Trabajo_Mundial_Info.ipynb

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  "metadata": {
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "***Taller de Pandas con Python***"
      ],
      "metadata": {
        "id": "pSse-z36lKbp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dCWDVC6OkeFp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/apdaza/ejemplos_colab.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7IwLg7qqlURU",
        "outputId": "75491a64-8f17-483f-84fc-3f02925ae504"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ejemplos_colab'...\n",
            "remote: Enumerating objects: 10, done.\u001b[K\n",
            "remote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (9/9), done.\u001b[K\n",
            "remote: Total 10 (delta 2), reused 3 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (10/10), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info = pd.read_csv(\"/content/ejemplos_colab/indicadores-mundiales_v2.csv\")"
      ],
      "metadata": {
        "id": "hEqOY8XZktal"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yAYLfYJ-qnYR",
        "outputId": "4e8e7941-23b7-4f44-a907-91e09ee887df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDlfFxjxqrvI",
        "outputId": "615cf20e-62b4-4a08-9024-ca7d7298f30d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyYMm8Dnqzdp",
        "outputId": "15ae876d-1201-42e3-c23a-8f55d547f336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Ano', 'Dias para iniciar un negocio', 'Emisiones de CO2',\n",
              "       'Esperanza de vida (mujeres)', 'Esperanza de vida (varones)',\n",
              "       'Facilidad para hacer negocios', 'Gasto en salud ( del PIB)',\n",
              "       'Gasto en salud (per capita)', 'Horas preparar los impuestos',\n",
              "       'Interes de prestamos', 'PIB', 'Pais', 'Poblacion 0-14',\n",
              "       'Poblacion 15-64', 'Poblacion 65+', 'Poblacion total',\n",
              "       'Poblacion urbana', 'Region', 'Tasa de mortalidad infantil',\n",
              "       'Tasa de natalidad', 'Tasa tributaria empresarial', 'Turismo emisor',\n",
              "       'Turismo receptivo', 'Uso de Internet', 'Uso de energia',\n",
              "       'Uso de telefonos celulares'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted(info.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6iD-0nUqq2UR",
        "outputId": "4ce17bc8-2fcb-4fe4-dab5-b97d823a5f64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Ano',\n",
              " 'Dias para iniciar un negocio',\n",
              " 'Emisiones de CO2',\n",
              " 'Esperanza de vida (mujeres)',\n",
              " 'Esperanza de vida (varones)',\n",
              " 'Facilidad para hacer negocios',\n",
              " 'Gasto en salud ( del PIB)',\n",
              " 'Gasto en salud (per capita)',\n",
              " 'Horas preparar los impuestos',\n",
              " 'Interes de prestamos',\n",
              " 'PIB',\n",
              " 'Pais',\n",
              " 'Poblacion 0-14',\n",
              " 'Poblacion 15-64',\n",
              " 'Poblacion 65+',\n",
              " 'Poblacion total',\n",
              " 'Poblacion urbana',\n",
              " 'Region',\n",
              " 'Tasa de mortalidad infantil',\n",
              " 'Tasa de natalidad',\n",
              " 'Tasa tributaria empresarial',\n",
              " 'Turismo emisor',\n",
              " 'Turismo receptivo',\n",
              " 'Uso de Internet',\n",
              " 'Uso de energia',\n",
              " 'Uso de telefonos celulares']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQuzE3G3q8pS",
        "outputId": "663b79eb-3a7c-4364-d4ac-d619c5053b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=1000, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFZ5YxP7rAaV",
        "outputId": "ddfa16b6-b842-4644-f7aa-318b0238143d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([['2000-12-01', nan, 87931.0, ..., 0.0, 26998.0, 0.0],\n",
              "       ['2000-12-01', nan, 9542.0, ..., 0.0, 7499.0, 0.0],\n",
              "       ['2000-12-01', nan, 1617.0, ..., 0.0, 1983.0, 0.0],\n",
              "       ...,\n",
              "       ['2004-12-01', 58.0, 410744.0, ..., 0.1, 159324.0, 0.4],\n",
              "       ['2004-12-01', 39.0, 4426.0, ..., 0.0, 2821.0, 0.1],\n",
              "       ['2004-12-01', 18.0, 5805.0, ..., 0.1, 2613.0, 0.4]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "U2v1l1co2Xrb",
        "outputId": "28532900-07c3-4b85-c619-25450fdf9b13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Dias para iniciar un negocio  Emisiones de CO2  \\\n",
              "count                    267.000000      9.220000e+02   \n",
              "mean                      49.528090      1.331001e+05   \n",
              "std                       38.162717      5.473218e+05   \n",
              "min                        3.000000      1.500000e+01   \n",
              "25%                       25.000000      1.283500e+03   \n",
              "50%                       39.000000      7.565000e+03   \n",
              "75%                       62.000000      5.687250e+04   \n",
              "max                      202.000000      5.790765e+06   \n",
              "\n",
              "       Esperanza de vida (mujeres)  Esperanza de vida (varones)  \\\n",
              "count                   950.000000                   950.000000   \n",
              "mean                     69.748421                    65.020000   \n",
              "std                      11.204060                     9.748395   \n",
              "min                      39.000000                    37.000000   \n",
              "25%                      62.000000                    59.000000   \n",
              "50%                      74.000000                    68.000000   \n",
              "75%                      78.000000                    73.000000   \n",
              "max                      86.000000                    80.000000   \n",
              "\n",
              "       Facilidad para hacer negocios  Gasto en salud ( del PIB)  \\\n",
              "count                            0.0                 884.000000   \n",
              "mean                             NaN                   6.125566   \n",
              "std                              NaN                   2.449476   \n",
              "min                              NaN                   0.800000   \n",
              "25%                              NaN                   4.500000   \n",
              "50%                              NaN                   5.800000   \n",
