DataVisualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CO2 Emission by Sector - Data Visualization\n",
    "### For CO2 Emission \n",
    "CO2Ems10YrResIndComTrn.csv\n",
    "\n",
    "### Consumption Totals \n",
    "MasterConsumptionResComTrnInd.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "import scipy.stats as stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from itertools import cycle, islice\n",
    "from decimal import Decimal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Read School and Student Data File and store into Pandas Data Frames\n",
    "co2_ems_df = pd.read_csv(\"CO2Ems10YrResIndComTrn.csv\")\n",
    "co2_ems_df = co2_ems_df[[\"sector\", \"source\", \"year\", \"value\"]]\n",
    "co2_ems_df = co2_ems_df.loc[co2_ems_df[\"source\"] != \"Total Energy\" ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector     object\n",
       "source     object\n",
       "year        int64\n",
       "value     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_ems_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2018</td>\n",
       "      <td>109.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2017</td>\n",
       "      <td>111.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2016</td>\n",
       "      <td>112.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2015</td>\n",
       "      <td>128.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2014</td>\n",
       "      <td>143.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2013</td>\n",
       "      <td>144.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2012</td>\n",
       "      <td>141.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2011</td>\n",
       "      <td>145.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2010</td>\n",
       "      <td>152.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2009</td>\n",
       "      <td>130.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2018</td>\n",
       "      <td>92.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2017</td>\n",
       "      <td>87.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2016</td>\n",
       "      <td>84.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2015</td>\n",
       "      <td>85.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2014</td>\n",
       "      <td>99.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2013</td>\n",
       "      <td>92.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2012</td>\n",
       "      <td>92.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2011</td>\n",
       "      <td>90.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2010</td>\n",
       "      <td>84.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2009</td>\n",
       "      <td>78.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2018</td>\n",
       "      <td>56.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2017</td>\n",
       "      <td>52.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2016</td>\n",
       "      <td>51.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2015</td>\n",
       "      <td>53.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2014</td>\n",
       "      <td>45.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2013</td>\n",
       "      <td>53.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2012</td>\n",
       "      <td>50.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2011</td>\n",
       "      <td>39.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2010</td>\n",
       "      <td>43.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2009</td>\n",
       "      <td>37.949</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>390</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2018</td>\n",
       "      <td>46.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2017</td>\n",
       "      <td>42.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2016</td>\n",
       "      <td>40.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2014</td>\n",
       "      <td>40.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2013</td>\n",
       "      <td>47.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2012</td>\n",
       "      <td>41.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2011</td>\n",
       "      <td>38.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2010</td>\n",
       "      <td>38.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2009</td>\n",
       "      <td>37.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2018</td>\n",
       "      <td>1865.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2017</td>\n",
       "      <td>1841.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2016</td>\n",
       "      <td>1827.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2015</td>\n",
       "      <td>1795.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2014</td>\n",
       "      <td>1770.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2013</td>\n",
       "      <td>1745.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2012</td>\n",
       "      <td>1728.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2011</td>\n",
       "      <td>1765.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2010</td>\n",
       "      <td>1800.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2009</td>\n",
       "      <td>1781.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2018</td>\n",
       "      <td>48.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2017</td>\n",
       "      <td>52.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2016</td>\n",
       "      <td>49.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2015</td>\n",
       "      <td>36.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2014</td>\n",
       "      <td>35.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2013</td>\n",
       "      <td>45.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2012</td>\n",
       "      <td>52.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2011</td>\n",
       "      <td>61.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2010</td>\n",
       "      <td>70.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2009</td>\n",
       "      <td>62.290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sector               source  year     value\n",
       "0        Industrial                 Coal  2018   109.967\n",
       "1        Industrial                 Coal  2017   111.616\n",
       "2        Industrial                 Coal  2016   112.702\n",
       "3        Industrial                 Coal  2015   128.941\n",
       "4        Industrial                 Coal  2014   143.075\n",
       "5        Industrial                 Coal  2013   144.563\n",
       "6        Industrial                 Coal  2012   141.470\n",
       "7        Industrial                 Coal  2011   145.983\n",
       "8        Industrial                 Coal  2010   152.679\n",
       "9        Industrial                 Coal  2009   130.545\n",
       "10       Industrial  Distillate Fuel Oil  2018    92.306\n",
       "11       Industrial  Distillate Fuel Oil  2017    87.969\n",
       "12       Industrial  Distillate Fuel Oil  2016    84.446\n",
       "13       Industrial  Distillate Fuel Oil  2015    85.380\n",
       "14       Industrial  Distillate Fuel Oil  2014    99.700\n",
       "15       Industrial  Distillate Fuel Oil  2013    92.428\n",
       "16       Industrial  Distillate Fuel Oil  2012    92.799\n",
       "17       Industrial  Distillate Fuel Oil  2011    90.210\n",
       "18       Industrial  Distillate Fuel Oil  2010    84.176\n",
       "19       Industrial  Distillate Fuel Oil  2009    78.017\n",
       "20       Industrial                  HGL  2018    56.836\n",
       "21       Industrial                  HGL  2017    52.732\n",
       "22       Industrial                  HGL  2016    51.074\n",
       "23       Industrial                  HGL  2015    53.360\n",
       "24       Industrial                  HGL  2014    45.533\n",
       "25       Industrial                  HGL  2013    53.056\n",
       "26       Industrial                  HGL  2012    50.339\n",
       "27       Industrial                  HGL  2011    39.529\n",
       "28       Industrial                  HGL  2010    43.444\n",
       "29       Industrial                  HGL  2009    37.949\n",
       "..              ...                  ...   ...       ...\n",
       "390  Transportation          Natural Gas  2018    46.299\n",
       "391  Transportation          Natural Gas  2017    42.375\n",
