DS_salary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import re\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import datetime as dt\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# West coast\n",
    "links = ['https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SAN+JOSE&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SANTA+CLARA&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SUNNYVALE&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=MOUNTAIN+VIEW&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=CUPERTINO&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=MENLO+PARK&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=PALO+ALTO&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=REDWOOD+CITY&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SAN+MATEO&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SAN+FRANCISCO&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SEATTLE&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=REDMOND&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SANTA+MONICA&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=LOS+ANGELES&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=AUSTIN&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=amazon&job=Data+Scientist+I&city=&year=All+Years'\n",
    "        ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boston and Seattle\n",
    "links = ['https://h1bdata.info/index.php?em=&job=Data+Scientist&city=BOSTON&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SEATTLE&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=REDMOND&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=ROCHESTER&year=All+Years',\n",
    "         'https://h1bdata.info/index.php?em=&job=Data+Scientist&city=SYRACUSE&year=All+Years'\n",
    "        ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs_list = []\n",
    "for link in links:\n",
    "    page_link = link\n",
    "    page_response = requests.get(page_link, timeout=1000)\n",
    "    page_content = BeautifulSoup(page_response.content, \"lxml\")\n",
    "    \n",
    "    for row in page_content.find_all(\"tr\")[1:]:\n",
    "        row_data = []\n",
    "        for i in row:\n",
    "            row_data.append(i.text)\n",
    "        jobs_list.append(row_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#put everything into a dataframe for processing easily\n",
    "ds_jobs_df = pd.DataFrame()\n",
    "ds_jobs_df['company'] = [i[0] for i in jobs_list]\n",
    "ds_jobs_df['title'] = [i[1] for i in jobs_list]\n",
    "ds_jobs_df['salary'] = [i[2].replace(',','') for i in jobs_list]\n",
    "ds_jobs_df['salary'] = ds_jobs_df['salary'].astype(float)\n",
    "ds_jobs_df['location'] = [i[3] for i in jobs_list]\n",
    "ds_jobs_df['date'] = [i[4] for i in jobs_list]\n",
    "ds_jobs_df['date'] = pd.to_datetime(ds_jobs_df['date'])\n",
    "ds_jobs_df['year'] = [i.year for i in ds_jobs_df['date']]\n",
    "\n",
    "#Drop pre 2014 data (very few obseravations pre 2014)\n",
    "ds_jobs_df.drop(ds_jobs_df[ds_jobs_df['year']<2014].index, axis=0, inplace=True)\n",
    "\n",
    "#Drop salaries over $1,000,000\n",
    "ds_jobs_df.drop(ds_jobs_df[ds_jobs_df['salary']>1000000].index, axis=0, inplace=True)\n",
    "\n",
    "#Sort by company and year\n",
    "ds_jobs_df.sort_values(by=['year', 'company'], inplace=True, ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.barplot(x=ds_jobs_df['year'].value_counts().sort_index().index,\n",
    "                 y=ds_jobs_df['year'].value_counts().sort_index().values)\n",
    "ax.set_xlabel(\"Year\", fontsize=16)\n",
    "ax.set_ylabel(\"Number of H1B Data Scientist Salaries\", fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113152.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_jobs_df['salary'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>year</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>110191.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>107100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>119000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>115000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>115000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>95160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    salary\n",
       "0  2014  110191.5\n",
       "1  2015  107100.0\n",
       "2  2016  119000.0\n",
       "3  2017  115000.0\n",
       "4  2018  115000.0\n",
       "5  2019   95160.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_salary = ds_jobs_df.groupby(by=['year']).median().reset_index()\n",
    "median_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax = sns.barplot(x=median_salary['year'],\n",
    "                 y=median_salary['salary'])\n",