              "75%                              NaN                   7.500000   \n",
              "max                              NaN                  22.500000   \n",
              "\n",
              "       Gasto en salud (per capita)  Horas preparar los impuestos  \\\n",
              "count                   884.000000                           0.0   \n",
              "mean                    553.334842                           NaN   \n",
              "std                    1041.309561                           NaN   \n",
              "min                       2.000000                           NaN   \n",
              "25%                      27.000000                           NaN   \n",
              "50%                     119.500000                           NaN   \n",
              "75%                     408.500000                           NaN   \n",
              "max                    6355.000000                           NaN   \n",
              "\n",
              "       Interes de prestamos           PIB  ...  Poblacion total  \\\n",
              "count            725.000000  9.320000e+02  ...     1.000000e+03   \n",
              "mean              16.794621  1.898312e+11  ...     3.086253e+07   \n",
              "std               16.442455  9.158183e+11  ...     1.218139e+08   \n",
              "min                1.800000  6.310127e+07  ...     1.887600e+04   \n",
              "25%                8.400000  2.201272e+09  ...     9.408075e+05   \n",
              "50%               13.300000  8.894314e+09  ...     5.465395e+06   \n",
              "75%               19.700000  6.670608e+10  ...     1.896108e+07   \n",
              "max              278.900000  1.227700e+13  ...     1.296075e+09   \n",
              "\n",
              "       Poblacion urbana  Tasa de mortalidad infantil  Tasa de natalidad  \\\n",
              "count        991.000000                   906.000000         948.000000   \n",
              "mean          54.935318                     3.802980           2.358228   \n",
              "std           24.575174                     3.347618           1.196830   \n",
              "min            8.200000                     0.200000           0.700000   \n",
              "25%           34.350000                     1.000000           1.300000   \n",
              "50%           54.900000                     2.500000           2.100000   \n",
              "75%           75.150000                     6.200000           3.200000   \n",
              "max          100.000000                    14.100000           5.300000   \n",
              "\n",
              "       Tasa tributaria empresarial  Turismo emisor  Turismo receptivo  \\\n",
              "count                          0.0    8.080000e+02       8.690000e+02   \n",
              "mean                           NaN    3.479308e+09       3.475463e+09   \n",
              "std                            NaN    1.061927e+10       1.044699e+10   \n",
              "min                            NaN    2.000000e+05       8.000000e+05   \n",
              "25%                            NaN    8.000000e+07       8.400000e+07   \n",
              "50%                            NaN    3.015000e+08       4.310000e+08   \n",
              "75%                            NaN    1.881750e+09       2.390000e+09   \n",
              "max                            NaN    9.476400e+10       1.209120e+11   \n",
              "\n",
              "       Uso de Internet  Uso de energia  Uso de telefonos celulares  \n",
              "count       919.000000    6.740000e+02                  928.000000  \n",
              "mean          0.133297    7.365907e+04                    0.264224  \n",
              "std           0.191943    2.397725e+05                    0.299644  \n",
              "min           0.000000    2.800000e+01                    0.000000  \n",
              "25%           0.000000    3.841500e+03                    0.000000  \n",
              "50%           0.000000    1.406250e+04                    0.100000  \n",
              "75%           0.200000    4.048100e+04                    0.500000  \n",
              "max           0.800000    2.307819e+06                    1.200000  \n",
              "\n",
              "[8 rows x 23 columns]"
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              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dias para iniciar un negocio</th>\n",
              "      <th>Emisiones de CO2</th>\n",
              "      <th>Esperanza de vida (mujeres)</th>\n",
              "      <th>Esperanza de vida (varones)</th>\n",
              "      <th>Facilidad para hacer negocios</th>\n",
              "      <th>Gasto en salud ( del PIB)</th>\n",
              "      <th>Gasto en salud (per capita)</th>\n",
              "      <th>Horas preparar los impuestos</th>\n",
              "      <th>Interes de prestamos</th>\n",
              "      <th>PIB</th>\n",
              "      <th>...</th>\n",
              "      <th>Poblacion total</th>\n",
              "      <th>Poblacion urbana</th>\n",
              "      <th>Tasa de mortalidad infantil</th>\n",
              "      <th>Tasa de natalidad</th>\n",
              "      <th>Tasa tributaria empresarial</th>\n",
              "      <th>Turismo emisor</th>\n",
              "      <th>Turismo receptivo</th>\n",
              "      <th>Uso de Internet</th>\n",
              "      <th>Uso de energia</th>\n",
              "      <th>Uso de telefonos celulares</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>267.000000</td>\n",
              "      <td>9.220000e+02</td>\n",
              "      <td>950.000000</td>\n",
              "      <td>950.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>884.000000</td>\n",
              "      <td>884.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>725.000000</td>\n",
              "      <td>9.320000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>991.000000</td>\n",
              "      <td>906.000000</td>\n",
              "      <td>948.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.080000e+02</td>\n",
              "      <td>8.690000e+02</td>\n",
              "      <td>919.000000</td>\n",
              "      <td>6.740000e+02</td>\n",
              "      <td>928.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>49.528090</td>\n",
              "      <td>1.331001e+05</td>\n",
              "      <td>69.748421</td>\n",
              "      <td>65.020000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.125566</td>\n",
              "      <td>553.334842</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.794621</td>\n",
