Data Driven Insights and Anomaly Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c461eb71",
   "metadata": {},
   "source": [
    "## Import Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "56ba856d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For Data Processing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#For Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sb\n",
    "from mpl_toolkits import mplot3d\n",
    "sb.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b079e934",
   "metadata": {},
   "source": [
    "## Import Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3e373734",
   "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>Country</th>\n",
       "      <th>Hydro(TWh)</th>\n",
       "      <th>Biofuel(TWh)</th>\n",
       "      <th>Solar PV (TWh)</th>\n",
       "      <th>Geothermal (TWh)</th>\n",
       "      <th>Total (TWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>1189.84</td>\n",
       "      <td>295.0200</td>\n",
       "      <td>79.43</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>1819.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USA</td>\n",
       "      <td>315.62</td>\n",
       "      <td>277.9100</td>\n",
       "      <td>58.95</td>\n",
       "      <td>18.9600</td>\n",
       "      <td>758.6190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>370.90</td>\n",
       "      <td>42.3700</td>\n",
       "      <td>52.25</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>466.3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canada</td>\n",
       "      <td>383.48</td>\n",
       "      <td>29.6500</td>\n",
       "      <td>7.12</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>424.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>141.80</td>\n",
       "      <td>51.0600</td>\n",
       "      <td>43.76</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>262.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Germany</td>\n",
       "      <td>24.17</td>\n",
       "      <td>111.5900</td>\n",
       "      <td>45.10</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>227.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Russia</td>\n",
       "      <td>187.13</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.4300</td>\n",
       "      <td>188.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Japan</td>\n",
       "      <td>90.67</td>\n",
       "      <td>7.6300</td>\n",
       "      <td>19.01</td>\n",
       "      <td>2.4400</td>\n",
       "      <td>187.3490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>France</td>\n",
       "      <td>70.13</td>\n",
       "      <td>28.5000</td>\n",
       "      <td>5.87</td>\n",
       "      <td>0.1300</td>\n",
       "      <td>114.8200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Italy</td>\n",
       "      <td>50.92</td>\n",
       "      <td>17.4900</td>\n",
       "      <td>16.85</td>\n",
       "      <td>6.0800</td>\n",
       "      <td>113.9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UK</td>\n",
       "      <td>7.96</td>\n",
       "      <td>57.1100</td>\n",
       "      <td>32.08</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>110.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Spain</td>\n",
       "      <td>36.74</td>\n",
       "      <td>50.8100</td>\n",
       "      <td>5.48</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.5400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>59.74</td>\n",
       "      <td>19.8800</td>\n",
       "      <td>2.63</td>\n",
       "      <td>6.9000</td>\n",
       "      <td>96.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>32.52</td>\n",
       "      <td>13.0700</td>\n",
       "      <td>1.88</td>\n",
       "      <td>5.3600</td>\n",
       "      <td>56.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Australia</td>\n",
       "      <td>16.02</td>\n",
       "      <td>15.1700</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>44.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>18.63</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.23</td>\n",
       "      <td>12.7600</td>\n",
       "      <td>41.6406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>15.38</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>30.5401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>7.28</td>\n",
       "      <td>2.4600</td>\n",
       "      <td>6.82</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>25.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Iran</td>\n",
       "      <td>15.05</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>8.87</td>\n",
       "      <td>1.7300</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>12.4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country  Hydro(TWh)  Biofuel(TWh)  Solar PV (TWh)  Geothermal (TWh)  \\\n",
       "0         China     1189.84      295.0200           79.43            0.1250   \n",
       "1           USA      315.62      277.9100           58.95           18.9600   \n",
       "2        Brazil      370.90       42.3700           52.25            0.0000   \n",
       "3        Canada      383.48       29.6500            7.12            0.0000   \n",
       "4         India      141.80       51.0600           43.76            0.0000   \n",
       "5       Germany       24.17      111.5900           45.10            0.1600   \n",
       "6        Russia      187.13        0.1400            0.08            0.4300   \n",
       "7         Japan       90.67        7.6300           19.01            2.4400   \n",
       "8        France       70.13       28.5000            5.87            0.1300   \n",
       "9         Italy       50.92       17.4900           16.85            6.0800   \n",
       "10           UK        7.96       57.1100           32.08            0.0000   \n",
       "11        Spain       36.74       50.8100            5.48            0.0000   \n",
       "12       Turkey       59.74       19.8800            2.63            6.9000   \n",
       "13       Mexico       32.52       13.0700            1.88            5.3600   \n",
       "14    Australia       16.02       15.1700            3.51            0.0000   \n",
       "15    Indonesia       18.63        0.0006            0.23           12.7600   \n",
       "16     Thailand        9.52        1.1000           15.38            0.0001   \n",
       "17  South Korea        7.28        2.4600            6.82            0.0000   \n",
       "18         Iran       15.05        0.3000            0.02            0.0000   \n",
       "19       Taiwan        8.87        1.7300            0.11            0.0000   \n",
       "\n",
       "    Total (TWh)  \n",
       "0     1819.9400  \n",
       "1      758.6190  \n",
       "2      466.3500  \n",
       "3      424.0900  \n",
       "4      262.6500  \n",
       "5      227.1800  \n",
       "6      188.3300  \n",
       "7      187.3490  \n",
       "8      114.8200  \n",
       "9      113.9900  \n",
       "10     110.0700  \n",
       "11     100.5400  \n",
       "12      96.6200  \n",
       "13      56.0200  \n",
       "14      44.6200  \n",
       "15      41.6406  \n",
       "16      30.5401  \n",
       "17      25.3200  \n",
       "18      15.4500  \n",
       "19      12.4000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load the dataset\n",
    "#API Link: \"kaggle datasets download -d jamesvandenberg/renewable-power-generation\"\n",
    "dataset = pd.read_csv(\"top20CountriesPowerGeneration.csv\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dc2001a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_display(datasetName, colName):\n",
    "    \"\"\"\n",
    "    Function to sort and display a given dataset according to a specific variable. \n",
    "    Additionally, it displays the barplot of the variable against 'Country'\n",
    "    \n",
    "    \"\"\"\n",
    "    datasetName = datasetName.sort_values(by=[colName], ascending=False)\n",
    "    datasetName.head()\n",
    "    \n",
    "    plt.xticks(rotation=90)\n",
    "    sb.barplot(data=datasetName, x=datasetName[\"Country\"], y=datasetName[colName])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9097b781",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_display(dataset, 'Hydro(TWh)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a56f718",
   "metadata": {},
   "source": [
    "Here we see that China is the lead consumer of Hydro energy, followed by Canada and Brazil. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e362acdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_display(dataset, 'Biofuel(TWh)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d838c0fb",
