EnegySupply.ipynb

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        "<a href=\"https://colab.research.google.com/github/jmelendezgeo/Exploratory-analysis-/blob/main/EnegySupply.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Introduction\n",
        "**English**\n",
        "\n",
        "In this notebook an exploratory analysis of various data sets is made with certain information from countries. The information will go through a Data Cleaning process to be able to unify the format in a DataFrame with which we will continue working to obtain certain insights and statistics. \n",
        "\n",
        "**Spanish**.\n",
        "\n",
        "En esta libreta se hace un **análisis exploratorio** de diversos set de datos con cierta información de países. La información pasará por un proceso de **Limpieza de Datos** para poder unificar el formato en un DataFrame con el que continuaremos trabajando para obtener ciertos insights y estadísticas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ImmGneRpwNF"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcynGuHwqKlz"
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      "source": [
        "## Data load & Data Cleaning\n",
        "\n",
        "**English**\n",
        "\n",
        "The information contained in 'Energy Indicators' corresponds to the supply of energy and production of renewable electricity of United Nations of 2013 \n",
        "\n",
        "\n",
        "**Spanish**\n",
        "\n",
        "La información contenida en 'Energy Indicators' corresponde a *Suministro de energía y producción de electricidad renovable* de [United nations](https://unstats.un.org/unsd/environment/excel_file_tables/2013/Energy%20Indicators.xls) del 2013"
      ]
    },
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        "outputId": "21c38bab-04b0-4894-8f16-f8a954af1473"
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      "source": [
        "#First, we are going to load the data contained in an .xls file (excel file). \n",
        "#In the file we have information in the header and in the footer so we exclude this information from our DataFrame \n",
        "Energy = pd.read_excel('Energy Indicators.xls', skiprows = 17, skipfooter = 38)\n",
        "Energy.head()"
      ],
      "execution_count": 3,
      "outputs": [
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              "   Unnamed: 0      Unnamed: 1      Unnamed: 2 Petajoules Gigajoules           %\n",
              "0         NaN     Afghanistan     Afghanistan        321         10   78.669280\n",
              "1         NaN         Albania         Albania        102         35  100.000000\n",
              "2         NaN         Algeria         Algeria       1959         51    0.551010\n",
              "3         NaN  American Samoa  American Samoa        ...        ...    0.641026\n",
              "4         NaN         Andorra         Andorra          9        121   88.695650"
            ]
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      "source": [
        "# The first two columns are unnecessary. We also need to name our columns appropriately \n",
        "Energy.drop(columns = ['Unnamed: 0','Unnamed: 1'], inplace = True)\n",
        "Energy.columns = ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable']\n",
        "Energy.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Country</th>\n",
              "      <th>Energy Supply</th>\n",
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              "      <th>% Renewable</th>\n",
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              "      <td>Albania</td>\n",
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              "      <td>Algeria</td>\n",
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              "      <th>3</th>\n",
              "      <td>American Samoa</td>\n",
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            "text/plain": [
              "          Country Energy Supply Energy Supply per Capita  % Renewable\n",
              "0     Afghanistan           321                       10    78.669280\n",
              "1         Albania           102                       35   100.000000\n",
              "2         Algeria          1959                       51     0.551010\n",
              "3  American Samoa           ...                      ...     0.641026\n",
              "4         Andorra             9                      121    88.695650"
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        "id": "GQqut3sXrotX",
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      "source": [
        "# It seems that in our Energy Supply column there is information that is not real (eg '...'). \n",
        "# When reviewing the dataset information, the '...' means no data. \n",
        "# We will use np.nan to replace these strings\n",
        "~Energy.applymap(np.isreal)"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Country</th>\n",
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              "      <td>...</td>\n",
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              "      <th>222</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>227 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Country  Energy Supply  Energy Supply per Capita  % Renewable\n",
              "0       True          False                     False        False\n",
              "1       True          False                     False        False\n",
              "2       True          False                     False        False\n",
              "3       True           True                      True        False\n",
              "4       True          False                     False        False\n",
              "..       ...            ...                       ...          ...\n",
              "222     True          False                     False        False\n",
              "223     True          False                     False        False\n",
              "224     True          False                     False        False\n",
              "225     True          False                     False        False\n",
              "226     True          False                     False        False\n",
              "\n",
              "[227 rows x 4 columns]"
            ]
          },
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            "tags": []
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          "execution_count": 5
        }
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      "source": [
        "# True's mean that the values are not real. With this we could recognize the strings.\n",
        "# In our columns, except for 'Country', we should have real numbers\n",
        "\n",
        "# Replace empty data (as ...) to np.nan values\n",
        "Energy.replace('\\.{2,}',np.nan, regex = True, inplace = True)\n",
        "\n",
        "# PetaJoule to GigaJoule. Let's convert to the same units of energy \n",
        "Energy['Energy Supply'] = Energy['Energy Supply'] * 1000000\n",
        "Energy[10:15]"
      ],
      "execution_count": 6,
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>Country</th>\n",
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              "      <th>% Renewable</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>1.200000e+07</td>\n",
              "      <td>120.0</td>\n",
              "      <td>14.870690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Australia1</td>\n",
              "      <td>5.386000e+09</td>\n",
              "      <td>231.0</td>\n",
              "      <td>11.810810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Austria</td>\n",
              "      <td>1.391000e+09</td>\n",
              "      <td>164.0</td>\n",
              "      <td>72.452820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>5.670000e+08</td>\n",
              "      <td>60.0</td>\n",
              "      <td>6.384345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>4.500000e+07</td>\n",