              "      <td>1.898312e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>3.086253e+07</td>\n",
              "      <td>54.935318</td>\n",
              "      <td>3.802980</td>\n",
              "      <td>2.358228</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.479308e+09</td>\n",
              "      <td>3.475463e+09</td>\n",
              "      <td>0.133297</td>\n",
              "      <td>7.365907e+04</td>\n",
              "      <td>0.264224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>38.162717</td>\n",
              "      <td>5.473218e+05</td>\n",
              "      <td>11.204060</td>\n",
              "      <td>9.748395</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.449476</td>\n",
              "      <td>1041.309561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.442455</td>\n",
              "      <td>9.158183e+11</td>\n",
              "      <td>...</td>\n",
              "      <td>1.218139e+08</td>\n",
              "      <td>24.575174</td>\n",
              "      <td>3.347618</td>\n",
              "      <td>1.196830</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.061927e+10</td>\n",
              "      <td>1.044699e+10</td>\n",
              "      <td>0.191943</td>\n",
              "      <td>2.397725e+05</td>\n",
              "      <td>0.299644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>6.310127e+07</td>\n",
              "      <td>...</td>\n",
              "      <td>1.887600e+04</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000e+05</td>\n",
              "      <td>8.000000e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.800000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.283500e+03</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.400000</td>\n",
              "      <td>2.201272e+09</td>\n",
              "      <td>...</td>\n",
              "      <td>9.408075e+05</td>\n",
              "      <td>34.350000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000e+07</td>\n",
              "      <td>8.400000e+07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.841500e+03</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>7.565000e+03</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>119.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.300000</td>\n",
              "      <td>8.894314e+09</td>\n",
              "      <td>...</td>\n",
              "      <td>5.465395e+06</td>\n",
              "      <td>54.900000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.015000e+08</td>\n",
              "      <td>4.310000e+08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.406250e+04</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>5.687250e+04</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>408.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.700000</td>\n",
              "      <td>6.670608e+10</td>\n",
              "      <td>...</td>\n",
              "      <td>1.896108e+07</td>\n",
              "      <td>75.150000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.881750e+09</td>\n",
              "      <td>2.390000e+09</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>4.048100e+04</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>202.000000</td>\n",
              "      <td>5.790765e+06</td>\n",
              "      <td>86.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>6355.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>278.900000</td>\n",
              "      <td>1.227700e+13</td>\n",
              "      <td>...</td>\n",
              "      <td>1.296075e+09</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>14.100000</td>\n",
              "      <td>5.300000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.476400e+10</td>\n",
              "      <td>1.209120e+11</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>2.307819e+06</td>\n",
              "      <td>1.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38dba07b-14ac-45d1-a8ba-7c22249c05e0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38dba07b-14ac-45d1-a8ba-7c22249c05e0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-38dba07b-14ac-45d1-a8ba-7c22249c05e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se genera Array con la info a filtar, agrupar y graficar\n",
        "Info_general = info.loc[:,['Region', 'Pais', 'Ano' ,'Poblacion total','PIB', 'Uso de energia']]\n",
        "\n",
        "#Se liminan valores en nulo por que afectan los totales\n",
        "Info_general = Info_general[ ( Info_general['PIB'].notnull() ) & (Info_general['Uso de energia'].notnull() ) ]  \n",
        "\n",
        "#Se elimina fecha y se deja solo el campo de año \n",
        "Info_general['Ano'] = Info_general['Ano'].str.replace('-12-01','')\n",
        "\n",
        "\n",
        "Info_general_agrupada = Info_general.sort_values(by=['Region','Pais','Ano']).groupby(['Region','Pais','Ano']).sum().reset_index()"
      ],
      "metadata": {
        "id": "aevzWk4j5-6r"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Info_general_agrupada"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BigOnRnv8KbP",
        "outputId": "d84852fe-6598-4c8c-e799-5ddfd6360a9a"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Region     Pais   Ano  Poblacion total           PIB  \\\n",
              "0           Africa  Algeria  2000         31719449  5.479006e+10   \n",
              "1           Africa  Algeria  2001         32150198  5.474472e+10   \n",
              "2           Africa  Algeria  2002         32572977  5.676029e+10   \n",
              "3           Africa  Algeria  2003         33003442  6.786383e+10   \n",
              "4           Africa  Algeria  2004         33461345  8.532500e+10   \n",
              "..             ...      ...   ...              ...           ...   \n",
              "650  Oriente Medio    Siria  2003         17298476  2.182814e+10   \n",
              "651  Oriente Medio    Yemen  2000         17522537  9.636342e+09   \n",
              "652  Oriente Medio    Yemen  2001         18029989  9.854042e+09   \n",
              "653  Oriente Medio    Yemen  2002         18551068  1.069328e+10   \n",
              "654  Oriente Medio    Yemen  2003         19081306  1.177777e+10   \n",
              "\n",
              "     Uso de energia  \n",
              "0           26998.0  \n",
              "1           27072.0  \n",
              "2           28773.0  \n",
              "3           30698.0  \n",
              "4           30974.0  \n",
              "..              ...  \n",
              "650         16543.0  \n",
              "651          4747.0  \n",
              "652          5265.0  \n",
