Hcktn_Graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statistics\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>description</th>\n",
       "      <th>units</th>\n",
       "      <th>source key</th>\n",
       "      <th>1-Jan</th>\n",
       "      <th>1-Feb</th>\n",
       "      <th>1-Mar</th>\n",
       "      <th>1-Apr</th>\n",
       "      <th>1-May</th>\n",
       "      <th>1-Jun</th>\n",
       "      <th>1-Jul</th>\n",
       "      <th>...</th>\n",
       "      <th>19-Apr</th>\n",
       "      <th>19-May</th>\n",
       "      <th>19-Jun</th>\n",
       "      <th>19-Jul</th>\n",
       "      <th>19-Aug</th>\n",
       "      <th>19-Sep</th>\n",
       "      <th>19-Oct</th>\n",
       "      <th>19-Nov</th>\n",
       "      <th>19-Dec</th>\n",
       "      <th>20-Jan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN..M</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>all fuels (utility-scale)</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.ALL-US-99.M</td>\n",
       "      <td>332493</td>\n",
       "      <td>282940</td>\n",
       "      <td>300707</td>\n",
       "      <td>278079</td>\n",
       "      <td>300492</td>\n",
       "      <td>327694</td>\n",
       "      <td>357614</td>\n",
       "      <td>...</td>\n",
       "      <td>294577.0</td>\n",
       "      <td>328269.0</td>\n",
       "      <td>351363.0</td>\n",
       "      <td>411616.0</td>\n",
       "      <td>401665.0</td>\n",
       "      <td>359545.0</td>\n",
       "      <td>321875.0</td>\n",
       "      <td>316672.0</td>\n",
       "      <td>337253.0</td>\n",
       "      <td>339320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>coal</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.COW-US-99.M</td>\n",
       "      <td>177287</td>\n",
       "      <td>149735</td>\n",
       "      <td>155269</td>\n",
       "      <td>140671</td>\n",
       "      <td>151593</td>\n",
       "      <td>162616</td>\n",
       "      <td>179060</td>\n",
       "      <td>...</td>\n",
       "      <td>60008.0</td>\n",
       "      <td>71883.0</td>\n",
       "      <td>78610.0</td>\n",
       "      <td>100981.0</td>\n",
       "      <td>94177.0</td>\n",
       "      <td>85918.0</td>\n",
       "      <td>66829.0</td>\n",
       "      <td>75560.0</td>\n",
       "      <td>72554.0</td>\n",
       "      <td>65170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>petroleum liquids</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.PEL-US-99.M</td>\n",
       "      <td>17300</td>\n",
       "      <td>9626</td>\n",
       "      <td>10981</td>\n",
       "      <td>10237</td>\n",
       "      <td>9641</td>\n",
       "      <td>10997</td>\n",
       "      <td>10050</td>\n",
       "      <td>...</td>\n",
       "      <td>788.0</td>\n",
       "      <td>943.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>942.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>911.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>petroleum coke</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.PC-US-99.M</td>\n",
       "      <td>812</td>\n",
       "      <td>716</td>\n",
       "      <td>752</td>\n",
       "      <td>627</td>\n",
       "      <td>749</td>\n",
       "      <td>825</td>\n",
       "      <td>992</td>\n",
       "      <td>...</td>\n",
       "      <td>446.0</td>\n",
       "      <td>747.0</td>\n",
       "      <td>555.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>687.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>natural gas</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.NG-US-99.M</td>\n",
       "      <td>42389</td>\n",
       "      <td>37967</td>\n",
       "      <td>44364</td>\n",
       "      <td>45843</td>\n",
       "      <td>50934</td>\n",
       "      <td>57603</td>\n",
       "      <td>73030</td>\n",
       "      <td>...</td>\n",
       "      <td>103006.0</td>\n",
       "      <td>116236.0</td>\n",
       "      <td>136994.0</td>\n",
       "      <td>174341.0</td>\n",
       "      <td>176458.0</td>\n",
       "      <td>150753.0</td>\n",
       "      <td>133667.0</td>\n",
       "      <td>117762.0</td>\n",
       "      <td>129342.0</td>\n",
       "      <td>132980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>other gases</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.OOG-US-99.M</td>\n",
       "      <td>718</td>\n",
       "      <td>676</td>\n",
       "      <td>769</td>\n",
       "      <td>698</td>\n",
       "      <td>785</td>\n",
       "      <td>733</td>\n",
       "      <td>840</td>\n",
       "      <td>...</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>1139.0</td>\n",
       "      <td>997.0</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>1211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>nuclear</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.NUC-US-99.M</td>\n",