              "      <td>118.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "       Country  Energy Supply  Energy Supply per Capita  % Renewable\n",
              "10       Aruba   1.200000e+07                     120.0    14.870690\n",
              "11  Australia1   5.386000e+09                     231.0    11.810810\n",
              "12     Austria   1.391000e+09                     164.0    72.452820\n",
              "13  Azerbaijan   5.670000e+08                      60.0     6.384345\n",
              "14     Bahamas   4.500000e+07                     118.0     0.000000"
            ]
          },
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          },
          "execution_count": 6
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      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Here are some country names to clean up. This problem is very common. \n",
        "# We will pass a filter that removes the information between parentheses and numbers in the name \n",
        "# We also correct the names of some countries with a dictionary \n",
        "Energy['Country']=Energy['Country'].str.extract('(^[a-zA-Z\\s\\,]+)',expand = False)\n",
        "# Corregir algunos nombres de paises\n",
        "Energy.replace({\"Republic of Korea\": \"South Korea\",\n",
        "            \"United States of America\": \"United States\",\n",
        "            \"United Kingdom of Great Britain and Northern Ireland\": \"United Kingdom\",\n",
        "            \"China, Hong Kong Special Administrative Region\": \"Hong Kong\",\n",
        "               \"Iran \":'Iran'}, inplace=True)\n",
        "# Finally, we assign the name of the country as index\n",
        "Energy = Energy.set_index('Country')\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "douy_f4-vRQg",
        "outputId": "1b3d918f-752d-48e6-ea25-29d8df94285d"
      },
      "source": [
        "Energy.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Energy Supply</th>\n",
              "      <th>Energy Supply per Capita</th>\n",
              "      <th>% Renewable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>3.210000e+08</td>\n",
              "      <td>10.0</td>\n",
              "      <td>78.669280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>1.020000e+08</td>\n",
              "      <td>35.0</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>1.959000e+09</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.551010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>American Samoa</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.641026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>9.000000e+06</td>\n",
              "      <td>121.0</td>\n",
              "      <td>88.695650</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Energy Supply  Energy Supply per Capita  % Renewable\n",
              "Country                                                             \n",
              "Afghanistan      3.210000e+08                      10.0    78.669280\n",
              "Albania          1.020000e+08                      35.0   100.000000\n",
              "Algeria          1.959000e+09                      51.0     0.551010\n",
              "American Samoa            NaN                       NaN     0.641026\n",
              "Andorra          9.000000e+06                     121.0    88.695650"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SWyDsnndvVER",
        "outputId": "64c06658-a0d7-449d-85a8-fb0a29a7517c"
      },
      "source": [
        "# With this dataset clean and ready for enrichment, let's look at some metrics \n",
        "Energy.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Energy Supply</th>\n",
              "      <th>Energy Supply per Capita</th>\n",
              "      <th>% Renewable</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.220000e+02</td>\n",
              "      <td>222.000000</td>\n",
              "      <td>227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.457982e+09</td>\n",
              "      <td>90.666667</td>\n",
              "      <td>28.086077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.103915e+10</td>\n",
              "      <td>116.234887</td>\n",
              "      <td>31.903505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.300000e+07</td>\n",
              "      <td>21.250000</td>\n",
              "      <td>0.022893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.895000e+08</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>14.814810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.962500e+08</td>\n",
              "      <td>117.750000</td>\n",
              "      <td>50.168620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.271910e+11</td>\n",
              "      <td>957.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Energy Supply  Energy Supply per Capita  % Renewable\n",
              "count   2.220000e+02                222.000000   227.000000\n",
              "mean    2.457982e+09                 90.666667    28.086077\n",
              "std     1.103915e+10                116.234887    31.903505\n",
              "min     0.000000e+00                  2.000000     0.000000\n",
              "25%     3.300000e+07                 21.250000     0.022893\n",
              "50%     1.895000e+08                 51.000000    14.814810\n",
              "75%     9.962500e+08                117.750000    50.168620\n",
              "max     1.271910e+11                957.000000   100.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ib2Rt2j1s-bH"
      },
      "source": [
        "Now we are going to load information from the [World Bank](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD) on the GDP of countries. \n",
        "\n",
        "We will do the necessary cleaning and limit with a specific range of years between 2006-2015"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "aEfDWZi4-6pd",
        "outputId": "d2708bf7-5939-4f92-aece-c9ab78f57a1e"
      },
      "source": [
        "# Skip some rows and select the header information\n",
        "GDP = pd.read_csv('world_bank.csv', skiprows = 3, header = 1)\n",
        "GDP.replace({\"Korea, Rep.\": \"South Korea\", \n",
        "         \"Iran, Islamic Rep.\": \"Iran\",\n",
        "         \"Hong Kong SAR, China\": \"Hong Kong\"}, inplace = True)\n",
        "GDP=GDP.set_index('Country Name')\n",
        "columns_to_keep = ['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
        "   '2014', '2015']\n",
        "GDP = GDP[columns_to_keep]\n",
        "GDP.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country Name</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>Aruba</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.467704e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>4.018196e+09</td>\n",
              "      <td>4.021331e+09</td>\n",
              "      <td>3.675728e+09</td>\n",
              "      <td>3.535389e+09</td>\n",
              "      <td>3.346317e+09</td>\n",
              "      <td>3.185605e+09</td>\n",
              "      <td>3.129538e+09</td>\n",
              "      <td>3.127550e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>1.030523e+10</td>\n",
              "      <td>1.172119e+10</td>\n",
              "      <td>1.214448e+10</td>\n",
              "      <td>1.469733e+10</td>\n",
              "      <td>1.593680e+10</td>\n",
              "      <td>1.691113e+10</td>\n",
              "      <td>1.935220e+10</td>\n",
              "      <td>1.973134e+10</td>\n",
              "      <td>1.999032e+10</td>\n",
              "      <td>2.029415e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>5.581103e+10</td>\n",
              "      <td>6.842044e+10</td>\n",
              "      <td>7.787420e+10</td>\n",
              "      <td>7.975320e+10</td>\n",
              "      <td>8.247091e+10</td>\n",
              "      <td>8.570262e+10</td>\n",
              "      <td>9.012096e+10</td>\n",