       "392  Transportation          Natural Gas  2016    40.174\n",
       "393  Transportation          Natural Gas  2015    39.519\n",
       "394  Transportation          Natural Gas  2014    40.307\n",
       "395  Transportation          Natural Gas  2013    47.076\n",
       "396  Transportation          Natural Gas  2012    41.372\n",
       "397  Transportation          Natural Gas  2011    38.920\n",
       "398  Transportation          Natural Gas  2010    38.145\n",
       "399  Transportation          Natural Gas  2009    37.929\n",
       "400  Transportation            Petroleum  2018  1865.319\n",
       "401  Transportation            Petroleum  2017  1841.610\n",
       "402  Transportation            Petroleum  2016  1827.162\n",
       "403  Transportation            Petroleum  2015  1795.433\n",
       "404  Transportation            Petroleum  2014  1770.374\n",
       "405  Transportation            Petroleum  2013  1745.080\n",
       "406  Transportation            Petroleum  2012  1728.024\n",
       "407  Transportation            Petroleum  2011  1765.592\n",
       "408  Transportation            Petroleum  2010  1800.046\n",
       "409  Transportation            Petroleum  2009  1781.950\n",
       "410  Transportation        Residual Fuel  2018    48.188\n",
       "411  Transportation        Residual Fuel  2017    52.367\n",
       "412  Transportation        Residual Fuel  2016    49.100\n",
       "413  Transportation        Residual Fuel  2015    36.514\n",
       "414  Transportation        Residual Fuel  2014    35.186\n",
       "415  Transportation        Residual Fuel  2013    45.793\n",
       "416  Transportation        Residual Fuel  2012    52.835\n",
       "417  Transportation        Residual Fuel  2011    61.171\n",
       "418  Transportation        Residual Fuel  2010    70.285\n",
       "419  Transportation        Residual Fuel  2009    62.290\n",
       "\n",
       "[390 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_ems_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#combine sources unique for a sector\n",
    "\n",
    "d = {\"Jet Fuel\":\"Sector Specific\",\n",
    "     \"Electric Power\":\"Sector Specific\", \n",
    "     \"Other\":\"Sector Specific\", \n",
    "     \"Aviation Gasoline\":\"Sector Specific\",\n",
    "    \"Transportations\":\"Sector Specific\",\n",
    "    \"Residentials\":\"Sector Specific\",\n",
    "    \"Electric Power\":\"Sector Specific\"}\n",
    "\n",
    "co2_ems_nosingle_df = co2_ems_df.replace(d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>Commercial</th>\n",
       "      <th>Industrial</th>\n",
       "      <th>Residental</th>\n",
       "      <th>Transportation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Coal</td>\n",
       "      <td>3.9984</td>\n",
       "      <td>132.1541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>26.1913</td>\n",
       "      <td>88.7431</td>\n",
       "      <td>36.9063</td>\n",
       "      <td>428.4636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HGL</td>\n",
       "      <td>9.4346</td>\n",
       "      <td>48.3852</td>\n",
       "      <td>29.6618</td>\n",
       "      <td>0.5806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kerosene</td>\n",
       "      <td>0.1613</td>\n",
       "      <td>0.2014</td>\n",
       "      <td>1.0520</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lubricants</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.3793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Motor Gasoline</td>\n",
       "      <td>11.6340</td>\n",
       "      <td>16.8014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1082.8919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>174.5048</td>\n",
       "      <td>470.6199</td>\n",
       "      <td>255.0470</td>\n",
       "      <td>41.2116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Petroleum</td>\n",
       "      <td>49.5564</td>\n",
       "      <td>344.4765</td>\n",
       "      <td>67.6202</td>\n",
       "      <td>1792.0590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Petroleum Coke</td>\n",
       "      <td>0.0376</td>\n",
       "      <td>65.5721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2.0979</td>\n",
       "      <td>4.9684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.3729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Sector Specific</td>\n",
       "      <td>718.5307</td>\n",
       "      <td>115.0174</td>\n",
       "      <td>752.8539</td>\n",
       "      <td>75.8181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sector               source  Commercial  Industrial  Residental  \\\n",
       "0                      Coal      3.9984    132.1541         NaN   \n",
       "1       Distillate Fuel Oil     26.1913     88.7431     36.9063   \n",
       "2                       HGL      9.4346     48.3852     29.6618   \n",
       "3                  Kerosene      0.1613      0.2014      1.0520   \n",
       "4                Lubricants         NaN      4.7878         NaN   \n",
       "5            Motor Gasoline     11.6340     16.8014         NaN   \n",
       "6               Natural Gas    174.5048    470.6199    255.0470   \n",
       "7                 Petroleum     49.5564    344.4765     67.6202   \n",
       "8            Petroleum Coke      0.0376     65.5721         NaN   \n",
       "9             Residual Fuel      2.0979      4.9684         NaN   \n",
       "10          Sector Specific    718.5307    115.0174    752.8539   \n",
       "\n",
       "sector  Transportation  \n",
       "0               0.0000  \n",
       "1             428.4636  \n",
       "2               0.5806  \n",
       "3                  NaN  \n",
       "4               5.3793  \n",
       "5            1082.8919  \n",
       "6              41.2116  \n",
       "7            1792.0590  \n",
       "8                  NaN  \n",
       "9              51.3729  \n",
       "10             75.8181  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "co2_ems_pivot_df = co2_ems_nosingle_df.pivot_table(values = \"value\", \n",
    "                           index = [\"source\"],columns = \"sector\" ).reset_index()\n",
    "co2_ems_pivot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "my_colors = list(islice(cycle(['black', 'red', 'blue', 'green']), None, len(co2_ems_pivot_df)))\n",
    "                 \n",
    "ax = co2_ems_pivot_df.plot(kind='bar', stacked=True, color=my_colors, figsize=(10, 6))\n",
    "# ax.figure(figsize=(12, 8))\n",
    "ax.set_title(\"Source vs Sectors\")\n",
    "ax.set_xlabel(\"Source\")\n",
    "ax.set_ylabel(\"CO2 Emission (MMT)\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':')\n",
    "\n",
    "for p in ax.patches:\n",
    "    if p.get_height() >= 50:\n",
    "        ax.annotate(str(int(p.get_height())), (p.get_x() * 1, p.get_height() * 1))\n",
    "        \n",
    "plt.xticks(rotation=70)\n",
    "plt.tight_layout()  \n",
    "plt.savefig(\"CO2EmissionSourcesVsSectors.png\")\n",
    "# plt.show()           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>sector</th>\n",
       "      <th>total_emissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>9961.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>12917.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Residental</td>\n",
       "      <td>11431.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>36294.132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sector  total_emissions\n",
       "0      Commercial         9961.470\n",
       "1      Industrial        12917.273\n",
       "2      Residental        11431.412\n",
       "3  Transportation        36294.132"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CO2 Emission by Sector\n",
    "co2_ems_10Y_gdf = co2_ems_nosingle_df.groupby([\"sector\"])[\"value\"].sum().rename(columns={'value': 'values'})\n",
    "\n",
    "co2_ems_10Y_gdf1 = pd.DataFrame({\"total_emissions\": co2_ems_10Y_gdf})\n",
    "\n",
    "co2_ems_10Y_gdf1 = co2_ems_10Y_gdf1.reset_index()\n",
    "\n",
    "co2_ems_10Y_gdf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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": [
    "\n",
    "ax = co2_ems_10Y_gdf1.plot(x= 'sector',\n",
    "                           y='total_emissions',\n",
    "                           kind='bar', \n",
    "                           title=\"CO2 Emission by Sectors\",\n",
    "                           color = (['black', 'red', 'blue', 'green']),\n",
    "                           figsize=(10, 6))\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Sector\")\n",
    "ax.set_ylabel(\"CO2 Emission (MMT)\")\n",
