Part 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import os \n",
    "import csv\n",
    "import datetime as dt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_filepath = os.path.join(\"Resources\\Raw Data\\Table 2 - US_electricity_net_gen_from_renewable_en_1990_2020.csv\")\n",
    "USD_filepath = os.path.join(\"Resources\\Raw Data\\Table 3 - US-dollar-index-historical-chart.csv\")\n",
    "\n",
    "US_electricity = pd.read_csv(US_filepath)\n",
    "USD_data = pd.read_csv(USD_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#isolating US_electricity data for only 2005 to 2019 data \n",
    "\n",
    "US_electricity = US_electricity.loc[(US_electricity[\"YYYYMM\"] <= 201912) & (US_electricity[\"YYYYMM\"] >= 200501) ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#no days were provided in date data. Will assume data was collected at the first day of the month. \n",
    "\n",
    "US_electricity[\"YYYYMM\"] = US_electricity['YYYYMM'].astype(str)\n",
    "\n",
    "US_electricity[\"Date (YYYY-MM-01)\"] = US_electricity[\"YYYYMM\"]+str(\"01\")\n",
    "\n",
    "# there is a 13th month for the cumulative total. Removing this data. \n",
    "\n",
    "US_electricity[\"Month\"] = US_electricity[\"YYYYMM\"].str[-2:]\n",
    "US_electricity[\"Month\"] = US_electricity[\"Month\"].astype(int)\n",
    "\n",
    "US_electricity = US_electricity.loc[ US_electricity[\"Month\"] != 13]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean up columns \n",
    "#US_electricity[unit] only includes million kilowatthours (can remove)\n",
    "\n",
    "US_electricity = US_electricity.drop(columns=[\"YYYYMM\", \"Month\", \"MSN\", \"Column_Order\", \"Unit\"])\n",
    "US_electricity = US_electricity.rename(columns= {\"Date (YYYY-MM-01)\" : \"Date\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "      <th>Description</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>177013.907</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "      <td>2005-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>155818.014</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "      <td>2005-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>163612.7</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "      <td>2005-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>143083.239</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "      <td>2005-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>153957.962</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "      <td>2005-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Value                                        Description       Date\n",
       "440  177013.907  Electricity Net Generation From Coal, All Sectors 2005-01-01\n",
       "441  155818.014  Electricity Net Generation From Coal, All Sectors 2005-02-01\n",
       "442    163612.7  Electricity Net Generation From Coal, All Sectors 2005-03-01\n",
       "443  143083.239  Electricity Net Generation From Coal, All Sectors 2005-04-01\n",
       "444  153957.962  Electricity Net Generation From Coal, All Sectors 2005-05-01"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#format date to datetime objects \n",
    "US_electricity[\"Date\"]=pd.to_datetime(US_electricity[\"Date\"], format='%Y-%m-%d')\n",
    "\n",
    "US_electricity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping by renewable energy and crude oil \n",
    "#Renewable sources include : hydroelectric pumped storage, wood, waste, geothermal, solar, wind,and conventional hydroelectric power  \n",
    "#crude oil sources include: coal, petroleum, natural gas, and other gases\n",
    "\n",
    "US_electricity = US_electricity.loc[US_electricity[\"Value\"] != \"Not Available\"]\n",
    "\n",
    "US_renewable = US_electricity.loc[ (US_electricity[\"Description\"] == \"Electricity Net Generation From Hydroelectric Pumped Storage, All Sectors\") | \n",
    "                                (US_electricity[\"Description\"] == \"Electricity Net Generation From Wood, All Sectors\") | \n",
    "                                (US_electricity[\"Description\"] == \"Electricity Net Generation From Waste, All Sectors\") |\n",
    "                               (US_electricity[\"Description\"] == \"Electricity Net Generation From Geothermal, All Sectors\") | \n",
    "                               (US_electricity[\"Description\"] == \"Electricity Net Generation From Solar, All Sectors\") | \n",
    "                               (US_electricity[\"Description\"] == \"Electricity Net Generation From Wind, All Sectors\")|\n",
    "                                (US_electricity[\"Description\"] == \"Electricity Net Generation From Conventional Hydroelectric Power, All Sectors\")]\n",
    "\n",
    "US_oil = US_electricity.loc[(US_electricity[\"Description\"] == \"Electricity Net Generation From Coal, All Sectors\") |\n",
    "                            (US_electricity[\"Description\"] == \"Electricity Net Generation From Petroleum, All Sectors\") | \n",
    "                            (US_electricity[\"Description\"] == \"Electricity Net Generation From Natural Gas, All Sectors\") |\n",
    "                            (US_electricity[\"Description\"] == \"Electricity Net Generation From Other Gases, All Sectors\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>107.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1973-02-01</td>\n",
       "      <td>103.046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1973-03-01</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1973-04-01</td>\n",
       "      <td>100.376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1973-05-01</td>\n",
       "      <td>99.263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date    Value\n",
       "15  1973-01-01  107.616\n",
       "16  1973-02-01  103.046\n",
       "17  1973-03-01      100\n",
       "18  1973-04-01  100.376\n",
       "19  1973-05-01   99.263"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaning data - need to remove top 30 rows in USD_data \n",
    "USD_data = USD_data.rename(columns = {\"Macrotrends Data Download\" : \"Date\", \n",
    "                                      \"Unnamed: 1\" : \"Value\"})\n",
    "rows = list(range(0,15))\n",
    "USD_data = USD_data.drop(rows)\n",
    "\n",
    "USD_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "USD_data[\"Date\"]=pd.to_datetime(USD_data[\"Date\"], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#isolating for 2005 to 2019 \n",
    "sdate = dt.datetime(2005,1,1)   # start date\n",
    "edate = dt.datetime(2019,12,30)   # end date\n",
    "\n",
    "USD_data = USD_data [(USD_data[\"Date\"]>=sdate ) & (USD_data[\"Date\"]<= edate)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value (million kWh)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>-725.387</td>\n",
       "      <td>Electricity Net Generation From Hydroelectric ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>-345.608</td>\n",
       "      <td>Electricity Net Generation From Hydroelectric ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>-496.889</td>\n",
       "      <td>Electricity Net Generation From Hydroelectric ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>-337.538</td>\n",
       "      <td>Electricity Net Generation From Hydroelectric ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>-466.254</td>\n",
       "      <td>Electricity Net Generation From Hydroelectric ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Value (million kWh)  \\\n",
       "Date                             \n",
       "2005-01-01            -725.387   \n",
       "2005-02-01            -345.608   \n",
       "2005-03-01            -496.889   \n",
       "2005-04-01            -337.538   \n",
       "2005-05-01            -466.254   \n",
       "\n",
       "                                                  Description  \n",
       "Date                                                           \n",
       "2005-01-01  Electricity Net Generation From Hydroelectric ...  \n",
       "2005-02-01  Electricity Net Generation From Hydroelectric ...  \n",
       "2005-03-01  Electricity Net Generation From Hydroelectric ...  \n",
       "2005-04-01  Electricity Net Generation From Hydroelectric ...  \n",
       "2005-05-01  Electricity Net Generation From Hydroelectric ...  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#setting date as index\n",
    "\n",
    "US_renewable = US_renewable.set_index(US_renewable[\"Date\"])\n",
    "US_renewable = US_renewable.drop(columns=[\"Date\"])\n",
    "US_renewable = US_renewable.rename(columns={\"Value\" : \"Value (million kWh)\"})\n",
    "US_renewable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value (million kWh)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>177013.907</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>155818.014</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>163612.7</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>143083.239</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>153957.962</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Value (million kWh)  \\\n",