              "653          5100.0  \n",
              "654          5667.0  \n",
              "\n",
              "[655 rows x 6 columns]"
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              "      <td>16543.0</td>\n",
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              "      <th>651</th>\n",
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              "      <td>9.636342e+09</td>\n",
              "      <td>4747.0</td>\n",
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              "    <tr>\n",
              "      <th>652</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>Yemen</td>\n",
              "      <td>2001</td>\n",
              "      <td>18029989</td>\n",
              "      <td>9.854042e+09</td>\n",
              "      <td>5265.0</td>\n",
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              "    <tr>\n",
              "      <th>653</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>Yemen</td>\n",
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              "      <td>18551068</td>\n",
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              "      <td>Oriente Medio</td>\n",
              "      <td>Yemen</td>\n",
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              "<p>655 rows × 6 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bed4ba53-25c0-4c9f-8026-a9132ca6e1e4')\"\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-bed4ba53-25c0-4c9f-8026-a9132ca6e1e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bed4ba53-25c0-4c9f-8026-a9132ca6e1e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se traen librerias para graficar\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "xc8NQ7Uv6mNz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Se separa un array con los años\n",
        "anos_consulta = Info_general_agrupada[['Ano']].groupby(['Ano']).mean().reset_index()\n",
        "anos_consulta\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HzXNF1l66qdr",
        "outputId": "d6b7ef32-3eee-4184-914f-c40298d48e6d"
      },
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Ano\n",
              "0  2000\n",
              "1  2001\n",
              "2  2002\n",
              "3  2003\n",
              "4  2004"
            ],
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              "      <th>4</th>\n",
              "      <td>2004</td>\n",
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              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44fdc36d-7e7b-42f5-afd9-5dd2095b9bd8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      <script>\n",
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              "          document.querySelector('#df-44fdc36d-7e7b-42f5-afd9-5dd2095b9bd8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-44fdc36d-7e7b-42f5-afd9-5dd2095b9bd8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se calcula promedios por regiones\n",
        "Consumo_energia_agrupada_promedio = Info_general_agrupada.groupby([\"Region\",\"Ano\"])[\"Uso de energia\"].transform(\"mean\")\n",
        "Info_general_agrupada['Consumo energia media por región'] = Consumo_energia_agrupada_promedio\n",
        "\n",
        "PIB_agrupada_promedio = Info_general_agrupada.groupby([\"Region\", \"Ano\"])[\"PIB\"].transform(\"mean\")\n",
        "Info_general_agrupada['PIB media por región'] = PIB_agrupada_promedio\n",
        "\n",
        "Info_general_agrupada[\"Poblacion total\"] = Info_general_agrupada[\"Poblacion total\"].astype(\"int64\")\n",
        "Info_general_agrupada[\"Consumo energia media por región\"] = Info_general_agrupada[\"Consumo energia media por región\"].astype(\"int64\")\n",
        "Info_general_agrupada[\"Uso de energia\"] = Info_general_agrupada[\"Uso de energia\"].astype(\"int64\")\n",
        "Info_general_agrupada[\"PIB\"] = Info_general_agrupada[\"PIB\"].astype(\"int64\")\n",
        "Info_general_agrupada[\"PIB media por región\"] = Info_general_agrupada[\"PIB media por región\"].astype(\"int64\")\n",
        "\n",
        "     "
      ],
      "metadata": {
        "id": "tylE7rLFIMPp"
      },
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Consumo_energia_agrupada_promedio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yr6BIwB2mtze",
        "outputId": "e2892531-cb6c-4016-f0a7-435c0b203b08"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      17212.269231\n",
              "1      17926.923077\n",
              "2      18290.884615\n",
              "3      18933.153846\n",
              "4      14535.555556\n",
              "           ...     \n",
              "650    34070.416667\n",
              "651    29199.833333\n",
              "652    31215.583333\n",
              "653    33005.583333\n",
              "654    34070.416667\n",
              "Name: Uso de energia, Length: 655, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Info_general_agrupada[ (Info_general_agrupada['Region'] == 'Africa')&( Info_general_agrupada['Ano'] == '2000' ) ]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "_EtdOTL9jahH",
        "outputId": "ab07fa90-cb51-4b35-bad2-5182763fe778"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Region              Pais   Ano  Poblacion total           PIB  \\\n",
              "0    Africa           Algeria  2000         31719449   54790058957   \n",
              "5    Africa            Angola  2000         13924930    9129594819   \n",
              "10   Africa             Benin  2000          6949366    2359122303   \n",
              "15   Africa          Botsuana  2000          1755375    5788311645   \n",
              "20   Africa           Camerun  2000         15927713    9287367569   \n",
              "26   Africa  Congo (Kinshasa)  2000         50075448   22307956971   \n",
              "31   Africa   Costa de Marfil  2000         16131332   10417060605   \n",
              "36   Africa            Egipto  2000         66136590   99838540997   \n",
              "41   Africa           Eritrea  2000          3939348     706370812   \n",
              "46   Africa           Etiopia  2000         66024199    8091384891   \n",
              "51   Africa             Gabon  2000          1225527    5067865503   \n",
              "57   Africa             Ghana  2000         18825034    4982849054   \n",
              "64   Africa             Kenia  2000         31285050   12705350153   \n",
              "70   Africa             Libia  2000          5176185   33896600871   \n",
              "75   Africa         Marruecos  2000         28710123   37020609825   \n",
              "81   Africa        Mozambique  2000         18275618    4310090791   \n",