    "\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':')\n",
    "plt.xticks(rotation=70)\n",
    "plt.legend().remove()\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(int(p.get_height())), (p.get_x() * 1, p.get_height() * 1.005))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"CO2EmissionBySectorTL.png\")   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Total Emissions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Coal</th>\n",
       "      <td>1361.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distillate Fuel Oil</th>\n",
       "      <td>5803.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HGL</th>\n",
       "      <td>880.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerosene</th>\n",
       "      <td>14.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lubricants</th>\n",
       "      <td>101.671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motor Gasoline</th>\n",
       "      <td>11113.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Natural Gas</th>\n",
       "      <td>9413.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petroleum</th>\n",
       "      <td>22537.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petroleum Coke</th>\n",
       "      <td>656.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual Fuel</th>\n",
       "      <td>584.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sector Specific</th>\n",
       "      <td>18138.563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Total Emissions\n",
       "source                              \n",
       "Coal                        1361.525\n",
       "Distillate Fuel Oil         5803.043\n",
       "HGL                          880.622\n",
       "Kerosene                      14.147\n",
       "Lubricants                   101.671\n",
       "Motor Gasoline             11113.273\n",
       "Natural Gas                 9413.833\n",
       "Petroleum                  22537.121\n",
       "Petroleum Coke               656.097\n",
       "Residual Fuel                584.392\n",
       "Sector Specific            18138.563"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#CO2 Emission by Source\n",
    "co2_ems_10Y_gbso_df = co2_ems_nosingle_df.groupby([\"source\"])[\"value\"].sum()\n",
    "\n",
    "co2_ems_10Y_gbso_df1 = pd.DataFrame({\"Total Emissions\": co2_ems_10Y_gbso_df})\n",
    "\n",
    "co2_ems_10Y_gbso_df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": [
    "ax = co2_ems_10Y_gbso_df1.plot(kind='bar', width=.975, figsize=(10, 6))\n",
    "\n",
    "ax.set_title(\"CO2 Emission by Source\")\n",
    "ax.set_xlabel(\"Source\")\n",
    "ax.set_ylabel(\"CO2 Emission (MMT)\")\n",
    "\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':')\n",
    "plt.xticks(rotation=70)\n",
    "\n",
    "for p in ax.patches:\n",
    "    if p.get_height() >= 50:\n",
    "        ax.annotate(str(int(p.get_height())), (p.get_x() * 1, p.get_height() * 1))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"CO2EmissionBySource.png\")           \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "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>sector</th>\n",
       "      <th>source</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2018</td>\n",
       "      <td>109.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2017</td>\n",
       "      <td>111.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2016</td>\n",
       "      <td>112.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2015</td>\n",
       "      <td>128.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2014</td>\n",
       "      <td>143.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2013</td>\n",
       "      <td>144.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2012</td>\n",
       "      <td>141.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2011</td>\n",
       "      <td>145.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2010</td>\n",
       "      <td>152.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Coal</td>\n",
       "      <td>2009</td>\n",
       "      <td>130.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2018</td>\n",
       "      <td>92.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2017</td>\n",
       "      <td>87.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2016</td>\n",
       "      <td>84.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2015</td>\n",
       "      <td>85.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2014</td>\n",
       "      <td>99.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2013</td>\n",
       "      <td>92.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2012</td>\n",
       "      <td>92.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2011</td>\n",
       "      <td>90.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2010</td>\n",
       "      <td>84.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>Distillate Fuel Oil</td>\n",
       "      <td>2009</td>\n",
       "      <td>78.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2018</td>\n",
       "      <td>56.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2017</td>\n",
       "      <td>52.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2016</td>\n",
       "      <td>51.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2015</td>\n",
       "      <td>53.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2014</td>\n",
       "      <td>45.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2013</td>\n",
       "      <td>53.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2012</td>\n",
       "      <td>50.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2011</td>\n",
       "      <td>39.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2010</td>\n",
       "      <td>43.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>HGL</td>\n",
       "      <td>2009</td>\n",
       "      <td>37.949</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>390</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2018</td>\n",
       "      <td>46.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2017</td>\n",
       "      <td>42.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2016</td>\n",
       "      <td>40.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2014</td>\n",
       "      <td>40.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2013</td>\n",
       "      <td>47.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2012</td>\n",
       "      <td>41.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2011</td>\n",
       "      <td>38.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2010</td>\n",
       "      <td>38.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Natural Gas</td>\n",
       "      <td>2009</td>\n",
       "      <td>37.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2018</td>\n",
       "      <td>1865.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2017</td>\n",
       "      <td>1841.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2016</td>\n",
       "      <td>1827.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2015</td>\n",
       "      <td>1795.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2014</td>\n",
       "      <td>1770.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2013</td>\n",
       "      <td>1745.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2012</td>\n",
       "      <td>1728.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2011</td>\n",
       "      <td>1765.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2010</td>\n",
       "      <td>1800.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Petroleum</td>\n",
       "      <td>2009</td>\n",
       "      <td>1781.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2018</td>\n",
       "      <td>48.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2017</td>\n",
       "      <td>52.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2016</td>\n",
       "      <td>49.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2015</td>\n",
       "      <td>36.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2014</td>\n",
       "      <td>35.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2013</td>\n",
       "      <td>45.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2012</td>\n",
       "      <td>52.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2011</td>\n",
       "      <td>61.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2010</td>\n",
       "      <td>70.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>Residual Fuel</td>\n",
       "      <td>2009</td>\n",