              "      <td>9.626143e+10</td>\n",
              "      <td>1.008863e+11</td>\n",
              "      <td>1.039106e+11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>9.771760e+09</td>\n",
              "      <td>1.034829e+10</td>\n",
              "      <td>1.112752e+10</td>\n",
              "      <td>1.150029e+10</td>\n",
              "      <td>1.192695e+10</td>\n",
              "      <td>1.223109e+10</td>\n",
              "      <td>1.240477e+10</td>\n",
              "      <td>1.254247e+10</td>\n",
              "      <td>1.279331e+10</td>\n",
              "      <td>1.312082e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      2006          2007  ...          2014          2015\n",
              "Country Name                              ...                            \n",
              "Aruba                  NaN           NaN  ...           NaN           NaN\n",
              "Andorra       4.018196e+09  4.021331e+09  ...           NaN           NaN\n",
              "Afghanistan   1.030523e+10  1.172119e+10  ...  1.999032e+10  2.029415e+10\n",
              "Angola        5.581103e+10  6.842044e+10  ...  1.008863e+11  1.039106e+11\n",
              "Albania       9.771760e+09  1.034829e+10  ...  1.279331e+10  1.312082e+10\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fh5L_6nuowPG"
      },
      "source": [
        "Finally, we load the [Sciamgo Journal and Country Rank data for Energy Engineering and Power Technology](https://www.scimagojr.com/countryrank.php?category=2102) which ranks countries based on their journal contributions in the aforementioned area. In this DataFrame, we want keep only the top 15 contries by rank. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "Y-moSoi6Axr2",
        "outputId": "cb8b6c77-bc8b-4d2e-98a4-8cccee7c9e0e"
      },
      "source": [
        "ScimEn = pd.read_excel('scimagojr-3.xlsx', header = 0)\n",
        "ScimEn=ScimEn.set_index('Country')\n",
        "ScimEn.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Rank</th>\n",
              "      <th>Documents</th>\n",
              "      <th>Citable documents</th>\n",
              "      <th>Citations</th>\n",
              "      <th>Self-citations</th>\n",
              "      <th>Citations per document</th>\n",
              "      <th>H index</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</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>China</th>\n",
              "      <td>1</td>\n",
              "      <td>127050</td>\n",
              "      <td>126767</td>\n",
              "      <td>597237</td>\n",
              "      <td>411683</td>\n",
              "      <td>4.70</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>2</td>\n",
              "      <td>96661</td>\n",
              "      <td>94747</td>\n",
              "      <td>792274</td>\n",
              "      <td>265436</td>\n",
              "      <td>8.20</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>3</td>\n",
              "      <td>30504</td>\n",
              "      <td>30287</td>\n",
              "      <td>223024</td>\n",
              "      <td>61554</td>\n",
              "      <td>7.31</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>4</td>\n",
              "      <td>20944</td>\n",
              "      <td>20357</td>\n",
              "      <td>206091</td>\n",
              "      <td>37874</td>\n",
              "      <td>9.84</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian Federation</th>\n",
              "      <td>5</td>\n",
              "      <td>18534</td>\n",
              "      <td>18301</td>\n",
              "      <td>34266</td>\n",
              "      <td>12422</td>\n",
              "      <td>1.85</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Rank  Documents  ...  Citations per document  H index\n",
              "Country                              ...                                 \n",
              "China                  1     127050  ...                    4.70      138\n",
              "United States          2      96661  ...                    8.20      230\n",
              "Japan                  3      30504  ...                    7.31      134\n",
              "United Kingdom         4      20944  ...                    9.84      139\n",
              "Russian Federation     5      18534  ...                    1.85       57\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "3O5Kx3qWA8TW",
        "outputId": "acd20315-6809-42ab-97e2-d0ea198333c1"
      },
      "source": [
        "# We want to keep the top 15 countries\n",
        "ScimEn=ScimEn[ScimEn['Rank'].le(15)]\n",
        "ScimEn"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Rank</th>\n",
              "      <th>Documents</th>\n",
              "      <th>Citable documents</th>\n",
              "      <th>Citations</th>\n",
              "      <th>Self-citations</th>\n",
              "      <th>Citations per document</th>\n",
              "      <th>H index</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</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>China</th>\n",
              "      <td>1</td>\n",
              "      <td>127050</td>\n",
              "      <td>126767</td>\n",
              "      <td>597237</td>\n",
              "      <td>411683</td>\n",
              "      <td>4.70</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>2</td>\n",
              "      <td>96661</td>\n",
              "      <td>94747</td>\n",
              "      <td>792274</td>\n",
              "      <td>265436</td>\n",
              "      <td>8.20</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>3</td>\n",
              "      <td>30504</td>\n",
              "      <td>30287</td>\n",
              "      <td>223024</td>\n",
              "      <td>61554</td>\n",
              "      <td>7.31</td>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>4</td>\n",
              "      <td>20944</td>\n",
              "      <td>20357</td>\n",
              "      <td>206091</td>\n",
              "      <td>37874</td>\n",
              "      <td>9.84</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian Federation</th>\n",
              "      <td>5</td>\n",
              "      <td>18534</td>\n",
              "      <td>18301</td>\n",
              "      <td>34266</td>\n",
              "      <td>12422</td>\n",
              "      <td>1.85</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>6</td>\n",
              "      <td>17899</td>\n",
              "      <td>17620</td>\n",
              "      <td>215003</td>\n",
              "      <td>40930</td>\n",
              "      <td>12.01</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>7</td>\n",
              "      <td>17027</td>\n",
              "      <td>16831</td>\n",
              "      <td>140566</td>\n",
              "      <td>27426</td>\n",
              "      <td>8.26</td>\n",
              "      <td>126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>8</td>\n",
              "      <td>15005</td>\n",
              "      <td>14841</td>\n",
              "      <td>128763</td>\n",
              "      <td>37209</td>\n",
              "      <td>8.58</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>9</td>\n",
              "      <td>13153</td>\n",
              "      <td>12973</td>\n",
              "      <td>130632</td>\n",
              "      <td>28601</td>\n",
              "      <td>9.93</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Korea</th>\n",
              "      <td>10</td>\n",
              "      <td>11983</td>\n",
              "      <td>11923</td>\n",
              "      <td>114675</td>\n",
              "      <td>22595</td>\n",
              "      <td>9.57</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>11</td>\n",
              "      <td>10964</td>\n",
              "      <td>10794</td>\n",
              "      <td>111850</td>\n",
              "      <td>26661</td>\n",
