>50400.0</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       salary_year_avg job_skills\n",
       "11412          50400.0     python\n",
       "11412          50400.0        sql\n",
       "11412          50400.0        aws\n",
       "11412          50400.0      azure\n",
       "11412          50400.0      excel"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ind = 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_ind = df_ind.dropna(subset=['salary_year_avg'])\n",
    "\n",
    "df_ind_explode = df_ind.explode('job_skills')\n",
    "\n",
    "df_ind_explode[['salary_year_avg', 'job_skills']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>skill_count</th>\n",
       "      <th>median_salary</th>\n",
       "      <th>skill_percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_skills</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sql</th>\n",
       "      <td>46</td>\n",
       "      <td>96050.0</td>\n",
       "      <td>48.936170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excel</th>\n",
       "      <td>39</td>\n",
       "      <td>98500.0</td>\n",
       "      <td>41.489362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>python</th>\n",
       "      <td>36</td>\n",
       "      <td>96050.0</td>\n",
       "      <td>38.297872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tableau</th>\n",
       "      <td>20</td>\n",
       "      <td>108087.5</td>\n",
       "      <td>21.276596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r</th>\n",
       "      <td>18</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>19.148936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postgresql</th>\n",
       "      <td>1</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>1.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ms access</th>\n",
       "      <td>1</td>\n",
       "      <td>64600.0</td>\n",
       "      <td>1.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mysql</th>\n",
       "      <td>1</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>1.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neo4j</th>\n",
       "      <td>1</td>\n",
       "      <td>163782.0</td>\n",
       "      <td>1.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zoom</th>\n",
       "      <td>1</td>\n",
       "      <td>104500.0</td>\n",
       "      <td>1.063830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            skill_count  median_salary  skill_percent\n",
       "job_skills                                           \n",
       "sql                  46        96050.0      48.936170\n",
       "excel                39        98500.0      41.489362\n",
       "python               36        96050.0      38.297872\n",
       "tableau              20       108087.5      21.276596\n",
       "r                    18        79200.0      19.148936\n",
       "...                 ...            ...            ...\n",
       "postgresql            1       165000.0       1.063830\n",
       "ms access             1        64600.0       1.063830\n",
       "mysql                 1       165000.0       1.063830\n",
       "neo4j                 1       163782.0       1.063830\n",
       "zoom                  1       104500.0       1.063830\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_DA_skills = df_ind_explode.groupby('job_skills')['salary_year_avg'].agg(['count', 'median']).sort_values(by='count', ascending=False)\n",
    "\n",
    "df_DA_skills = df_DA_skills.rename(columns={'count': 'skill_count', 'median': 'median_salary'})\n",
    "\n",
    "DA_job_count = len(df_ind)  # Find count of Data Analyst jobs in India with salary info\n",
    "\n",
    "df_DA_skills['skill_percent'] = df_DA_skills['skill_count'] / DA_job_count * 100\n",
    "\n",
    "df_DA_skills = df_DA_skills[df_DA_skills['skill_count'] > 0]\n",
    "\n",
    "df_DA_skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "skill_limit = 5\n",
    "\n",
    "df_DA_skills_high_demand = df_DA_skills[df_DA_skills['skill_percent'] > skill_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from adjustText import adjust_text\n",
    "\n",
    "plt.scatter(df_DA_skills_high_demand['skill_percent'], df_DA_skills_high_demand['median_salary'])\n",
    "plt.xlabel('Percent of Data Analyst Jobs')\n",
    "plt.ylabel('Median Salary ($USD)')  # Assuming this is the label you want for y-axis\n",
    "plt.title('Most Optimal Skills for Data Analysts in India')\n",
    "\n",
    "# Get current axes, set limits, and format axes\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, pos: f'${int(y/1000)}K'))  # Example formatting y-axis\n",
    "\n",
    "# Add labels to points and collect them in a list\n",
    "texts = []\n",