   "metadata": {},
   "source": [
    "Here we see that China is the lead consumer of BioFuel energy, followed by USA and Germany. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "209ff62c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_display(dataset, 'Solar PV (TWh)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a961693",
   "metadata": {},
   "source": [
    "Here we see that China is the lead consumer of Solar PV energy, followed by USA and Brazil. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e7d19fd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_display(dataset, 'Geothermal (TWh)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0974e9",
   "metadata": {},
   "source": [
    "Here we see that USA is the lead consumer of Geothermal energy, followed by Indonesia and Turkey. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5a4a0ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_display(dataset, 'Total (TWh)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288ed90a",
   "metadata": {},
   "source": [
    "Here we see that China is the lead consumer of total energy, followed by USA and Brazil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "393114c9",
   "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>China</th>\n",
       "      <th>United States</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Czechia</th>\n",
       "      <th>France</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Italy</th>\n",
       "      <th>Netherlands</th>\n",
       "      <th>Poland</th>\n",
       "      <th>...</th>\n",
       "      <th>Australia</th>\n",
       "      <th>New Zealand</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Egypt</th>\n",
       "      <th>Nigeria</th>\n",
       "      <th>South Africa</th>\n",
       "      <th>Iran</th>\n",
       "      <th>Kuwait</th>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <th>United Arab Emirates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</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",
       "      <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>1990.0</th>\n",
       "      <td>874.000000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991.0</th>\n",
       "      <td>848.000000</td>\n",
       "      <td>1925.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>877.000000</td>\n",
       "      <td>1964.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993.0</th>\n",
       "      <td>929.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994.0</th>\n",
       "      <td>973.000000</td>\n",
       "      <td>2036.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>333.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995.0</th>\n",
       "      <td>1045.000000</td>\n",
       "      <td>2063.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996.0</th>\n",
       "      <td>1074.000000</td>\n",
       "      <td>2108.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>1073.000000</td>\n",
       "      <td>2130.000000</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>244.000000</td>\n",
       "      <td>345.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998.0</th>\n",
       "      <td>1079.000000</td>\n",
       "      <td>2148.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>1099.000000</td>\n",
       "      <td>2206.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>1130.000000</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001.0</th>\n",
       "      <td>1168.000000</td>\n",
       "      <td>2226.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>1246.000000</td>\n",
       "      <td>2256.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>20.00000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003.0</th>\n",
       "      <td>1420.000000</td>\n",
       "      <td>2262.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>1615.000000</td>\n",
       "      <td>2308.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>341.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>1782.000000</td>\n",
       "      <td>2320.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>273.000000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>1950.000000</td>\n",
       "      <td>2298.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>2099.000000</td>\n",
       "      <td>2338.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>265.000000</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>25.00000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>2155.000000</td>\n",
       "      <td>2278.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>2297.000000</td>\n",
       "      <td>2165.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>2536.000000</td>\n",
       "      <td>2217.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>204.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>2723.000000</td>\n",
       "      <td>2191.000000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>2821.000000</td>\n",
       "      <td>2152.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>189.000000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>2912.000000</td>\n",
       "      <td>2190.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>2966.000000</td>\n",
       "      <td>2216.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>27.00000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>2994.000000</td>\n",
       "      <td>2192.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>2977.000000</td>\n",
       "      <td>2168.000000</td>\n",
       "      <td>287.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>127.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>36.00000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>3070.000000</td>\n",
       "      <td>2161.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>152.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>34.00000</td>\n",
       "      <td>219.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018.0</th>\n",
       "      <td>3201.000000</td>\n",
       "      <td>2236.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>266.000000</td>\n",
       "      <td>34.00000</td>\n",
       "      <td>214.000000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019.0</th>\n",
       "      <td>3309.000000</td>\n",
       "      <td>2214.000000</td>\n",
       "      <td>293.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>36.00000</td>\n",
       "      <td>215.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020.0</th>\n",
       "      <td>3381.000000</td>\n",
       "      <td>2046.000000</td>\n",
       "      <td>286.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Average Consumption</th>\n",
       "      <td>1923.322581</td>\n",
       "      <td>2167.451613</td>\n",
       "      <td>223.451613</td>\n",
       "      <td>54.903226</td>\n",
       "      <td>43.258065</td>\n",
       "      <td>251.193548</td>\n",
       "      <td>327.903226</td>\n",
       "      <td>162.903226</td>\n",
       "      <td>74.870968</td>\n",
       "      <td>97.419355</td>\n",
       "      <td>...</td>\n",
       "      <td>112.645161</td>\n",
       "      <td>17.612903</td>\n",
       "      <td>37.258065</td>\n",
       "      <td>60.935484</td>\n",
       "      <td>108.967742</td>\n",
       "      <td>118.193548</td>\n",
       "      <td>169.064516</td>\n",
       "      <td>23.16129</td>\n",
       "      <td>138.387097</td>\n",
       "      <td>49.064516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           China  United States      Brazil    Belgium  \\\n",
       "Year                                                                     \n",
       "1990.0                874.000000    1910.000000  141.000000  48.000000   \n",
       "1991.0                848.000000    1925.000000  143.000000  50.000000   \n",
       "1992.0                877.000000    1964.000000  145.000000  51.000000   \n",
       "1993.0                929.000000    1998.000000  148.000000  49.000000   \n",
       "1994.0                973.000000    2036.000000  156.000000  52.000000   \n",
       "1995.0               1045.000000    2063.000000  162.000000  53.000000   \n",
       "1996.0               1074.000000    2108.000000  170.000000  56.000000   \n",
       "1997.0               1073.000000    2130.000000  179.000000  56.000000   \n",
       "1998.0               1079.000000    2148.000000  183.000000  57.000000   \n",