              "86   Africa           Namibia  2000          1897953    3908645566   \n",
              "91   Africa           Nigeria  2000        122876727   46385996027   \n",
              "97   Africa           Senegal  2000          9861679    4679604922   \n",
              "104  Africa         Sudafrica  2000         44000000  132877640158   \n",
              "109  Africa             Sudan  2000         27729798   12257299163   \n",
              "114  Africa          Tanzania  2000         34020512   10185786171   \n",
              "119  Africa              Togo  2000          4864753    1294250280   \n",
              "124  Africa             Tunez  2000          9563500   21473188882   \n",
              "130  Africa            Zambia  2000         10100981    3253551750   \n",
              "135  Africa          Zimbabue  2000         12503652    6689957610   \n",
              "\n",
              "     Uso de energia  Consumo energia media por región  PIB media por región  \n",
              "0             26998                             17212           21680963703  \n",
              "5              7499                             17212           21680963703  \n",
              "10             1983                             17212           21680963703  \n",
              "15             1836                             17212           21680963703  \n",
              "20             6310                             17212           21680963703  \n",
              "26            17493                             17212           21680963703  \n",
              "31             6734                             17212           21680963703  \n",
              "36            40658                             17212           21680963703  \n",
              "41              708                             17212           21680963703  \n",
              "46            25242                             17212           21680963703  \n",
              "51             1463                             17212           21680963703  \n",
              "57             7736                             17212           21680963703  \n",
              "64            14055                             17212           21680963703  \n",
              "70            15901                             17212           21680963703  \n",
              "75            10238                             17212           21680963703  \n",
              "81             7173                             17212           21680963703  \n",
              "86              978                             17212           21680963703  \n",
              "91            90596                             17212           21680963703  \n",
              "97             2398                             17212           21680963703  \n",
              "104          109264                             17212           21680963703  \n",
              "109           13340                             17212           21680963703  \n",
              "114           13390                             17212           21680963703  \n",
              "119            2111                             17212           21680963703  \n",
              "124            7306                             17212           21680963703  \n",
              "130            6244                             17212           21680963703  \n",
              "135            9865                             17212           21680963703  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b17679a-aa01-44a4-aebc-93ae0182ac96\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Region</th>\n",
              "      <th>Pais</th>\n",
              "      <th>Ano</th>\n",
              "      <th>Poblacion total</th>\n",
              "      <th>PIB</th>\n",
              "      <th>Uso de energia</th>\n",
              "      <th>Consumo energia media por región</th>\n",
              "      <th>PIB media por región</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>2000</td>\n",
              "      <td>31719449</td>\n",
              "      <td>54790058957</td>\n",
              "      <td>26998</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Angola</td>\n",
              "      <td>2000</td>\n",
              "      <td>13924930</td>\n",
              "      <td>9129594819</td>\n",
              "      <td>7499</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Benin</td>\n",
              "      <td>2000</td>\n",
              "      <td>6949366</td>\n",
              "      <td>2359122303</td>\n",
              "      <td>1983</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Botsuana</td>\n",
              "      <td>2000</td>\n",
              "      <td>1755375</td>\n",
              "      <td>5788311645</td>\n",
              "      <td>1836</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Camerun</td>\n",
              "      <td>2000</td>\n",
              "      <td>15927713</td>\n",
              "      <td>9287367569</td>\n",
              "      <td>6310</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Congo (Kinshasa)</td>\n",
              "      <td>2000</td>\n",
              "      <td>50075448</td>\n",
              "      <td>22307956971</td>\n",
              "      <td>17493</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Costa de Marfil</td>\n",
              "      <td>2000</td>\n",
              "      <td>16131332</td>\n",
              "      <td>10417060605</td>\n",
              "      <td>6734</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Egipto</td>\n",
              "      <td>2000</td>\n",
              "      <td>66136590</td>\n",
              "      <td>99838540997</td>\n",
              "      <td>40658</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Eritrea</td>\n",
              "      <td>2000</td>\n",
              "      <td>3939348</td>\n",
              "      <td>706370812</td>\n",
              "      <td>708</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Etiopia</td>\n",
              "      <td>2000</td>\n",
              "      <td>66024199</td>\n",
              "      <td>8091384891</td>\n",
              "      <td>25242</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Gabon</td>\n",
              "      <td>2000</td>\n",
              "      <td>1225527</td>\n",
              "      <td>5067865503</td>\n",
              "      <td>1463</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Ghana</td>\n",
              "      <td>2000</td>\n",
              "      <td>18825034</td>\n",
              "      <td>4982849054</td>\n",