    "for i, txt in enumerate(df_DA_skills_high_demand.index):\n",
    "    texts.append(plt.text(df_DA_skills_high_demand['skill_percent'].iloc[i], df_DA_skills_high_demand['median_salary'].iloc[i], \" \" + txt))\n",
    "\n",
    "# Adjust text to avoid overlap and add arrows\n",
    "adjust_text(texts, arrowprops=dict(arrowstyle='->', color='gray'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'analyst_tools': ['sap',\n",
       "  'ssrs',\n",
       "  'sheets',\n",
       "  'powerbi',\n",
       "  'tableau',\n",
       "  'excel',\n",
       "  'powerpoint',\n",
       "  'splunk',\n",
       "  'microstrategy',\n",
       "  'word',\n",
       "  'looker',\n",
       "  'ms access',\n",
       "  'spreadsheet',\n",
       "  'sharepoint',\n",
       "  'ssis',\n",
       "  'outlook',\n",
       "  'spss',\n",
       "  'dax',\n",
       "  'visio',\n",
       "  'datarobot',\n",
       "  'esquisse',\n",
       "  'nuix',\n",
       "  'sas',\n",
       "  'alteryx',\n",
       "  'cognos',\n",
       "  'qlik',\n",
       "  'msaccess',\n",
       "  'power bi'],\n",
       " 'programming': ['c#',\n",
       "  'scala',\n",
       "  'visualbasic',\n",
       "  'crystal',\n",
       "  'r',\n",
       "  'no-sql',\n",
       "  'f#',\n",
       "  'erlang',\n",
       "  'c++',\n",
       "  'powershell',\n",
       "  'sass',\n",
       "  'lisp',\n",
       "  'ocaml',\n",
       "  'mongo',\n",
       "  'julia',\n",
       "  'visual basic',\n",
       "  'kotlin',\n",
       "  't-sql',\n",
       "  'pascal',\n",
       "  'objective-c',\n",
       "  'clojure',\n",
       "  'elixir',\n",
       "  'vb.net',\n",
       "  'groovy',\n",
       "  'matlab',\n",
       "  'nosql',\n",
       "  'haskell',\n",
       "  'java',\n",
       "  'swift',\n",
       "  'bash',\n",
       "  'perl',\n",
       "  'fortran',\n",
       "  'delphi',\n",
       "  'sql',\n",
       "  'vba',\n",
       "  'lua',\n",
       "  'typescript',\n",
       "  'php',\n",
       "  'html',\n",
       "  'assembly',\n",
       "  'c',\n",
       "  'apl',\n",
       "  'javascript',\n",
       "  'shell',\n",
       "  'golang',\n",
       "  'cobol',\n",
       "  'go',\n",
       "  'dart',\n",
       "  'rust',\n",
       "  'python',\n",
       "  'solidity',\n",
       "  'sas',\n",
       "  'mongodb',\n",
       "  'css',\n",
       "  'ruby'],\n",
       " 'cloud': ['bigquery',\n",
       "  'vmware',\n",
       "  'databricks',\n",
       "  'aws',\n",
       "  'snowflake',\n",
       "  'oracle',\n",
       "  'gcp',\n",
       "  'ovh',\n",
       "  'openstack',\n",
       "  'redshift',\n",
       "  'aurora',\n",
       "  'firebase',\n",
       "  'watson',\n",
       "  'ibm cloud',\n",
       "  'digitalocean',\n",
       "  'azure',\n",
       "  'heroku',\n",
       "  'linode',\n",
       "  'colocation'],\n",
       " 'libraries': ['tidyverse',\n",
       "  'mlr',\n",
       "  'huggingface',\n",
       "  'tidyr',\n",
       "  'dlib',\n",
       "  'ionic',\n",
       "  'keras',\n",
       "  'spring',\n",
       "  'tensorflow',\n",
       "  'electron',\n",
       "  'plotly',\n",
       "  'matplotlib',\n",
       "  'hugging face',\n",
       "  'graphql',\n",
       "  'jupyter',\n",
       "  'rshiny',\n",
       "  'selenium',\n",
       "  'shogun',\n",
       "  'cordova',\n",
       "  'pandas',\n",
       "  'spark',\n",
       "  'theano',\n",
       "  'kafka',\n",
       "  'scikit-learn',\n",
       "  'nltk',\n",
       "  'seaborn',\n",
       "  'ggplot2',\n",
       "  'chainer',\n",
       "  'pyspark',\n",
       "  'xamarin',\n",
       "  'numpy',\n",
       "  'dplyr',\n",
       "  'mlpack',\n",
       "  'pytorch',\n",
       "  'opencv',\n",
       "  'gtx',\n",
       "  'gdpr',\n",
       "  'flutter',\n",
       "  'airflow',\n",
       "  'react',\n",
       "  'qt',\n",
       "  'hadoop',\n",
       "  'capacitor',\n",
       "  'mxnet'],\n",
       " 'other': ['codecommit',\n",
       "  'git',\n",
       "  'ansible',\n",
       "  'bitbucket',\n",
       "  'unity',\n",
       "  'svn',\n",
       "  'jenkins',\n",
       "  'docker',\n",
       "  'kubernetes',\n",
       "  'pulumi',\n",
       "  'unreal',\n",
       "  'puppet',\n",
       "  'terraform',\n",
       "  'flow',\n",
       "  'chef',\n",
       "  'npm',\n",
       "  'yarn',\n",
       "  'terminal',\n",
       "  'github',\n",
       "  'gitlab',\n",
       "  'atlassian',\n",
       "  'homebrew'],\n",
       " 'databases': ['neo4j',\n",
       "  'firebase',\n",
       "  'mariadb',\n",
       "  'couchdb',\n",
       "  'sqlite',\n",
       "  'mongodb',\n",
       "  'dynamodb',\n",
       "  'cassandra',\n",
       "  'db2',\n",
       "  'postgresql',\n",
       "  'elasticsearch',\n",