       "1999.0               1099.000000    2206.000000  187.000000  57.000000   \n",
       "2000.0               1130.000000    2269.000000  188.000000  58.000000   \n",
       "2001.0               1168.000000    2226.000000  191.000000  58.000000   \n",
       "2002.0               1246.000000    2256.000000  196.000000  56.000000   \n",
       "2003.0               1420.000000    2262.000000  199.000000  58.000000   \n",
       "2004.0               1615.000000    2308.000000  211.000000  58.000000   \n",
       "2005.0               1782.000000    2320.000000  216.000000  58.000000   \n",
       "2006.0               1950.000000    2298.000000  224.000000  57.000000   \n",
       "2007.0               2099.000000    2338.000000  236.000000  56.000000   \n",
       "2008.0               2155.000000    2278.000000  250.000000  58.000000   \n",
       "2009.0               2297.000000    2165.000000  242.000000  56.000000   \n",
       "2010.0               2536.000000    2217.000000  267.000000  60.000000   \n",
       "2011.0               2723.000000    2191.000000  271.000000  56.000000   \n",
       "2012.0               2821.000000    2152.000000  282.000000  53.000000   \n",
       "2013.0               2912.000000    2190.000000  294.000000  56.000000   \n",
       "2014.0               2966.000000    2216.000000  303.000000  53.000000   \n",
       "2015.0               2994.000000    2192.000000  297.000000  52.000000   \n",
       "2016.0               2977.000000    2168.000000  287.000000  56.000000   \n",
       "2017.0               3070.000000    2161.000000  292.000000  55.000000   \n",
       "2018.0               3201.000000    2236.000000  288.000000  53.000000   \n",
       "2019.0               3309.000000    2214.000000  293.000000  55.000000   \n",
       "2020.0               3381.000000    2046.000000  286.000000  51.000000   \n",
       "Average Consumption  1923.322581    2167.451613  223.451613  54.903226   \n",
       "\n",
       "                       Czechia      France     Germany       Italy  \\\n",
       "Year                                                                 \n",
       "1990.0               50.000000  225.000000  351.000000  147.000000   \n",
       "1991.0               45.000000  237.000000  344.000000  150.000000   \n",
       "1992.0               44.000000  234.000000  338.000000  149.000000   \n",
       "1993.0               43.000000  238.000000  335.000000  149.000000   \n",
       "1994.0               41.000000  231.000000  333.000000  147.000000   \n",
       "1995.0               42.000000  240.000000  336.000000  159.000000   \n",
       "1996.0               43.000000  251.000000  348.000000  159.000000   \n",
       "1997.0               43.000000  244.000000  345.000000  162.000000   \n",
       "1998.0               42.000000  252.000000  343.000000  166.000000   \n",
       "1999.0               39.000000  252.000000  335.000000  169.000000   \n",
       "2000.0               41.000000  255.000000  337.000000  172.000000   \n",
       "2001.0               42.000000  260.000000  347.000000  172.000000   \n",
       "2002.0               43.000000  261.000000  339.000000  174.000000   \n",
       "2003.0               45.000000  268.000000  338.000000  182.000000   \n",
       "2004.0               46.000000  271.000000  341.000000  183.000000   \n",
       "2005.0               45.000000  273.000000  339.000000  187.000000   \n",
       "2006.0               46.000000  268.000000  349.000000  185.000000   \n",
       "2007.0               46.000000  265.000000  331.000000  184.000000   \n",
       "2008.0               45.000000  266.000000  335.000000  182.000000   \n",
       "2009.0               43.000000  255.000000  313.000000  170.000000   \n",
       "2010.0               45.000000  263.000000  330.000000  174.000000   \n",
       "2011.0               43.000000  258.000000  313.000000  168.000000   \n",
       "2012.0               43.000000  258.000000  315.000000  162.000000   \n",
       "2013.0               43.000000  259.000000  322.000000  156.000000   \n",
       "2014.0               42.000000  249.000000  307.000000  147.000000   \n",
       "2015.0               42.000000  253.000000  309.000000  153.000000   \n",
       "2016.0               41.000000  248.000000  310.000000  151.000000   \n",
       "2017.0               43.000000  247.000000  311.000000  154.000000   \n",
       "2018.0               43.000000  246.000000  302.000000  151.000000   \n",
       "2019.0               43.000000  243.000000  294.000000  149.000000   \n",
       "2020.0               39.000000  217.000000  275.000000  137.000000   \n",
       "Average Consumption  43.258065  251.193548  327.903226  162.903226   \n",
       "\n",
       "                     Netherlands      Poland  ...   Australia  New Zealand  \\\n",
       "Year                                          ...                            \n",
       "1990.0                 67.000000  103.000000  ...   86.000000    14.000000   \n",
       "1991.0                 69.000000  101.000000  ...   85.000000    14.000000   \n",
       "1992.0                 69.000000   99.000000  ...   87.000000    14.000000   \n",
       "1993.0                 70.000000  101.000000  ...   91.000000    15.000000   \n",
       "1994.0                 70.000000   96.000000  ...   91.000000    15.000000   \n",
       "1995.0                 73.000000   99.000000  ...   93.000000    15.000000   \n",
       "1996.0                 77.000000  104.000000  ...   99.000000    16.000000   \n",
       "1997.0                 75.000000  102.000000  ...  101.000000    16.000000   \n",
       "1998.0                 75.000000   95.000000  ...  104.000000    16.000000   \n",
       "1999.0                 74.000000   93.000000  ...  106.000000    17.000000   \n",
       "2000.0                 75.000000   89.000000  ...  108.000000    17.000000   \n",
       "2001.0                 77.000000   90.000000  ...  106.000000    17.000000   \n",
       "2002.0                 78.000000   89.000000  ...  109.000000    17.000000   \n",
       "2003.0                 80.000000   91.000000  ...  111.000000    17.000000   \n",
       "2004.0                 81.000000   91.000000  ...  113.000000    18.000000   \n",
       "2005.0                 80.000000   92.000000  ...  113.000000    17.000000   \n",
       "2006.0                 79.000000   97.000000  ...  118.000000    17.000000   \n",
       "2007.0                 79.000000   97.000000  ...  121.000000    17.000000   \n",
       "2008.0                 78.000000   98.000000  ...  126.000000    18.000000   \n",
       "2009.0                 77.000000   94.000000  ...  126.000000    18.000000   \n",
       "2010.0                 83.000000  101.000000  ...  126.000000    19.000000   \n",
       "2011.0                 77.000000  101.000000  ...  127.000000    18.000000   \n",
       "2012.0                 77.000000   98.000000  ...  126.000000    19.000000   \n",
       "2013.0                 75.000000   98.000000  ...  127.000000    19.000000   \n",
       "2014.0                 71.000000   95.000000  ...  127.000000    21.000000   \n",
       "2015.0                 72.000000   96.000000  ...  127.000000    21.000000   \n",
       "2016.0                 74.000000  100.000000  ...  127.000000    21.000000   \n",
       "2017.0                 74.000000  104.000000  ...  128.000000    21.000000   \n",
       "2018.0                 73.000000  106.000000  ...  128.000000    20.000000   \n",
       "2019.0                 72.000000  103.000000  ...  129.000000    21.000000   \n",
       "2020.0                 70.000000   97.000000  ...  126.000000    21.000000   \n",
       "Average Consumption    74.870968   97.419355  ...  112.645161    17.612903   \n",
       "\n",
       "                       Algeria      Egypt     Nigeria  South Africa  \\\n",
       "Year                                                                  \n",
       "1990.0               22.000000  33.000000   66.000000     90.000000   \n",
       "1991.0               23.000000  33.000000   70.000000     92.000000   \n",
       "1992.0               24.000000  34.000000   72.000000     88.000000   \n",
       "1993.0               24.000000  35.000000   74.000000     94.000000   \n",
       "1994.0               23.000000  34.000000   72.000000     98.000000   \n",
       "1995.0               24.000000  36.000000   73.000000    104.000000   \n",
       "1996.0               23.000000  38.000000   76.000000    107.000000   \n",