       "      <td>68707</td>\n",
       "      <td>61272</td>\n",
       "      <td>62141</td>\n",
       "      <td>56003</td>\n",
       "      <td>61512</td>\n",
       "      <td>68023</td>\n",
       "      <td>69166</td>\n",
       "      <td>...</td>\n",
       "      <td>60581.0</td>\n",
       "      <td>67124.0</td>\n",
       "      <td>68805.0</td>\n",
       "      <td>72199.0</td>\n",
       "      <td>71911.0</td>\n",
       "      <td>66064.0</td>\n",
       "      <td>62033.0</td>\n",
       "      <td>64125.0</td>\n",
       "      <td>73074.0</td>\n",
       "      <td>74204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>conventional hydroelectric</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.HYC-US-99.M</td>\n",
       "      <td>18852</td>\n",
       "      <td>17473</td>\n",
       "      <td>20477</td>\n",
       "      <td>18013</td>\n",
       "      <td>19176</td>\n",
       "      <td>20728</td>\n",
       "      <td>18079</td>\n",
       "      <td>...</td>\n",
       "      <td>25483.0</td>\n",
       "      <td>30061.0</td>\n",
       "      <td>26469.0</td>\n",
       "      <td>23730.0</td>\n",
       "      <td>21041.0</td>\n",
       "      <td>16324.0</td>\n",
       "      <td>16292.0</td>\n",
       "      <td>20520.0</td>\n",
       "      <td>22206.0</td>\n",
       "      <td>24286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>wind</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.WND-US-99.M</td>\n",
       "      <td>389</td>\n",
       "      <td>431</td>\n",
       "      <td>532</td>\n",
       "      <td>685</td>\n",
       "      <td>635</td>\n",
       "      <td>670</td>\n",
       "      <td>635</td>\n",
       "      <td>...</td>\n",
       "      <td>29711.0</td>\n",
       "      <td>25973.0</td>\n",
       "      <td>22947.0</td>\n",
       "      <td>22024.0</td>\n",
       "      <td>19869.0</td>\n",
       "      <td>24385.0</td>\n",
       "      <td>28136.0</td>\n",
       "      <td>25603.0</td>\n",
       "      <td>27183.0</td>\n",
       "      <td>28403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>geothermal</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.GEO-US-99.M</td>\n",
       "      <td>1229</td>\n",
       "      <td>1073</td>\n",
       "      <td>1190</td>\n",
       "      <td>1095</td>\n",
       "      <td>1071</td>\n",
       "      <td>1088</td>\n",
       "      <td>1179</td>\n",
       "      <td>...</td>\n",
       "      <td>1239.0</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>1412.0</td>\n",
       "      <td>1409.0</td>\n",
       "      <td>1384.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>1112.0</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>1255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>biomass</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.BIO-US-99.M</td>\n",
       "      <td>4400</td>\n",
       "      <td>3793</td>\n",
       "      <td>4042</td>\n",
       "      <td>4008</td>\n",
       "      <td>3951</td>\n",
       "      <td>4116</td>\n",
       "      <td>4339</td>\n",
       "      <td>...</td>\n",
       "      <td>4491.0</td>\n",
       "      <td>4907.0</td>\n",
       "      <td>4892.0</td>\n",
       "      <td>5157.0</td>\n",
       "      <td>5319.0</td>\n",
       "      <td>4788.0</td>\n",
       "      <td>4647.0</td>\n",
       "      <td>4604.0</td>\n",
       "      <td>4996.0</td>\n",
       "      <td>4957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>wood and wood-derived fuels</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.WWW-US-99.M</td>\n",
       "      <td>3191</td>\n",
       "      <td>2697</td>\n",
       "      <td>2853</td>\n",
       "      <td>2821</td>\n",
       "      <td>2740</td>\n",
       "      <td>2891</td>\n",
       "      <td>3053</td>\n",
       "      <td>...</td>\n",
       "      <td>3027.0</td>\n",
       "      <td>3365.0</td>\n",
       "      <td>3339.0</td>\n",
       "      <td>3569.0</td>\n",
       "      <td>3717.0</td>\n",
       "      <td>3282.0</td>\n",
       "      <td>3081.0</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>3407.0</td>\n",
       "      <td>3349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>other biomass</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.WAS-US-99.M</td>\n",
       "      <td>1209</td>\n",
       "      <td>1096</td>\n",
       "      <td>1189</td>\n",
       "      <td>1187</td>\n",
       "      <td>1211</td>\n",
       "      <td>1225</td>\n",
       "      <td>1286</td>\n",
       "      <td>...</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>1587.0</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>1506.0</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>1497.0</td>\n",
       "      <td>1588.0</td>\n",
       "      <td>1609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>hydro-electric pumped storage</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.HPS-US-99.M</td>\n",