       "Date                             \n",
       "2005-01-01          177013.907   \n",
       "2005-02-01          155818.014   \n",
       "2005-03-01            163612.7   \n",
       "2005-04-01          143083.239   \n",
       "2005-05-01          153957.962   \n",
       "\n",
       "                                                  Description  \n",
       "Date                                                           \n",
       "2005-01-01  Electricity Net Generation From Coal, All Sectors  \n",
       "2005-02-01  Electricity Net Generation From Coal, All Sectors  \n",
       "2005-03-01  Electricity Net Generation From Coal, All Sectors  \n",
       "2005-04-01  Electricity Net Generation From Coal, All Sectors  \n",
       "2005-05-01  Electricity Net Generation From Coal, All Sectors  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_oil = US_oil.set_index(US_oil[\"Date\"])\n",
    "US_oil = US_oil.drop(columns=[\"Date\"])\n",
    "US_oil = US_oil.rename(columns={\"Value\" : \"Value (million kWh)\"})\n",
    "US_oil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>95.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>95.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>95.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>96.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>96.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Value\n",
       "Date              \n",
       "2005-01-01   95.45\n",
       "2005-02-01  95.698\n",
       "2005-03-01  95.307\n",
       "2005-04-01  96.234\n",
       "2005-05-01   96.47"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USD_data = USD_data.set_index(USD_data[\"Date\"])\n",
    "USD_data = USD_data.drop(columns=[\"Date\"])\n",
    "USD_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting clean data set as CSV \n",
    "\n",
    "US_renewable.to_csv(r\"Clean Data\\Table 2a.csv\", index=True)\n",
    "\n",
    "US_oil.to_csv(r\"Clean Data\\Table 2b.csv\", index=True)\n",
    "\n",
    "USD_data.to_csv(r\"Clean Data\\Table 3.csv\", index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value (million kWh)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>177013.907</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>155818.014</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-03-01</td>\n",
       "      <td>163612.700</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-04-01</td>\n",
       "      <td>143083.239</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-05-01</td>\n",
       "      <td>153957.962</td>\n",
       "      <td>Electricity Net Generation From Coal, All Sectors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Value (million kWh)  \\\n",
       "0  2005-01-01           177013.907   \n",
       "1  2005-02-01           155818.014   \n",
       "2  2005-03-01           163612.700   \n",
       "3  2005-04-01           143083.239   \n",
       "4  2005-05-01           153957.962   \n",
       "\n",
       "                                         Description  \n",
       "0  Electricity Net Generation From Coal, All Sectors  \n",
       "1  Electricity Net Generation From Coal, All Sectors  \n",
       "2  Electricity Net Generation From Coal, All Sectors  \n",
       "3  Electricity Net Generation From Coal, All Sectors  \n",
       "4  Electricity Net Generation From Coal, All Sectors  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pulling in clean data \n",
    "\n",
    "Table2a_filepath = os.path.join(\"Clean Data\\Table 2a.csv\")\n",
    "Table2b_filepath = os.path.join(\"Clean Data\\Table 2b.csv\")\n",
    "Table3_filepath = os.path.join(\"Clean Data\\Table 3.csv\")\n",
    "\n",
    "US_renewable = pd.read_csv(Table2a_filepath)\n",
    "US_oil = pd.read_csv(Table2b_filepath)\n",
    "USD_data = pd.read_csv(Table3_filepath)\n",
    "\n",
    "US_oil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electricity Net Generation From Coal, All Sectors',\n",
       "       'Electricity Net Generation From Petroleum, All Sectors',\n",
       "       'Electricity Net Generation From Natural Gas, All Sectors',\n",
       "       'Electricity Net Generation From Other Gases, All Sectors'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_oil[\"Description\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Date</th>\n",
       "      <th>Coal (mil kWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>177013.907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>155818.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-03-01</td>\n",
       "      <td>163612.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-04-01</td>\n",
       "      <td>143083.239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-05-01</td>\n",
       "      <td>153957.962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Coal (mil kWh)\n",
       "0  2005-01-01      177013.907\n",
       "1  2005-02-01      155818.014\n",
       "2  2005-03-01      163612.700\n",
       "3  2005-04-01      143083.239\n",
       "4  2005-05-01      153957.962"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by source types \n",
    "\n",
    "Coal = US_oil.loc[US_oil[\"Description\"] == \"Electricity Net Generation From Coal, All Sectors\"]\n",
    "Coal = Coal.rename(columns={\"Value (million kWh)\" : \"Coal (mil kWh)\"})\n",
    "Coal = Coal.drop(columns=[\"Description\"])\n",
    "\n",
    "Petroleum = US_oil.loc[US_oil[\"Description\"] == \"Electricity Net Generation From Petroleum, All Sectors\"]\n",
    "Petroleum = Petroleum.rename(columns={\"Value (million kWh)\" : \"Petroleum (mil kWh)\"})\n",
    "Petroleum = Petroleum.drop(columns=[\"Description\"])\n",
    "\n",
    "Natural_Gas = US_oil.loc[US_oil[\"Description\"] == \"Electricity Net Generation From Natural Gas, All Sectors\"]\n",
    "Natural_Gas = Natural_Gas.rename(columns={\"Value (million kWh)\" : \"Natural_Gas (mil kWh)\"})\n",
    "Natural_Gas = Natural_Gas.drop(columns=[\"Description\"])\n",
    "\n",
    "Other_Gas = US_oil.loc[US_oil[\"Description\"] == \"Electricity Net Generation From Other Gases, All Sectors\"]\n",
    "Other_Gas = Other_Gas.rename(columns={\"Value (million kWh)\" : \"Other_Gas (mil kWh)\"})\n",
    "Other_Gas = Other_Gas.drop(columns=[\"Description\"])\n",
    "\n",
    "Coal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>Coal (mil kWh)</th>\n",
       "      <th>Petroleum (mil kWh)</th>\n",
       "      <th>Natural_Gas (mil kWh)</th>\n",
       "      <th>Other_Gas (mil kWh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>177013.907</td>\n",
       "      <td>12190.178</td>\n",
       "      <td>51337.627</td>\n",
       "      <td>1126.191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>155818.014</td>\n",
       "      <td>7314.510</td>\n",
       "      <td>44912.617</td>\n",
       "      <td>1076.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>163612.700</td>\n",
       "      <td>8351.900</td>\n",
       "      <td>51896.973</td>\n",
       "      <td>1214.487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>143083.239</td>\n",
       "      <td>6952.153</td>\n",
       "      <td>52016.286</td>\n",
       "      <td>1121.459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>153957.962</td>\n",
       "      <td>6713.918</td>\n",
       "      <td>54826.004</td>\n",
       "      <td>1191.303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Coal (mil kWh)  Petroleum (mil kWh)  Natural_Gas (mil kWh)  \\\n",
       "Date                                                                     \n",
       "2005-01-01      177013.907            12190.178              51337.627   \n",
       "2005-02-01      155818.014             7314.510              44912.617   \n",
       "2005-03-01      163612.700             8351.900              51896.973   \n",
       "2005-04-01      143083.239             6952.153              52016.286   \n",
       "2005-05-01      153957.962             6713.918              54826.004   \n",
       "\n",
       "            Other_Gas (mil kWh)  \n",
       "Date                             \n",
       "2005-01-01             1126.191  \n",
       "2005-02-01             1076.051  \n",
       "2005-03-01             1214.487  \n",
       "2005-04-01             1121.459  \n",
       "2005-05-01             1191.303  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining data frames \n",
    "\n",
    "US_oil = Coal.merge(Petroleum, on=\"Date\", how=\"left\")\n",
    "US_oil = US_oil.merge(Natural_Gas, on=\"Date\", how=\"left\")\n",
    "US_oil = US_oil.merge(Other_Gas, on=\"Date\", how=\"left\")\n",
    "\n",
    "US_oil = US_oil.set_index([\"Date\"])\n",
    "US_oil.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing data type so we can get the total sum\n",