       "1997.0               24.000000  40.000000   79.000000    109.000000   \n",
       "1998.0               25.000000  42.000000   80.000000    111.000000   \n",
       "1999.0               27.000000  43.000000   83.000000    109.000000   \n",
       "2000.0               27.000000  41.000000   87.000000    109.000000   \n",
       "2001.0               27.000000  47.000000   91.000000    105.000000   \n",
       "2002.0               29.000000  48.000000   94.000000    100.000000   \n",
       "2003.0               31.000000  50.000000   98.000000    113.000000   \n",
       "2004.0               31.000000  54.000000   99.000000    123.000000   \n",
       "2005.0               32.000000  62.000000  105.000000    118.000000   \n",
       "2006.0               35.000000  66.000000  106.000000    120.000000   \n",
       "2007.0               37.000000  70.000000  110.000000    131.000000   \n",
       "2008.0               37.000000  73.000000  113.000000    142.000000   \n",
       "2009.0               41.000000  75.000000  111.000000    144.000000   \n",
       "2010.0               42.000000  74.000000  127.000000    134.000000   \n",
       "2011.0               43.000000  77.000000  139.000000    137.000000   \n",
       "2012.0               47.000000  79.000000  140.000000    131.000000   \n",
       "2013.0               49.000000  78.000000  143.000000    130.000000   \n",
       "2014.0               54.000000  80.000000  147.000000    138.000000   \n",
       "2015.0               56.000000  81.000000  145.000000    129.000000   \n",
       "2016.0               56.000000  88.000000  150.000000    135.000000   \n",
       "2017.0               57.000000  93.000000  152.000000    136.000000   \n",
       "2018.0               61.000000  96.000000  160.000000    134.000000   \n",
       "2019.0               65.000000  97.000000  157.000000    129.000000   \n",
       "2020.0               59.000000  92.000000  159.000000    124.000000   \n",
       "Average Consumption  37.258065  60.935484  108.967742    118.193548   \n",
       "\n",
       "                           Iran    Kuwait  Saudi Arabia  United Arab Emirates  \n",
       "Year                                                                           \n",
       "1990.0                69.000000   9.00000     58.000000             20.000000  \n",
       "1991.0                77.000000   3.00000     68.000000             23.000000  \n",
       "1992.0                81.000000   9.00000     77.000000             22.000000  \n",
       "1993.0                87.000000  12.00000     80.000000             23.000000  \n",
       "1994.0                97.000000  14.00000     84.000000             26.000000  \n",
       "1995.0               101.000000  15.00000     85.000000             28.000000  \n",
       "1996.0                98.000000  14.00000     90.000000             29.000000  \n",
       "1997.0               109.000000  15.00000     86.000000             31.000000  \n",
       "1998.0               111.000000  17.00000     92.000000             32.000000  \n",
       "1999.0               126.000000  18.00000     94.000000             32.000000  \n",
       "2000.0               123.000000  19.00000     98.000000             31.000000  \n",
       "2001.0               133.000000  20.00000    100.000000             40.000000  \n",
       "2002.0               138.000000  20.00000    114.000000             39.000000  \n",
       "2003.0               143.000000  21.00000    115.000000             38.000000  \n",
       "2004.0               156.000000  24.00000    115.000000             42.000000  \n",
       "2005.0               173.000000  27.00000    123.000000             44.000000  \n",
       "2006.0               180.000000  25.00000    132.000000             46.000000  \n",
       "2007.0               191.000000  25.00000    138.000000             50.000000  \n",
       "2008.0               205.000000  28.00000    151.000000             59.000000  \n",
       "2009.0               204.000000  29.00000    159.000000             64.000000  \n",
       "2010.0               204.000000  29.00000    175.000000             61.000000  \n",
       "2011.0               208.000000  28.00000    184.000000             60.000000  \n",
       "2012.0               218.000000  29.00000    189.000000             64.000000  \n",
       "2013.0               222.000000  30.00000    188.000000             68.000000  \n",
       "2014.0               238.000000  27.00000    207.000000             74.000000  \n",
       "2015.0               238.000000  33.00000    216.000000             76.000000  \n",
       "2016.0               246.000000  36.00000    215.000000             79.000000  \n",
       "2017.0               262.000000  34.00000    219.000000             78.000000  \n",
       "2018.0               266.000000  34.00000    214.000000             78.000000  \n",
       "2019.0               269.000000  36.00000    215.000000             83.000000  \n",
       "2020.0               268.000000  38.00000    209.000000             81.000000  \n",
       "Average Consumption  169.064516  23.16129    138.387097             49.064516  \n",
       "\n",
       "[32 rows x 44 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making DataFrame for Countrywise Consunmption of Energy\n",
    "dataset_02 = pd.read_csv(\"Country_Consumption_TWH.csv\")\n",
    "\n",
    "#Remove NaN values, as they're <7% of the dataset.\n",
    "ds=dataset_02[:31]\n",
    "avg = []\n",
    "sum = 0\n",
    "ds.set_index(\"Year\", drop=True, append=False, inplace=True, verify_integrity=False)\n",
    "for i in ds:\n",
    "    for j in ds[i]:\n",
    "        sum+=j\n",
    "    avg.append(sum/31)\n",
    "    sum=0\n",
    "ds = ds.swapaxes(\"rows\", \"columns\")\n",
    "\n",
    "# Finding the average consumption for outlier detection.\n",
    "ds[\"Average Consumption\"] = avg \n",
    "ds_disp = ds.swapaxes(\"rows\", \"columns\")\n",
    "\n",
    "# Display the DataFrame with average consumption added in.\n",
    "ds_disp "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3b16e323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(data = ds[\"Average Consumption\"], orient = \"h\") # Visualizing outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e12cf2ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average Consumption</th>\n",
       "      <th>scores</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1923.322581</td>\n",
       "      <td>-0.279970</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2167.451613</td>\n",
       "      <td>-0.306135</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>223.451613</td>\n",
       "      <td>0.031433</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>54.903226</td>\n",
       "      <td>0.105165</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czechia</th>\n",
       "      <td>43.258065</td>\n",
       "      <td>0.093048</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>251.193548</td>\n",
       "      <td>0.012116</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>327.903226</td>\n",
       "      <td>-0.079787</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>162.903226</td>\n",
       "      <td>0.073485</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>74.870968</td>\n",
       "      <td>0.075290</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>97.419355</td>\n",
       "      <td>0.100261</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>22.225806</td>\n",
       "      <td>0.053086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>39.193548</td>\n",
       "      <td>0.085993</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>117.838710</td>\n",
       "      <td>0.092524</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>49.258065</td>\n",
       "      <td>0.102566</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>204.322581</td>\n",
       "      <td>0.047082</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>27.064516</td>\n",
       "      <td>0.078102</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>92.612903</td>\n",
       "      <td>0.103527</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kazakhstan</th>\n",
       "      <td>59.774194</td>\n",
       "      <td>0.109469</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>691.677419</td>\n",
       "      <td>-0.165450</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>139.064516</td>\n",
       "      <td>0.071295</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>46.774194</td>\n",