       "      <td>-589</td>\n",
       "      <td>-707</td>\n",
       "      <td>-773</td>\n",
       "      <td>-796</td>\n",
       "      <td>-623</td>\n",
       "      <td>-774</td>\n",
       "      <td>-871</td>\n",
       "      <td>...</td>\n",
       "      <td>-103.0</td>\n",
       "      <td>-368.0</td>\n",
       "      <td>-385.0</td>\n",
       "      <td>-622.0</td>\n",
       "      <td>-579.0</td>\n",
       "      <td>-671.0</td>\n",
       "      <td>-373.0</td>\n",
       "      <td>-509.0</td>\n",
       "      <td>-529.0</td>\n",
       "      <td>-406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>other</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.OTH-US-99.M</td>\n",
       "      <td>992</td>\n",
       "      <td>872</td>\n",
       "      <td>931</td>\n",
       "      <td>957</td>\n",
       "      <td>986</td>\n",
       "      <td>978</td>\n",
       "      <td>1022</td>\n",
       "      <td>...</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>1087.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>1084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>all solar</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.TSN-US-99.M</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>...</td>\n",
       "      <td>10089.0</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>11620.0</td>\n",
       "      <td>11941.0</td>\n",
       "      <td>11510.0</td>\n",
       "      <td>9968.0</td>\n",
       "      <td>8971.0</td>\n",
       "      <td>6608.0</td>\n",
       "      <td>5541.0</td>\n",
       "      <td>6848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>small-scale solar photovoltaic</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.DPV-US-99.M</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>--</td>\n",
       "      <td>...</td>\n",
       "      <td>3253.0</td>\n",
       "      <td>3558.0</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>3772.0</td>\n",
       "      <td>3623.0</td>\n",
       "      <td>3216.0</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>2232.0</td>\n",
       "      <td>2046.0</td>\n",
       "      <td>2293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>all utility-scale solar</td>\n",
       "      <td>thousand megawatthours</td>\n",
       "      <td>ELEC.GEN.SUN-US-99.M</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>81</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>6835.0</td>\n",
       "      <td>7191.0</td>\n",
       "      <td>8006.0</td>\n",
       "      <td>8169.0</td>\n",
       "      <td>7888.0</td>\n",
       "      <td>6752.0</td>\n",
       "      <td>6131.0</td>\n",
       "      <td>4377.0</td>\n",
       "      <td>3494.0</td>\n",
       "      <td>4555.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19 rows × 232 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       description                   units  \\\n",
       "0                    United States  thousand megawatthours   \n",
       "1        all fuels (utility-scale)  thousand megawatthours   \n",
       "2                             coal  thousand megawatthours   \n",
       "3                petroleum liquids  thousand megawatthours   \n",
       "4                   petroleum coke  thousand megawatthours   \n",
       "5                      natural gas  thousand megawatthours   \n",
       "6                      other gases  thousand megawatthours   \n",
       "7                          nuclear  thousand megawatthours   \n",
       "8       conventional hydroelectric  thousand megawatthours   \n",
       "9                             wind  thousand megawatthours   \n",
       "10                      geothermal  thousand megawatthours   \n",
       "11                         biomass  thousand megawatthours   \n",
       "12     wood and wood-derived fuels  thousand megawatthours   \n",
       "13                   other biomass  thousand megawatthours   \n",
       "14   hydro-electric pumped storage  thousand megawatthours   \n",
       "15                           other  thousand megawatthours   \n",
       "16                       all solar  thousand megawatthours   \n",
       "17  small-scale solar photovoltaic  thousand megawatthours   \n",
       "18         all utility-scale solar  thousand megawatthours   \n",
       "\n",
       "              source key   1-Jan   1-Feb   1-Mar   1-Apr   1-May   1-Jun  \\\n",
       "0            ELEC.GEN..M     NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1   ELEC.GEN.ALL-US-99.M  332493  282940  300707  278079  300492  327694   \n",
       "2   ELEC.GEN.COW-US-99.M  177287  149735  155269  140671  151593  162616   \n",
       "3   ELEC.GEN.PEL-US-99.M   17300    9626   10981   10237    9641   10997   \n",