    "US_oil = US_oil.astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Coal (mil kWh)</th>\n",
       "      <th>Petroleum (mil kWh)</th>\n",
       "      <th>Natural_Gas (mil kWh)</th>\n",
       "      <th>Other_Gas (mil kWh)</th>\n",
       "      <th>Total Oil (mil kWh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>177013.907</td>\n",
       "      <td>12190.178</td>\n",
       "      <td>51337.627</td>\n",
       "      <td>1126.191</td>\n",
       "      <td>241667.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>155818.014</td>\n",
       "      <td>7314.510</td>\n",
       "      <td>44912.617</td>\n",
       "      <td>1076.051</td>\n",
       "      <td>209121.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>163612.700</td>\n",
       "      <td>8351.900</td>\n",
       "      <td>51896.973</td>\n",
       "      <td>1214.487</td>\n",
       "      <td>225076.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>143083.239</td>\n",
       "      <td>6952.153</td>\n",
       "      <td>52016.286</td>\n",
       "      <td>1121.459</td>\n",
       "      <td>203173.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>153957.962</td>\n",
       "      <td>6713.918</td>\n",
       "      <td>54826.004</td>\n",
       "      <td>1191.303</td>\n",
       "      <td>216689.187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Coal (mil kWh)  Petroleum (mil kWh)  Natural_Gas (mil kWh)  \\\n",
       "Date                                                                     \n",
       "2005-01-01      177013.907            12190.178              51337.627   \n",
       "2005-02-01      155818.014             7314.510              44912.617   \n",
       "2005-03-01      163612.700             8351.900              51896.973   \n",
       "2005-04-01      143083.239             6952.153              52016.286   \n",
       "2005-05-01      153957.962             6713.918              54826.004   \n",
       "\n",
       "            Other_Gas (mil kWh)  Total Oil (mil kWh)  \n",
       "Date                                                  \n",
       "2005-01-01             1126.191           241667.903  \n",
       "2005-02-01             1076.051           209121.192  \n",
       "2005-03-01             1214.487           225076.060  \n",
       "2005-04-01             1121.459           203173.137  \n",
       "2005-05-01             1191.303           216689.187  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting total electricity production\n",
    "\n",
    "US_oil[\"Total Oil (mil kWh)\"] = US_oil.sum(axis=1)\n",
    "US_oil.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Electricity Net Generation From Hydroelectric Pumped Storage, All Sectors',\n",
       "       'Electricity Net Generation From Conventional Hydroelectric Power, All Sectors',\n",
       "       'Electricity Net Generation From Wood, All Sectors',\n",
       "       'Electricity Net Generation From Waste, All Sectors',\n",
       "       'Electricity Net Generation From Geothermal, All Sectors',\n",
       "       'Electricity Net Generation From Solar, All Sectors',\n",
       "       'Electricity Net Generation From Wind, All Sectors'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_renewable[\"Description\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Date</th>\n",
       "      <th>Wind (mil kWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>1131.569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>966.482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>2005-03-01</td>\n",
       "      <td>1560.626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>2005-04-01</td>\n",
       "      <td>1697.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>2005-05-01</td>\n",
       "      <td>1746.430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Wind (mil kWh)\n",
       "1080  2005-01-01        1131.569\n",
       "1081  2005-02-01         966.482\n",
       "1082  2005-03-01        1560.626\n",
       "1083  2005-04-01        1697.504\n",
       "1084  2005-05-01        1746.430"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by source types \n",
    "#hydro electric pump is just a storage unit\n",
    "\n",
    "wood = US_renewable.loc[US_renewable[\"Description\"] == \"Electricity Net Generation From Wood, All Sectors\"]\n",
    "wood = wood.rename(columns={\"Value (million kWh)\" : \"Wood (mil kWh)\"})\n",
    "wood = wood.drop(columns=[\"Description\"])\n",
    "\n",
    "waste = US_renewable.loc[US_renewable[\"Description\"] == \"Electricity Net Generation From Waste, All Sectors\"]\n",
    "waste = waste.rename(columns={\"Value (million kWh)\" : \"Waste (mil kWh)\"})\n",
    "waste = waste.drop(columns=[\"Description\"])\n",
    "\n",
    "geothermal = US_renewable.loc[US_renewable[\"Description\"] == \"Electricity Net Generation From Geothermal, All Sectors\"]\n",
    "geothermal = geothermal.rename(columns={\"Value (million kWh)\" : \"Geothermal (mil kWh)\"})\n",
    "geothermal = geothermal.drop(columns=[\"Description\"])\n",
    "\n",
    "solar = US_renewable.loc[US_renewable[\"Description\"] == \"Electricity Net Generation From Solar, All Sectors\"]\n",
    "solar = solar.rename(columns={\"Value (million kWh)\" : \"Solar (mil kWh)\"})\n",
    "solar = solar.drop(columns=[\"Description\"])\n",
    "\n",
    "wind = US_renewable.loc[US_renewable[\"Description\"] == \"Electricity Net Generation From Wind, All Sectors\"]\n",
    "wind = wind.rename(columns={\"Value (million kWh)\" : \"Wind (mil kWh)\"})\n",
    "wind = wind.drop(columns=[\"Description\"])\n",
    "\n",
    "Conv_Power = US_renewable.loc[US_renewable[\"Description\"] == \"Electricity Net Generation From Conventional Hydroelectric Power, All Sectors\"]\n",
    "Conv_Power = Conv_Power.rename(columns={\"Value (million kWh)\" : \"Conventional Hydroelectric Power (mil kWh)\"})\n",
    "Conv_Power = Conv_Power.drop(columns=[\"Description\"])\n",
    "\n",
    "\n",
    "wind.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Wood (mil kWh)</th>\n",
       "      <th>Waste (mil kWh)</th>\n",
       "      <th>Geothermal (mil kWh)</th>\n",
       "      <th>Solar (mil kWh)</th>\n",
       "      <th>Wind (mil kWh)</th>\n",
       "      <th>Conventional Hydroelectric Power (mil kWh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>3333.076</td>\n",
       "      <td>1278.347</td>\n",
       "      <td>1252.144</td>\n",
       "      <td>8.668</td>\n",
       "      <td>1131.569</td>\n",
       "      <td>24272.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>3055.295</td>\n",
       "      <td>1123.407</td>\n",
       "      <td>1063.014</td>\n",
       "      <td>13.082</td>\n",
       "      <td>966.482</td>\n",
       "      <td>21606.774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>3287.717</td>\n",
       "      <td>1277.929</td>\n",
       "      <td>1204.252</td>\n",
       "      <td>38.157</td>\n",
       "      <td>1560.626</td>\n",
       "      <td>22936.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>3035.547</td>\n",
       "      <td>1222.542</td>\n",
       "      <td>1187.134</td>\n",
       "      <td>58.263</td>\n",
       "      <td>1697.504</td>\n",
       "      <td>23058.359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>3100.697</td>\n",
       "      <td>1351.152</td>\n",
       "      <td>1264.308</td>\n",
       "      <td>81.153</td>\n",
       "      <td>1746.430</td>\n",
       "      <td>27278.511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Wood (mil kWh)  Waste (mil kWh)  Geothermal (mil kWh)  \\\n",
       "Date                                                                \n",
       "2005-01-01        3333.076         1278.347              1252.144   \n",
       "2005-02-01        3055.295         1123.407              1063.014   \n",
       "2005-03-01        3287.717         1277.929              1204.252   \n",
       "2005-04-01        3035.547         1222.542              1187.134   \n",
       "2005-05-01        3100.697         1351.152              1264.308   \n",
       "\n",
       "            Solar (mil kWh)  Wind (mil kWh)  \\\n",
       "Date                                          \n",
       "2005-01-01            8.668        1131.569   \n",
       "2005-02-01           13.082         966.482   \n",
       "2005-03-01           38.157        1560.626   \n",
       "2005-04-01           58.263        1697.504   \n",
       "2005-05-01           81.153        1746.430   \n",
       "\n",
       "            Conventional Hydroelectric Power (mil kWh)  \n",
       "Date                                                    \n",
       "2005-01-01                                   24272.165  \n",
       "2005-02-01                                   21606.774  \n",
       "2005-03-01                                   22936.068  \n",
       "2005-04-01                                   23058.359  \n",