       "      <td>62.290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sector               source  year     value\n",
       "0        Industrial                 Coal  2018   109.967\n",
       "1        Industrial                 Coal  2017   111.616\n",
       "2        Industrial                 Coal  2016   112.702\n",
       "3        Industrial                 Coal  2015   128.941\n",
       "4        Industrial                 Coal  2014   143.075\n",
       "5        Industrial                 Coal  2013   144.563\n",
       "6        Industrial                 Coal  2012   141.470\n",
       "7        Industrial                 Coal  2011   145.983\n",
       "8        Industrial                 Coal  2010   152.679\n",
       "9        Industrial                 Coal  2009   130.545\n",
       "10       Industrial  Distillate Fuel Oil  2018    92.306\n",
       "11       Industrial  Distillate Fuel Oil  2017    87.969\n",
       "12       Industrial  Distillate Fuel Oil  2016    84.446\n",
       "13       Industrial  Distillate Fuel Oil  2015    85.380\n",
       "14       Industrial  Distillate Fuel Oil  2014    99.700\n",
       "15       Industrial  Distillate Fuel Oil  2013    92.428\n",
       "16       Industrial  Distillate Fuel Oil  2012    92.799\n",
       "17       Industrial  Distillate Fuel Oil  2011    90.210\n",
       "18       Industrial  Distillate Fuel Oil  2010    84.176\n",
       "19       Industrial  Distillate Fuel Oil  2009    78.017\n",
       "20       Industrial                  HGL  2018    56.836\n",
       "21       Industrial                  HGL  2017    52.732\n",
       "22       Industrial                  HGL  2016    51.074\n",
       "23       Industrial                  HGL  2015    53.360\n",
       "24       Industrial                  HGL  2014    45.533\n",
       "25       Industrial                  HGL  2013    53.056\n",
       "26       Industrial                  HGL  2012    50.339\n",
       "27       Industrial                  HGL  2011    39.529\n",
       "28       Industrial                  HGL  2010    43.444\n",
       "29       Industrial                  HGL  2009    37.949\n",
       "..              ...                  ...   ...       ...\n",
       "390  Transportation          Natural Gas  2018    46.299\n",
       "391  Transportation          Natural Gas  2017    42.375\n",
       "392  Transportation          Natural Gas  2016    40.174\n",
       "393  Transportation          Natural Gas  2015    39.519\n",
       "394  Transportation          Natural Gas  2014    40.307\n",
       "395  Transportation          Natural Gas  2013    47.076\n",
       "396  Transportation          Natural Gas  2012    41.372\n",
       "397  Transportation          Natural Gas  2011    38.920\n",
       "398  Transportation          Natural Gas  2010    38.145\n",
       "399  Transportation          Natural Gas  2009    37.929\n",
       "400  Transportation            Petroleum  2018  1865.319\n",
       "401  Transportation            Petroleum  2017  1841.610\n",
       "402  Transportation            Petroleum  2016  1827.162\n",
       "403  Transportation            Petroleum  2015  1795.433\n",
       "404  Transportation            Petroleum  2014  1770.374\n",
       "405  Transportation            Petroleum  2013  1745.080\n",
       "406  Transportation            Petroleum  2012  1728.024\n",
       "407  Transportation            Petroleum  2011  1765.592\n",
       "408  Transportation            Petroleum  2010  1800.046\n",
       "409  Transportation            Petroleum  2009  1781.950\n",
       "410  Transportation        Residual Fuel  2018    48.188\n",
       "411  Transportation        Residual Fuel  2017    52.367\n",
       "412  Transportation        Residual Fuel  2016    49.100\n",
       "413  Transportation        Residual Fuel  2015    36.514\n",
       "414  Transportation        Residual Fuel  2014    35.186\n",
       "415  Transportation        Residual Fuel  2013    45.793\n",
       "416  Transportation        Residual Fuel  2012    52.835\n",
       "417  Transportation        Residual Fuel  2011    61.171\n",
       "418  Transportation        Residual Fuel  2010    70.285\n",
       "419  Transportation        Residual Fuel  2009    62.290\n",
       "\n",
       "[390 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_ems_nosingle_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "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>sector</th>\n",
       "      <th>year</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2009</td>\n",
       "      <td>1054.233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2010</td>\n",
       "      <td>1071.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2011</td>\n",
       "      <td>1034.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2012</td>\n",
       "      <td>971.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2013</td>\n",
       "      <td>997.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2014</td>\n",
       "      <td>1010.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2015</td>\n",
       "      <td>993.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2016</td>\n",
       "      <td>952.480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2017</td>\n",
       "      <td>924.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>951.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2009</td>\n",
       "      <td>1179.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2010</td>\n",
       "      <td>1263.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2011</td>\n",
       "      <td>1263.493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2012</td>\n",
       "      <td>1296.041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2013</td>\n",
       "      <td>1319.647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2014</td>\n",
       "      <td>1309.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2015</td>\n",
       "      <td>1297.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2016</td>\n",
       "      <td>1300.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2017</td>\n",
       "      <td>1323.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018</td>\n",
       "      <td>1364.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2009</td>\n",
       "      <td>1236.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2010</td>\n",
       "      <td>1286.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2011</td>\n",
       "      <td>1219.382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2012</td>\n",
       "      <td>1103.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2013</td>\n",
       "      <td>1165.584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2014</td>\n",
       "      <td>1186.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2015</td>\n",
       "      <td>1106.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2016</td>\n",
       "      <td>1041.843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2017</td>\n",
       "      <td>1006.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Residental</td>\n",
       "      <td>2018</td>\n",
       "      <td>1080.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2009</td>\n",
       "      <td>3606.489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2010</td>\n",
       "      <td>3642.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2011</td>\n",
       "      <td>3574.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2012</td>\n",
       "      <td>3501.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2013</td>\n",
       "      <td>3541.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2014</td>\n",
       "      <td>3585.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2015</td>\n",
       "      <td>3634.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2016</td>\n",
       "      <td>3698.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2017</td>\n",
       "      <td>3729.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2018</td>\n",
       "      <td>3780.524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sector  year    values\n",
       "0       Commercial  2009  1054.233\n",
       "1       Commercial  2010  1071.039\n",
       "2       Commercial  2011  1034.949\n",
       "3       Commercial  2012   971.230\n",
       "4       Commercial  2013   997.786\n",
       "5       Commercial  2014  1010.748\n",
       "6       Commercial  2015   993.390\n",
       "7       Commercial  2016   952.480\n",
       "8       Commercial  2017   924.376\n",