       "4    ELEC.GEN.PC-US-99.M     812     716     752     627     749     825   \n",
       "5    ELEC.GEN.NG-US-99.M   42389   37967   44364   45843   50934   57603   \n",
       "6   ELEC.GEN.OOG-US-99.M     718     676     769     698     785     733   \n",
       "7   ELEC.GEN.NUC-US-99.M   68707   61272   62141   56003   61512   68023   \n",
       "8   ELEC.GEN.HYC-US-99.M   18852   17473   20477   18013   19176   20728   \n",
       "9   ELEC.GEN.WND-US-99.M     389     431     532     685     635     670   \n",
       "10  ELEC.GEN.GEO-US-99.M    1229    1073    1190    1095    1071    1088   \n",
       "11  ELEC.GEN.BIO-US-99.M    4400    3793    4042    4008    3951    4116   \n",
       "12  ELEC.GEN.WWW-US-99.M    3191    2697    2853    2821    2740    2891   \n",
       "13  ELEC.GEN.WAS-US-99.M    1209    1096    1189    1187    1211    1225   \n",
       "14  ELEC.GEN.HPS-US-99.M    -589    -707    -773    -796    -623    -774   \n",
       "15  ELEC.GEN.OTH-US-99.M     992     872     931     957     986     978   \n",
       "16  ELEC.GEN.TSN-US-99.M      --      --      --      --      --      --   \n",
       "17  ELEC.GEN.DPV-US-99.M      --      --      --      --      --      --   \n",
       "18  ELEC.GEN.SUN-US-99.M       7      13      31      39      81      91   \n",
       "\n",
       "     1-Jul  ...    19-Apr    19-May    19-Jun    19-Jul    19-Aug    19-Sep  \\\n",
       "0      NaN  ...       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "1   357614  ...  294577.0  328269.0  351363.0  411616.0  401665.0  359545.0   \n",
       "2   179060  ...   60008.0   71883.0   78610.0  100981.0   94177.0   85918.0   \n",
       "3    10050  ...     788.0     943.0     976.0    1029.0    1084.0     942.0   \n",
       "4      992  ...     446.0     747.0     555.0     746.0     687.0     638.0   \n",
       "5    73030  ...  103006.0  116236.0  136994.0  174341.0  176458.0  150753.0   \n",
       "6      840  ...    1071.0    1101.0    1025.0    1290.0    1202.0    1139.0   \n",
       "7    69166  ...   60581.0   67124.0   68805.0   72199.0   71911.0   66064.0   \n",
       "8    18079  ...   25483.0   30061.0   26469.0   23730.0   21041.0   16324.0   \n",
       "9      635  ...   29711.0   25973.0   22947.0   22024.0   19869.0   24385.0   \n",
       "10    1179  ...    1239.0    1347.0    1362.0    1412.0    1409.0    1384.0   \n",
       "11    4339  ...    4491.0    4907.0    4892.0    5157.0    5319.0    4788.0   \n",
       "12    3053  ...    3027.0    3365.0    3339.0    3569.0    3717.0    3282.0   \n",
       "13    1286  ...    1464.0    1542.0    1554.0    1587.0    1602.0    1506.0   \n",
       "14    -871  ...    -103.0    -368.0    -385.0    -622.0    -579.0    -671.0   \n",
       "15    1022  ...    1020.0    1124.0    1107.0    1162.0    1199.0    1128.0   \n",
       "16      --  ...   10089.0   10750.0   11620.0   11941.0   11510.0    9968.0   \n",
       "17      --  ...    3253.0    3558.0    3615.0    3772.0    3623.0    3216.0   \n",
       "18      92  ...    6835.0    7191.0    8006.0    8169.0    7888.0    6752.0   \n",
       "\n",
       "      19-Oct    19-Nov    19-Dec    20-Jan  \n",
       "0        NaN       NaN       NaN       NaN  \n",
       "1   321875.0  316672.0  337253.0  339320.0  \n",
       "2    66829.0   75560.0   72554.0   65170.0  \n",
       "3      955.0     911.0     961.0     934.0  \n",
       "4      198.0     339.0     409.0     687.0  \n",
       "5   133667.0  117762.0  129342.0  132980.0  \n",
       "6      997.0    1196.0    1136.0    1211.0  \n",
       "7    62033.0   64125.0   73074.0   74204.0  \n",
       "8    16292.0   20520.0   22206.0   24286.0  \n",
       "9    28136.0   25603.0   27183.0   28403.0  \n",
       "10    1277.0    1112.0    1301.0    1255.0  \n",
       "11    4647.0    4604.0    4996.0    4957.0  \n",
       "12    3081.0    3107.0    3407.0    3349.0  \n",
       "13    1565.0    1497.0    1588.0    1609.0  \n",
       "14    -373.0    -509.0    -529.0    -406.0  \n",
       "15    1087.0    1070.0    1126.0    1084.0  \n",
       "16    8971.0    6608.0    5541.0    6848.0  \n",
       "17    2840.0    2232.0    2046.0    2293.0  \n",
       "18    6131.0    4377.0    3494.0    4555.0  \n",
       "\n",
       "[19 rows x 232 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading csv file of monthly energy consumption broken down by type of energy source \n",
    "df = pd.read_csv('Monthly_NetGeneration_USA.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# cleaning the data\n",
    "df = df.fillna(0)\n",
    "df = df.replace('--',0)\n",
    "df = df.drop(df.columns[2], axis=1)\n",
    "df = df[1:18]\n",
    "df=df.T\n",
    "df.columns = df.iloc[0]\n",
    "df = df[2:]\n",
    "for col in df.columns:\n",
    "    df[col] = df[col].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               non_renewable  renewable\n",