              "      <td>10.20</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>12</td>\n",
              "      <td>9428</td>\n",
              "      <td>9330</td>\n",
              "      <td>123336</td>\n",
              "      <td>23964</td>\n",
              "      <td>13.08</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iran</th>\n",
              "      <td>13</td>\n",
              "      <td>8896</td>\n",
              "      <td>8819</td>\n",
              "      <td>57470</td>\n",
              "      <td>19125</td>\n",
              "      <td>6.46</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>14</td>\n",
              "      <td>8831</td>\n",
              "      <td>8725</td>\n",
              "      <td>90765</td>\n",
              "      <td>15606</td>\n",
              "      <td>10.28</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>15</td>\n",
              "      <td>8668</td>\n",
              "      <td>8596</td>\n",
              "      <td>60702</td>\n",
              "      <td>14396</td>\n",
              "      <td>7.00</td>\n",
              "      <td>86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Rank  Documents  ...  Citations per document  H index\n",
              "Country                              ...                                 \n",
              "China                  1     127050  ...                    4.70      138\n",
              "United States          2      96661  ...                    8.20      230\n",
              "Japan                  3      30504  ...                    7.31      134\n",
              "United Kingdom         4      20944  ...                    9.84      139\n",
              "Russian Federation     5      18534  ...                    1.85       57\n",
              "Canada                 6      17899  ...                   12.01      149\n",
              "Germany                7      17027  ...                    8.26      126\n",
              "India                  8      15005  ...                    8.58      115\n",
              "France                 9      13153  ...                    9.93      114\n",
              "South Korea           10      11983  ...                    9.57      104\n",
              "Italy                 11      10964  ...                   10.20      106\n",
              "Spain                 12       9428  ...                   13.08      115\n",
              "Iran                  13       8896  ...                    6.46       72\n",
              "Australia             14       8831  ...                   10.28      107\n",
              "Brazil                15       8668  ...                    7.00       86\n",
              "\n",
              "[15 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x1vKFFW-BWFH"
      },
      "source": [
        "## Join data and transform it \n",
        "At this point we have already loaded and cleaned 3 different datasets. Our data is stored in 3 pandas DataFrames where we have conditioned the information to be able to work them together.\n",
        "\n",
        "Now we will create a DataFrame that is the result of the union of our data. This new DataFrame has the information of countries related to: Their energy production, GDP between 2006-2015 of the top 15 countries ranked by their journal contributions in the area of Energy Engineering and Power Technology "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "TmDOvgNzBH9G",
        "outputId": "279535e0-fd9b-4979-df25-3ecfdc741f56"
      },
      "source": [
        "df = pd.merge(ScimEn,Energy, how='inner', left_index = True, right_index = True)\n",
        "df=pd.merge(df,GDP,how='inner',left_index=True,right_index=True)\n",
        "df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Rank</th>\n",
              "      <th>Documents</th>\n",
              "      <th>Citable documents</th>\n",
              "      <th>Citations</th>\n",
              "      <th>Self-citations</th>\n",
              "      <th>Citations per document</th>\n",
              "      <th>H index</th>\n",
              "      <th>Energy Supply</th>\n",
              "      <th>Energy Supply per Capita</th>\n",
              "      <th>% Renewable</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>14</td>\n",
              "      <td>8831</td>\n",
              "      <td>8725</td>\n",
              "      <td>90765</td>\n",
              "      <td>15606</td>\n",
              "      <td>10.28</td>\n",
              "      <td>107</td>\n",
              "      <td>5.386000e+09</td>\n",
              "      <td>231.0</td>\n",
              "      <td>11.81081</td>\n",
              "      <td>1.021939e+12</td>\n",
              "      <td>1.060340e+12</td>\n",
              "      <td>1.099644e+12</td>\n",
              "      <td>1.119654e+12</td>\n",
              "      <td>1.142251e+12</td>\n",
              "      <td>1.169431e+12</td>\n",
              "      <td>1.211913e+12</td>\n",
              "      <td>1.241484e+12</td>\n",
              "      <td>1.272520e+12</td>\n",
              "      <td>1.301251e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>15</td>\n",
              "      <td>8668</td>\n",
              "      <td>8596</td>\n",
              "      <td>60702</td>\n",
              "      <td>14396</td>\n",
              "      <td>7.00</td>\n",
              "      <td>86</td>\n",
              "      <td>1.214900e+10</td>\n",
              "      <td>59.0</td>\n",
              "      <td>69.64803</td>\n",
              "      <td>1.845080e+12</td>\n",
              "      <td>1.957118e+12</td>\n",
              "      <td>2.056809e+12</td>\n",
              "      <td>2.054215e+12</td>\n",
              "      <td>2.208872e+12</td>\n",
              "      <td>2.295245e+12</td>\n",
              "      <td>2.339209e+12</td>\n",
              "      <td>2.409740e+12</td>\n",
              "      <td>2.412231e+12</td>\n",
              "      <td>2.319423e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>6</td>\n",
              "      <td>17899</td>\n",
              "      <td>17620</td>\n",
              "      <td>215003</td>\n",
              "      <td>40930</td>\n",
              "      <td>12.01</td>\n",
              "      <td>149</td>\n",
              "      <td>1.043100e+10</td>\n",
              "      <td>296.0</td>\n",
              "      <td>61.94543</td>\n",
              "      <td>1.564469e+12</td>\n",
              "      <td>1.596740e+12</td>\n",
              "      <td>1.612713e+12</td>\n",
              "      <td>1.565145e+12</td>\n",
              "      <td>1.613406e+12</td>\n",
              "      <td>1.664087e+12</td>\n",
              "      <td>1.693133e+12</td>\n",
              "      <td>1.730688e+12</td>\n",
              "      <td>1.773486e+12</td>\n",
              "      <td>1.792609e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>China</th>\n",
              "      <td>1</td>\n",
              "      <td>127050</td>\n",
              "      <td>126767</td>\n",
              "      <td>597237</td>\n",
              "      <td>411683</td>\n",
              "      <td>4.70</td>\n",
              "      <td>138</td>\n",
              "      <td>1.271910e+11</td>\n",
              "      <td>93.0</td>\n",
              "      <td>19.75491</td>\n",
              "      <td>3.992331e+12</td>\n",
              "      <td>4.559041e+12</td>\n",
              "      <td>4.997775e+12</td>\n",
              "      <td>5.459247e+12</td>\n",
              "      <td>6.039659e+12</td>\n",
              "      <td>6.612490e+12</td>\n",
              "      <td>7.124978e+12</td>\n",
              "      <td>7.672448e+12</td>\n",
              "      <td>8.230121e+12</td>\n",
              "      <td>8.797999e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>9</td>\n",
              "      <td>13153</td>\n",
              "      <td>12973</td>\n",
              "      <td>130632</td>\n",
              "      <td>28601</td>\n",
