GDS_1_emissions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.common.by import By\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "url = 'https://racetozerodataexplorer.org/proceed/'\n",
    "\n",
    "names = []\n",
    "e1 = []\n",
    "e2l = []\n",
    "e2m = []\n",
    "e_master = [e1, e2l, e2m]\n",
    "\n",
    "non_click = ['-', '-', '-', '-']\n",
    "\n",
    "with webdriver.Chrome() as driver:\n",
    "    driver.get(url)\n",
    "    table = driver.find_elements(By.XPATH, \"//*[@class= 'styles_table__jWPKo']/tbody/tr\")\n",
    "    for row in table:\n",
    "        name = row.find_element(By.TAG_NAME, 'th')\n",
    "        names.append(name.text)\n",
    "\n",
    "        for i, cell in enumerate(row.find_elements(By.TAG_NAME, 'td')):\n",
    "            if i % 9 in [0,1,2]:\n",
    "                try:\n",
    "                    cell.find_element(By.CLASS_NAME, 'styles_button__IS06I').click()\n",
    "                    WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CLASS_NAME, 'styles_axisValue__ipOq1')))\n",
    "                    values = driver.find_elements(By.CLASS_NAME, 'styles_axisValue__ipOq1')\n",
    "                    e_master[i].append([value.text.replace(',','') for value in values])\n",
    "                    driver.find_element(By.CLASS_NAME, 'styles_close__cpVjT').click()\n",
    "                except:\n",
    "                    e_master[i].append(non_click)\n",
    "                    continue"
   ]
  },
  {
   "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>Company</th>\n",
       "      <th>E1-2018</th>\n",
       "      <th>E1-2019</th>\n",
       "      <th>E1-2020</th>\n",
       "      <th>E1-2021</th>\n",
       "      <th>E2L-2018</th>\n",
       "      <th>E2L-2019</th>\n",
       "      <th>E2L-2020</th>\n",
       "      <th>E2L-2021</th>\n",
       "      <th>E2M-2018</th>\n",
       "      <th>E2M-2019</th>\n",
       "      <th>E2M-2020</th>\n",
       "      <th>E2M-2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37 Interactive Entertainment</td>\n",
       "      <td>-</td>\n",
       "      <td>129</td>\n",
       "      <td>169</td>\n",
       "      <td>152</td>\n",
       "      <td>-</td>\n",
       "      <td>2701</td>\n",
       "      <td>2395</td>\n",
       "      <td>1794</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A.P. Moller - Maersk</td>\n",
       "      <td>38749000</td>\n",
       "      <td>36204000</td>\n",
       "      <td>33902000</td>\n",
       "      <td>36863000</td>\n",
       "      <td>338000</td>\n",
       "      <td>287000</td>\n",
       "      <td>289000</td>\n",
       "      <td>310170</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>337000</td>\n",
       "      <td>330182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAK AB</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>269380</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>18632</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>74351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AB Volvo</td>\n",
       "      <td>-</td>\n",
       "      <td>245000</td>\n",
       "      <td>205000</td>\n",
       "      <td>245000</td>\n",
       "      <td>-</td>\n",
       "      <td>215000</td>\n",
       "      <td>193000</td>\n",
       "      <td>204000</td>\n",
       "      <td>-</td>\n",
       "      <td>124000</td>\n",
       "      <td>121000</td>\n",
       "      <td>114000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABB</td>\n",
       "      <td>375000</td>\n",
       "      <td>257500</td>\n",
       "      <td>225547</td>\n",
       "      <td>190900</td>\n",
       "      <td>650799</td>\n",
       "      <td>456000</td>\n",
       "      <td>414000</td>\n",
       "      <td>370000</td>\n",
       "      <td>627903</td>\n",
       "      <td>410800</td>\n",
       "      <td>335300</td>\n",
       "      <td>214000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Yum China Holdings, Inc.</td>\n",
       "      <td>-</td>\n",
       "      <td>114720</td>\n",
       "      <td>135615</td>\n",
       "      <td>158887</td>\n",
       "      <td>-</td>\n",
       "      <td>1956072</td>\n",
       "      <td>1873757</td>\n",
       "      <td>2158856</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Yum! Brands, Inc.</td>\n",
       "      <td>48047</td>\n",
       "      <td>23545</td>\n",
       "      <td>38907</td>\n",
       "      <td>39013</td>\n",
       "      <td>155439</td>\n",
       "      <td>140341</td>\n",
       "      <td>128129</td>\n",
       "      <td>117621</td>\n",
       "      <td>159403</td>\n",
       "      <td>144313</td>\n",
       "      <td>130953</td>\n",
       "      <td>112665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>Yutong Bus Co., Ltd.</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>498</th>\n",
       "      <td>Żabka Polska Sp. z o. o.</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>7653</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>499</th>\n",
       "      <td>ZF Friedrichshafen AG</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>384000</td>\n",
       "      <td>416000</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1323000</td>\n",
       "      <td>1377000</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1130000</td>\n",
       "      <td>941000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Company   E1-2018   E1-2019   E1-2020   E1-2021  \\\n",
       "0    37 Interactive Entertainment         -       129       169       152   \n",
       "1            A.P. Moller - Maersk  38749000  36204000  33902000  36863000   \n",
       "2                          AAK AB         -         -         -    269380   \n",
       "3                        AB Volvo         -    245000    205000    245000   \n",
       "4                             ABB    375000    257500    225547    190900   \n",
       "..                            ...       ...       ...       ...       ...   \n",
       "495      Yum China Holdings, Inc.         -    114720    135615    158887   \n",
       "496             Yum! Brands, Inc.     48047     23545     38907     39013   \n",
       "497          Yutong Bus Co., Ltd.         -         -         -         -   \n",
       "498      Żabka Polska Sp. z o. o.         -         -         -      7653   \n",
       "499         ZF Friedrichshafen AG         -         -    384000    416000   \n",
       "\n",
       "    E2L-2018 E2L-2019 E2L-2020 E2L-2021 E2M-2018 E2M-2019 E2M-2020 E2M-2021  \n",
       "0          -     2701     2395     1794        -        -        -        -  \n",
       "1     338000   287000   289000   310170        -        -   337000   330182  \n",
       "2          -        -        -    18632        -        -        -    74351  \n",
       "3          -   215000   193000   204000        -   124000   121000   114000  \n",
       "4     650799   456000   414000   370000   627903   410800   335300   214000  \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...  \n",
       "495        -  1956072  1873757  2158856        -        -        -        -  \n",
       "496   155439   140341   128129   117621   159403   144313   130953   112665  \n",
       "497        -        -        -        -        -        -        -        -  \n",
       "498        -        -        -        -        -        -        -        -  \n",
       "499        -        -  1323000  1377000        -        -  1130000   941000  \n",
       "\n",
       "[500 rows x 13 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(e1, columns=['E1-2018', 'E1-2019', 'E1-2020', 'E1-2021'])\n",
    "df2 = pd.DataFrame(e2l, columns=['E2L-2018', 'E2L-2019', 'E2L-2020', 'E2L-2021'])\n",
    "df3 = pd.DataFrame(e2m, columns=['E2M-2018', 'E2M-2019', 'E2M-2020', 'E2M-2021'])\n",
    "\n",
    "df = pd.concat([df1, df2, df3], axis=1)\n",
    "df.insert(0, 'Company', names)\n",
    "df.to_csv('emissions.csv', index=False)\n",
    "df"
   ]
  }
 ],
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