       "2005-05-01                                   27278.511  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#combining data frames \n",
    "\n",
    "US_renewable = wood.merge(waste, on=\"Date\", how=\"left\")\n",
    "US_renewable = US_renewable.merge(geothermal, on=\"Date\", how=\"left\")\n",
    "US_renewable = US_renewable.merge(solar, on=\"Date\", how=\"left\")\n",
    "US_renewable = US_renewable.merge(wind, on=\"Date\", how=\"left\")\n",
    "US_renewable = US_renewable.merge(Conv_Power, on=\"Date\", how=\"left\")\n",
    "\n",
    "US_renewable = US_renewable.set_index([\"Date\"])\n",
    "US_renewable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing data type so we can get the total sum\n",
    "US_renewable = US_renewable.astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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",
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       "    }\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>Wood (mil kWh)</th>\n",
       "      <th>Waste (mil kWh)</th>\n",
       "      <th>Geothermal (mil kWh)</th>\n",
       "      <th>Solar (mil kWh)</th>\n",
       "      <th>Wind (mil kWh)</th>\n",
       "      <th>Conventional Hydroelectric Power (mil kWh)</th>\n",
       "      <th>Total Renewable (mil kWh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>3333.076</td>\n",
       "      <td>1278.347</td>\n",
       "      <td>1252.144</td>\n",
       "      <td>8.668</td>\n",
       "      <td>1131.569</td>\n",
       "      <td>24272.165</td>\n",
       "      <td>31275.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>3055.295</td>\n",
       "      <td>1123.407</td>\n",
       "      <td>1063.014</td>\n",
       "      <td>13.082</td>\n",
       "      <td>966.482</td>\n",
       "      <td>21606.774</td>\n",
       "      <td>27828.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>3287.717</td>\n",
       "      <td>1277.929</td>\n",
       "      <td>1204.252</td>\n",
       "      <td>38.157</td>\n",
       "      <td>1560.626</td>\n",
       "      <td>22936.068</td>\n",
       "      <td>30304.749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>3035.547</td>\n",
       "      <td>1222.542</td>\n",
       "      <td>1187.134</td>\n",
       "      <td>58.263</td>\n",
       "      <td>1697.504</td>\n",
       "      <td>23058.359</td>\n",
       "      <td>30259.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>3100.697</td>\n",
       "      <td>1351.152</td>\n",
       "      <td>1264.308</td>\n",
       "      <td>81.153</td>\n",
       "      <td>1746.430</td>\n",
       "      <td>27278.511</td>\n",
       "      <td>34822.251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Wood (mil kWh)  Waste (mil kWh)  Geothermal (mil kWh)  \\\n",
       "Date                                                                \n",
       "2005-01-01        3333.076         1278.347              1252.144   \n",
       "2005-02-01        3055.295         1123.407              1063.014   \n",
       "2005-03-01        3287.717         1277.929              1204.252   \n",
       "2005-04-01        3035.547         1222.542              1187.134   \n",
       "2005-05-01        3100.697         1351.152              1264.308   \n",
       "\n",
       "            Solar (mil kWh)  Wind (mil kWh)  \\\n",
       "Date                                          \n",
       "2005-01-01            8.668        1131.569   \n",
       "2005-02-01           13.082         966.482   \n",
       "2005-03-01           38.157        1560.626   \n",
       "2005-04-01           58.263        1697.504   \n",
       "2005-05-01           81.153        1746.430   \n",
       "\n",
       "            Conventional Hydroelectric Power (mil kWh)  \\\n",
       "Date                                                     \n",
       "2005-01-01                                   24272.165   \n",
       "2005-02-01                                   21606.774   \n",
       "2005-03-01                                   22936.068   \n",
       "2005-04-01                                   23058.359   \n",
       "2005-05-01                                   27278.511   \n",
       "\n",
       "            Total Renewable (mil kWh)  \n",
       "Date                                   \n",
       "2005-01-01                  31275.969  \n",
       "2005-02-01                  27828.054  \n",
       "2005-03-01                  30304.749  \n",
       "2005-04-01                  30259.349  \n",
       "2005-05-01                  34822.251  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting total electricity production\n",
    "\n",
    "US_renewable[\"Total Renewable (mil kWh)\"] = US_renewable.sum(axis=1)\n",
    "US_renewable.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>95.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>95.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>95.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>96.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>96.470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Value\n",
       "Date              \n",
       "2005-01-01  95.450\n",
       "2005-02-01  95.698\n",
       "2005-03-01  95.307\n",
       "2005-04-01  96.234\n",
       "2005-05-01  96.470"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Set index as data \n",
    "USD_data = USD_data.set_index([\"Date\"], drop=True)\n",
    "USD_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>USD Index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>95.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>95.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>95.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>96.234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>96.470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            USD Index\n",
       "Date                 \n",
       "2005-01-01     95.450\n",
       "2005-02-01     95.698\n",
       "2005-03-01     95.307\n",
       "2005-04-01     96.234\n",
       "2005-05-01     96.470"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename column name \n",
    "USD_data = USD_data.rename( columns={ \"Value\" : \"USD Index\"})\n",
    "USD_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>USD Index</th>\n",
       "      <th>Wood (mil kWh)</th>\n",
       "      <th>Waste (mil kWh)</th>\n",
       "      <th>Geothermal (mil kWh)</th>\n",
       "      <th>Solar (mil kWh)</th>\n",
       "      <th>Wind (mil kWh)</th>\n",
       "      <th>Conventional Hydroelectric Power (mil kWh)</th>\n",
       "      <th>Total Renewable (mil kWh)</th>\n",
       "      <th>Coal (mil kWh)</th>\n",
       "      <th>Petroleum (mil kWh)</th>\n",
       "      <th>Natural_Gas (mil kWh)</th>\n",
       "      <th>Other_Gas (mil kWh)</th>\n",
       "      <th>Total Oil (mil kWh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>95.450</td>\n",
       "      <td>3333.076</td>\n",
       "      <td>1278.347</td>\n",
       "      <td>1252.144</td>\n",
       "      <td>8.668</td>\n",
       "      <td>1131.569</td>\n",
       "      <td>24272.165</td>\n",
       "      <td>31275.969</td>\n",
       "      <td>177013.907</td>\n",
       "      <td>12190.178</td>\n",
       "      <td>51337.627</td>\n",
       "      <td>1126.191</td>\n",
       "      <td>241667.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-01</th>\n",
       "      <td>95.698</td>\n",
       "      <td>3055.295</td>\n",
       "      <td>1123.407</td>\n",
       "      <td>1063.014</td>\n",
       "      <td>13.082</td>\n",
       "      <td>966.482</td>\n",
       "      <td>21606.774</td>\n",
       "      <td>27828.054</td>\n",
       "      <td>155818.014</td>\n",
       "      <td>7314.510</td>\n",
       "      <td>44912.617</td>\n",
       "      <td>1076.051</td>\n",
       "      <td>209121.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-03-01</th>\n",
       "      <td>95.307</td>\n",
       "      <td>3287.717</td>\n",
       "      <td>1277.929</td>\n",
       "      <td>1204.252</td>\n",
       "      <td>38.157</td>\n",
       "      <td>1560.626</td>\n",
       "      <td>22936.068</td>\n",
       "      <td>30304.749</td>\n",
       "      <td>163612.700</td>\n",
       "      <td>8351.900</td>\n",
       "      <td>51896.973</td>\n",
       "      <td>1214.487</td>\n",
       "      <td>225076.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-01</th>\n",
       "      <td>96.234</td>\n",
       "      <td>3035.547</td>\n",
       "      <td>1222.542</td>\n",
       "      <td>1187.134</td>\n",
       "      <td>58.263</td>\n",
       "      <td>1697.504</td>\n",
       "      <td>23058.359</td>\n",
       "      <td>30259.349</td>\n",
       "      <td>143083.239</td>\n",
       "      <td>6952.153</td>\n",
       "      <td>52016.286</td>\n",
       "      <td>1121.459</td>\n",
       "      <td>203173.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-01</th>\n",
