company_emissions_preprocessing_missing_values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "prostate-garden",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76020\n"
     ]
    },
    {
     "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>organization_id</th>\n",
       "      <th>StatementDetails.FinancialPeriodFiscalYear</th>\n",
       "      <th>EmissionDataPoints.CO2EquivalentsEmissionDirectScope1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66</td>\n",
       "      <td>2009</td>\n",
       "      <td>83134.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66</td>\n",
       "      <td>2010</td>\n",
       "      <td>53354.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>2011</td>\n",
       "      <td>79268.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>66</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>66</td>\n",
       "      <td>2016</td>\n",
       "      <td>127.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66</td>\n",
       "      <td>2017</td>\n",
       "      <td>156.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>66</td>\n",
       "      <td>2018</td>\n",
       "      <td>569.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>66</td>\n",
       "      <td>2019</td>\n",
       "      <td>795.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>66</td>\n",
       "      <td>2020</td>\n",
       "      <td>6119.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    organization_id  StatementDetails.FinancialPeriodFiscalYear  \\\n",
       "0                66                                        2009   \n",
       "1                66                                        2010   \n",
       "2                66                                        2011   \n",
       "3                66                                        2012   \n",
       "4                66                                        2013   \n",
       "5                66                                        2014   \n",
       "6                66                                        2015   \n",
       "7                66                                        2016   \n",
       "8                66                                        2017   \n",
       "9                66                                        2018   \n",
       "10               66                                        2019   \n",
       "11               66                                        2020   \n",
       "\n",
       "    EmissionDataPoints.CO2EquivalentsEmissionDirectScope1  \n",
       "0                                            83134.00      \n",
       "1                                            53354.10      \n",
       "2                                            79268.75      \n",
       "3                                                 NaN      \n",
       "4                                                 NaN      \n",
       "5                                                 NaN      \n",
       "6                                                 NaN      \n",
       "7                                              127.00      \n",
       "8                                              156.00      \n",
       "9                                              569.00      \n",
       "10                                             795.00      \n",
       "11                                            6119.00      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-90-64ea4701f689>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m \u001b[0;31m#MSE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memissions_per_company\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_int\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'scope'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    166\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    333\u001b[0m     \u001b[0;36m0.825\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \"\"\"\n\u001b[0;32m--> 335\u001b[0;31m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m    336\u001b[0m         y_true, y_pred, multioutput)\n\u001b[1;32m    337\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \"\"\"\n\u001b[1;32m     88\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m             _assert_all_finite(array,\n\u001b[0m\u001b[1;32m    664\u001b[0m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m    101\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[1;32m    102\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'infinity'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'NaN, infinity'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m    104\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m                     (type_err,\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "#open dataset\n",
    "df0 = pd.read_csv(\"20220105_company_emissions_data.csv\", sep=',')\n",
    "\n",
    "#drop irrelevant columns from dataset\n",
    "df1 = df0.drop(columns=['WorkforceDataPoints.AsianMinoritiesEmployeesPercent',\n",
    "                       'WorkforceDataPoints.AsianMinoritiesManagersPercent', \n",