    "ax.set_xlabel(\"Year\", fontsize=16)\n",
    "ax.set_ylabel(\"Median Data Scientist Salary\", fontsize=16)\n",
    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax = sns.boxplot(x='year', y='salary', data=ds_jobs_df, showfliers=False);\n",
    "ax.set_xlabel(\"Year\", fontsize=16)\n",
    "ax.set_ylabel(\"Data Scientist Salary\", fontsize=16)\n",
    "ax.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All 25th perctile: 90351.0\n",
      "All 25th perctile: 113152.0\n",
      "All 25th perctile: 127000.0\n"
     ]
    }
   ],
   "source": [
    "print(\"All 25th perctile:\", ds_jobs_df['salary'].quantile(0.25))\n",
    "print(\"All 25th perctile: \" + str(ds_jobs_df['salary'].quantile(0.5)))\n",
    "print(\"All 25th perctile: \" + str(ds_jobs_df['salary'].quantile(0.75)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015 25th perctile: 100000.0\n",
      "2015 50th perctile: 107100.0\n",
      "2015 75th perctile: 120000.0\n"
     ]
    }
   ],
   "source": [
    "print('2015 25th perctile: ' + str(ds_jobs_df[ds_jobs_df['year']==2015]['salary'].quantile(0.25)))\n",
    "print('2015 50th perctile: ' + str(ds_jobs_df[ds_jobs_df['year']==2015]['salary'].quantile(0.50)))\n",
    "print('2015 75th perctile: ' + str(ds_jobs_df[ds_jobs_df['year']==2015]['salary'].quantile(0.75)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019 25th perctile: 83849.0\n",
      "2019 50th perctile: 95160.0\n",
      "2019 75th perctile: 119850.0\n"
     ]
    }
   ],
   "source": [
    "print('2019 25th perctile: ' + str(ds_jobs_df[ds_jobs_df['year']>=2019]['salary'].quantile(0.25)))\n",
    "print('2019 50th perctile: ' + str(ds_jobs_df[ds_jobs_df['year']>=2019]['salary'].quantile(0.50)))\n",
    "print('2019 75th perctile: ' + str(ds_jobs_df[ds_jobs_df['year']>=2019]['salary'].quantile(0.75)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(14,7))\n",
    "ax = plt.hist(ds_jobs_df['salary'], bins=50, alpha=0.5)\n",
    "plt.axvline(ds_jobs_df['salary'].quantile(0.25), c='black')\n",
    "plt.axvline(ds_jobs_df['salary'].quantile(0.5), c='red')\n",
    "plt.axvline(ds_jobs_df['salary'].quantile(0.75), c='black')\n",
    "\n",
    "plt.xlabel('salary', fontsize=16)\n",
    "plt.ylabel('Frequency', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "company_df = pd.DataFrame()\n",
    "company_df['company'] = ds_jobs_df[['company','salary']].groupby(by=['company']).count().reset_index()['company']\n",
    "company_df['count'] = ds_jobs_df[['company','salary']].groupby(by=['company']).count().reset_index()['salary']\n",
    "company_df['salary'] = ds_jobs_df[['company','salary']].groupby(by=['company']).median().reset_index()['salary']\n",
    "sorted_df = company_df.sort_values(by='salary', ascending=False)\n",
    "sorted_df = sorted_df[sorted_df['count']>=10]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax = sns.barplot(x=sorted_df['salary'],\n",
    "                 y=sorted_df['company'])\n",
    "ax.set_xlabel(\"Median Data Scientist Salary\", fontsize=16)\n",
    "ax.set_ylabel(\"company\", fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>company</th>\n",
       "      <th>count</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>FACEBOOK INC</td>\n",
       "      <td>35</td>\n",
       "      <td>140250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>MICROSOFT CORPORATION</td>\n",
       "      <td>203</td>\n",
       "      <td>121367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>ZILLOW INC</td>\n",
       "      <td>14</td>\n",
       "      <td>111390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>THE BOSTON CONSULTING GROUP INC</td>\n",
       "      <td>17</td>\n",
       "      <td>95000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>WAYFAIR LLC</td>\n",
       "      <td>24</td>\n",
       "      <td>77854.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             company  count    salary\n",
       "52                      FACEBOOK INC     35  140250.0\n",
       "85             MICROSOFT CORPORATION    203  121367.0\n",
       "141                       ZILLOW INC     14  111390.0\n",
       "126  THE BOSTON CONSULTING GROUP INC     17   95000.0\n",
       "138                      WAYFAIR LLC     24   77854.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_df.iloc[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_df = company_df.sort_values(by='count', ascending=False)\n",
    "sorted_df = sorted_df[sorted_df['count']>=10]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax = sns.barplot(x=sorted_df['count'],\n",
    "                 y=sorted_df['company'])\n",
    "ax.set_xlabel(\"H1B Petitions Filed\", fontsize=16)\n",
    "ax.set_ylabel(\"Company\", fontsize=16)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}