       "      <td>96.470</td>\n",
       "      <td>3100.697</td>\n",
       "      <td>1351.152</td>\n",
       "      <td>1264.308</td>\n",
       "      <td>81.153</td>\n",
       "      <td>1746.430</td>\n",
       "      <td>27278.511</td>\n",
       "      <td>34822.251</td>\n",
       "      <td>153957.962</td>\n",
       "      <td>6713.918</td>\n",
       "      <td>54826.004</td>\n",
       "      <td>1191.303</td>\n",
       "      <td>216689.187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            USD Index  Wood (mil kWh)  Waste (mil kWh)  Geothermal (mil kWh)  \\\n",
       "Date                                                                           \n",
       "2005-01-01     95.450        3333.076         1278.347              1252.144   \n",
       "2005-02-01     95.698        3055.295         1123.407              1063.014   \n",
       "2005-03-01     95.307        3287.717         1277.929              1204.252   \n",
       "2005-04-01     96.234        3035.547         1222.542              1187.134   \n",
       "2005-05-01     96.470        3100.697         1351.152              1264.308   \n",
       "\n",
       "            Solar (mil kWh)  Wind (mil kWh)  \\\n",
       "Date                                          \n",
       "2005-01-01            8.668        1131.569   \n",
       "2005-02-01           13.082         966.482   \n",
       "2005-03-01           38.157        1560.626   \n",
       "2005-04-01           58.263        1697.504   \n",
       "2005-05-01           81.153        1746.430   \n",
       "\n",
       "            Conventional Hydroelectric Power (mil kWh)  \\\n",
       "Date                                                     \n",
       "2005-01-01                                   24272.165   \n",
       "2005-02-01                                   21606.774   \n",
       "2005-03-01                                   22936.068   \n",
       "2005-04-01                                   23058.359   \n",
       "2005-05-01                                   27278.511   \n",
       "\n",
       "            Total Renewable (mil kWh)  Coal (mil kWh)  Petroleum (mil kWh)  \\\n",
       "Date                                                                         \n",
       "2005-01-01                  31275.969      177013.907            12190.178   \n",
       "2005-02-01                  27828.054      155818.014             7314.510   \n",
       "2005-03-01                  30304.749      163612.700             8351.900   \n",
       "2005-04-01                  30259.349      143083.239             6952.153   \n",
       "2005-05-01                  34822.251      153957.962             6713.918   \n",
       "\n",
       "            Natural_Gas (mil kWh)  Other_Gas (mil kWh)  Total Oil (mil kWh)  \n",
       "Date                                                                         \n",
       "2005-01-01              51337.627             1126.191           241667.903  \n",
       "2005-02-01              44912.617             1076.051           209121.192  \n",
       "2005-03-01              51896.973             1214.487           225076.060  \n",
       "2005-04-01              52016.286             1121.459           203173.137  \n",
       "2005-05-01              54826.004             1191.303           216689.187  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part3 = USD_data.merge(US_renewable, on=\"Date\", how=\"left\")\n",
    "part3 = part3.merge(US_oil, on=\"Date\", how=\"left\")\n",
    "\n",
    "part3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting to CSV file\n",
    "part3.to_csv('Transformed Data\\Part 3.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Date</th>\n",
       "      <th>USD Index</th>\n",
       "      <th>Wood (mil kWh)</th>\n",
       "      <th>Waste (mil kWh)</th>\n",
       "      <th>Geothermal (mil kWh)</th>\n",
       "      <th>Solar (mil kWh)</th>\n",
       "      <th>Wind (mil kWh)</th>\n",
       "      <th>Conventional Hydroelectric Power (mil kWh)</th>\n",
       "      <th>Total Renewable (mil kWh)</th>\n",
       "      <th>Coal (mil kWh)</th>\n",
       "      <th>Petroleum (mil kWh)</th>\n",
       "      <th>Natural_Gas (mil kWh)</th>\n",
       "      <th>Other_Gas (mil kWh)</th>\n",
       "      <th>Total Oil (mil kWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>95.450</td>\n",
       "      <td>3333.076</td>\n",
       "      <td>1278.347</td>\n",
       "      <td>1252.144</td>\n",
       "      <td>8.668</td>\n",
       "      <td>1131.569</td>\n",
       "      <td>24272.165</td>\n",
       "      <td>31275.969</td>\n",
       "      <td>177013.907</td>\n",
       "      <td>12190.178</td>\n",
       "      <td>51337.627</td>\n",
       "      <td>1126.191</td>\n",
       "      <td>241667.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>95.698</td>\n",
       "      <td>3055.295</td>\n",
       "      <td>1123.407</td>\n",
       "      <td>1063.014</td>\n",
       "      <td>13.082</td>\n",
       "      <td>966.482</td>\n",
       "      <td>21606.774</td>\n",
       "      <td>27828.054</td>\n",
       "      <td>155818.014</td>\n",
       "      <td>7314.510</td>\n",
       "      <td>44912.617</td>\n",
       "      <td>1076.051</td>\n",
       "      <td>209121.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-03-01</td>\n",
       "      <td>95.307</td>\n",
       "      <td>3287.717</td>\n",
       "      <td>1277.929</td>\n",
       "      <td>1204.252</td>\n",
       "      <td>38.157</td>\n",
       "      <td>1560.626</td>\n",
       "      <td>22936.068</td>\n",
       "      <td>30304.749</td>\n",
       "      <td>163612.700</td>\n",
       "      <td>8351.900</td>\n",
       "      <td>51896.973</td>\n",
       "      <td>1214.487</td>\n",
       "      <td>225076.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-04-01</td>\n",
       "      <td>96.234</td>\n",
       "      <td>3035.547</td>\n",
       "      <td>1222.542</td>\n",
       "      <td>1187.134</td>\n",
       "      <td>58.263</td>\n",
       "      <td>1697.504</td>\n",
       "      <td>23058.359</td>\n",
       "      <td>30259.349</td>\n",
       "      <td>143083.239</td>\n",
       "      <td>6952.153</td>\n",
       "      <td>52016.286</td>\n",
       "      <td>1121.459</td>\n",
       "      <td>203173.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-05-01</td>\n",
       "      <td>96.470</td>\n",
       "      <td>3100.697</td>\n",
       "      <td>1351.152</td>\n",
       "      <td>1264.308</td>\n",
       "      <td>81.153</td>\n",
       "      <td>1746.430</td>\n",
       "      <td>27278.511</td>\n",
       "      <td>34822.251</td>\n",
       "      <td>153957.962</td>\n",
       "      <td>6713.918</td>\n",
       "      <td>54826.004</td>\n",
       "      <td>1191.303</td>\n",
       "      <td>216689.187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  USD Index  Wood (mil kWh)  Waste (mil kWh)  \\\n",
       "0  2005-01-01     95.450        3333.076         1278.347   \n",
       "1  2005-02-01     95.698        3055.295         1123.407   \n",
       "2  2005-03-01     95.307        3287.717         1277.929   \n",
       "3  2005-04-01     96.234        3035.547         1222.542   \n",
       "4  2005-05-01     96.470        3100.697         1351.152   \n",
       "\n",
       "   Geothermal (mil kWh)  Solar (mil kWh)  Wind (mil kWh)  \\\n",
       "0              1252.144            8.668        1131.569   \n",
       "1              1063.014           13.082         966.482   \n",
       "2              1204.252           38.157        1560.626   \n",
       "3              1187.134           58.263        1697.504   \n",
       "4              1264.308           81.153        1746.430   \n",
       "\n",
       "   Conventional Hydroelectric Power (mil kWh)  Total Renewable (mil kWh)  \\\n",
       "0                                   24272.165                  31275.969   \n",
       "1                                   21606.774                  27828.054   \n",
       "2                                   22936.068                  30304.749   \n",
       "3                                   23058.359                  30259.349   \n",
       "4                                   27278.511                  34822.251   \n",
       "\n",
       "   Coal (mil kWh)  Petroleum (mil kWh)  Natural_Gas (mil kWh)  \\\n",
       "0      177013.907            12190.178              51337.627   \n",
       "1      155818.014             7314.510              44912.617   \n",
       "2      163612.700             8351.900              51896.973   \n",
       "3      143083.239             6952.153              52016.286   \n",
       "4      153957.962             6713.918              54826.004   \n",
       "\n",
       "   Other_Gas (mil kWh)  Total Oil (mil kWh)  \n",
       "0             1126.191           241667.903  \n",