       "      <td>0.098285</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>66.064516</td>\n",
       "      <td>0.090802</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>259.516129</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>28.032258</td>\n",
       "      <td>0.079278</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>30.677419</td>\n",
       "      <td>0.079635</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>163.516129</td>\n",
       "      <td>0.072683</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>56.290323</td>\n",
       "      <td>0.106796</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indonesia</th>\n",
       "      <td>171.741935</td>\n",
       "      <td>0.063170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>476.741935</td>\n",
       "      <td>-0.119598</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malaysia</th>\n",
       "      <td>61.225806</td>\n",
       "      <td>0.110904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>212.419355</td>\n",
       "      <td>0.049602</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taiwan</th>\n",
       "      <td>89.032258</td>\n",
       "      <td>0.094738</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>95.612903</td>\n",
       "      <td>0.104238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>580.000000</td>\n",
       "      <td>-0.126174</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>112.645161</td>\n",
       "      <td>0.087782</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>17.612903</td>\n",
       "      <td>-0.016824</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>37.258065</td>\n",
       "      <td>0.083389</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>60.935484</td>\n",
       "      <td>0.110183</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nigeria</th>\n",
       "      <td>108.967742</td>\n",
       "      <td>0.092319</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>118.193548</td>\n",
       "      <td>0.092104</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>169.064516</td>\n",
       "      <td>0.068140</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kuwait</th>\n",
       "      <td>23.161290</td>\n",
       "      <td>0.060936</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>138.387097</td>\n",
       "      <td>0.073688</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>49.064516</td>\n",
       "      <td>0.103794</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Average Consumption    scores  anomaly\n",
       "China                         1923.322581 -0.279970       -1\n",
       "United States                 2167.451613 -0.306135       -1\n",
       "Brazil                         223.451613  0.031433        1\n",
       "Belgium                         54.903226  0.105165        1\n",
       "Czechia                         43.258065  0.093048        1\n",
       "France                         251.193548  0.012116        1\n",
       "Germany                        327.903226 -0.079787       -1\n",
       "Italy                          162.903226  0.073485        1\n",
       "Netherlands                     74.870968  0.075290        1\n",
       "Poland                          97.419355  0.100261        1\n",
       "Portugal                        22.225806  0.053086        1\n",
       "Romania                         39.193548  0.085993        1\n",
       "Spain                          117.838710  0.092524        1\n",
       "Sweden                          49.258065  0.102566        1\n",
       "United Kingdom                 204.322581  0.047082        1\n",
       "Norway                          27.064516  0.078102        1\n",
       "Turkey                          92.612903  0.103527        1\n",
       "Kazakhstan                      59.774194  0.109469        1\n",
       "Russia                         691.677419 -0.165450       -1\n",
       "Ukraine                        139.064516  0.071295        1\n",
       "Uzbekistan                      46.774194  0.098285        1\n",
       "Argentina                       66.064516  0.090802        1\n",
       "Canada                         259.516129  0.008150        1\n",
       "Chile                           28.032258  0.079278        1\n",
       "Colombia                        30.677419  0.079635        1\n",
       "Mexico                         163.516129  0.072683        1\n",
       "Venezuela                       56.290323  0.106796        1\n",
       "Indonesia                      171.741935  0.063170        1\n",
       "Japan                          476.741935 -0.119598       -1\n",
       "Malaysia                        61.225806  0.110904        1\n",
       "South Korea                    212.419355  0.049602        1\n",
       "Taiwan                          89.032258  0.094738        1\n",
       "Thailand                        95.612903  0.104238        1\n",
       "India                          580.000000 -0.126174       -1\n",
       "Australia                      112.645161  0.087782        1\n",
       "New Zealand                     17.612903 -0.016824       -1\n",
       "Algeria                         37.258065  0.083389        1\n",
       "Egypt                           60.935484  0.110183        1\n",
       "Nigeria                        108.967742  0.092319        1\n",
       "South Africa                   118.193548  0.092104        1\n",
       "Iran                           169.064516  0.068140        1\n",
       "Kuwait                          23.161290  0.060936        1\n",
       "Saudi Arabia                   138.387097  0.073688        1\n",
       "United Arab Emirates            49.064516  0.103794        1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest # Import IsolationForest for anomaly detection.\n",
    "\n",
    "df = pd.DataFrame(ds['Average Consumption'])\n",
    "model = IsolationForest(n_estimators=100, max_samples='auto', max_features=1.0)\n",
    "model.fit(df[['Average Consumption']]) # Fit the model.\n",
    "df['scores']=model.decision_function(df[['Average Consumption']])\n",
    "df['anomaly']=model.predict(df[['Average Consumption']])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "611f68b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Average Consumption    scores  anomaly  \\\n",
      "China                         1923.322581 -0.279970       -1   \n",
      "United States                 2167.451613 -0.306135       -1   \n",
      "Brazil                         223.451613  0.031433        1   \n",
      "Belgium                         54.903226  0.105165        1   \n",
      "Czechia                         43.258065  0.093048        1   \n",
      "France                         251.193548  0.012116        1   \n",
      "Germany                        327.903226 -0.079787       -1   \n",
      "Italy                          162.903226  0.073485        1   \n",
      "Netherlands                     74.870968  0.075290        1   \n",
      "Poland                          97.419355  0.100261        1   \n",
      "Portugal                        22.225806  0.053086        1   \n",
      "Romania                         39.193548  0.085993        1   \n",
      "Spain                          117.838710  0.092524        1   \n",
      "Sweden                          49.258065  0.102566        1   \n",
      "United Kingdom                 204.322581  0.047082        1   \n",
      "Norway                          27.064516  0.078102        1   \n",
      "Turkey                          92.612903  0.103527        1   \n",
      "Kazakhstan                      59.774194  0.109469        1   \n",
      "Russia                         691.677419 -0.165450       -1   \n",
      "Ukraine                        139.064516  0.071295        1   \n",
      "Uzbekistan                      46.774194  0.098285        1   \n",
      "Argentina                       66.064516  0.090802        1   \n",
      "Canada                         259.516129  0.008150        1   \n",
      "Chile                           28.032258  0.079278        1   \n",
      "Colombia                        30.677419  0.079635        1   \n",
      "Mexico                         163.516129  0.072683        1   \n",
      "Venezuela                       56.290323  0.106796        1   \n",
      "Indonesia                      171.741935  0.063170        1   \n",
      "Japan                          476.741935 -0.119598       -1   \n",
      "Malaysia                        61.225806  0.110904        1   \n",