              "      <td>7736</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Kenia</td>\n",
              "      <td>2000</td>\n",
              "      <td>31285050</td>\n",
              "      <td>12705350153</td>\n",
              "      <td>14055</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Libia</td>\n",
              "      <td>2000</td>\n",
              "      <td>5176185</td>\n",
              "      <td>33896600871</td>\n",
              "      <td>15901</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Marruecos</td>\n",
              "      <td>2000</td>\n",
              "      <td>28710123</td>\n",
              "      <td>37020609825</td>\n",
              "      <td>10238</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Mozambique</td>\n",
              "      <td>2000</td>\n",
              "      <td>18275618</td>\n",
              "      <td>4310090791</td>\n",
              "      <td>7173</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>2000</td>\n",
              "      <td>1897953</td>\n",
              "      <td>3908645566</td>\n",
              "      <td>978</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>2000</td>\n",
              "      <td>122876727</td>\n",
              "      <td>46385996027</td>\n",
              "      <td>90596</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>2000</td>\n",
              "      <td>9861679</td>\n",
              "      <td>4679604922</td>\n",
              "      <td>2398</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Sudafrica</td>\n",
              "      <td>2000</td>\n",
              "      <td>44000000</td>\n",
              "      <td>132877640158</td>\n",
              "      <td>109264</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Sudan</td>\n",
              "      <td>2000</td>\n",
              "      <td>27729798</td>\n",
              "      <td>12257299163</td>\n",
              "      <td>13340</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Tanzania</td>\n",
              "      <td>2000</td>\n",
              "      <td>34020512</td>\n",
              "      <td>10185786171</td>\n",
              "      <td>13390</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Togo</td>\n",
              "      <td>2000</td>\n",
              "      <td>4864753</td>\n",
              "      <td>1294250280</td>\n",
              "      <td>2111</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Tunez</td>\n",
              "      <td>2000</td>\n",
              "      <td>9563500</td>\n",
              "      <td>21473188882</td>\n",
              "      <td>7306</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>2000</td>\n",
              "      <td>10100981</td>\n",
              "      <td>3253551750</td>\n",
              "      <td>6244</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Africa</td>\n",
              "      <td>Zimbabue</td>\n",
              "      <td>2000</td>\n",
              "      <td>12503652</td>\n",
              "      <td>6689957610</td>\n",
              "      <td>9865</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Info_general_grafica = Info_general_agrupada.loc[:,[\"Region\",\"Ano\",\"Uso de energia\",\"Consumo energia media por región\",\"PIB\", \"PIB media por región\"]]\n",
        "Info_general_grafica = Info_general_grafica.groupby([\"Region\",\"Ano\",\"Consumo energia media por región\",\"PIB media por región\"]).sum().reset_index()"
      ],
      "metadata": {
        "id": "Zyu8hOJYVFPw"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Info_general_grafica"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "xSXVXdg1Vp63",
        "outputId": "7fd6e828-f15a-4db7-e5e8-fcb448fd1f3b"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Region   Ano  Consumo energia media por región  \\\n",
              "0          Africa  2000                             17212   \n",
              "1          Africa  2001                             17926   \n",
              "2          Africa  2002                             18290   \n",
              "3          Africa  2003                             18933   \n",
              "4          Africa  2004                             14535   \n",
              "5            Asia  2000                            112683   \n",
              "6            Asia  2001                            114260   \n",
              "7            Asia  2002                            118697   \n",
              "8            Asia  2003                            126880   \n",
              "9            Asia  2004                            124384   \n",
              "10         Europa  2000                             64096   \n",
              "11         Europa  2001                             65208   \n",
              "12         Europa  2002                             65126   \n",
              "13         Europa  2003                             66971   \n",
              "14         Europa  2004                             67609   \n",
              "15   Las Americas  2000                            129821   \n",
              "16   Las Americas  2001                            127997   \n",
              "17   Las Americas  2002                            129428   \n",
              "18   Las Americas  2003                            130570   \n",
              "19   Las Americas  2004                            124965   \n",
              "20        Oceania  2000                             62583   \n",
              "21        Oceania  2001                             61435   \n",
              "22        Oceania  2002                             63290   \n",
              "23        Oceania  2003                             63830   \n",
              "24  Oriente Medio  2000                             29199   \n",
              "25  Oriente Medio  2001                             31215   \n",
              "26  Oriente Medio  2002                             33005   \n",
              "27  Oriente Medio  2003                             34070   \n",
              "\n",
              "    PIB media por región  Uso de energia             PIB  \n",
              "0            21680963703          447519    563705056295  \n",
              "1            20392948198          466100    530216653172  \n",
              "2            20607796202          475563    535802701253  \n",
              "3            24941708226          492262    648484413891  \n",
              "4            22222737653          523280    800018555523  \n",
              "5           305105595436         2929770   7932745481338  \n",
              "6           286666237701         2970762   7453322180231  \n",