       "9       Commercial  2018   951.239\n",
       "10      Industrial  2009  1179.183\n",
       "11      Industrial  2010  1263.935\n",
       "12      Industrial  2011  1263.493\n",
       "13      Industrial  2012  1296.041\n",
       "14      Industrial  2013  1319.647\n",
       "15      Industrial  2014  1309.430\n",
       "16      Industrial  2015  1297.111\n",
       "17      Industrial  2016  1300.285\n",
       "18      Industrial  2017  1323.910\n",
       "19      Industrial  2018  1364.238\n",
       "20      Residental  2009  1236.056\n",
       "21      Residental  2010  1286.053\n",
       "22      Residental  2011  1219.382\n",
       "23      Residental  2012  1103.428\n",
       "24      Residental  2013  1165.584\n",
       "25      Residental  2014  1186.017\n",
       "26      Residental  2015  1106.128\n",
       "27      Residental  2016  1041.843\n",
       "28      Residental  2017  1006.176\n",
       "29      Residental  2018  1080.745\n",
       "30  Transportation  2009  3606.489\n",
       "31  Transportation  2010  3642.899\n",
       "32  Transportation  2011  3574.543\n",
       "33  Transportation  2012  3501.453\n",
       "34  Transportation  2013  3541.433\n",
       "35  Transportation  2014  3585.278\n",
       "36  Transportation  2015  3634.270\n",
       "37  Transportation  2016  3698.127\n",
       "38  Transportation  2017  3729.116\n",
       "39  Transportation  2018  3780.524"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by sector and year for yearly analysis\n",
    "\n",
    "co2_sec_group_value_df = co2_ems_nosingle_df[[\"year\", \"sector\", \"value\"]]\n",
    "\n",
    "co2_sec_group_value_df = co2_sec_group_value_df.groupby([\"sector\", \"year\" ])[\"value\"].sum()\n",
    "co2_sec_group_value_df = pd.DataFrame({\"values\":co2_sec_group_value_df})\n",
    "co2_sec_group_value_df = co2_sec_group_value_df.reset_index()\n",
    "co2_sec_group_value_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21b843c30b8>]"
      ]
     },
     "execution_count": 59,
     "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": [
    "\n",
    "x_ind = co2_sec_group_value_df[\"year\"].loc[co2_sec_group_value_df[\"sector\"] == \"Industrial\"]\n",
    "y_ind = co2_sec_group_value_df[\"values\"].loc[co2_sec_group_value_df[\"sector\"] == \"Industrial\"]\n",
    "plt.plot(x_ind, y_ind, color=\"red\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "\n",
    "x_com = co2_sec_group_value_df[\"year\"].loc[co2_sec_group_value_df[\"sector\"] == \"Commercial\"]\n",
    "y_com = co2_sec_group_value_df[\"values\"].loc[co2_sec_group_value_df[\"sector\"] == \"Commercial\"]\n",
    "plt.plot(x_com, y_com, color=\"blue\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "\n",
    "\n",
    "x_res = co2_sec_group_value_df[\"year\"].loc[co2_sec_group_value_df[\"sector\"] == \"Residental\"]\n",
    "y_res = co2_sec_group_value_df[\"values\"].loc[co2_sec_group_value_df[\"sector\"] == \"Residental\"]\n",
    "plt.plot(x_res, y_res, color=\"green\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "\n",
    "\n",
    "x_trn = co2_sec_group_value_df[\"year\"].loc[co2_sec_group_value_df[\"sector\"] == \"Transportation\"]\n",
    "y_trn = co2_sec_group_value_df[\"values\"].loc[co2_sec_group_value_df[\"sector\"] == \"Transportation\"]\n",
    "plt.plot(x_trn, y_trn, color=\"orange\", linestyle=(0, (.5,.5)), marker=\"o\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# above graph is not very usefull as one sector has very high CO2 Emissions\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.subplots_adjust(hspace=.5)\n",
    "\n",
    "plt.subplots_adjust (wspace=.5)\n",
    "\n",
    "plt.plot(x_ind, y_ind, color=\"red\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.title(\"Industrial\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO2 Emission(MMT)\")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(x_com, y_com, color=\"black\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.title(\"Commercial\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO2 Emission(MMT)\")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(x_res, y_res, color=\"blue\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.title(\"Residential\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO2 Emission(MMT)\")\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(x_trn, y_trn, color=\"green\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.title(\"Transportation\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO2 Emission(MMT)\")\n",
    "\n",
    "plt.savefig(\"CO2EmissionTrendSubplot.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "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>sector</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>109.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>111.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>112.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>128.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>143.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>144.563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>141.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>145.983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>152.679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>130.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>92.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>87.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>84.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>85.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>99.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>92.428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>92.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>90.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>84.176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>78.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>56.836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>52.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>51.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>53.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>45.533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>53.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>50.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>39.529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>43.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>37.949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>46.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>42.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>40.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>39.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>40.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>47.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>41.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>38.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>38.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>37.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1865.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1841.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1827.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1795.433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1770.374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1745.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1728.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1765.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1800.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>1781.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>48.188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>52.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>49.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>36.514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>35.186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>45.793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>52.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>61.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>70.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>62.290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>390 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sector     value\n",
       "0        Industrial   109.967\n",
       "1        Industrial   111.616\n",
       "2        Industrial   112.702\n",
       "3        Industrial   128.941\n",
       "4        Industrial   143.075\n",
       "5        Industrial   144.563\n",
       "6        Industrial   141.470\n",
       "7        Industrial   145.983\n",