      "South Korea                    212.419355  0.049602        1   \n",
      "Taiwan                          89.032258  0.094738        1   \n",
      "Thailand                        95.612903  0.104238        1   \n",
      "India                          580.000000 -0.126174       -1   \n",
      "Australia                      112.645161  0.087782        1   \n",
      "New Zealand                     17.612903 -0.016824       -1   \n",
      "Algeria                         37.258065  0.083389        1   \n",
      "Egypt                           60.935484  0.110183        1   \n",
      "Nigeria                        108.967742  0.092319        1   \n",
      "South Africa                   118.193548  0.092104        1   \n",
      "Iran                           169.064516  0.068140        1   \n",
      "Kuwait                          23.161290  0.060936        1   \n",
      "Saudi Arabia                   138.387097  0.073688        1   \n",
      "United Arab Emirates            49.064516  0.103794        1   \n",
      "\n",
      "                                   Country  \n",
      "China                                China  \n",
      "United States                United States  \n",
      "Brazil                              Brazil  \n",
      "Belgium                            Belgium  \n",
      "Czechia                            Czechia  \n",
      "France                              France  \n",
      "Germany                            Germany  \n",
      "Italy                                Italy  \n",
      "Netherlands                    Netherlands  \n",
      "Poland                              Poland  \n",
      "Portugal                          Portugal  \n",
      "Romania                            Romania  \n",
      "Spain                                Spain  \n",
      "Sweden                              Sweden  \n",
      "United Kingdom              United Kingdom  \n",
      "Norway                              Norway  \n",
      "Turkey                              Turkey  \n",
      "Kazakhstan                      Kazakhstan  \n",
      "Russia                              Russia  \n",
      "Ukraine                            Ukraine  \n",
      "Uzbekistan                      Uzbekistan  \n",
      "Argentina                        Argentina  \n",
      "Canada                              Canada  \n",
      "Chile                                Chile  \n",
      "Colombia                          Colombia  \n",
      "Mexico                              Mexico  \n",
      "Venezuela                        Venezuela  \n",
      "Indonesia                        Indonesia  \n",
      "Japan                                Japan  \n",
      "Malaysia                          Malaysia  \n",
      "South Korea                    South Korea  \n",
      "Taiwan                              Taiwan  \n",
      "Thailand                          Thailand  \n",
      "India                                India  \n",
      "Australia                        Australia  \n",
      "New Zealand                    New Zealand  \n",
      "Algeria                            Algeria  \n",
      "Egypt                                Egypt  \n",
      "Nigeria                            Nigeria  \n",
      "South Africa                  South Africa  \n",
      "Iran                                  Iran  \n",
      "Kuwait                              Kuwait  \n",
      "Saudi Arabia                  Saudi Arabia  \n",
      "United Arab Emirates  United Arab Emirates  \n"
     ]
    }
   ],
   "source": [
    "#Adding Country as the index in the DataFrame\n",
    "df['Country'] = df.index\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a43fe5ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tort_display(datasetName, colName):\n",
    "    datasetName = datasetName.sort_values(by=[colName], ascending=False)\n",
    "    datasetName.head()\n",
    "    \n",
    "    datasetName.plot(figsize=(20, 10))\n",
    "    plt.xticks(rotation=90)\n",
    "    sb.barplot(data=datasetName, x=datasetName[\"Country\"], y=datasetName[colName])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "48bc2af7",
   "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>Average Consumption</th>\n",
       "      <th>scores</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1923.322581</td>\n",
       "      <td>-0.279970</td>\n",
       "      <td>-1</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2167.451613</td>\n",
       "      <td>-0.306135</td>\n",
       "      <td>-1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>327.903226</td>\n",
       "      <td>-0.079787</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>691.677419</td>\n",
       "      <td>-0.165450</td>\n",
       "      <td>-1</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>476.741935</td>\n",
       "      <td>-0.119598</td>\n",
       "      <td>-1</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>580.000000</td>\n",
       "      <td>-0.126174</td>\n",
       "      <td>-1</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>17.612903</td>\n",
       "      <td>-0.016824</td>\n",
       "      <td>-1</td>\n",
       "      <td>New Zealand</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Average Consumption    scores  anomaly        Country\n",
       "China                  1923.322581 -0.279970       -1          China\n",
       "United States          2167.451613 -0.306135       -1  United States\n",
       "Germany                 327.903226 -0.079787       -1        Germany\n",
       "Russia                  691.677419 -0.165450       -1         Russia\n",
       "Japan                   476.741935 -0.119598       -1          Japan\n",
       "India                   580.000000 -0.126174       -1          India\n",
       "New Zealand              17.612903 -0.016824       -1    New Zealand"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Segregating countries based on Anomalous Consumption\n",
    "anomaly=df.loc[df['anomaly']==-1]\n",
    "anomaly_index=list(anomaly.index)\n",
    "anomaly # Print outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f8f53f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tort_display(df, 'Average Consumption')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010aa682",
   "metadata": {},
   "source": [
    "Here, we see that New Zealand is an anomaly because of its unusually low consumption. However, for the purposes of this project, we need to find those countries that consume energy the most (not the least). Thus, we remove New Zealand from the DataFrame of outliers in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "781179f0",
   "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>Average Consumption</th>\n",
       "      <th>scores</th>\n",
       "      <th>anomaly</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1923.322581</td>\n",
       "      <td>-0.27997</td>\n",
       "      <td>-1</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2167.451613</td>\n",
       "      <td>-0.306135</td>\n",
       "      <td>-1</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>327.903226</td>\n",
       "      <td>-0.079787</td>\n",
       "      <td>-1</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>691.677419</td>\n",
       "      <td>-0.16545</td>\n",
       "      <td>-1</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>476.741935</td>\n",
       "      <td>-0.119598</td>\n",
       "      <td>-1</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>580.0</td>\n",
       "      <td>-0.126174</td>\n",
       "      <td>-1</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Average Consumption    scores anomaly        Country\n",
       "China                 1923.322581  -0.27997      -1          China\n",
       "United States         2167.451613 -0.306135      -1  United States\n",
       "Germany                327.903226 -0.079787      -1        Germany\n",
       "Russia                 691.677419  -0.16545      -1         Russia\n",
       "Japan                  476.741935 -0.119598      -1          Japan\n",
       "India                       580.0 -0.126174      -1          India"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "anomaly = anomaly.swapaxes(\"rows\", \"columns\")\n",
    "del anomaly[\"New Zealand\"]\n",
    "anomaly = anomaly.swapaxes(\"rows\", \"columns\")\n",
    "anomaly # DataFrame of countries that consumed the most energy (renewable + non-renewable) on average from 1990 to 2020."
