company_emissions_preprocessing_predicting_values_fulldf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cultural-jackson",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "organization_id                                                        0\n",
      "StatementDetails.FinancialPeriodFiscalYear                             0\n",
      "EmissionDataPoints.CO2EquivalentsEmissionDirectScope1              76020\n",
      "EmissionDataPoints.AnalyticEstimatesCO2EquivalentsEmissionTotal    37094\n",
      "dtype: int64\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>2.718972e+06</td>\n",
       "      <td>3.303832e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.718871e+06</td>\n",
       "      <td>3.303725e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2.718869e+06</td>\n",
       "      <td>3.303723e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2.718867e+06</td>\n",
       "      <td>3.303720e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2.718865e+06</td>\n",
       "      <td>3.303718e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101101</th>\n",
       "      <td>14957.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>1.032016e+06</td>\n",
       "      <td>1.512969e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101102</th>\n",
       "      <td>14957.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.032014e+06</td>\n",
       "      <td>1.512966e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101103</th>\n",
       "      <td>14957.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.032012e+06</td>\n",
       "      <td>1.512964e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101104</th>\n",
       "      <td>14958.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>1.031902e+06</td>\n",
       "      <td>1.512847e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101105</th>\n",
       "      <td>14958.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>1.031900e+06</td>\n",
       "      <td>1.512845e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101106 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0       1             2             3\n",
       "0           1.0  2015.0  2.718972e+06  3.303832e+06\n",
       "1           2.0  2009.0  2.718871e+06  3.303725e+06\n",
       "2           2.0  2010.0  2.718869e+06  3.303723e+06\n",
       "3           2.0  2011.0  2.718867e+06  3.303720e+06\n",
       "4           2.0  2012.0  2.718865e+06  3.303718e+06\n",
       "...         ...     ...           ...           ...\n",
       "101101  14957.0  2013.0  1.032016e+06  1.512969e+06\n",
       "101102  14957.0  2014.0  1.032014e+06  1.512966e+06\n",
       "101103  14957.0  2015.0  1.032012e+06  1.512964e+06\n",
       "101104  14958.0  2014.0  1.031902e+06  1.512847e+06\n",
       "101105  14958.0  2015.0  1.031900e+06  1.512845e+06\n",
       "\n",
       "[101106 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "dtype: int64\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>83134.000000</td>\n",
       "      <td>86960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>53354.100000</td>\n",
       "      <td>57370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>79268.750000</td>\n",
       "      <td>81940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>-299316.403716</td>\n",
       "      <td>88421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>-303205.381302</td>\n",
       "      <td>84278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>-319661.494216</td>\n",
       "      <td>66747.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>-344951.586110</td>\n",
       "      <td>39805.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>1592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>2107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>795.000000</td>\n",
       "      <td>2556.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>66.0</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>6119.000000</td>\n",
       "      <td>7922.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0       1              2        3\n",
       "337  66.0  2009.0   83134.000000  86960.0\n",
       "338  66.0  2010.0   53354.100000  57370.0\n",
       "339  66.0  2011.0   79268.750000  81940.0\n",
       "340  66.0  2012.0 -299316.403716  88421.0\n",
       "341  66.0  2013.0 -303205.381302  84278.0\n",
       "342  66.0  2014.0 -319661.494216  66747.0\n",
       "343  66.0  2015.0 -344951.586110  39805.0\n",
       "344  66.0  2016.0     127.000000   1592.0\n",
       "345  66.0  2017.0     156.000000   1739.0\n",
       "346  66.0  2018.0     569.000000   2107.0\n",
       "347  66.0  2019.0     795.000000   2556.0\n",
       "348  66.0  2020.0    6119.000000   7922.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Predicted'}, xlabel='Year', ylabel='CO2 emission in tonnes'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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",
    "emissions_per_company = df.groupby([\"organization_id\", \"StatementDetails.FinancialPeriodFiscalYear\"])[[\"EmissionDataPoints.CO2EquivalentsEmissionDirectScope1\", \"EmissionDataPoints.AnalyticEstimatesCO2EquivalentsEmissionTotal\"\n",
    "]].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",
    "print(emissions_per_company.isnull().sum())\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "test_df = emissions_per_company[emissions_per_company[\"EmissionDataPoints.CO2EquivalentsEmissionDirectScope1\"].isnull()]\n",
    "\n",
    "imp_mean = IterativeImputer()\n",
    "imp_mean.fit(emissions_per_company)\n",
    "\n",
    "pred_df = pd.DataFrame(imp_mean.transform(emissions_per_company))\n",
    "display(pred_df)\n",
    "\n",
    "\n",
    "print(pred_df.isnull().sum())\n",
    "\n",
    "df5 = pred_df.loc[pred_df[0] == 66.0]\n",
    "\n",
    "display(df5)\n",
    "\n",
    "df5.plot(x = 1, \n",
    "     y = 2, title='Predicted', color='blue', linestyle='dotted', xlabel='Year', ylabel='CO2 emission in tonnes', label='Organization 66')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eligible-century",
   "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
}