              "7           291415084000         3086123   7576792184024  \n",
              "8           321271937512         3298904   8353070375321  \n",
              "9           321535272542         3607163   9324522903730  \n",
              "10          232912384942         2627956   9549407782661  \n",
              "11          235342388214         2673534   9649037916781  \n",
              "12          257670235745         2670205  10564479665585  \n",
              "13          314294926482         2745842  12886091985775  \n",
              "14          366286464645         2771993  15017745050471  \n",
              "15          552256703092         3115705  13254160874219  \n",
              "16          563305812249         3071939  13519339493992  \n",
              "17          568067069429         3106287  13633609666309  \n",
              "18          598223747075         3133686  14357369929807  \n",
              "19          613169736407         3124146  15329243410194  \n",
              "20          233609905578          125166    467219811157  \n",
              "21          215973953142          122871    431947906285  \n",
              "22          230231720313          126580    460463440627  \n",
              "23          277051901044          127661    554103802089  \n",
              "24           54837530157          350398    658050361891  \n",
              "25           55302865846          374587    663634390155  \n",
              "26           56329575160          396067    675954901929  \n",
              "27           63409249120          408845    760910989444  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a33f797-7e63-4f1e-a388-eb15323e5923\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Region</th>\n",
              "      <th>Ano</th>\n",
              "      <th>Consumo energia media por región</th>\n",
              "      <th>PIB media por región</th>\n",
              "      <th>Uso de energia</th>\n",
              "      <th>PIB</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Africa</td>\n",
              "      <td>2000</td>\n",
              "      <td>17212</td>\n",
              "      <td>21680963703</td>\n",
              "      <td>447519</td>\n",
              "      <td>563705056295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa</td>\n",
              "      <td>2001</td>\n",
              "      <td>17926</td>\n",
              "      <td>20392948198</td>\n",
              "      <td>466100</td>\n",
              "      <td>530216653172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Africa</td>\n",
              "      <td>2002</td>\n",
              "      <td>18290</td>\n",
              "      <td>20607796202</td>\n",
              "      <td>475563</td>\n",
              "      <td>535802701253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Africa</td>\n",
              "      <td>2003</td>\n",
              "      <td>18933</td>\n",
              "      <td>24941708226</td>\n",
              "      <td>492262</td>\n",
              "      <td>648484413891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Africa</td>\n",
              "      <td>2004</td>\n",
              "      <td>14535</td>\n",
              "      <td>22222737653</td>\n",
              "      <td>523280</td>\n",
              "      <td>800018555523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2000</td>\n",
              "      <td>112683</td>\n",
              "      <td>305105595436</td>\n",
              "      <td>2929770</td>\n",
              "      <td>7932745481338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2001</td>\n",
              "      <td>114260</td>\n",
              "      <td>286666237701</td>\n",
              "      <td>2970762</td>\n",
              "      <td>7453322180231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2002</td>\n",
              "      <td>118697</td>\n",
              "      <td>291415084000</td>\n",
              "      <td>3086123</td>\n",
              "      <td>7576792184024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2003</td>\n",
              "      <td>126880</td>\n",
              "      <td>321271937512</td>\n",
              "      <td>3298904</td>\n",
              "      <td>8353070375321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2004</td>\n",
              "      <td>124384</td>\n",
              "      <td>321535272542</td>\n",
              "      <td>3607163</td>\n",
              "      <td>9324522903730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Europa</td>\n",
              "      <td>2000</td>\n",
              "      <td>64096</td>\n",
              "      <td>232912384942</td>\n",
              "      <td>2627956</td>\n",
              "      <td>9549407782661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Europa</td>\n",
              "      <td>2001</td>\n",
              "      <td>65208</td>\n",
              "      <td>235342388214</td>\n",
              "      <td>2673534</td>\n",
              "      <td>9649037916781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Europa</td>\n",
              "      <td>2002</td>\n",
              "      <td>65126</td>\n",
              "      <td>257670235745</td>\n",
              "      <td>2670205</td>\n",
              "      <td>10564479665585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Europa</td>\n",
              "      <td>2003</td>\n",
              "      <td>66971</td>\n",
              "      <td>314294926482</td>\n",
              "      <td>2745842</td>\n",
              "      <td>12886091985775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Europa</td>\n",
              "      <td>2004</td>\n",
              "      <td>67609</td>\n",
              "      <td>366286464645</td>\n",
              "      <td>2771993</td>\n",
              "      <td>15017745050471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2000</td>\n",
              "      <td>129821</td>\n",
              "      <td>552256703092</td>\n",
              "      <td>3115705</td>\n",
              "      <td>13254160874219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2001</td>\n",
              "      <td>127997</td>\n",
              "      <td>563305812249</td>\n",
              "      <td>3071939</td>\n",
              "      <td>13519339493992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2002</td>\n",
              "      <td>129428</td>\n",
              "      <td>568067069429</td>\n",
              "      <td>3106287</td>\n",
              "      <td>13633609666309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2003</td>\n",