              "      <td>9.93</td>\n",
              "      <td>114</td>\n",
              "      <td>1.059700e+10</td>\n",
              "      <td>166.0</td>\n",
              "      <td>17.02028</td>\n",
              "      <td>2.607840e+12</td>\n",
              "      <td>2.669424e+12</td>\n",
              "      <td>2.674637e+12</td>\n",
              "      <td>2.595967e+12</td>\n",
              "      <td>2.646995e+12</td>\n",
              "      <td>2.702032e+12</td>\n",
              "      <td>2.706968e+12</td>\n",
              "      <td>2.722567e+12</td>\n",
              "      <td>2.729632e+12</td>\n",
              "      <td>2.761185e+12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Rank  Documents  ...          2014          2015\n",
              "Australia    14       8831  ...  1.272520e+12  1.301251e+12\n",
              "Brazil       15       8668  ...  2.412231e+12  2.319423e+12\n",
              "Canada        6      17899  ...  1.773486e+12  1.792609e+12\n",
              "China         1     127050  ...  8.230121e+12  8.797999e+12\n",
              "France        9      13153  ...  2.729632e+12  2.761185e+12\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3n0y2BJCZdJ",
        "outputId": "f68e77f1-49b5-4474-ac84-0a499fa6bc4a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "tazqQhhtCakX",
        "outputId": "b55b6a8d-c51c-4fd4-cf4b-68183b53e050"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Rank</th>\n",
              "      <th>Documents</th>\n",
              "      <th>Citable documents</th>\n",
              "      <th>Citations</th>\n",
              "      <th>Self-citations</th>\n",
              "      <th>Citations per document</th>\n",
              "      <th>H index</th>\n",
              "      <th>Energy Supply</th>\n",
              "      <th>Energy Supply per Capita</th>\n",
              "      <th>% Renewable</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>1.400000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>27703.133333</td>\n",
              "      <td>27394.066667</td>\n",
              "      <td>201776.933333</td>\n",
              "      <td>69698.800000</td>\n",
              "      <td>8.484667</td>\n",
              "      <td>119.466667</td>\n",
              "      <td>2.615287e+10</td>\n",
              "      <td>157.600000</td>\n",
              "      <td>22.824394</td>\n",
              "      <td>2.978152e+12</td>\n",
              "      <td>3.095871e+12</td>\n",
              "      <td>3.141171e+12</td>\n",
              "      <td>3.085834e+12</td>\n",
              "      <td>3.219173e+12</td>\n",
              "      <td>3.313404e+12</td>\n",
              "      <td>3.391038e+12</td>\n",
              "      <td>3.470126e+12</td>\n",
              "      <td>3.562304e+12</td>\n",
              "      <td>3.875826e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.472136</td>\n",
              "      <td>35131.532962</td>\n",
              "      <td>34859.707837</td>\n",
              "      <td>210915.794366</td>\n",
              "      <td>113291.694884</td>\n",
              "      <td>2.815531</td>\n",
              "      <td>39.631998</td>\n",
              "      <td>3.533846e+10</td>\n",
              "      <td>78.960026</td>\n",
              "      <td>19.821536</td>\n",
              "      <td>3.525351e+12</td>\n",
              "      <td>3.589045e+12</td>\n",
              "      <td>3.580506e+12</td>\n",
              "      <td>3.492797e+12</td>\n",
              "      <td>3.602331e+12</td>\n",
              "      <td>3.677913e+12</td>\n",
              "      <td>3.788807e+12</td>\n",
              "      <td>3.876953e+12</td>\n",
              "      <td>3.996887e+12</td>\n",
              "      <td>4.185453e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8668.000000</td>\n",
              "      <td>8596.000000</td>\n",
              "      <td>34266.000000</td>\n",
              "      <td>12422.000000</td>\n",
              "      <td>1.850000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>4.923000e+09</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>2.279353</td>\n",
              "      <td>3.895523e+11</td>\n",
              "      <td>4.250646e+11</td>\n",
              "      <td>4.289909e+11</td>\n",
              "      <td>4.389208e+11</td>\n",
              "      <td>4.677902e+11</td>\n",
              "      <td>4.853309e+11</td>\n",
              "      <td>4.532569e+11</td>\n",
              "      <td>4.445926e+11</td>\n",
              "      <td>4.639027e+11</td>\n",
              "      <td>1.266580e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.500000</td>\n",
              "      <td>10196.000000</td>\n",
              "      <td>10062.000000</td>\n",
              "      <td>101307.500000</td>\n",
              "      <td>20860.000000</td>\n",
              "      <td>7.155000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>8.546000e+09</td>\n",
              "      <td>107.500000</td>\n",
              "      <td>11.085725</td>\n",
              "      <td>1.325843e+12</td>\n",
              "      <td>1.421505e+12</td>\n",
              "      <td>1.456445e+12</td>\n",
              "      <td>1.445337e+12</td>\n",
              "      <td>1.478295e+12</td>\n",
              "      <td>1.503649e+12</td>\n",
              "      <td>1.513046e+12</td>\n",
              "      <td>1.512037e+12</td>\n",
              "      <td>1.527157e+12</td>\n",
              "      <td>1.660264e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>15005.000000</td>\n",
              "      <td>14841.000000</td>\n",
              "      <td>128763.000000</td>\n",
              "      <td>27426.000000</td>\n",
              "      <td>8.580000</td>\n",
              "      <td>115.000000</td>\n",
              "      <td>1.100700e+10</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>17.020280</td>\n",
              "      <td>1.845080e+12</td>\n",
              "      <td>1.957118e+12</td>\n",
              "      <td>2.056809e+12</td>\n",
              "      <td>2.054215e+12</td>\n",
              "      <td>2.125185e+12</td>\n",
              "      <td>2.137439e+12</td>\n",
              "      <td>2.077184e+12</td>\n",
              "      <td>2.051982e+12</td>\n",
              "      <td>2.200617e+12</td>\n",
              "      <td>2.343315e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.500000</td>\n",
              "      <td>19739.000000</td>\n",
              "      <td>19329.000000</td>\n",
              "      <td>210547.000000</td>\n",
              "      <td>39402.000000</td>\n",
              "      <td>10.065000</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>2.484650e+10</td>\n",
              "      <td>217.500000</td>\n",
              "      <td>26.711070</td>\n",
              "      <td>2.970365e+12</td>\n",
              "      <td>3.055492e+12</td>\n",
              "      <td>3.076723e+12</td>\n",
              "      <td>2.939653e+12</td>\n",
              "      <td>3.032146e+12</td>\n",
              "      <td>3.122201e+12</td>\n",
              "      <td>3.131846e+12</td>\n",
              "      <td>3.144942e+12</td>\n",
              "      <td>3.177009e+12</td>\n",
              "      <td>3.454463e+12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>127050.000000</td>\n",
              "      <td>126767.000000</td>\n",
              "      <td>792274.000000</td>\n",
              "      <td>411683.000000</td>\n",
              "      <td>13.080000</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>1.271910e+11</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>69.648030</td>\n",
              "      <td>1.479230e+13</td>\n",
              "      <td>1.505540e+13</td>\n",
              "      <td>1.501149e+13</td>\n",
              "      <td>1.459484e+13</td>\n",
              "      <td>1.496437e+13</td>\n",
              "      <td>1.520402e+13</td>\n",