       "8        Industrial   152.679\n",
       "9        Industrial   130.545\n",
       "10       Industrial    92.306\n",
       "11       Industrial    87.969\n",
       "12       Industrial    84.446\n",
       "13       Industrial    85.380\n",
       "14       Industrial    99.700\n",
       "15       Industrial    92.428\n",
       "16       Industrial    92.799\n",
       "17       Industrial    90.210\n",
       "18       Industrial    84.176\n",
       "19       Industrial    78.017\n",
       "20       Industrial    56.836\n",
       "21       Industrial    52.732\n",
       "22       Industrial    51.074\n",
       "23       Industrial    53.360\n",
       "24       Industrial    45.533\n",
       "25       Industrial    53.056\n",
       "26       Industrial    50.339\n",
       "27       Industrial    39.529\n",
       "28       Industrial    43.444\n",
       "29       Industrial    37.949\n",
       "..              ...       ...\n",
       "390  Transportation    46.299\n",
       "391  Transportation    42.375\n",
       "392  Transportation    40.174\n",
       "393  Transportation    39.519\n",
       "394  Transportation    40.307\n",
       "395  Transportation    47.076\n",
       "396  Transportation    41.372\n",
       "397  Transportation    38.920\n",
       "398  Transportation    38.145\n",
       "399  Transportation    37.929\n",
       "400  Transportation  1865.319\n",
       "401  Transportation  1841.610\n",
       "402  Transportation  1827.162\n",
       "403  Transportation  1795.433\n",
       "404  Transportation  1770.374\n",
       "405  Transportation  1745.080\n",
       "406  Transportation  1728.024\n",
       "407  Transportation  1765.592\n",
       "408  Transportation  1800.046\n",
       "409  Transportation  1781.950\n",
       "410  Transportation    48.188\n",
       "411  Transportation    52.367\n",
       "412  Transportation    49.100\n",
       "413  Transportation    36.514\n",
       "414  Transportation    35.186\n",
       "415  Transportation    45.793\n",
       "416  Transportation    52.835\n",
       "417  Transportation    61.171\n",
       "418  Transportation    70.285\n",
       "419  Transportation    62.290\n",
       "\n",
       "[390 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "co2_sec_value = co2_ems_nosingle_df[[\"sector\", \"value\"]]\n",
    "\n",
    "co2_sec_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21b843a4080>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "co2_sec_value.boxplot(\"value\", by=\"sector\", figsize=(20, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=9.77611676380672, pvalue=3.175321817861175e-06)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate out sectors and do ANOVA\n",
    "co2_sec_value=co2_sec_value.round({'value':0}).dropna()\n",
    "\n",
    "group2 = co2_sec_value[co2_sec_value[\"sector\"] == \"Transportation\"][\"value\"]\n",
    "group4 = co2_sec_value[co2_sec_value[\"sector\"] == \"Commercial\"][\"value\"]\n",
    "group3 = co2_sec_value[co2_sec_value[\"sector\"] == \"Residental\"][\"value\"]\n",
    "group1 = co2_sec_value[co2_sec_value[\"sector\"] == \"Industrial\"][\"value\"]\n",
    "\n",
    "\n",
    "# Perform the ANOVA\n",
    "stats.f_oneway(group1, group2, group3, group4)\n",
    "\n",
    "# low pvalue tells atleast one of these is different\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.9990015499478924, pvalue=0.00010166620358948755)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the population mean for Transportation and Commercial\n",
    "\n",
    "tran = co2_sec_value[co2_sec_value[\"sector\"] == \"Transportation\"]\n",
    "tran.value.mean()\n",
    "com = co2_sec_value[co2_sec_value[\"sector\"] == \"Commercial\"]\n",
    "com.value.mean()\n",
    "\n",
    "# Calculate Independent (Two Sample) T-Test\n",
    "stats.ttest_ind(tran.value, com.value, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.187456572677531, pvalue=0.030109137158130787)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the population mean for Transportation and Residental\n",
    "\n",
    "tran = co2_sec_value[co2_sec_value[\"sector\"] == \"Transportation\"]\n",
    "tran.value.mean()\n",
    "res = co2_sec_value[co2_sec_value[\"sector\"] == \"Residental\"]\n",
    "res.value.mean()\n",
    "\n",
    "# Calculate Independent (Two Sample) T-Test\n",
    "stats.ttest_ind(tran.value, res.value, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.844929124973709, pvalue=0.00019172090150524653)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the population mean for Transportation and Industrial\n",
    "\n",
    "tran = co2_sec_value[co2_sec_value[\"sector\"] == \"Transportation\"]\n",
    "tran.value.mean()\n",
    "ind = co2_sec_value[co2_sec_value[\"sector\"] == \"Industrial\"]\n",
    "ind.value.mean()\n",
    "\n",
    "# Calculate Independent (Two Sample) T-Test\n",
    "stats.ttest_ind(tran.value, ind.value, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.230194236512595, pvalue=0.027891182128721166)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the population mean for Commercial and Residental\n",
    "\n",
    "com = co2_sec_value[co2_sec_value[\"sector\"] == \"Commercial\"]\n",
    "com.value.mean()\n",
    "res = co2_sec_value[co2_sec_value[\"sector\"] == \"Residental\"]\n",
    "res.value.mean()\n",
    "\n",
    "# Calculate Independent (Two Sample) T-Test\n",
    "stats.ttest_ind(com.value, res.value, equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.69502955633174, pvalue=0.48796805168862656)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the population mean for Commercial and Industrial\n",
    "\n",
    "com = co2_sec_value[co2_sec_value[\"sector\"] == \"Commercial\"]\n",
    "com.value.mean()\n",
    "ind = co2_sec_value[co2_sec_value[\"sector\"] == \"Industrial\"]\n",
    "ind.value.mean()\n",
    "\n",
    "# Calculate Independent (Two Sample) T-Test\n",
    "stats.ttest_ind(com.value, ind.value, equal_var=False)\n",
    "##############################################################\n",
    "# High P value indicates Commercial and Industrial are sigificantly different\n",
    "##############################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=1.9542319519698639, pvalue=0.054223544745046294)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the population mean for Residental and Industrial\n",
    "res = co2_sec_value[co2_sec_value[\"sector\"] == \"Residental\"]\n",
    "res.value.mean()\n",
    "ind = co2_sec_value[co2_sec_value[\"sector\"] == \"Industrial\"]\n",
    "ind.value.mean()\n",
    "\n",
    "# Calculate Independent (Two Sample) T-Test\n",
    "stats.ttest_ind(res.value, ind.value, equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consumption Data for Combined by Sector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "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>sector</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2009</td>\n",
       "      <td>17887.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2009</td>\n",
       "      <td>26588.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2009</td>\n",
       "      <td>28465.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2009</td>\n",
       "      <td>21081.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2010</td>\n",
       "      <td>30670.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2010</td>\n",
       "      <td>18058.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2010</td>\n",
       "      <td>26978.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2010</td>\n",
       "      <td>21894.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2011</td>\n",
       "      <td>26598.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2011</td>\n",
       "      <td>21381.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2011</td>\n",
       "      <td>17981.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2011</td>\n",
       "      <td>30981.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2012</td>\n",
       "      <td>17421.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2012</td>\n",
       "      <td>31060.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2012</td>\n",
       "      <td>19869.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2012</td>\n",
       "      <td>26125.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2013</td>\n",
       "      <td>26611.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2013</td>\n",
       "      <td>31627.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2013</td>\n",