   ]
  },
  {
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       "                            \n",
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       "            observer.disconnect();\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter_3d(df,x='scores',\n",
    "                       y='Country',\n",
    "                       z='Average Consumption',\n",
    "                       color='anomaly')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4a2fd219",
   "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>Year</th>\n",
       "      <th>Hydro(TWh)</th>\n",
       "      <th>Biofuel(TWh)</th>\n",
       "      <th>Solar PV (TWh)</th>\n",
       "      <th>Geothermal (TWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>2191.67</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.09</td>\n",
       "      <td>36.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>2268.63</td>\n",
       "      <td>4.19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>37.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>2267.16</td>\n",
       "      <td>4.63</td>\n",
       "      <td>0.12</td>\n",
       "      <td>39.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>2397.67</td>\n",
       "      <td>5.61</td>\n",
       "      <td>0.15</td>\n",
       "      <td>40.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>2419.73</td>\n",
       "      <td>7.31</td>\n",
       "      <td>0.17</td>\n",
       "      <td>41.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>2545.96</td>\n",
       "      <td>7.95</td>\n",
       "      <td>0.19</td>\n",
       "      <td>39.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1996</td>\n",
       "      <td>2583.18</td>\n",
       "      <td>9.45</td>\n",
       "      <td>0.22</td>\n",
       "      <td>42.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1997</td>\n",
       "      <td>2614.54</td>\n",
       "      <td>12.08</td>\n",
       "      <td>0.27</td>\n",
       "      <td>42.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1998</td>\n",
       "      <td>2628.63</td>\n",
       "      <td>16.07</td>\n",
       "      <td>0.35</td>\n",
       "      <td>45.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1999</td>\n",
       "      <td>2636.26</td>\n",
       "      <td>21.52</td>\n",
       "      <td>0.61</td>\n",
       "      <td>48.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>2695.85</td>\n",
       "      <td>31.34</td>\n",
       "      <td>0.99</td>\n",
       "      <td>51.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2001</td>\n",
       "      <td>2638.20</td>\n",
       "      <td>38.45</td>\n",
       "      <td>1.32</td>\n",
       "      <td>51.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2002</td>\n",
       "      <td>2711.12</td>\n",
       "      <td>52.85</td>\n",
       "      <td>1.58</td>\n",
       "      <td>52.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003</td>\n",
       "      <td>2726.33</td>\n",
       "      <td>64.23</td>\n",
       "      <td>2.01</td>\n",
       "      <td>54.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2004</td>\n",
       "      <td>2894.22</td>\n",
       "      <td>84.43</td>\n",
       "      <td>2.66</td>\n",
       "      <td>56.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2005</td>\n",
       "      <td>3019.50</td>\n",
       "      <td>103.92</td>\n",
       "      <td>3.92</td>\n",
       "      <td>58.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2006</td>\n",
       "      <td>3124.34</td>\n",
       "      <td>133.05</td>\n",
       "      <td>5.52</td>\n",
       "      <td>59.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2007</td>\n",
       "      <td>3165.71</td>\n",
       "      <td>170.83</td>\n",
       "      <td>7.47</td>\n",
       "      <td>62.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2008</td>\n",
       "      <td>3285.59</td>\n",
       "      <td>221.05</td>\n",
       "      <td>11.92</td>\n",
       "      <td>64.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2009</td>\n",
       "      <td>3338.82</td>\n",
       "      <td>277.44</td>\n",
       "      <td>20.04</td>\n",
       "      <td>67.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2010</td>\n",
       "      <td>3530.72</td>\n",
       "      <td>341.38</td>\n",
       "      <td>32.22</td>\n",
       "      <td>68.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2011</td>\n",
       "      <td>2603.78</td>\n",
       "      <td>436.01</td>\n",
       "      <td>63.76</td>\n",
       "      <td>69.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012</td>\n",
       "      <td>3765.96</td>\n",
       "      <td>525.04</td>\n",
       "      <td>99.03</td>\n",
       "      <td>70.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>2898.28</td>\n",
       "      <td>646.36</td>\n",
       "      <td>139.64</td>\n",
       "      <td>71.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014</td>\n",
       "      <td>3976.01</td>\n",
       "      <td>718.07</td>\n",
       "      <td>190.25</td>\n",
       "      <td>77.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015</td>\n",
       "      <td>3989.28</td>\n",
       "      <td>838.31</td>\n",
       "      <td>250.75</td>\n",
       "      <td>80.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016</td>\n",
       "      <td>4162.26</td>\n",
       "      <td>958.15</td>\n",
       "      <td>329.14</td>\n",
       "      <td>82.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017</td>\n",
       "      <td>4197.29</td>\n",
       "      <td>1127.31</td>\n",
       "      <td>443.55</td>\n",
       "      <td>85.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Hydro(TWh)  Biofuel(TWh)  Solar PV (TWh)  Geothermal (TWh)\n",
       "0   1990     2191.67          3.88            0.09             36.42\n",
       "1   1991     2268.63          4.19            0.10             37.39\n",
       "2   1992     2267.16          4.63            0.12             39.30\n",
       "3   1993     2397.67          5.61            0.15             40.23\n",
       "4   1994     2419.73          7.31            0.17             41.05\n",
       "5   1995     2545.96          7.95            0.19             39.89\n",
       "6   1996     2583.18          9.45            0.22             42.18\n",
       "7   1997     2614.54         12.08            0.27             42.38\n",
       "8   1998     2628.63         16.07            0.35             45.35\n",
       "9   1999     2636.26         21.52            0.61             48.66\n",
       "10  2000     2695.85         31.34            0.99             51.98\n",
       "11  2001     2638.20         38.45            1.32             51.57\n",
       "12  2002     2711.12         52.85            1.58             52.29\n",
       "13  2003     2726.33         64.23            2.01             54.09\n",
       "14  2004     2894.22         84.43            2.66             56.50\n",