              "      <td>1.554216e+13</td>\n",
              "      <td>1.577367e+13</td>\n",
              "      <td>1.615662e+13</td>\n",
              "      <td>1.654857e+13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Rank      Documents  ...          2014          2015\n",
              "count  15.000000      15.000000  ...  1.500000e+01  1.400000e+01\n",
              "mean    8.000000   27703.133333  ...  3.562304e+12  3.875826e+12\n",
              "std     4.472136   35131.532962  ...  3.996887e+12  4.185453e+12\n",
              "min     1.000000    8668.000000  ...  4.639027e+11  1.266580e+12\n",
              "25%     4.500000   10196.000000  ...  1.527157e+12  1.660264e+12\n",
              "50%     8.000000   15005.000000  ...  2.200617e+12  2.343315e+12\n",
              "75%    11.500000   19739.000000  ...  3.177009e+12  3.454463e+12\n",
              "max    15.000000  127050.000000  ...  1.615662e+13  1.654857e+13\n",
              "\n",
              "[8 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xj86UpXVEy7m"
      },
      "source": [
        "\n",
        "Here we can extract valuable information for an analysis, for example:\n",
        "* We have the top 15 countries ranked for their journal contributions in Energy Engineering and Power Technology. These countries are a reference for the world in research and technological advancement in this area.\n",
        "* Of these countries, the average per capita Energy Supply is 157.6 gigajoules where on average 22.8% is renewable. The highest value of renewable energy is 69.64% "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Cc7bRXRCt-b",
        "outputId": "0fc2bb90-db04-4f69-c4dc-20c3b6b10641"
      },
      "source": [
        "# What country has the maximum % Renewable and what is the percentage?\n",
        "max_renewable=df['% Renewable'].max()\n",
        "(df[df['% Renewable']==max_renewable].index[0], max_renewable)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Brazil', 69.64803)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bNIDD69FYyL",
        "outputId": "70b91000-f44b-4a90-93cb-901e8f70967a"
      },
      "source": [
        "# What country has the minimum % Renewable and what is the percentage?\n",
        "min_renewable=df['% Renewable'].min()\n",
        "(df[df['% Renewable']==min_renewable].index[0], min_renewable)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('South Korea', 2.279353)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Yhxqvr6GPEx"
      },
      "source": [
        "With an investigation in [Our world indata](https://ourworldindata.org) we see that a large percentage of energy in South Korea comes from Oil, Coal and gas:\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3tw8RnmHdYL"
      },
      "source": [
        "On the other hand, clearly Brazil produces energy from these sources but hydroelectric energy occupies a large percentage (along with other renewable energies). **Both data come from Energy Consumption by source**\n",
        "\n",
        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)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "ixDr06mFFz9i",
        "outputId": "ad4f020f-51fa-4f9e-f18e-3ceff71bb9b9"
      },
      "source": [
        "# We are going to add some columns that would be interesting to have in our df \n",
        "# Let's do some simple calculations \n",
        "\n",
        "# Population by country estimates using energy supply and energy per capita:\n",
        "df['Population'] = df['Energy Supply'] / df['Energy Supply per Capita']\n",
        "df.sort_values(by='Population',ascending=False)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Rank</th>\n",
              "      <th>Documents</th>\n",
              "      <th>Citable documents</th>\n",
              "      <th>Citations</th>\n",
              "      <th>Self-citations</th>\n",
              "      <th>Citations per document</th>\n",
              "      <th>H index</th>\n",
              "      <th>Energy Supply</th>\n",
              "      <th>Energy Supply per Capita</th>\n",
              "      <th>% Renewable</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>Population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>China</th>\n",
              "      <td>1</td>\n",
              "      <td>127050</td>\n",
              "      <td>126767</td>\n",
              "      <td>597237</td>\n",
              "      <td>411683</td>\n",
              "      <td>4.70</td>\n",
              "      <td>138</td>\n",
              "      <td>1.271910e+11</td>\n",
              "      <td>93.0</td>\n",
              "      <td>19.754910</td>\n",
              "      <td>3.992331e+12</td>\n",
              "      <td>4.559041e+12</td>\n",
              "      <td>4.997775e+12</td>\n",
              "      <td>5.459247e+12</td>\n",
              "      <td>6.039659e+12</td>\n",
              "      <td>6.612490e+12</td>\n",
              "      <td>7.124978e+12</td>\n",
              "      <td>7.672448e+12</td>\n",
              "      <td>8.230121e+12</td>\n",
              "      <td>8.797999e+12</td>\n",
              "      <td>1.367645e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India</th>\n",
              "      <td>8</td>\n",
              "      <td>15005</td>\n",
              "      <td>14841</td>\n",
              "      <td>128763</td>\n",
              "      <td>37209</td>\n",
              "      <td>8.58</td>\n",
              "      <td>115</td>\n",
              "      <td>3.319500e+10</td>\n",
              "      <td>26.0</td>\n",
              "      <td>14.969080</td>\n",
              "      <td>1.265894e+12</td>\n",
              "      <td>1.374865e+12</td>\n",
              "      <td>1.428361e+12</td>\n",
              "      <td>1.549483e+12</td>\n",
              "      <td>1.708459e+12</td>\n",
              "      <td>1.821872e+12</td>\n",
              "      <td>1.924235e+12</td>\n",
              "      <td>2.051982e+12</td>\n",
              "      <td>2.200617e+12</td>\n",
              "      <td>2.367206e+12</td>\n",
              "      <td>1.276731e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United States</th>\n",
              "      <td>2</td>\n",
              "      <td>96661</td>\n",
              "      <td>94747</td>\n",
              "      <td>792274</td>\n",
              "      <td>265436</td>\n",
              "      <td>8.20</td>\n",
              "      <td>230</td>\n",
              "      <td>9.083800e+10</td>\n",
              "      <td>286.0</td>\n",
              "      <td>11.570980</td>\n",
              "      <td>1.479230e+13</td>\n",
              "      <td>1.505540e+13</td>\n",
              "      <td>1.501149e+13</td>\n",
              "      <td>1.459484e+13</td>\n",
              "      <td>1.496437e+13</td>\n",
              "      <td>1.520402e+13</td>\n",
              "      <td>1.554216e+13</td>\n",
              "      <td>1.577367e+13</td>\n",
              "      <td>1.615662e+13</td>\n",
              "      <td>1.654857e+13</td>\n",
              "      <td>3.176154e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>15</td>\n",
              "      <td>8668</td>\n",
              "      <td>8596</td>\n",
              "      <td>60702</td>\n",
              "      <td>14396</td>\n",
              "      <td>7.00</td>\n",
              "      <td>86</td>\n",
              "      <td>1.214900e+10</td>\n",
              "      <td>59.0</td>\n",
              "      <td>69.648030</td>\n",
              "      <td>1.845080e+12</td>\n",
              "      <td>1.957118e+12</td>\n",
              "      <td>2.056809e+12</td>\n",
              "      <td>2.054215e+12</td>\n",
              "      <td>2.208872e+12</td>\n",
              "      <td>2.295245e+12</td>\n",
              "      <td>2.339209e+12</td>\n",
              "      <td>2.409740e+12</td>\n",
              "      <td>2.412231e+12</td>\n",
              "      <td>2.319423e+12</td>\n",