       "1             1076.051           209121.192  \n",
       "2             1214.487           225076.060  \n",
       "3             1121.459           203173.137  \n",
       "4             1191.303           216689.187  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pulling in transformed files \n",
    "\n",
    "#renewable production vs. oil production over time \n",
    "data_filepath = \"Transformed Data\\Part 3.csv\"\n",
    "data = pd.read_csv(data_filepath)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q5. Electricity production from renewable sources over time in the US.\n",
    "\n",
    "analyzing electricity production over 2005 to 2019, looking at the contribution of the different sources. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at electricity production from renewable sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MlseJmnOBPSVtSVhf5jYLk6K+V3dNTdaVwIG1VpPjOI7TP3TKm+4oQtBCCMHzFgKY2UJJtXVRtiIEM6yxIKatYOA6GbX02jXzo6yVkhYTIgM/XsVDOI7jrAvMXTSTux66gGUrHmHMyM3ZY+LJTJvQdFWKSqm8ZSRpPcJywFc0O7VBWtp6HrWwEVnHkjqcJGm2pNmPPZa58rbjOM46zdxFM7n5wbNYtuJhwFi24mFufvAs5i6a2VW9OtFNdzBhjZdH4v4jseuN+PloTF/AwEW9tiYsxrUgfq9PH3CNpBGEUPdP1CtgZhea2XQzm77ppoPmWjmO4wwZ7nroAlbWrbW50p7jrocu6JJGgU4Yo6NZ20UHYfnr4+L34wjraNTSj4oeci8kOCrcGbv0lkraK44HHVt3TU3WEcBvzIPtOY7jpLJsxSOF0jtFpWNGkjYAXsPAJW8/B1wu6QTCwk5HApjZXyRdTlj0bCXwPjNbFa95L3AxYfG6mXED+A7wfUlzCS2io6p8HsdxnH5nzMjNYxfd4PRuMuSidk+fPt08HJDjOEOV2phRsqtuhEaz7+TTO+LEIOluM5tenz7kYtM5juNUQS96qDWiplOv6erGyHEcp03qWxs1DzWgrUK+bANXL++AKWd23QjV8Nh0juM4bVKFh1rZLti96tJdw42R4zhOm1ThoVa2getVl+4abowcx3HaJM0TrR0PtbINXK+6dNdwY+Q4jtMme0w8mREaPSBthEazx8STW5Y5avjYhumtGrgqDGaZuAOD4zhOC9Q7A2w3/lDmL7mlFGeDuYtm8vyqpwelD9PIlg3cHhNPbujS3Y7BLBM3Ro7jOAVp5D133xPXljZX566HLsBYOSh9ta1YM8ZT9D696tJdw42R4zhOQdKcAWbNOwNoz50bssdx2nEbnzbh4J4xPvX4mJHjOE5B0oyFsboUd+lm4zi95AVXFm6MHMdxCpJlLMowFI0cIurpFS+4snBj5DiOU5BmxqJdQzFtwsHsO/l0xozcIvWcXvGCKws3Ro7jOAWpGQulFKFlGIppEw7m6F2u44ApZ5XuNt6LuAOD4zhOC9QcAap2l+51L7iycGPkOI7TIp0yFL3sBVcWbowcx3HaoGxD0S9LUZSNGyPHcZweoV+WoqiCTGMk6RXAMcC+wJbAs8CfgeuAS81sceUaOo7jDBGyImu3E1oozcDV7tkLRirVGEmaCTwEXA18BngUGA1sDxwAXC3pXDO7phOKOo7j9APttEI6uRTFrfPPYZU9X3orrFWyWkbvMLPH69KWAffE7UuSNqlMM8dxnD6j3W62MSM3j4vfDU5vlTRDtnz1kkFp7bbC2iF1nlEDQ9TSOY7jOEOFdhewq2IpiqKGrFuRHZpOepX0Zkn3SVosaYmkpZIGm1THcZwhTrvdbAMjL4gxI7doOxJ4moEbNXxcw/O7FdkhjzfdOcAbzOzeqpVxHMfpZ8roZivbVTxtLhRUP2G3CHmM0SNuiBzHcZrTqwvYZRm4fvCme3P8OlvSZcBPgeW142b2k4p1cxzH6Sv6LXRPL0V2yGoZvSHx/RngtYl9A9wYOY7j1NFLBXw/kWWMPmRmT3ZME8dxHGfIkmWM/i7pMeBW4HfArWb2f51Ry3EcxxlKZM0z2gx4E8EQ7Q38RNIjkq6WdGqnFHQcx3HWfTK96WJL6P+AiyVtC7we+ABh/Oic6tVzHMdZt+mHIKadIMubbm9Ci+gVwCTgn8DthMCp93REO8dxnHWYqqJ0t6JHtw1iVsvoFoLRORf4qZk90xmVHMdxhgZVROkuSq8YxKxwQBOBs4HdgF9IulXSBZLeLmmbPMIlbSzpSkl/k3SvpFdIGi/p+hhi6HpJL0icf5qkuZL+Lul1ifTdJc2Jx86XpJg+StJlMf0OSVNbyQTHcZxuUEWU7qK0G0+vLLIcGB42s5+Y2X+b2SuBVwN/Az4F3JdT/nnAL8xsR2BX4F7go8ANZrYdcEPcR9LOwFHAi4CDgK9LGh7lfAM4CdgubgfF9BOAJ81sGvBl4PM59XIcx+k6aWGCOhkfrhcMImQYI0njJB0k6dOSfg3MB94B/Ax4WzPBksYCrwS+A2Bmz5vZU8DhwCXxtEuAN8bvhwM/MrPlZnY/MBfYU9KWwFgzu83MDPhe3TU1WVcCB9ZaTY7jOL1OFVG6a8xdNJMZcw7honumM2POIcxdNLPheb1gECF7zGguwWHhVuBM4E4ze7aA7G2Ax4DvStoVuJvgibe5mS0EMLOFkjaL528V71djQUxbEb/Xp9eumR9lrZS0GJgADFjaQtJJhJYVkydPLvAIjuM41VFV+KAi40B54ul1wsEh1RiZ2aYAkqaa2QPJY5L2MLO7csjeDfhPM7tD0nnELrkUGrVoLCM965qBCWYXAhcCTJ8+fdBxx3GcblFF+KAijhHNDGKnHBzyRO3+saTDzOxfAJL2Ay4Admly3QJggZndEfevJBijRyRtGVtFWxKWM6+dPylx/daEZc8XxO/16clrFkgaAYwDnsjxTI7jOOssRceBmkX17oTHX9PF9YD3AD+VtIWk1xOcEl7f7CIzexiYL2mHmHQg8FfgGuC4mHYccHX8fg1wVPSQeyHBUeHO2KW3VNJecTzo2LprarKOAH4Tx5Ucx3HWkHf8ZF2hzHGgTjk4NG0Zmdldkt4P/Ap4DniNmT2WU/5/Aj+QtB5h0uw7CQbwckknAA8CR8b7/EXS5QSDtRJ4n5mtinLeC1wMrA/MjBsE54jvS5pLaBEdlVMvx3GGCL0yj6YZZY7LlLmuUhkLBuZBaQ0JST9j4PjLzsBC4EkAMzusVE06xPTp02327NndVsNxnA4xY84hKYXpFhy9y3Vd0Ggw9QYTgvFoZ8nxsoxb2bpJutvMptenZ7WMvlj4Lo7jOD1Gr8yjyaKKcZmyHCM6tWBgljfdTaXeyXEcpwt0qpupHXrdYHZiwcA8DgyO4zh9S5UTS8uiVyaedhM3Ro7jrNNMm3Aw+04+nTEjtwDEmJFbtDUWUwX9YDCrJs88I8dxnL6mE91M7VDmuEwvLAfRClnrGc2hQTQDQtQDM7OXVKaV4zhOD1JlQV+GwSzTjb3TRi3LtXtK1oVmNq8SjSrGXbsdx2mFKtyvy6YsN/ZGzwowavg49t76w209b2HX7n41No7jOFXQCwvhNaMsr7xGzwqwfNXiyiYMZy0hcUv8XCppSWJbKmlJqVo4juP0OL3ufg3leeVlPVNVC+9ltYz2iZ8blX5Xx3GcLtDOOEg/zFdqJwxQMm+EsIYuA4EqDHAub7q4NPik5Plmdk/p2jiO41REu4P7ZcZ7a5c0o9qqV1593mQZIqjGADc1RpLOBI4nBDpdHZMNeFXp2jiO41REO2M+tcJ/pT2HGIaxmjEjt8gs6KvwRpu7aCa3zj+H5avXjpTUG9VWvPLSxogaUZUBzjPp9a3Atma2n5kdEDc3RI7j9BWtjvnUWg21LjpjNWIEK1Y/y43zPt5wSYqB19gag9HO0hU1mUlDVKPdcZz0PBAHTDmrIxOG83TT/RnYmLWL4DmO4/QdrY75NGo1GCtZvmox0Li7L28rrEjrqVnrJc2g5LlHVt50asJwnpbRZ4HfS/qlpGtqW9WKOY7jlEmrIXfyDNbXt0zytMKKtp6a6dHIqOa9Ry+EI8rTMroE+Dwwh7VjRo7jOH3FwMH9hxHDBhiRtNp/WquhnqSxyNMKKzqGlaXHCI1m0th94qTXtS2gvPfo1DIRWeQxRo+b2fmVa+I4jlMxtcK1iFddIy+6RiQNTR7Pu6JjWGl6jBo+jm02fg33PXHtoGdK07nRPbodvy+PMbpb0meBa4DltUR37XYcpx8p2iKpbzWMGrYRK+xZVtuKNefUG5o8LY2iY1hZMmfMOaThM9U8//LeA7oXaDWPMXpZ/Nwrkeau3Y7j9CWteNXVtxryFNjNWhqtzFtKk5mmu7GaERqd+x5lBlotSlNjZGYHVKqB4zhOBykjkkIZXVpljtOkP9MWa8aOWvXY61T8vawlJI4BfmhmDZ0WJG0LbGlmt1SlnOM4Tjs0asF0M5JCI32KRNNOI+uZihjObsbfy2oZTSC4dN8N3A08BowGpgH7AY8DH61cQ8dxnBZI63Lad/Lp7Dv59I6Pi1TZBVZWK6ub8fdS1zMCkDScMDb0b8CWwLPAvcBMM3uwcu0qwNczcpx1n7mLZjJr3hkpg/fF1vYpi7S1hsQw9p/y6Z5YhqITazYVXs8IwMxWAdfHzXEcp+epFaiNDBF0b8mHLCeDTjkJNKOb841yRe12HMdJo1uuwGncuuALmXOCurXkQ9ak1XacBMrO/27NN3Jj5DhDjDILr266AqfpU4sZ14huLfkAzSfPttJi67X8b4c8sekcx1lHKDuadJYrcDfIuq8YVlnE6TxMm3Aw+04+HaUUu6202Hot/9shy7X7Q1kXmtm55avjOE6VlD2PpNeW4s66by84CTQKRwStt9h6Lf/bIatltFHcpgPvBbaK23uAnatXzXGcsim78EqrzXdzXKYRo4aN7bohqlFrIZWxRlCv5X87pLaMzOxTAJJ+BexmZkvj/ieBKzqineM4pVL2PJJeWoo7S5+9J53aFX3SKMtJoNfyvx3yjBlNBp5P7D8PTK1EG8dxKqXsdWsG1vIZsCxDO6uatkqZrY5+YF163sxJrwCS/oew9PhVhACpbwIuN7OzmwqXHgCWAquAlWY2XdJ44DKCQXsAeKuZPRnPPw04IZ7/fjP7ZUzfHbgYWB/4OfABMzNJo4DvAbsDi4C3mdkDWTr5pFenCnrNvTmLsnRNymkUyRpgpwlHsM+U08pS3VkHSJv02tQYxYt3A/aNu781s9/nvOkDwHQzezyRdg7whJl9TtJHgReY2Uck7QzMAPYEJgK/BrY3s1WS7gQ+ANxOMEbnm9lMSf8BvMTM3iPpKOBNZva2LJ3cGDll04lZ671Go2dO44ApZ/V0PvRTRaKeftQ9zRjlde3eAFhiZucBCyS9sA1dDiesHkv8fGMi/UdmttzM7gfmAntK2hIYa2a3WbCc36u7pibrSuBASWpDN8cpzLrkXpuXRs+cdW6vUrareyfpZ90b0dQYSfoE8BGg1tYeCVyaU74Bv5J0t6STYtrmZrYQIH5uFtO3AuYnrl3AWg++BQ3SB1xjZiuBxYQAr47TEeYumpk6q37Ziof7tmBoRhHvu151M67Fr+vXisS6VgnKE4HhTYQF9u4BMLOHJG2UU/6/xfM3A66X9LeMcxu1aCwjPeuagYKDITwJYPLkydkaO05OajXTLHppNnyZXTpZoW0andtr9Gr8uiKsS3OMIF833fOxe8wAJG2YV7iZPRQ/HyU4QOwJPBK73oifj8bTFwCTEpdvDTwU07dukD7gGkkjgHHAEw30uNDMppvZ9E033TSv+o6TSZ6uql6pqZbdpdPIK69R3bBVT725i2YyY84hXHTPdGbMOaT0Fmaz364XDWg969IcI8hnjC6X9C1gY0knEhwLLmp2kaQNay2oaMBeC/wZuAY4Lp52HHB1/H4NcJSkUXFMajvgztiVt1TSXnE86Ni6a2qyjgB+Y3k8MhynBPLWQHuhplp2l04jl+IDppzJAVPOatvNuBNjIVm/Sb/M0ynbTb/b5Fl2/IuSXgMsAXYAzjCzPEtKbA5cFf0JRhBWjf2FpLsIBu4E4EHgyHifv0i6HPgrsBJ4X1zCAkIEiIsJrt0z4wbwHeD7kuYSWkRH5dDLcUohb1dVL9RU07t0Huaie6a31G2XNnGz3S7JTix9nfbbVRW/rgqvt24u91AFeeYZvRBYaBb+HZLWJzghPFC9euXjrt1OWeRxb+4VF++0hd2S9IKucxfN5MZ5p6ccFSfuVs672yl3/LmLZnLr/HNYvnrJgPReyOtu0Y5r9xUwYJRvFR4OyHEAGK711nwfNXwcO004oidnwzce4xlIt8e3mjmElNnC7ETkgtrz1Bsi6H5e9yJ5vOlGmNmacEBm9ryUeAMdZwjSqGa9avVythjz0lIjDpTVvVPfpdPA6RTo7vhWllNBFWMheeLDtZP/zZwkemEssZfIY4wek3SYmV0DIOlw4PEm1zjOOk0nxjXKXjgtWfimddt1c3wrq3DO02ope/+0EcoAACAASURBVFym3fxvZmx6YSyxl8jTTfce4GOSHpQ0nzAB9t+rVctxeptOzPGoclJjK55YVblb1+SmtdbGjNwilyEq0wOvjAmxWcamn73eqqKpMTKzf5jZXoQ1jHY2s73NbG71qjlO79KJOR5VGryiYyZVuVsPlDuYvIV2mYa7rAmxaeN0o4aP65mxxF6iaTedpDPq9gEws09XpJPj9DydWEem7LWH6imypk5V3ZJZ4ypjRm6Ru6utTMNd1oTYdc31umryjBk9nfg+GjgUuLcadRynP+hEQdOOwSt7/KSqVlr69eLoXa5b04XX7DnKNNxlTogtaxG9oUCeSa9fSu5L+iIh8oHjDGmqKGjqjch24w9l/pJbChmVsh0foLpWWpbcIs9RZku10xNinUDeJSSSbABsU7YijjPUaTQuc98T17LHxJM5cbfZHL3LdbkKwqLjJ3kcE6oKPZMlt8hzlDlvKE2n/ad82g1RheQZM5rDWjeX4cCmgI8XOU7JlDUuU6RLLW/ro6puySy5N877eO7nqMkqw1j4WE93yDNmdGji+0rgkbh2kOM4JVLWuEyRLrUiBrBIYZ81ZtXo2NG7XNfWc5SNj/V0nlRjJGl8/Lq07tBYSZjZoKUaHMcJtOJAkFb4jhq2Ua5B/BpFxk+qcEzIam0BXRkHcnqfrJbR3axd3G4y8GT8vjEh2nY7S487zjpLqw4EjQpfgOWrl6yJb5ZHVpFupipaH83GehodmzXvDB5e9odBzhr7Tj7du8uGCHmidn8TuMbMfh73DwZebWb/1QH9SsejdjtVkx5qZ4uG3VFJ0qI8tyIrD1VEr77onuk0jqZQW3wv35JjQzmy9bpMO1G796gZIgAzmwnsV6ZyjrMu0U7X17QJBzNy+AYt36NoyJ4qoldnRaco0uJq5DlX9QqwTvfI48DwuKTTgUsJVZpjgEWVauU4fUy7XV95jFYjWa12D5Y9WN9srKfZGlBJknlRxfwpp3fI0zI6muDOfRXwU2CzmOY4fUuVNex25+Q0M1ppsqoMrFqErNZW7ZhyTnFM5kXVz+etru6SJwLDE8AHJI0FVpvZsurVcpzqqLqG3e48lTRHBsiO19aJSOJ5yWpt1dLzrJKbNLpVPp+3urpPnkmvuwDfA8bH/ceB48zszxXr5jiV0Im1iNrp+mrVmDULrdNLXmmNnnHS2H0yQx9VOe+oE/8JJ5s8Y0bfAj5kZjcCSNofuBDYu0K9HKcyeqkFkUYrxixtrGbS2H16stZf9BmrnHfUD/+JdZ08Hbcb1gwRgJnNAjasTCPHqZhOrEWUlzLHKdLGauYvuaUnxpLaodayW2nPrRlvKsPzr0baby/kY0cdIk/L6J+SPg58P+4fA9xfnUqOUy29MrO/inGKRq2NojHe0nTtVjdffT4Zq9f8XmXpkDZOZ6zuiVbkUCBPy+hdBG+6n8RtE+CdVSrlOFVSxdyaVuiU91u7LcGqVnnNSyfyKcvLr99akf1KZstI0nDgCjN7dYf0cZyO0AuBMIuOU7TaOmm3Jdjtwf1Ojee0EincKY/MlpGZrQKekTSuQ/o4zpChSIulndZJuy3Bbg/up7fgrPT5QL00njjUyDNm9BwwR9L1JJYgN7P3V6aV4wwBirRY2m2dtNMS7OZSDpA976psz8BeGU8ciuQxRtfFzXE6Sq/NjSmbIvOJutk66XYBPTCfBhvFMrsMfWG97pEZtVvSG4FpwBwz+2XHtKoQj9rdH1QRTbqfaScSeBn0SsUgPSI4hC5INx69TlrU7qzF9b4OvAi4FThT0p5mdmaFOjrOGro9aN4JihTwvdA66YV8T+syDFjPTOh1ipPVTfdKYFczWyVpA+BmwI2R0xHSu6UeZu6imX1f0BSdY9Rq91G7LZpeaRHVyBo/qrGuVVqGClnG6PnoTYeZPSNJGec6Tqlk1YDXhZpvKy2/oq2TdifV9mLw0HqjnNZl567Y/UeWMdpR0p/idwHbxn0BZmYvqVw7p68osxadVQPuVs137fM9jBiGsTozinYWnXBIaLers1e7SpNGOX0szV2x+40sY7RTGTeIE2dnA/8ys0MljQcuA6YCDwBvNbMn47mnAScAq4D315wmJO0OXAysD/wc+ICZmaRRhIjiuxMW/HubmT1Qht5OMcquRdeuuXHe6Q2Pd7rm2ygkTdCjtefshLt0uwavCoNZdrdft8fSnPJInfRqZvOytgL3+ABwb2L/o8ANZrYdcEPcR9LOwFEEp4mDgK9HQwbwDeAkYLu4HRTTTwCeNLNpwJeBzxfQyymRKkK2TJtwcJyoOZh2Cu1WgpM2er4aac+ZdZ92F+DLQ7sTOMueAFpFWKFeCe3ktE++5RZbRNLWwCHAtxPJhwOXxO+XAG9MpP/IzJab2f3AXGBPSVsCY83sNgt+6N+ru6Ym60rgQB/b6g5VdTuVXWi3WiA2e476483u04lCtN28Kzvvq4oxN23CwRy9y3WcuNtsjt7lOjdEfUqeSa/t8BXgVGCjRNrmZrYQwMwWStospm8F3J44b0FMWxG/16fXrpkfZa2UtBiYADyeVELSSYSWFZMnT27/qZxBVNXtVPYkxFbHQbJdigc/Z7OCN/k8B0w5s5ICtN28Kzvvux1WyOlt8qz0eijwczNbXURwvO5RM7s7LsjX9JIGaZaRnnXNwASzCwkLAjJ9+vT0Wb5Oy1TZd1/mHJdWC8Qsh4pGz5nlmt5JD7W8eZc2llNm3nc7rJDT2+RpGR0FnCfpx8B3zezeZhdE/g04TNLrgdHAWEmXAo9I2jK2irYEHo3nLwAmJa7fGngopm/dID15zQJJI4BxwBM59et5em2ORxZl1qKrfO5WC8T6kDTNvOnS7iOG9ZyHWpUu3MnfctSwjRimkay2FWuOt9vl2i/vh9OcpsbIzI6RNBY4GviuJAO+C8wws6UZ150GnAZrlir/7yjrC8BxwOfi59XxkmuAH0o6F5hIcFS4M066XSppL+AO4Fjgq4lrjgNuA44AfmNZ8Y36iF6c49GMMmrRVT93Ky24+kLvgClnNdUl7T5pThDd7Koq2nWZ1wjU/5bLVy9BjGDU8HEsX7Wk7QpLv70fTja5xozMbElsGa0PnAK8CfiwpPPN7KvZVw/ic8Dlkk4AHgSOjPf4i6TLgb8CK4H31SbdAu9lrWv3zLgBfAf4vqS5hBbRUQV16Vl6dY5HGmXVUqt+7qItuFYLvbT7pAX7LNJVVXaLIKtL8aJ7pg+4R5H8aPRbGisZOWx9jt31Ny3rmya7l98Ppzl5xowOI6zsui1h6fE9zezRGCLoXta2UlIxs1nArPh9EXBgynmfAT7TIH028OIG6c8Rjdm6Rj8N9pZZS+3EcxdpwbVT6KXdp52xtSpaBEXivRXJjyp/y356P5x85HHtPgL4spm9xMy+YGaPQggRRFiS3KmAflrkq0yX3TKWyC46hyiLsgu9dl26q3CPbuTCXU/tHkXyo8r/cD+9H04+8hijhWb222SCpM8DmNkNlWjlpBYQK1Y/W+rKlmVQZoHdztyWVuYQNTNeVRR67cyLqaJFUG8gs+5dJD+qnNjbiUnDTmfJY4xe0yDNO2UrplZAjBo2dkD68lWL2561XjZlFtjttByKthryGK9OFXp5W3RVtQiSBjIr6kWR/KhyYq9HXlj3SF1cT9J7gf8gjBXNTRzaCPidmR1TvXrl02+L63V7UbU89MpCeOkLr4kTdxv8m+fN26pdiIvkX9q5240/lPlLbilFx2b6uEu10w6FF9cDfkjwWvssMX5cZKmZrTNzeXqdfhio7ZWlmovOIcqbt1UvLFfEKaBRXk8auw/3PXFt6UFq037PXlloz1m3yDJGZmYPSHpf/QFJ490gVUey5imENajt99pAbS8UUEXnEPVKRICiFY76vJ4x55DS3Zx74fd0hhbNWkaHAnczOCyPAdtUqNeQZfBSBYMNkQ/UNqZoC63Tyw+kdW+1axT7ofXsOM1INUZmdmj8fGHn1HHSlioI4WfM++ibUKRG38nuxaz5Qe0axTJbeFWNB/k4k9OMPJNe30QIs7M47m8M7G9mP61auaFA/UuaNvnQsIaD8L1IPxU8neqOyhoXqjlLtJpnk8buw72LrhyQ1koLr6oQOx66x8lDnnBAnzCzq2o7ZvaUpE8AbozapNFLmkavjRGl0YsFTy8Yx2Zdaa0axbmLZnLfE9cOSt9u/KGF5VUVYsdD9zh5yGOMGs1FqnodpCFB1uqhSfppjKiqoJtFqI8UvcKeXRMpuohxLFO3qpwl0v5D85fcUlhWVWNPPqbl5CHPpNfZks6VtK2kbSR9meDU4LRJ1svYr5P5ihQ8VSxDXS9z+eolA5YsgHzhc8rWrarJs2UW9FVNqPXQPU4e8hij/wSeBy4DrgCeAwa5ew9lWo2Hlv6SbtHWMsplx2crQpGCJ60VNWveGS3rnre1uWzFw5myy44BV1XEgDIL+qoMpofucfKQZz2jpxk46dVJ0M4YSRWuxc30qXr8pMgzpdXejdUt616kRZD1O1UVA67sFm6Z/6GqvAt7ZVK009ukGiNJXzGzUyT9jMZLeR9WqWZ9QlbtHlpb86aqwWKgcueCIs+UvXRBa7rnkVkvu4huveQuDeX/h6ryLvRJtE4zsmLT7W5md0var9FxM7upUs0qouzYdOnx0HovPlt6AdudOHeNYqA1Jr/ujWSKERgrU2U3cpkvK95er8Ttc5xeoXBsumiIhgMn9mtQ1E6QVRPvhvtqVo2+17ya6mv1IfTR6kHn5dG93oNu+LBRA5a2LrrCalktDndrdpx8ZI4ZmdkqSZtKWs/Mnu+UUv1Eoz77JLWB8jLdlbMKxqwxhDKWvC6bZPdNWiuime711y1fvYQRGs0BU84ckEdFx1bK6FrqtQqA4/QqeeYLPQD8TtI1wNO1RDM7tyql+olaYTVr3hkNa/WQPVCehyJOEs1q9FU4TLTSesi6rqjueVofzWRXNa7TK8FYHafXyWOMHorbMMJaRpA2SDJESSssa7TbLVO0qyetRl/2YHernoTNriuq+43zPt7wPnmXgqgyakSVwVh7IbKE45RFHmP0VzO7Ipkg6ciK9OlbaoXAjfNOb3g8T7dMWuHSbldPVYVWq+MhrV6XZkzabX1UOa5TlVtzL4Zdcpx2yDPp9bScaUOesBxA+pLNWWTN+G9nYmMjuTfOO51b5n226bXNaNVIlj2O0u6kyqrHdaZNOLitScyNKHtSruN0m1RjJOlgSV8FtpJ0fmK7GFL9ZIc8rRaMWYVLO4VtWkSCexdd2XbYHQ1Y4motzYxkmVEDaq2+lfYcin/notEN+jFcjTtGOOsaWS2jh4DZhPA/dye2a4DXVa9af9JK2Je5i2amuocvW/FIW6FksgqnVmvRtdZWI4eNPEayrPAwA1t9IXJDTU6R1kc/hqvpRwPqOFlkzTP6I/BHSVcBT5vZKoA492hUh/TrS4q4BNcK1DRqhUurbsZZ86BarUVnLQCYx0j22hyefgxX0+lVah2navI4MPwKeDWwLO6vH9P2rkqpoURWYM+yglSmOVW0WotOjylnhVZZrXoOTxHHjXp9asFme9U49aMBdZws8hij0WZWM0SY2TJJG1SoU09SlUdaVuukjJAx0yYczMPL/jBoJVAxghWrn+Wie6YXfp5uz52p/RZpMwwaTYQtuo5RP3iqebw3Z10ijzfd05J2q+1I2h14tjqVeo8q1t2pkbWMRFkFzT5TTuOAKWetGXMaNWwskli+ajFFnmdta2GwIepUF1H9OFGaHu14m7mnmuN0njzG6BTgCkk3S7qZsK7RkOqYrrJw6tTgedK9eOTwDRouODdr3hmpBinLCKQ5VFSxrlJWt2ZSj3a8zdxTzXE6T571jO6StCOwAyDgb2Z1Jdk6TpWFUzf6/rPWEUrrjkozAmkRv6vq6krPcw3Qo9WuxJrLujXoAnRPNcepjqbGKI4PfQiYYmYnStpO0g5mdm316vUG6R5pxkX37A7AqOHj2HvrD7dU0Ha677+VSONFDXJVUQ3yGplWvM3adVl3HKd18nTTfZew7Pgr4v4CIN0XOSJptKQ7Jf1R0l8kfSqmj5d0vaT74ucLEtecJmmupL9Lel0ifXdJc+Kx8yUppo+SdFlMv0PS1NxPXoBGXWn1LF+1mJse/FRHl/iuJ2+3WLPnaWRgis5rqao1mbdbs5W5We26rDuO0zp5vOm2NbO3SToawMyerRmDJiwHXhW970YCt0iaCbwZuMHMPifpo4QlzT8iaWfgKOBFwETg15K2j/ObvgGcBNwO/Bw4CJgJnAA8aWbTJB0FfB54W/7Hz0fedXdW24rcNf+yvfNaieydFmm8kYEp2tKoyuOuSLdm0RZnGS7rjuO0Rh5j9Lyk9Yl+tJK2JRiaTCwsIVtzCR8ZNwMOB/aP6ZcAs4CPxPQfmdly4H5Jc4E9JT0AjDWz2+L9vwe8kWCMDgc+GWVdCVwgSZa2fG0bJAu2sJpqY/IGRG3H7bhRQdxKZG/Iv6RE0bGtKidlVtWt2W2XdccZyuQxRp8AfgFMkvQD4N+A4/MIj9Ea7gamAV8zszskbW5mCwHMbKGkzeLpWxFaPjUWxLQV8Xt9eu2a+VHWSkmLgQnA43V6nERoWTF58uQ8qmeSNeaSp+BqdTwly4i10i1W1MAUMQL9OCnToxo4TvfI4013vaR7gL0I3nQfMLPHm1xWu3YV8FJJGwNXSXpxxumNuv4sIz3rmno9LgQuBJg+fXrbraY9Jp7MrHmfxOrixQ7TyFwFV6vjKVlGrNVafZXOE0XDInXbcPWjAXWcdYVUY5Sc6BpZGD8nS5psZvfkvYmZPSVpFmGs5xFJW8ZW0ZbAo/G0BcCkxGVbE4K1Lojf69OT1yyQNAIYBzyRV69WqRVOt84/h+WrlwCNvenSCthWDUeWETtgyplt1eq7aQx6KeKBRzVwnO6Q1TL6UsYxA16VJVjSpsCKaIjWJ8S3+zwh6vdxwOfi59XxkmuAH0o6l+DAsB1wp5mtkrRU0l7AHcCxwFcT1xwH3AYcAfymivGiRjQrtLIK2Fa7g9KM2KhhGw1YRsFYzZiRWxRaArybxqDKxe0cx+kPsqJ2H9Cm7C2BS+K40TDgcjO7VtJtwOWSTgAeBI6M9/uLpMuBvxLWS3pfLVI48F7gYkKQ1plxA/gO8P3o7PAEwRuvJ8gqYGuTM4u2RBoZMTGCFfYsy1eEFloryyh02xh4xAPHcbK66U41s3Pi9yOTS49LOtvMPpYl2Mz+BLysQfoi4MCUaz4DfKZB+mxg0HiTmT1HNGa9RrMCtpXuoEZjGitWPxtjzK2lqCEpYgyq6M5zLzbHcbK66Y4CzonfTwOuSBw7CMg0RusqeQvjKufZJO+X5mZepFXRTNe1zzzwnLK681qNluCOBo6z7pAVgUEp3xvtDwmKRO/uVADUMlb8zNK1WZTsMgLGFo2WUGUUdcdxukNWy8hSvjfaHxIUGVvplJtwGXNjsnSdMeeQ1CjZNcoKGNsvY1yO45RPljHaVdISQito/fiduJ8dqG0dpehAeyfchMsyemm65jE0nR7bcYcHx1n3yPKmG95JRfqBXh1or9LoZUWbgO5EKOjV38FxnNbJE7XbiZQxDlTFgnNVkhXhO08k7E7p5GF7HKe/yRObzom02yXW6cmlZXiclT321Ys6OY7TfdShgAU9w/Tp02327NldufeMOYekLtvdaLXUdqg3fBBaD91cl+eWeZ/l3kVXDkjrtk6O43QWSXeb2aA5Kd5N10E6OfCe5XHWDeYumjnIEEF3dXIcp3dwY9RBypgTlJde8zjLMjjuBec4jhujDlLFwHuaQ0QnDV8esgyOe8E5juPGqEPUBu5rkbWhfW+0rEgEveRxNnfRTJQRtMO94BzHcW+6DlDvTNBKZO1GMmfNOwNj9YD0diODl03t2ev1rLHThCPcecFxHDdGVdPMaLRSEDcr4NuJDF5/n3aNWSNHCgAxjP2nfNoNkeM4gBujSslrNIqSVsDXKGMMpqw5UWnPaJgbIsdx1uBjRhVSldHIMmJljAvVWnNluIb3miOF4zi9iRujCqnKaKQV5GJY2xNIy27N9ZIjheM4vYsbowqpymikFfDtjsGktYiSFG3RFF2ryHGcoYmPGVVI2lpD7RbGVcRma9YigtZbNJ1YSsNxnP7GjVGFVBnQs+wCvtn4VhldgI7jOGm4MaqYfmkVNBvfckPkOE6V+JiRA1TrFOE4jtMMN0YOUJ1ThOM4Th68m84BfME6x3G6ixsjZw39Mr7lOM66h3fTOY7jOF3HjZHjOI7TddwYOY7jOF3HjZHjOI7TddwYOY7jOF1HZtZtHTqKpMeAeW2I2AR4vCR1qpY71HUd6s9flVzX1XVthylmtml94pAzRu0iabaZTe8HuUNd16H+/FXJdV1d1yrwbjrHcRyn67gxchzHcbqOG6PiXNhHcoe6rkP9+auS67q6rqXjY0aO4zhO1/GWkeM4jtN13Bg5juM4XceNUQ8iab2K5L6gCrlVIUn9ILMqqtK1onwdXrbMKvB3K9CL74EbozaQdICkl5Qs8zXAuySNK1nuq4CvS9qqRJm7StpZ0vZlyYxyt47PX1oBJ2lSLDAqKTTLLIwlTZQ0FhhZlswo94WSNgFK+29Jmi5popmtklRaeeLvVv+8W2XhxqhFJL0W+DYwJpHWVm0jviwXA3PNbHFbCg6U+zrgu8CewKSY1tZvL+lQ4PvAR4D/krRVGYWRpMOi3G8A/y3poBJkvhG4HPgR8PGoe9tIOkzSVwBiYdz2Cx51+yHwE+CDkl7Yrswo9xBgBvBN4BRJm5Twf50K/Az4iaStzWx1Sf8Bf7f65N0qFTPzreAG7Af8DXhV3N+QsFDhei3KU7z+28A7Ytp4YCKwbZu6HgbcA+wIvAP4AzC+TZlTgD8BLwM2J7zkWwEbtil3MvBH4MXADsAHgZuAw9uQuQkwB9gL2AU4DrgaOLpNXfcE5gNPAj9MpA9vQ+aBwF+BXYFXAt8BDinh//pa4PfAHlH2T4GJ7cqNsr8Wf/+7gG1KkOfvVp+8W2VvvtJra+xJKIRuizXXswldKvdJ+rWZ3VBEmIV/y0pJ9wP/lLQh8HPgPmBrSVeZ2fkt6roX8BEz+5ukhcC+wG7AryUNM7PVLcjcCHjMzH4fm/z7AOcDT0uaaWYzWtR1DPC4mf0Z1vTD7wccJ+kpM7upBZkrgL8DfzCz5yQ9CDwFHC/pSTP7RYu6vgB4v5ldJen3kmaY2dEWW0hmtqoFmS8GvmZmfwSQtDNwlKSZhL9Jq/MwdgA+ZmZ3SdoU2Ak4R9I9wB+L/l+jbsMIPSurCQX9XsDFks4DVpjZNS3qOp3+ebf2pPx3a0Oqebc2pPx3q1S8m64FzOwLwPXANcCPCTWMrwILgcMkbdRit4IBnyfUWr4FHA+cCryt1f5zM/uYmV0fC48lcfv3eKyVl4X4hx4u6XeEgv5C4BTgKuBISVNalPtXYLGkC2Oh8QZgLvBbYGqLMhcDTxO6J2r7vyUUSLtDa11AZvZL4Oa4uxuwvaTL4rFVkjZvQd2vAZcldJoLjDaz1WZmkjZqQSZm9lUzmylpNMFw/C9wHsFQv0XSuCJ5IElRp5WEvHyxmX0RuJ/QxTghnle4fDGzLwG/JLRe2363EjqsAj5Hue/W6fHdEuW9W38Fhkm6hXLfrXuBpyR9q6x3q3S63TTrlw14DfA+4IOJtP8GPpTYfzHhJRpTUOYHEmlfJ0TY3T2R9m1g5xZ0fX/cV+LYKOBW4F0tPv8pibRphDGI2uTpCYSxmSmtygW2J4xtXAlcGdNeHeXm6gIDtgM2T+xvROjX/0oi7WUEg5Trt2okN6YNr30CdwMXAUcQarPrF5GZ/J3i/g7AVfH7MYTCM1d3VZpcYIvE9xcRuuw2akHmsPj5ekLr5eWE1salhG7RIv+BRvn6sbr/WtF3q5HMbwOPtvluDcrXRF60+m410nUnQlnQzrtV/x5MIxj4y1t9t6rcunrzftkITeXHgBOAWwgDgHskjtf+MG8BbgBe0KpMYANCi+seYDNCDe4eYFKLun41po1InHMC8LkWn/938SXZOx77MvCN+P1w4HZyjkc0kHsesGc8th5rC/rjCcZkRA6ZhwMPEloZk2u/D7Azof/9KmBj4O3ArDy/VZbc+JnM2yXAImCXdmTG79sRnC7eA/wZ2LENXYc1kP9m4DfkGOdoJDPxO/2Y0PX5ppj2EeCFreqaODYy8b3Iu5Wm60hgJjCb1t6trP9ALX+LvltZz38B8PUW362k3Cl1x8Yk9D2enO9W1ZuHA8qBpA8Rao+fil0dpwNjgRlmdls85xTCIOZxFvtlW5T5XQv9xecCzxIM1AfN7C9t6DoGuCyh616Erop9zWxJizLHAZcQCt/zCUZ0HHCMmc1pQ9dxBIeA22L3x4mEltM7zOxPTeSNA35AGAB/lDAAfL6ZzYuy1ie84OsRBp3fbWZ/yKFnI7nnmdmDscvK4nn7E17sQ5v9Xk1kDiN02W5NcDz4B+F/9bcydI15cQqhtdX0/9pE5noEQ/FA4v+1Jk/a1TWel/vdypKZOOeLwHIKvFsFdH0Fobeg6bvVTFdJ2xEqaGMo8G41kLsFoVfgwbrzTiLnu9URum0N+2EjdCX9Atg+7o8idE1ckDjnDHLUhnPI/GbdeaPL1jWmF+meSpN5XuKcnYFNKsjXY4EdCsh8IcGo7wl8BjiXulp6vE8h76QUuVPrzjkImFayzEuBXcvUlTCY/UnCWE/b+Uqi1UVdV2NJefDxgu9WU5nxvKLvVrN8rVXui7xbeZ5/xxberTxyjynyblW9dV2BXt0IcwZGEWolIwk1lBOALePx0cCdwDsrkFm0zzmv3OMT12QWGjll3gWcUJGuufOA4LY6yMAQxjHOji/isLg/oQK5ewDjSpa5J6E2nKtwLyB3OrBxiTKHx+ev7hXW7QAABIVJREFUKl/HVpCvuQv2gvm6ac53K2++7knObuQW8iBXvnZyc2+6BihMEJxJGG/5NqGZ+