       "      <td>21051.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2013</td>\n",
       "      <td>17929.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2014</td>\n",
       "      <td>18264.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2014</td>\n",
       "      <td>31797.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2014</td>\n",
       "      <td>26868.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2014</td>\n",
       "      <td>21444.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2015</td>\n",
       "      <td>27238.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2015</td>\n",
       "      <td>20616.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2015</td>\n",
       "      <td>18156.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2015</td>\n",
       "      <td>31471.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2016</td>\n",
       "      <td>20181.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2016</td>\n",
       "      <td>18030.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2016</td>\n",
       "      <td>31452.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2016</td>\n",
       "      <td>27785.537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2017</td>\n",
       "      <td>28014.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2017</td>\n",
       "      <td>32005.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2017</td>\n",
       "      <td>17899.252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2017</td>\n",
       "      <td>19889.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2018</td>\n",
       "      <td>21621.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2018</td>\n",
       "      <td>28390.532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>18607.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018</td>\n",
       "      <td>32617.767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sector  year      value\n",
       "240      Commercial  2009  17887.561\n",
       "241  Transportation  2009  26588.888\n",
       "242      Industrial  2009  28465.439\n",
       "243     Residential  2009  21081.368\n",
       "244      Industrial  2010  30670.436\n",
       "245      Commercial  2010  18058.632\n",
       "246  Transportation  2010  26978.400\n",
       "247     Residential  2010  21894.159\n",
       "248  Transportation  2011  26598.624\n",
       "249     Residential  2011  21381.247\n",
       "250      Commercial  2011  17981.378\n",
       "251      Industrial  2011  30981.322\n",
       "252      Commercial  2012  17421.351\n",
       "253      Industrial  2012  31060.724\n",
       "254     Residential  2012  19869.589\n",
       "255  Transportation  2012  26125.738\n",
       "256  Transportation  2013  26611.709\n",
       "257      Industrial  2013  31627.437\n",
       "258     Residential  2013  21051.185\n",
       "259      Commercial  2013  17929.446\n",
       "260      Commercial  2014  18264.465\n",
       "261      Industrial  2014  31797.941\n",
       "262  Transportation  2014  26868.857\n",
       "263     Residential  2014  21444.938\n",
       "264  Transportation  2015  27238.037\n",
       "265     Residential  2015  20616.830\n",
       "266      Commercial  2015  18156.728\n",
       "267      Industrial  2015  31471.524\n",
       "268     Residential  2016  20181.113\n",
       "269      Commercial  2016  18030.223\n",
       "270      Industrial  2016  31452.839\n",
       "271  Transportation  2016  27785.537\n",
       "272  Transportation  2017  28014.167\n",
       "273      Industrial  2017  32005.673\n",
       "274      Commercial  2017  17899.252\n",
       "275     Residential  2017  19889.581\n",
       "276     Residential  2018  21621.074\n",
       "277  Transportation  2018  28390.532\n",
       "278      Commercial  2018  18607.551\n",
       "279      Industrial  2018  32617.767"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#comsumption data, group by sector and year\n",
    "energy_cons_df = pd.read_csv(\"MasterConsumptionResComTrnInd.csv\")\n",
    "energy_cons_df = energy_cons_df[[\"sector\", \"year\", \"value\"]]\n",
    "energy_cons_df = energy_cons_df.loc[energy_cons_df[\"year\"] > 2008 ]\n",
    "energy_cons_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "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>sector</th>\n",
       "      <th>year</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2009</td>\n",
       "      <td>17887.561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2010</td>\n",
       "      <td>18058.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2011</td>\n",
       "      <td>17981.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2012</td>\n",
       "      <td>17421.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2013</td>\n",
       "      <td>17929.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2014</td>\n",
       "      <td>18264.465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2015</td>\n",
       "      <td>18156.728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2016</td>\n",
       "      <td>18030.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2017</td>\n",
       "      <td>17899.252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>18607.551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2009</td>\n",
       "      <td>28465.439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2010</td>\n",
       "      <td>30670.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2011</td>\n",
       "      <td>30981.322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2012</td>\n",
       "      <td>31060.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2013</td>\n",
       "      <td>31627.437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2014</td>\n",
       "      <td>31797.941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2015</td>\n",
       "      <td>31471.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2016</td>\n",
       "      <td>31452.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2017</td>\n",
       "      <td>32005.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Industrial</td>\n",
       "      <td>2018</td>\n",
       "      <td>32617.767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2009</td>\n",
       "      <td>21081.368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2010</td>\n",
       "      <td>21894.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2011</td>\n",
       "      <td>21381.247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2012</td>\n",
       "      <td>19869.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2013</td>\n",
       "      <td>21051.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2014</td>\n",
       "      <td>21444.938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2015</td>\n",
       "      <td>20616.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2016</td>\n",
       "      <td>20181.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2017</td>\n",
       "      <td>19889.581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Residential</td>\n",
       "      <td>2018</td>\n",
       "      <td>21621.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2009</td>\n",
       "      <td>26588.888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2010</td>\n",
       "      <td>26978.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2011</td>\n",
       "      <td>26598.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2012</td>\n",
       "      <td>26125.738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2013</td>\n",
       "      <td>26611.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2014</td>\n",
       "      <td>26868.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2015</td>\n",
       "      <td>27238.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2016</td>\n",
       "      <td>27785.537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2017</td>\n",
       "      <td>28014.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Transportation</td>\n",
       "      <td>2018</td>\n",
       "      <td>28390.532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sector  year     values\n",
       "0       Commercial  2009  17887.561\n",
       "1       Commercial  2010  18058.632\n",
       "2       Commercial  2011  17981.378\n",
       "3       Commercial  2012  17421.351\n",
       "4       Commercial  2013  17929.446\n",
       "5       Commercial  2014  18264.465\n",
       "6       Commercial  2015  18156.728\n",
       "7       Commercial  2016  18030.223\n",
       "8       Commercial  2017  17899.252\n",