      "non_renewable       1.000000   0.206699\n",
      "renewable           0.206699   1.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x21f9c836e08>"
      ]
     },
     "execution_count": 309,
     "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": [
    "# splitting the data by two energy source categories: renewable energy and non-renewable energy \n",
    "non_renewable = df['all fuels (utility-scale)']+df['coal']+df['petroleum liquids']+df['petroleum coke']+df['natural gas']+df['other gases']\n",
    "renewable = df['nuclear']+df['conventional hydroelectric']+df['wind']+df['all solar']+df['geothermal']+df['biomass']+df['wood and wood-derived fuels']+df['other biomass']+df['hydro-electric pumped storage']+df['other']\n",
    "is_renewable = pd.DataFrame({'non_renewable':non_renewable,'renewable':renewable})\n",
    "\n",
    "#checking the correlation between the two\n",
    "correlations = is_renewable.corr()\n",
    "print(correlations)\n",
    "\n",
    "a=sns.regplot(x=renewable, y=non_renewable, label='Usage of both both types of energy sources')\n",
    "a.set(xlabel='Renewable Source')\n",
    "a.set(ylabel='Non-renewable Source')\n",
    "a.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We see that the two categoris have a positive correlation. It make sense because at times of high usage, both categories\n",
    "# are being used more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept:  154.7650830927305 \n",
      " Coefficient:  5.082487985625197\n"
     ]
    },
    {
     "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": [
    "# Creating a Linear Regression model\n",
    "x = np.array(renewable)\n",
    "y = np.array(non_renewable)\n",
    "m_x, m_y = np.mean(x), np.mean(y)\n",
    "SS_xy = np.sum(y*x) - np.size(x)*m_y*m_x \n",
    "SS_xx = np.sum(x*x) - np.size(x)*m_x*m_x\n",
    "b1 = SS_xy / SS_xx \n",
    "b0 = m_y - b1*m_x\n",
    "\n",
    "b=sns.regplot(x=x, y=y, label='Linear Regression')\n",
    "b.set(xlabel='Renewable Source')\n",
    "b.set(ylabel='Non-renewable Source')\n",
    "b.legend()\n",
    "\n",
    "print('Intercept: ', b0, '\\n','Coefficient: ', b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared (uncentered):</th>      <td>1197.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>1202.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>  -228.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 30 Mar 2020</td> <th>  Prob (F-statistic):</th>           <td>  1.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>03:23:09</td>     <th>  Log-Likelihood:    </th>          <td> -2928.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   229</td>      <th>  AIC:               </th>          <td>   5859.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   228</td>      <th>  BIC:               </th>          <td>   5862.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>    5.0147</td> <td>    0.051</td> <td>   98.357</td> <td> 0.000</td> <td>    4.914</td> <td>    5.115</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.089</td> <th>  Durbin-Watson:     </th> <td>   0.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  11.856</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.556</td> <th>  Prob(JB):          </th> <td> 0.00266</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.930</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                      y   R-squared (uncentered):                1197.213\n",
       "Model:                            OLS   Adj. R-squared (uncentered):           1202.460\n",
       "Method:                 Least Squares   F-statistic:                             -228.2\n",
       "Date:                Mon, 30 Mar 2020   Prob (F-statistic):                        1.00\n",
       "Time:                        03:23:09   Log-Likelihood:                         -2928.3\n",
       "No. Observations:                 229   AIC:                                      5859.\n",
       "Df Residuals:                     228   BIC:                                      5862.\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1             5.0147      0.051     98.357      0.000       4.914       5.115\n",
       "==============================================================================\n",
       "Omnibus:                       11.089   Durbin-Watson:                   0.238\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               11.856\n",
       "Skew:                          -0.556   Prob(JB):                      0.00266\n",
       "Kurtosis:                       2.930   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.OLS(y, x).fit()\n",
    "predictions = model.predict(x)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can conclude that we don't have enough data for regression analysis"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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