              "      <td>2.059153e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Russian Federation</th>\n",
              "      <td>5</td>\n",
              "      <td>18534</td>\n",
              "      <td>18301</td>\n",
              "      <td>34266</td>\n",
              "      <td>12422</td>\n",
              "      <td>1.85</td>\n",
              "      <td>57</td>\n",
              "      <td>3.070900e+10</td>\n",
              "      <td>214.0</td>\n",
              "      <td>17.288680</td>\n",
              "      <td>1.385793e+12</td>\n",
              "      <td>1.504071e+12</td>\n",
              "      <td>1.583004e+12</td>\n",
              "      <td>1.459199e+12</td>\n",
              "      <td>1.524917e+12</td>\n",
              "      <td>1.589943e+12</td>\n",
              "      <td>1.645876e+12</td>\n",
              "      <td>1.666934e+12</td>\n",
              "      <td>1.678709e+12</td>\n",
              "      <td>1.616149e+12</td>\n",
              "      <td>1.435000e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>3</td>\n",
              "      <td>30504</td>\n",
              "      <td>30287</td>\n",
              "      <td>223024</td>\n",
              "      <td>61554</td>\n",
              "      <td>7.31</td>\n",
              "      <td>134</td>\n",
              "      <td>1.898400e+10</td>\n",
              "      <td>149.0</td>\n",
              "      <td>10.232820</td>\n",
              "      <td>5.496542e+12</td>\n",
              "      <td>5.617036e+12</td>\n",
              "      <td>5.558527e+12</td>\n",
              "      <td>5.251308e+12</td>\n",
              "      <td>5.498718e+12</td>\n",
              "      <td>5.473738e+12</td>\n",
              "      <td>5.569102e+12</td>\n",
              "      <td>5.644659e+12</td>\n",
              "      <td>5.642884e+12</td>\n",
              "      <td>5.669563e+12</td>\n",
              "      <td>1.274094e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>7</td>\n",
              "      <td>17027</td>\n",
              "      <td>16831</td>\n",
              "      <td>140566</td>\n",
              "      <td>27426</td>\n",
              "      <td>8.26</td>\n",
              "      <td>126</td>\n",
              "      <td>1.326100e+10</td>\n",
              "      <td>165.0</td>\n",
              "      <td>17.901530</td>\n",
              "      <td>3.332891e+12</td>\n",
              "      <td>3.441561e+12</td>\n",
              "      <td>3.478809e+12</td>\n",
              "      <td>3.283340e+12</td>\n",
              "      <td>3.417298e+12</td>\n",
              "      <td>3.542371e+12</td>\n",
              "      <td>3.556724e+12</td>\n",
              "      <td>3.567317e+12</td>\n",
              "      <td>3.624386e+12</td>\n",
              "      <td>3.685556e+12</td>\n",
              "      <td>8.036970e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iran</th>\n",
              "      <td>13</td>\n",
              "      <td>8896</td>\n",
              "      <td>8819</td>\n",
              "      <td>57470</td>\n",
              "      <td>19125</td>\n",
              "      <td>6.46</td>\n",
              "      <td>72</td>\n",
              "      <td>9.172000e+09</td>\n",
              "      <td>119.0</td>\n",
              "      <td>5.707721</td>\n",
              "      <td>3.895523e+11</td>\n",
              "      <td>4.250646e+11</td>\n",
              "      <td>4.289909e+11</td>\n",
              "      <td>4.389208e+11</td>\n",
              "      <td>4.677902e+11</td>\n",
              "      <td>4.853309e+11</td>\n",
              "      <td>4.532569e+11</td>\n",
              "      <td>4.445926e+11</td>\n",
              "      <td>4.639027e+11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.707563e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>4</td>\n",
              "      <td>20944</td>\n",
              "      <td>20357</td>\n",
              "      <td>206091</td>\n",
              "      <td>37874</td>\n",
              "      <td>9.84</td>\n",
              "      <td>139</td>\n",
              "      <td>7.920000e+09</td>\n",
              "      <td>124.0</td>\n",
              "      <td>10.600470</td>\n",
              "      <td>2.419631e+12</td>\n",
              "      <td>2.482203e+12</td>\n",
              "      <td>2.470614e+12</td>\n",
              "      <td>2.367048e+12</td>\n",
              "      <td>2.403504e+12</td>\n",
              "      <td>2.450911e+12</td>\n",
              "      <td>2.479809e+12</td>\n",
              "      <td>2.533370e+12</td>\n",
              "      <td>2.605643e+12</td>\n",
              "      <td>2.666333e+12</td>\n",
              "      <td>6.387097e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>9</td>\n",
              "      <td>13153</td>\n",
              "      <td>12973</td>\n",
              "      <td>130632</td>\n",
              "      <td>28601</td>\n",
              "      <td>9.93</td>\n",
              "      <td>114</td>\n",
              "      <td>1.059700e+10</td>\n",
              "      <td>166.0</td>\n",
              "      <td>17.020280</td>\n",
              "      <td>2.607840e+12</td>\n",
              "      <td>2.669424e+12</td>\n",
              "      <td>2.674637e+12</td>\n",
              "      <td>2.595967e+12</td>\n",
              "      <td>2.646995e+12</td>\n",
              "      <td>2.702032e+12</td>\n",
              "      <td>2.706968e+12</td>\n",
              "      <td>2.722567e+12</td>\n",
              "      <td>2.729632e+12</td>\n",
              "      <td>2.761185e+12</td>\n",
              "      <td>6.383735e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>11</td>\n",
              "      <td>10964</td>\n",
              "      <td>10794</td>\n",
              "      <td>111850</td>\n",
              "      <td>26661</td>\n",
              "      <td>10.20</td>\n",
              "      <td>106</td>\n",
              "      <td>6.530000e+09</td>\n",
              "      <td>109.0</td>\n",
              "      <td>33.667230</td>\n",
              "      <td>2.202170e+12</td>\n",
              "      <td>2.234627e+12</td>\n",
              "      <td>2.211154e+12</td>\n",
              "      <td>2.089938e+12</td>\n",
              "      <td>2.125185e+12</td>\n",
              "      <td>2.137439e+12</td>\n",
              "      <td>2.077184e+12</td>\n",
              "      <td>2.040871e+12</td>\n",
              "      <td>2.033868e+12</td>\n",
              "      <td>2.049316e+12</td>\n",
              "      <td>5.990826e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Korea</th>\n",
              "      <td>10</td>\n",
              "      <td>11983</td>\n",
              "      <td>11923</td>\n",
              "      <td>114675</td>\n",
              "      <td>22595</td>\n",
              "      <td>9.57</td>\n",
              "      <td>104</td>\n",
              "      <td>1.100700e+10</td>\n",
              "      <td>221.0</td>\n",
              "      <td>2.279353</td>\n",
              "      <td>9.410199e+11</td>\n",
              "      <td>9.924316e+11</td>\n",
              "      <td>1.020510e+12</td>\n",
              "      <td>1.027730e+12</td>\n",
              "      <td>1.094499e+12</td>\n",
              "      <td>1.134796e+12</td>\n",
              "      <td>1.160809e+12</td>\n",
              "      <td>1.194429e+12</td>\n",
              "      <td>1.234340e+12</td>\n",
              "      <td>1.266580e+12</td>\n",
              "      <td>4.980543e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>12</td>\n",
              "      <td>9428</td>\n",
              "      <td>9330</td>\n",
              "      <td>123336</td>\n",
              "      <td>23964</td>\n",
              "      <td>13.08</td>\n",
              "      <td>115</td>\n",
              "      <td>4.923000e+09</td>\n",
              "      <td>106.0</td>\n",
              "      <td>37.968590</td>\n",
              "      <td>1.414823e+12</td>\n",