       "9       Commercial  2018  18607.551\n",
       "10      Industrial  2009  28465.439\n",
       "11      Industrial  2010  30670.436\n",
       "12      Industrial  2011  30981.322\n",
       "13      Industrial  2012  31060.724\n",
       "14      Industrial  2013  31627.437\n",
       "15      Industrial  2014  31797.941\n",
       "16      Industrial  2015  31471.524\n",
       "17      Industrial  2016  31452.839\n",
       "18      Industrial  2017  32005.673\n",
       "19      Industrial  2018  32617.767\n",
       "20     Residential  2009  21081.368\n",
       "21     Residential  2010  21894.159\n",
       "22     Residential  2011  21381.247\n",
       "23     Residential  2012  19869.589\n",
       "24     Residential  2013  21051.185\n",
       "25     Residential  2014  21444.938\n",
       "26     Residential  2015  20616.830\n",
       "27     Residential  2016  20181.113\n",
       "28     Residential  2017  19889.581\n",
       "29     Residential  2018  21621.074\n",
       "30  Transportation  2009  26588.888\n",
       "31  Transportation  2010  26978.400\n",
       "32  Transportation  2011  26598.624\n",
       "33  Transportation  2012  26125.738\n",
       "34  Transportation  2013  26611.709\n",
       "35  Transportation  2014  26868.857\n",
       "36  Transportation  2015  27238.037\n",
       "37  Transportation  2016  27785.537\n",
       "38  Transportation  2017  28014.167\n",
       "39  Transportation  2018  28390.532"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_sec_group_value_df = energy_cons_df[[\"year\", \"sector\", \"value\"]]\n",
    "\n",
    "ec_sec_group_value_df = ec_sec_group_value_df.groupby([\"sector\", \"year\" ])[\"value\"].sum()\n",
    "ec_sec_group_value_df = pd.DataFrame({\"values\":ec_sec_group_value_df})\n",
    "ec_sec_group_value_df = ec_sec_group_value_df.reset_index()\n",
    "ec_sec_group_value_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graph variables\n",
    "x_resCO2 = co2_sec_group_value_df[\"year\"].loc[co2_sec_group_value_df[\"sector\"] == \"Residental\"]\n",
    "y_resCO2 = co2_sec_group_value_df[\"values\"].loc[co2_sec_group_value_df[\"sector\"] == \"Residental\"]\n",
    "\n",
    "\n",
    "x_trnCO2 = co2_sec_group_value_df[\"year\"].loc[co2_sec_group_value_df[\"sector\"] == \"Transportation\"]\n",
    "y_trnCO2 = co2_sec_group_value_df[\"values\"].loc[co2_sec_group_value_df[\"sector\"] == \"Transportation\"]\n",
    "\n",
    "x_resEC = ec_sec_group_value_df[\"year\"].loc[ec_sec_group_value_df[\"sector\"] == \"Residential\"]\n",
    "y_resEC = ec_sec_group_value_df[\"values\"].loc[ec_sec_group_value_df[\"sector\"] == \"Residential\"]\n",
    "\n",
    "\n",
    "x_trnEC = ec_sec_group_value_df[\"year\"].loc[ec_sec_group_value_df[\"sector\"] == \"Transportation\"]\n",
    "y_trnEC = ec_sec_group_value_df[\"values\"].loc[ec_sec_group_value_df[\"sector\"] == \"Transportation\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector     object\n",
       "year        int64\n",
       "values    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_sec_group_value_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sector     object\n",
       "year        int64\n",
       "values    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_sec_group_value_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.subplots_adjust(hspace=.5)\n",
    "\n",
    "plt.subplots_adjust (wspace=.5)\n",
    "\n",
    "           \n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x_resCO2, y_resCO2, color=\"green\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.title(\"CO2 Emission - Residential\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"CO2 Emission(MMT)\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x_resEC, y_resEC, color=\"red\", linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "plt.title(\"Energy Consumption - Residential\")\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Energy Consumption(Trillion Btu)\")\n",
    "plt.savefig(\"EnergyConsumptionAndCO2Emission.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CO2 Data is in Million Metric Ton and Consumption is in Trillion Btu, Use Two axis Graph\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "color = 'tab:red'\n",
    "\n",
    "plt.title(\"Consumption vs CO2 Emission - Residential\")\n",
    "\n",
    "ax1.set_xlabel((\"Year\"))\n",
    "ax1.set_ylabel('CO2 Emission(MMT)', color=color)\n",
    "plt.plot(x_resCO2, y_resCO2, color=color, linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Energy Consumption (Trillion Btu) ', color=color)  \n",
    "plt.plot(x_resEC, y_resEC, color=color, linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  \n",
    "plt.savefig(\"EnergyConsumAndCO2EmsResCombined.png\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined for Totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>94023.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>97601.627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>96942.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>94477.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>97219.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>98376.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>97483.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>97449.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>97808.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>101236.924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year      values\n",
       "0  2009   94023.256\n",
       "1  2010   97601.627\n",
       "2  2011   96942.571\n",
       "3  2012   94477.402\n",
       "4  2013   97219.777\n",
       "5  2014   98376.201\n",
       "6  2015   97483.119\n",
       "7  2016   97449.712\n",
       "8  2017   97808.673\n",
       "9  2018  101236.924"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "energy_cons_df = pd.read_csv(\"MasterConsumptionResComTrnInd.csv\")\n",
    "energy_cons_df = energy_cons_df.loc[energy_cons_df[\"year\"] > 2008 ]\n",
    "energy_cons_df\n",
    "\n",
    "ec_group_yv_df = energy_cons_df.groupby([\"year\" ])[\"value\"].sum()\n",
    "ec_group_yv_df = pd.DataFrame({\"values\":ec_group_yv_df})\n",
    "ec_group_yv_df = ec_group_yv_df.reset_index()\n",
    "ec_group_yv_df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>7075.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>7263.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>7092.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>6872.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>7024.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014</td>\n",
       "      <td>7091.473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>7030.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016</td>\n",
       "      <td>6992.735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017</td>\n",
       "      <td>6983.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>7176.746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    values\n",
       "0  2009  7075.961\n",
       "1  2010  7263.926\n",
       "2  2011  7092.367\n",
       "3  2012  6872.152\n",
       "4  2013  7024.450\n",
       "5  2014  7091.473\n",
       "6  2015  7030.899\n",
       "7  2016  6992.735\n",
       "8  2017  6983.578\n",
       "9  2018  7176.746"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2_group_yv_df = co2_sec_group_value_df.groupby([\"year\" ])[\"values\"].sum()\n",
    "co2_group_yv_df = pd.DataFrame({\"values\":co2_group_yv_df})\n",
    "co2_group_yv_df = co2_group_yv_df.reset_index()\n",
    "co2_group_yv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_allCO2 = co2_group_yv_df[\"year\"]\n",
    "y_allCO2 = co2_group_yv_df[\"values\"]\n",
    "\n",
    "x_allEC = ec_group_yv_df[\"year\"]\n",
    "y_allEC = ec_group_yv_df[\"values\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "color = 'tab:orange'\n",
    "\n",
    "plt.title(\"Consumption vs CO2 Emission - All Sectors\")\n",
    "\n",
    "ax1.set_xlabel((\"Year\"))\n",
    "ax1.set_ylabel('CO2 Emission(MMT)', color=color)\n",
    "plt.plot(x_allCO2, y_allCO2, color=color, linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "plt.grid(b=True, which='major', color='black', linestyle=':', alpha=0.5)\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Energy Consumption (Trillion Btu) ', color=color)  \n",
    "plt.plot(x_allEC, y_allEC, color=color, linestyle=(0, (.5,.5)), marker=\"o\")\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  \n",
    "plt.savefig(\"EnergyConsumAndCO2EmsAllSec.png\")\n",
    "plt.show()"
   ]
  },
  {
   "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
}