32C6+YhknY0s+cIE/5yufAWlLmyIl3XePhY/Je2KfMagkdWFbrmyoMo8+dR5ncl7ZB4xjsIg94LCeNnvyR0J5Yt93pyRDVoRWbW79Si3BsINeayZN4cda0iX39NGN8rU+avCN21Veg6Oh5r9m7lzddfEZxvytb1V+TI147TbWvYSxuhm2ESwRtof0If7qmEyY2TCbWfLxHcTc8DHgF26rRM1zVV5n8BDwEvqjv3KwS346bdUlXIdV1d137StVtb1xXotY3QPL6QMOu51gf8QYJnytZxf1/g3eQcH6hCpuuaKvP9wL9YOw71AsLM85e1qWtbcl1X17WfdO3G1nUFemUj+ODvQfDnvww4te74RwmeUk3njlQp03XNJfNUghv3BnE/10B1FXJdV9e1n3Tt5tZ1BXphAw4lxIO6ieD6exjwAHBa4pyphNpH3gHl0mW6roVkfou1tcSmsquQ67q6rv2ka7e3rivQ7Q3Ym+CP/7K4fyFwFiGQ4oOEuS/TCJPDZpNv0l3pMl1X19V1dV2rlNvtresKdHuLP+zxif1Ngevi920Icby+Hn/UXHMdqpDpurqurqvrWqXcbm9dV6DbG2Hwb2zie23We23eyxRCCPpc80iqkum6uq6uq+tapdxub0N+npGZrbK1YTtEiLH1hJktlHQMIWDjSCuwIFcVMl1X19V1dV2rlNttPDZdAyRdTJgg9lpCczhXrLVOy6xKruvqurqurmuncWOUIAaQHAncGz8PNLP7ek2m6+q6uq6ua5Vyu4EbowZIOh64y3JGyu6WzKrkuq6uq+vqunYaN0YNyBsCv9syq5Lrurqurqvr2mncGDmO4zhdZ8h70zmO4zjdx42R4ziO03XcGDmO4zhdx42R4ziO03XcGDlOl1DgFkkHJ9LeKukX3dTLcbqBe9M5TheR9GLgCuBlhDhjfwAOMrN/tCBruJmtKllFx+kIbowcp8tIOgd4Gtgwfk4BdiEEu/ykmV0taSrw/XgOwMlmdquk/YFPEELBvJSw2NrlhOCZw4Ezzeyyjj2M47SIGyPH6TKSNgTuAZ4HrgX+YmaXStoYuJPQajJgtZk9J2k7YIaZTY/G6DrgxWZ2v6S3EFpWJ0bZ4/otYKYzNHFj5Dg9gKRPA8uAtwKjgZXx0HjgdcBDhBU9XwqsArY3sw1qLSMzOyDK2R74JaF1dK2Z3dzJ53CcVhnRbQUcxwFgddwEvMXM/p48KOmTwCPArgTHo+cSh5+ufTGz/5O0O/B64LOSfmVmn65Yd8dpG/emc5ze4pfAf8ZozEh6WUwfByw0s9XAOwjjQYOQNBF4xswuBb4I7Fa9yo7TPt4ycpze4kzgK8CfokF6ADiUsIz0jyUdCdxIojVUxy7AFyStBlYA761cY8cpAR8zchzHcbqOd9M5juM4XceNkeM4jtN13Bg5juM4XceNkeM4jtN13Bg5juM4XceNkeM4jtN13Bg5juM4Xef/AxophtrrEP7pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# major ticks for x-axis (by year)\n",
    "years = np.arange(2005, 2020, 1)\n",
    "ticks = np.arange(0, len(data[\"Date\"]), 12)\n",
    "\n",
    "plt.scatter(data[\"Date\"], data[\"Total Renewable (mil kWh)\"], color=\"yellowgreen\")\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(ticks, years, rotation=45)\n",
    "plt.title(\"Electricity Produced by Renewable Sources (2005 - 2019)\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"Plots\\Question 5 - Time vs. Renewable.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# regression analysis\n",
    "data[\"Date_ordinal\"] = pd.to_datetime(data[\"Date\"]).apply(lambda date: date.toordinal())\n",
    "\n",
    "r_slope, r_intercept, r_rvalue, r_pvalue, r_stderr = stats.linregress(data[\"Date_ordinal\"], data[\"Total Renewable (mil kWh)\"])\n",
    "regressed_renewable = data[\"Date_ordinal\"] * r_slope + r_intercept \n",
    "\n",
    "plt.annotate(f\"Renewable = {round(r_slope,2)}*x + {round(r_intercept,0)}\", (0,60000))\n",
    "plt.scatter(data[\"Date\"], data[\"Total Renewable (mil kWh)\"], color=\"yellowgreen\")\n",
    "plt.plot(data[\"Date\"], regressed_renewable, color=\"green\")\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(ticks, years, rotation=45)\n",
    "plt.title(\"Electricity Produced by Renewable Sources (2005 - 2019)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"Plots\\Question 5 - Time vs. Renewable (with Regression).png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing oil production and renewable source production of electricity\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# major ticks for x-axis (by year)\n",
    "years = np.arange(2005, 2020, 1)\n",
    "ticks = np.arange(0, len(data[\"Date\"]), 12)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Renewable (mil kWh)\"], c=\"lightgreen\", label=\"Renewable\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Oil (mil kWh)\"], c=\"palegoldenrod\", label=\"Oil\")\n",
    "fig.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(ticks, years, rotation=45)\n",
    "plt.title(\"Electricity Produced (2005 - 2019)\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"Plots\\Question 5 - time vs. Oil and Renewable.png\", bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#Regression Analysis \n",
    "\n",
    "o_slope, o_intercept, o_rvalue, o_pvalue, o_stderr = stats.linregress(data[\"Date_ordinal\"], data[\"Total Oil (mil kWh)\"])\n",
    "regressed_oil = data[\"Date_ordinal\"] * o_slope + o_intercept \n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Renewable (mil kWh)\"], c=\"lightgreen\", label=\"Renewable\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Oil (mil kWh)\"], c=\"palegoldenrod\", label=\"Oil\")\n",
    "ax1.scatter(data[\"Date\"], regressed_oil, c=\"black\", s=1)\n",
    "ax1.scatter(data[\"Date\"], regressed_renewable, c=\"black\", s=1)\n",
    "\n",
    "plt.annotate(f\"Renewable = {round(r_slope,2)}*x + {round(r_intercept,0)}\", (0,80000))\n",
    "plt.annotate(f\"Oil = {round(o_slope,2)}*x + {round(o_intercept,0)}\", (0,100000))\n",
    "fig.legend(bbox_to_anchor=(1, 1), loc='upper left')\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(ticks, years, rotation=45)\n",
    "plt.title(\"Electricity Produced (2005 - 2019)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"Plots\\Question 5 - Time vs. Oil and Renewable (with Regression).png\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2055, 11, 12)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#intercept of when renewable production = oil production \n",
    "\n",
    "#regressed_oil = renewable_oil[\"Date_ordinal\"] * o_slope + o_intercept \n",
    "#regressed_renewable = renewable_oil[\"Date_ordinal\"] * r_slope + r_intercept \n",
    "\n",
    "intercept = (o_intercept-r_intercept)/(r_slope-o_slope)\n",
    "\n",
    "dt.date.fromordinal(int(intercept))\n",
    "\n",
    "#If the trend from 2013 onwards is maintained, it is predicted that electricity from renewable sources will surpass oil sources in 205. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis on types of renewable sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Renewable (mil kWh)\"], c=\"lightgreen\", label=\"Total\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Wood (mil kWh)\"], c=\"brown\", label=\"Wood\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Waste (mil kWh)\"], c=\"palegoldenrod\", label=\"Waste\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Geothermal (mil kWh)\"], c=\"tan\", label=\"Geothermal\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Solar (mil kWh)\"], c=\"gold\", label=\"Solar\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Wind (mil kWh)\"], c=\"bisque\", label=\"Wind\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Conventional Hydroelectric Power (mil kWh)\"], c=\"indianred\", label=\"Conv. Hydroelectric\")\n",
    "\n",
    "fig.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(ticks, years, rotation=45)\n",
    "plt.title(\"Electricity Produced from Different Renewable Sources(2005 - 2019)\")\n",
    "\n",
    "plt.savefig(\"Plots\\Question 5 - Time vs. Types of Renewable Sources.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis on types of oil sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot()\n",
    "\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Oil (mil kWh)\"], c=\"lightgreen\", label=\"Total\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Coal (mil kWh)\"], c=\"brown\", label=\"Coal\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Petroleum (mil kWh)\"], c=\"palegoldenrod\", label=\"Petroleum\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Natural_Gas (mil kWh)\"], c=\"tan\", label=\"Natural Gas\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Other_Gas (mil kWh)\"], c=\"gold\", label=\"Other Gas\")\n",
    "\n",
    "\n",
    "fig.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(ticks, years, rotation=45)\n",
    "plt.title(\"Electricity Produced from Different Oil Sources (2005 - 2019)\")\n",
    "\n",
    "plt.savefig(\"Plots\\Question 5 - Time vs. Types of Oil Sources.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect of US electricity production from renewable sources on the US dollar (index)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# major ticks for x-axis (by year)\n",
    "years = np.arange(2005, 2020, 2)\n",
    "ticks = np.arange(0, len(data[\"Date\"]), 24)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "ax1.set_xlabel(\"Year\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Renewable (mil kWh)\"], c=\"lightgreen\", label=\"Renewable\")\n",
    "ax1.scatter(data[\"Date\"], data[\"Total Oil (mil kWh)\"], c=\"palegoldenrod\", label=\"Oil\")\n",
    "\n",
    "plt.ylabel(\"Electricity Produced (mil kWh)\")\n",
    "\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "ax2.set_ylabel(\"USD Index\")\n",
    "lns3=ax2.scatter(data[\"Date\"], data[\"USD Index\"], c=\"black\", label=\"USD Index\")\n",
    "\n",
    "plt.xticks(ticks, years, rotation = 45)\n",
    "fig.legend(bbox_to_anchor=(0.9, 1), loc='upper left')\n",
    "plt.ylabel(\"USD Index\")\n",
    "\n",
    "plt.xlabel(\"Years\")\n",
    "plt.title(\"Electricity Produced vs. USD Index (2005 - 2019)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"Plots\\Question 6 - Sources vs. USD Index.png\", bbox_inches='tight')\n"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}