    "                      'WorkforceDataPoints.AverageEmployeeLengthOfService',\n",
    "                      'WorkforceDataPoints.AverageTrainingHours', \n",
    "                      'WorkforceDataPoints.BbbeeLevel', \n",
    "                      'WorkforceDataPoints.BlackOrAfricanAmericanMinoritiesEmployeesPercent',\n",
    "                      'WorkforceDataPoints.BlackOrAfricanAmericanMinoritiesManagersPercent',\n",
    "                      'WorkforceDataPoints.ContractorAccidents',\n",
    "                      'WorkforceDataPoints.ContractorFatalities',\n",
    "                      'WorkforceDataPoints.ContractorLostWorkingDays',\n",
    "                      'WorkforceDataPoints.DayCareServices',\n",
    "                      'WorkforceDataPoints.DiversityAndOpportunityControversies',\n",
    "                      'WorkforceDataPoints.EmployeeAccidents',\n",
    "                      'WorkforceDataPoints.EmployeeFatalities',\n",
    "                      'WorkforceDataPoints.EmployeeHealthAndSafetyTrainingHours',\n",
    "                      'WorkforceDataPoints.EmployeeLostWorkingDays',\n",
    "                      'WorkforceDataPoints.EmployeeResourceGroups',\n",
    "                      'WorkforceDataPoints.EmployeeSatisfaction',\n",
    "                      'WorkforceDataPoints.EmployeesHealthAndSafetyControversies',\n",
    "                      'WorkforceDataPoints.EmployeesHealthAndSafetyOhsas18001',\n",
    "                      'WorkforceDataPoints.EmployeesHealthAndSafetyTeam',\n",
    "                      'WorkforceDataPoints.EmployeesWithDisabilities',\n",
    "                      'WorkforceDataPoints.FlexibleWorkingHours',\n",
    "                      'WorkforceDataPoints.GenderPayGapPercentage',\n",
    "                      'WorkforceDataPoints.HealthAndSafetyTraining',\n",
    "                      'WorkforceDataPoints.HispanicOrLatinoMinoritiesEmployeesPercent',\n",
    "                      'WorkforceDataPoints.HispanicOrLatinoMinoritiesManagersPercent',\n",
    "                      'WorkforceDataPoints.HivAidsProgram',\n",
    "                      'WorkforceDataPoints.HrcCorporateEqualityIndex',\n",
    "                      'WorkforceDataPoints.HsmsCertifiedPercentage',\n",
    "                      'WorkforceDataPoints.InternalPromotion',\n",
    "                      'WorkforceDataPoints.InvoluntaryTurnoverOfEmployees',\n",
    "                      'WorkforceDataPoints.LostTimeInjuryRateContractors',\n",
    "                      'WorkforceDataPoints.LostTimeInjuryRateEmployees',\n",
    "                      'WorkforceDataPoints.LostTimeInjuryRateTotal',\n",
    "                      'WorkforceDataPoints.LostWorkingDays',\n",
    "                      'WorkforceDataPoints.ManagementDepartures',\n",
    "                      'WorkforceDataPoints.ManagementTraining',\n",
    "                      'WorkforceDataPoints.MinoritiesEmployeesPercent',\n",
    "                      'WorkforceDataPoints.MinoritiesManagersPercent',\n",
    "                      'WorkforceDataPoints.MinoritiesSalaryGapPercent',\n",
    "                      'WorkforceDataPoints.NewWomenEmployees',\n",
    "                      'WorkforceDataPoints.NumberOfEmployeesFromCsrReporting',\n",
    "                      'WorkforceDataPoints.OccupationalDiseases',\n",
    "                      'WorkforceDataPoints.OtherMinoritiesEmployeesPercent',\n",
    "                      'WorkforceDataPoints.OtherMinoritiesManagersPercent',\n",
    "                      'WorkforceDataPoints.PolicyCareerDevelopment',\n",
    "                      'WorkforceDataPoints.PolicyDiversityAndOpportunity',\n",
    "                      'WorkforceDataPoints.PolicyEmployeeHealthAndSafety',\n",
    "                      'WorkforceDataPoints.PolicySkillsTraining',\n",
    "                      'WorkforceDataPoints.PolicySupplyChainHealthAndSafety',\n",
    "                      'WorkforceDataPoints.RecentDiversityOpportunityControversies',\n",
    "                      'WorkforceDataPoints.RecentEmployeeHealthAndSafetyControversies',\n",
    "                      'WorkforceDataPoints.RecentWagesWorkingConditionControversies',\n",
    "                      'WorkforceDataPoints.SalariesAndWagesFromCsrReporting',\n",
    "                      'WorkforceDataPoints.Strikes',\n",
    "                      'WorkforceDataPoints.SupplierESGTraining',\n",
    "                      'WorkforceDataPoints.SupplyChainHealthAndSafetyImprovements',\n",
    "                      'WorkforceDataPoints.SupplyChainHealthAndSafetyTraining',\n",
    "                      'WorkforceDataPoints.TargetsDiversityAndOpportunity',\n",
    "                      'WorkforceDataPoints.TotalInjuryRateContractors',\n",
    "                      'WorkforceDataPoints.TotalInjuryRateEmployees',\n",
    "                      'WorkforceDataPoints.TotalInjuryRateTotal',\n",
    "                      'WorkforceDataPoints.TradeUnionRepresentation',\n",
    "                      'WorkforceDataPoints.TrainingCostsTotal',\n",