              "      <td>1.468146e+12</td>\n",
              "      <td>1.484530e+12</td>\n",
              "      <td>1.431475e+12</td>\n",
              "      <td>1.431673e+12</td>\n",
              "      <td>1.417355e+12</td>\n",
              "      <td>1.380216e+12</td>\n",
              "      <td>1.357139e+12</td>\n",
              "      <td>1.375605e+12</td>\n",
              "      <td>1.419821e+12</td>\n",
              "      <td>4.644340e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>6</td>\n",
              "      <td>17899</td>\n",
              "      <td>17620</td>\n",
              "      <td>215003</td>\n",
              "      <td>40930</td>\n",
              "      <td>12.01</td>\n",
              "      <td>149</td>\n",
              "      <td>1.043100e+10</td>\n",
              "      <td>296.0</td>\n",
              "      <td>61.945430</td>\n",
              "      <td>1.564469e+12</td>\n",
              "      <td>1.596740e+12</td>\n",
              "      <td>1.612713e+12</td>\n",
              "      <td>1.565145e+12</td>\n",
              "      <td>1.613406e+12</td>\n",
              "      <td>1.664087e+12</td>\n",
              "      <td>1.693133e+12</td>\n",
              "      <td>1.730688e+12</td>\n",
              "      <td>1.773486e+12</td>\n",
              "      <td>1.792609e+12</td>\n",
              "      <td>3.523986e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>14</td>\n",
              "      <td>8831</td>\n",
              "      <td>8725</td>\n",
              "      <td>90765</td>\n",
              "      <td>15606</td>\n",
              "      <td>10.28</td>\n",
              "      <td>107</td>\n",
              "      <td>5.386000e+09</td>\n",
              "      <td>231.0</td>\n",
              "      <td>11.810810</td>\n",
              "      <td>1.021939e+12</td>\n",
              "      <td>1.060340e+12</td>\n",
              "      <td>1.099644e+12</td>\n",
              "      <td>1.119654e+12</td>\n",
              "      <td>1.142251e+12</td>\n",
              "      <td>1.169431e+12</td>\n",
              "      <td>1.211913e+12</td>\n",
              "      <td>1.241484e+12</td>\n",
              "      <td>1.272520e+12</td>\n",
              "      <td>1.301251e+12</td>\n",
              "      <td>2.331602e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Rank  Documents  ...          2015    Population\n",
              "China                  1     127050  ...  8.797999e+12  1.367645e+09\n",
              "India                  8      15005  ...  2.367206e+12  1.276731e+09\n",
              "United States          2      96661  ...  1.654857e+13  3.176154e+08\n",
              "Brazil                15       8668  ...  2.319423e+12  2.059153e+08\n",
              "Russian Federation     5      18534  ...  1.616149e+12  1.435000e+08\n",
              "Japan                  3      30504  ...  5.669563e+12  1.274094e+08\n",
              "Germany                7      17027  ...  3.685556e+12  8.036970e+07\n",
              "Iran                  13       8896  ...           NaN  7.707563e+07\n",
              "United Kingdom         4      20944  ...  2.666333e+12  6.387097e+07\n",
              "France                 9      13153  ...  2.761185e+12  6.383735e+07\n",
              "Italy                 11      10964  ...  2.049316e+12  5.990826e+07\n",
              "South Korea           10      11983  ...  1.266580e+12  4.980543e+07\n",
              "Spain                 12       9428  ...  1.419821e+12  4.644340e+07\n",
              "Canada                 6      17899  ...  1.792609e+12  3.523986e+07\n",
              "Australia             14       8831  ...  1.301251e+12  2.331602e+07\n",
              "\n",
              "[15 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EE9iGM-yKgix"
      },
      "source": [
        "Clearly, if a country produces more energy than that consumed per capita, that is, it has greater energy capacity than demanded, in our estimate it will have a \"larger population.\" We see that China, India, the United States and Brazil are ours the countries with the largest population "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZUV7-uJKew8",
        "outputId": "096b7e05-2235-41ce-87d9-c32243b4f36a"
      },
      "source": [
        "# Let's see which country has the highest ratio of self citations to total citations \n",
        "df['Self/total Citations ratio']=df['Self-citations']/(df['Citations'])\n",
        "max_ratio=df['Self/total Citations ratio'].max()\n",
        "country_name=df[df['Self/total Citations ratio'] == max_ratio].index[0]\n",
        "(country_name, max_ratio)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('China', 0.6893126179389422)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "IWP8LZmUMBoS",
        "outputId": "f70f2688-9d3b-443a-b23d-3a021417b69d"
      },
      "source": [
        "# Let's see the behavior by continent. We will group the countries according to their continents \n",
        "ContinentDict  = {'China':'Asia', \n",
        "              'United States':'North America', \n",
        "              'Japan':'Asia', \n",
        "              'United Kingdom':'Europe', \n",
        "              'Russian Federation':'Europe', \n",
        "              'Canada':'North America', \n",
        "              'Germany':'Europe', \n",
        "              'India':'Asia',\n",
        "              'France':'Europe', \n",
        "              'South Korea':'Asia', \n",
        "              'Italy':'Europe', \n",
        "              'Spain':'Europe', \n",
        "              'Iran':'Asia',\n",
        "              'Australia':'Australia', \n",
        "              'Brazil':'South America'}\n",
        "df['Continent']=df.index.map(ContinentDict)\n",
        "\n",
        "new_df = pd.DataFrame({'size': df.groupby('Continent').count()['Rank'],\n",
        "                    'sum': df.groupby('Continent').sum()['Population'],\n",
        "                    'mean': df.groupby('Continent').mean()['Population'],\n",
        "                    'std': df.groupby('Continent').std()['Population']})\n",
        "new_df"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>size</th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Continent</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Asia</th>\n",
              "      <td>5</td>\n",
              "      <td>2.898666e+09</td>\n",
              "      <td>5.797333e+08</td>\n",
              "      <td>6.790979e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>1</td>\n",
              "      <td>2.331602e+07</td>\n",
              "      <td>2.331602e+07</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>6</td>\n",
              "      <td>4.579297e+08</td>\n",
              "      <td>7.632161e+07</td>\n",
              "      <td>3.464767e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>North America</th>\n",
              "      <td>2</td>\n",
              "      <td>3.528552e+08</td>\n",
              "      <td>1.764276e+08</td>\n",
              "      <td>1.996696e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South America</th>\n",
              "      <td>1</td>\n",
              "      <td>2.059153e+08</td>\n",
              "      <td>2.059153e+08</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               size           sum          mean           std\n",
              "Continent                                                    \n",
              "Asia              5  2.898666e+09  5.797333e+08  6.790979e+08\n",
              "Australia         1  2.331602e+07  2.331602e+07           NaN\n",
              "Europe            6  4.579297e+08  7.632161e+07  3.464767e+07\n",
              "North America     2  3.528552e+08  1.764276e+08  1.996696e+08\n",
              "South America     1  2.059153e+08  2.059153e+08           NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LE1DrXyoNE19"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}