       "15  2005     3019.50        103.92            3.92             58.28\n",
       "16  2006     3124.34        133.05            5.52             59.61\n",
       "17  2007     3165.71        170.83            7.47             62.29\n",
       "18  2008     3285.59        221.05           11.92             64.91\n",
       "19  2009     3338.82        277.44           20.04             67.03\n",
       "20  2010     3530.72        341.38           32.22             68.10\n",
       "21  2011     2603.78        436.01           63.76             69.29\n",
       "22  2012     3765.96        525.04           99.03             70.27\n",
       "23  2013     2898.28        646.36          139.64             71.74\n",
       "24  2014     3976.01        718.07          190.25             77.52\n",
       "25  2015     3989.28        838.31          250.75             80.56\n",
       "26  2016     4162.26        958.15          329.14             82.18\n",
       "27  2017     4197.29       1127.31          443.55             85.34"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Making DataFrame for different contributions in renwable energy consumption\n",
    "reg_data = pd.read_csv(\"renewablePowerGeneration97-17.csv\")\n",
    "reg_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5ef047f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Hydro(TWh)  Biofuel(TWh)  Solar PV (TWh)  Geothermal (TWh)\n",
      "0         2191.67          3.88            0.09             36.42\n",
      "1         2268.63          4.19            0.10             37.39\n",
      "2         2267.16          4.63            0.12             39.30\n",
      "3         2397.67          5.61            0.15             40.23\n",
      "4         2419.73          7.31            0.17             41.05\n",
      "5         2545.96          7.95            0.19             39.89\n",
      "6         2583.18          9.45            0.22             42.18\n",
      "7         2614.54         12.08            0.27             42.38\n",
      "8         2628.63         16.07            0.35             45.35\n",
      "9         2636.26         21.52            0.61             48.66\n",
      "10        2695.85         31.34            0.99             51.98\n",
      "11        2638.20         38.45            1.32             51.57\n",
      "12        2711.12         52.85            1.58             52.29\n",
      "13        2726.33         64.23            2.01             54.09\n",
      "14        2894.22         84.43            2.66             56.50\n",
      "15        3019.50        103.92            3.92             58.28\n",
      "16        3124.34        133.05            5.52             59.61\n",
      "17        3165.71        170.83            7.47             62.29\n",
      "18        3285.59        221.05           11.92             64.91\n",
      "19        3338.82        277.44           20.04             67.03\n",
      "20        3530.72        341.38           32.22             68.10\n",
      "21        2603.78        436.01           63.76             69.29\n",
      "22        3765.96        525.04           99.03             70.27\n",
      "23        2898.28        646.36          139.64             71.74\n",
      "24        3976.01        718.07          190.25             77.52\n",
      "25        3989.28        838.31          250.75             80.56\n",
      "26        4162.26        958.15          329.14             82.18\n",
      "27        4197.29       1127.31          443.55             85.34\n",
      "Total    83276.69       6860.91         1608.04           1596.40\n"
     ]
    }
   ],
   "source": [
    "#Calculating 'Total' for all renewable sources \n",
    "reg_data = pd.read_csv(\"renewablePowerGeneration97-17.csv\")\n",
    "regnew_data = reg_data[[\"Hydro(TWh)\",\"Biofuel(TWh)\",\"Solar PV (TWh)\", \"Geothermal (TWh)\"]]\n",
    "total1 = regnew_data.sum()\n",
    "total1.name = 'Total'\n",
    "# Assign sum of all rows of DataFrame as a new Row\n",
    "regnew_data = regnew_data.append(total1.transpose())\n",
    "print(regnew_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3c45576e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Hydro(TWh)': 83276.68999999999, 'Biofuel(TWh)': 6860.91, 'Solar PV (TWh)': 1608.0399999999997, 'Geothermal (TWh)': 1596.3999999999999}\n"
     ]
    }
   ],
   "source": [
    "#Adding renewable energy and their respective 'total' values as key-value pairs in Dictionary \n",
    "ret = {}\n",
    "for column in regnew_data.columns:\n",
    "    ret[column]=regnew_data[column].iloc[28]\n",
    "print(ret)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f6eaf655",
   "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>Hydro(TWh)</th>\n",
       "      <th>Biofuel(TWh)</th>\n",
       "      <th>Solar PV (TWh)</th>\n",
       "      <th>Geothermal (TWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83276.69</td>\n",
       "      <td>6860.91</td>\n",
       "      <td>1608.04</td>\n",
       "      <td>1596.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hydro(TWh)  Biofuel(TWh)  Solar PV (TWh)  Geothermal (TWh)\n",
       "0    83276.69       6860.91         1608.04            1596.4"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retdf = pd.DataFrame([ret])\n",
    "retdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a2081462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total Generation of Re-newable En')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting barplot for better comparisons\n",
    "f,ax = plt.subplots(figsize=(12,8))\n",
    "plt.bar(ret.keys(),ret.values())\n",
    "plt.xticks(rotation = 0, ha = 'right')\n",
    "ax.set_title(\"Total Generation of Re-newable En\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7448df8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting line graphs for different renewable sources for contribution\n",
    "plt.plot(reg_data['Year'],reg_data['Hydro(TWh)'],label='Hydro(TWh)',linewidth=4)\n",
    "plt.plot(reg_data['Year'],reg_data['Biofuel(TWh)'],label='Biofuel(TWh)',linewidth=4)\n",
    "plt.plot(reg_data['Year'],reg_data['Solar PV (TWh)'],label='Solar PV (TWh)',linewidth=4)\n",
    "plt.plot(reg_data['Year'],reg_data['Geothermal (TWh)'],label='Geothermal (TWh)',linewidth=4)\n",
    "\n",
    "\n",
    "\n",
    "#add title and axis labels\n",
    "plt.title('Renewable Energy Generation through different sources over the years')\n",
    "plt.xlabel('Different Sources')\n",
    "plt.ylabel('Consumption')\n",
    "\n",
    "#add legend\n",
    "plt.legend()\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))\n",
    "\n",
    "#display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b71edb",
   "metadata": {},
   "source": [
    "This barplot clearly indicates the supremacy of Hydro Renewable Energy source generation over the years.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af43eab2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "113e6cbe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}