       "  'sqlserver',\n",
       "  'firestore',\n",
       "  'sql server',\n",
       "  'couchbase',\n",
       "  'redis',\n",
       "  'mysql'],\n",
       " 'os': ['redhat',\n",
       "  'wsl',\n",
       "  'debian',\n",
       "  'windows',\n",
       "  'centos',\n",
       "  'fedora',\n",
       "  'unix',\n",
       "  'kali',\n",
       "  'arch',\n",
       "  'suse',\n",
       "  'ubuntu',\n",
       "  'macos',\n",
       "  'linux'],\n",
       " 'async': ['monday.com',\n",
       "  'wimi',\n",
       "  'planner',\n",
       "  'microsoft lists',\n",
       "  'asana',\n",
       "  'airtable',\n",
       "  'wrike',\n",
       "  'workfront',\n",
       "  'notion',\n",
       "  'dingtalk',\n",
       "  'smartsheet',\n",
       "  'confluence',\n",
       "  'jira',\n",
       "  'trello',\n",
       "  'clickup'],\n",
       " 'webframeworks': ['symfony',\n",
       "  'ruby on rails',\n",
       "  'angular',\n",
       "  'laravel',\n",
       "  'rubyon rails',\n",
       "  'gatsby',\n",
       "  'play framework',\n",
       "  'phoenix',\n",
       "  'asp.netcore',\n",
       "  'vue.js',\n",
       "  'asp.net core',\n",
       "  'fastapi',\n",
       "  'ember.js',\n",
       "  'asp.net',\n",
       "  'drupal',\n",
       "  'node',\n",
       "  'svelte',\n",
       "  'fastify',\n",
       "  'express',\n",
       "  'angular.js',\n",
       "  'react.js',\n",
       "  'nuxt.js',\n",
       "  'blazor',\n",
       "  'django',\n",
       "  'next.js',\n",
       "  'node.js',\n",
       "  'flask',\n",
       "  'vue',\n",
       "  'deno',\n",
       "  'ruby',\n",
       "  'jquery'],\n",
       " 'sync': ['slack',\n",
       "  'twilio',\n",
       "  'rocketchat',\n",
       "  'zoom',\n",
       "  'unify',\n",
       "  'microsoft teams',\n",
       "  'ringcentral',\n",
       "  'mattermost',\n",
       "  'webex',\n",
       "  'wire',\n",
       "  'symphony',\n",
       "  'google chat']}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_technology = dataset['job_type_skills'].copy()\n",
    "\n",
    "# remove duplicates\n",
    "df_technology = df_technology.drop_duplicates()\n",
    "\n",
    "# remove NaN values\n",
    "df_technology = df_technology.dropna()\n",
    "\n",
    "# combine all dictionaries into one\n",
    "technology_dict = {}\n",
    "for row in df_technology:\n",
    "    row_dict = ast.literal_eval(row)  # convert string to dictionary\n",
    "    for key, value in row_dict.items():\n",
    "        if key in technology_dict:  # if key already exists in technology_dict, add value to existing value\n",
    "            technology_dict[key] += value\n",
    "        else:                       # if key does not exist in technology_dict, add key and value\n",
    "            technology_dict[key] = value\n",
    "\n",
    "# remove duplicates by converting values to set then back to list\n",
    "for key, value in technology_dict.items():\n",
    "    technology_dict[key] = list(set(value))\n",
    "\n",
    "technology_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>technology</th>\n",
       "      <th>skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>sap</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>ssrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>sheets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>powerbi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>tableau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sync</td>\n",
       "      <td>mattermost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sync</td>\n",
       "      <td>webex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sync</td>\n",
       "      <td>wire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sync</td>\n",
       "      <td>symphony</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sync</td>\n",
       "      <td>google chat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       technology       skills\n",
       "0   analyst_tools          sap\n",
       "0   analyst_tools         ssrs\n",
       "0   analyst_tools       sheets\n",
       "0   analyst_tools      powerbi\n",
       "0   analyst_tools      tableau\n",
       "..            ...          ...\n",
       "9            sync   mattermost\n",
       "9            sync        webex\n",
       "9            sync         wire\n",
       "9            sync     symphony\n",
       "9            sync  google chat\n",
       "\n",
       "[256 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turn dictionary into dataframe\n",
    "df_technology = pd.DataFrame(list(technology_dict.items()), columns=['technology', 'skills'])\n",
    "\n",
    "df_technology = df_technology.explode('skills')\n",
    "\n",