    "                      'WorkforceDataPoints.TrainingHoursTotal',\n",
    "                      'WorkforceDataPoints.TurnoverOfEmployees',\n",
    "                      'WorkforceDataPoints.VoluntaryTurnoverOfEmployees',\n",
    "                      'WorkforceDataPoints.WagesWorkingConditionControversiesCount',\n",
    "                      'WorkforceDataPoints.WhiteMinoritiesEmployeesPercent',\n",
    "                      'WorkforceDataPoints.WhiteMinoritiesManagersPercent',\n",
    "                      'WorkforceDataPoints.WomenEmployees',\n",
    "                      'WorkforceDataPoints.WomenManagers',\n",
    "                      'WorkforceIndicators.AnnouncedLayOffsToTotalEmployees',\n",
    "                      'WorkforceIndicators.HealthAndSafetyPolicy',\n",
    "                      'WorkforceIndicators.InjuriesToMillionHours',\n",
    "                      'WorkforceIndicators.LostDaysToTotalDays',\n",
    "                      'WorkforceIndicators.NetEmploymentCreation',\n",
    "                      'WorkforceIndicators.SalaryGap',\n",
    "                      'WorkforceIndicators.TrainingAndDevelopmentPolicy',\n",
    "                      'WorkforceIndicators.TrainingCostsPerEmployee',\n",
    "                      'WorkforceIndicators.WagesWorkingConditionControversies'],\n",
    "                      axis=1)\n",
    "    \n",
    "#number of columns in (cleaned) dataset\n",
    "len(df0.columns)\n",
    "len(df1.columns)\n",
    "\n",
    "#save cleaned dataset as new file\n",
    "df2 = df1.to_csv('company_emissions_cleaned.csv', index=True)\n",
    "df3 = pd.read_csv(\"company_emissions_cleaned.csv\", sep=',')\n",
    "\n",
    "\n",
    "from scipy.interpolate import interp1d\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "scope_values = df3['EmissionDataPoints.CO2EquivalentsEmissionDirectScope1']\n",
    "print(scope_values.isnull().sum())\n",
    "\n",
    "df_numeric = df3.apply(pd.to_numeric, args=('coerce',))\n",
    "\n",
    "df = df_numeric.sort_values(by='StatementDetails.FinancialPeriodFiscalYear')\n",
    "\n",
    "df5 = df.loc[df[\"organization_id\"] == 66]\n",
    "\n",
    "emissions_per_company = df5.groupby([\"organization_id\", \"StatementDetails.FinancialPeriodFiscalYear\"])[[\"EmissionDataPoints.CO2EquivalentsEmissionDirectScope1\"]].sum(min_count=1).reset_index()\n",
    "\n",
    "#zeroes = df[df.isna().any(axis=1)]\n",
    "#display(zeroes[[\"organization_id\", \"StatementDetails.FinancialPeriodFiscalYear\",'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1']])\n",
    "\n",
    "display(emissions_per_company)\n",
    "\n",
    "\n",
    "#Actual\n",
    "emissions_per_company.plot(x = 'StatementDetails.FinancialPeriodFiscalYear',\n",
    "        y = 'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1', title='Actual', xlabel='Year', ylabel='CO2 emissions in tonnes', label='Organization 66')\n",
    "\n",
    "#Backward fill\n",
    "df_bf = emissions_per_company.bfill(axis = 0)\n",
    "\n",
    "df_bf.plot(x = 'StatementDetails.FinancialPeriodFiscalYear', \n",
    "        y = 'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1', title='Backward Fill', linestyle='dotted', xlabel='Year', ylabel='CO2 emission in tonnes', label='Organization 66')\n",
    "\n",
    "#Forward fill\n",
    "df_ff = emissions_per_company.ffill(axis = 0)\n",
    "\n",
    "df_ff.plot(x = 'StatementDetails.FinancialPeriodFiscalYear', \n",
    "        y = 'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1', title='Forward Fill', linestyle='dotted', xlabel='Year', ylabel='CO2 emission in tonnes', label='Organization 66')\n",
    "\n",
    "#Interpolation\n",
    "df_int = emissions_per_company.interpolate(axis = 0)\n",
    "\n",
    "df_int.plot(x = 'StatementDetails.FinancialPeriodFiscalYear', \n",
    "        y = 'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1', title='Interpolation', color='blue', linestyle='dotted', xlabel='Year', ylabel='CO2 emission in tonnes', label='Organization 66')\n",
    "\n",
    "from sklearn import linear_model\n",
    "\n",
    "df_int = df_int.rename(columns={'EmissionDataPoints.CO2EquivalentsEmissionDirectScope1': 'scope', 'StatementDetails.FinancialPeriodFiscalYear': \"year\"})\n",
    "\n",
    "y = df_int.scope.values\n",
    "x = df_int.year.values\n",
    "\n",
    "length = len(df_int) \n",
    "x = x.reshape(length, 1)   \n",
    "y = y.reshape(length, 1)\n",
    "\n",
    "reg = linear_model.LinearRegression()\n",
    "reg = reg.fit(x,y)\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.plot(x, reg.predict(x), color='red', linewidth=3)\n",
    "plt.show()\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "#MSE\n",
    "error = np.round(mean_squared_error(emissions_per_company['EmissionDataPoints.CO2EquivalentsEmissionDirectScope1'], df_int['scope']), 2)\n",
    "print(error)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "proper-watch",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "terminal-stupid",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}