    "df_technology\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>skill_count</th>\n",
       "      <th>median_salary</th>\n",
       "      <th>skill_percent</th>\n",
       "      <th>technology</th>\n",
       "      <th>skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46</td>\n",
       "      <td>96050.0</td>\n",
       "      <td>48.936170</td>\n",
       "      <td>programming</td>\n",
       "      <td>sql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>98500.0</td>\n",
       "      <td>41.489362</td>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>excel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>96050.0</td>\n",
       "      <td>38.297872</td>\n",
       "      <td>programming</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>108087.5</td>\n",
       "      <td>21.276596</td>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>tableau</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>79200.0</td>\n",
       "      <td>19.148936</td>\n",
       "      <td>programming</td>\n",
       "      <td>r</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>80</th>\n",
       "      <td>1</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>1.063830</td>\n",
       "      <td>databases</td>\n",
       "      <td>postgresql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>64600.0</td>\n",
       "      <td>1.063830</td>\n",
       "      <td>analyst_tools</td>\n",
       "      <td>ms access</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>1.063830</td>\n",
       "      <td>databases</td>\n",
       "      <td>mysql</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1</td>\n",
       "      <td>163782.0</td>\n",
       "      <td>1.063830</td>\n",
       "      <td>databases</td>\n",
       "      <td>neo4j</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>104500.0</td>\n",
       "      <td>1.063830</td>\n",
       "      <td>sync</td>\n",
       "      <td>zoom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    skill_count  median_salary  skill_percent     technology      skills\n",
       "0            46        96050.0      48.936170    programming         sql\n",
       "1            39        98500.0      41.489362  analyst_tools       excel\n",
       "2            36        96050.0      38.297872    programming      python\n",
       "3            20       108087.5      21.276596  analyst_tools     tableau\n",
       "4            18        79200.0      19.148936    programming           r\n",
       "..          ...            ...            ...            ...         ...\n",
       "80            1       165000.0       1.063830      databases  postgresql\n",
       "81            1        64600.0       1.063830  analyst_tools   ms access\n",
       "82            1       165000.0       1.063830      databases       mysql\n",
       "83            1       163782.0       1.063830      databases       neo4j\n",
       "84            1       104500.0       1.063830           sync        zoom\n",
       "\n",
       "[85 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge df_DA_skills and df_technology\n",
    "df_DA_skills_tech = df_DA_skills.merge(df_technology, left_on='job_skills', right_on='skills')\n",
    "\n",
    "df_DA_skills_tech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_DA_skills_tech_high_demand = df_DA_skills_tech[df_DA_skills_tech['skill_percent'] > skill_limit]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(\n",
    "    data=df_DA_skills_tech_high_demand,\n",
    "    x='skill_percent',\n",
    "    y='median_salary',\n",
    "    hue='technology'\n",
    ")\n",
    "\n",
    "sns.despine()\n",
    "sns.set_theme(style='ticks')\n",
    "\n",
    "# Prepare texts for adjustText\n",
    "texts = []\n",
    "for i, txt in enumerate(df_DA_skills_high_demand.index):\n",
    "    texts.append(plt.text(df_DA_skills_high_demand['skill_percent'].iloc[i], df_DA_skills_high_demand['median_salary'].iloc[i], txt))\n",
    "\n",
    "# Adjust text to avoid overlap\n",
    "adjust_text(texts, arrowprops=dict(arrowstyle='->', color='gray'))\n",
    "\n",
    "# Set axis labels, title, and legend\n",
    "plt.xlabel('Percent of Data Analyst Jobs')\n",
    "plt.ylabel('Median Yearly Salary')\n",
    "plt.title('Most Optimal Skills for Data Analysts in India')\n",
    "plt.legend(title='Technology')\n",
    "\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "ax = plt.gca()\n",
    "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, pos: f'${int(y/1000)}K'))\n",
    "ax.xaxis.set_major_formatter(PercentFormatter(decimals=0))\n",
    "\n",
    "# Adjust layout and display plot \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "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
}