              "      <td>130570</td>\n",
              "      <td>598223747075</td>\n",
              "      <td>3133686</td>\n",
              "      <td>14357369929807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2004</td>\n",
              "      <td>124965</td>\n",
              "      <td>613169736407</td>\n",
              "      <td>3124146</td>\n",
              "      <td>15329243410194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Oceania</td>\n",
              "      <td>2000</td>\n",
              "      <td>62583</td>\n",
              "      <td>233609905578</td>\n",
              "      <td>125166</td>\n",
              "      <td>467219811157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Oceania</td>\n",
              "      <td>2001</td>\n",
              "      <td>61435</td>\n",
              "      <td>215973953142</td>\n",
              "      <td>122871</td>\n",
              "      <td>431947906285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Oceania</td>\n",
              "      <td>2002</td>\n",
              "      <td>63290</td>\n",
              "      <td>230231720313</td>\n",
              "      <td>126580</td>\n",
              "      <td>460463440627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Oceania</td>\n",
              "      <td>2003</td>\n",
              "      <td>63830</td>\n",
              "      <td>277051901044</td>\n",
              "      <td>127661</td>\n",
              "      <td>554103802089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>2000</td>\n",
              "      <td>29199</td>\n",
              "      <td>54837530157</td>\n",
              "      <td>350398</td>\n",
              "      <td>658050361891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>2001</td>\n",
              "      <td>31215</td>\n",
              "      <td>55302865846</td>\n",
              "      <td>374587</td>\n",
              "      <td>663634390155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>2002</td>\n",
              "      <td>33005</td>\n",
              "      <td>56329575160</td>\n",
              "      <td>396067</td>\n",
              "      <td>675954901929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>2003</td>\n",
              "      <td>34070</td>\n",
              "      <td>63409249120</td>\n",
              "      <td>408845</td>\n",
              "      <td>760910989444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a33f797-7e63-4f1e-a388-eb15323e5923')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6a33f797-7e63-4f1e-a388-eb15323e5923 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6a33f797-7e63-4f1e-a388-eb15323e5923');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Se grafica el consumo total de energia por año en las regiones\n",
        "len(anos_consulta)\n",
        "pos_for = 0\n",
        "data_grafica = Info_general_grafica[ (Info_general_grafica['Ano'] == anos_consulta['Ano'][pos_for]) ]\n",
        "\n",
        "plt.title(\"Consumo Por Region año \"+anos_consulta['Ano'][pos_for])\n",
        "\n",
        "x_data_axis = data_grafica[\"Region\"]\n",
        "y_data_axis = data_grafica[\"Uso de energia\"]\n",
        "z_data_axis = data_grafica[\"Consumo energia media por región\"]\n",
        "\n",
        "plt.bar(np.arange(len(x_data_axis)) - 0.2, y_data_axis, 0.4, label = \"Consumo de energia Total\")\n",
        "plt.bar(np.arange(len(x_data_axis)) + 0.2, z_data_axis, 0.4, label = \"consumo de energia medio\")\n",
        "plt.xticks(np.arange(len(x_data_axis)), x_data_axis)\n",
        "plt.xlabel(\"Región\")\n",
        "plt.ylabel(\"Consumo\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "  \n",
        "\n",
        "  \n",
        "   "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "uavlqe3lB3ZF",
        "outputId": "53654f65-544f-4c5e-d48d-9302e34585c5"
      },
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_grafica"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ve1ED2GGnQx_",
        "outputId": "ccde0f5f-3e79-441f-fb76-ef0595ae3fa6"
      },
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Region   Ano  Consumo energia media por región  \\\n",
              "0          Africa  2000                             17212   \n",
              "5            Asia  2000                            112683   \n",
              "10         Europa  2000                             64096   \n",
              "15   Las Americas  2000                            129821   \n",
              "20        Oceania  2000                             62583   \n",
              "24  Oriente Medio  2000                             29199   \n",
              "\n",
              "    PIB media por región  Uso de energia             PIB  \n",
              "0            21680963703          447519    563705056295  \n",
              "5           305105595436         2929770   7932745481338  \n",
              "10          232912384942         2627956   9549407782661  \n",
              "15          552256703092         3115705  13254160874219  \n",
              "20          233609905578          125166    467219811157  \n",
              "24           54837530157          350398    658050361891  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Region</th>\n",
              "      <th>Ano</th>\n",
              "      <th>Consumo energia media por región</th>\n",
              "      <th>PIB media por región</th>\n",
              "      <th>Uso de energia</th>\n",
              "      <th>PIB</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>Africa</td>\n",
              "      <td>2000</td>\n",
              "      <td>17212</td>\n",
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              "      <td>447519</td>\n",
              "      <td>563705056295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2000</td>\n",
              "      <td>112683</td>\n",
              "      <td>305105595436</td>\n",
              "      <td>2929770</td>\n",
              "      <td>7932745481338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Europa</td>\n",
              "      <td>2000</td>\n",
              "      <td>64096</td>\n",
              "      <td>232912384942</td>\n",
              "      <td>2627956</td>\n",
              "      <td>9549407782661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2000</td>\n",
              "      <td>129821</td>\n",
              "      <td>552256703092</td>\n",
              "      <td>3115705</td>\n",
              "      <td>13254160874219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Oceania</td>\n",
              "      <td>2000</td>\n",
              "      <td>62583</td>\n",
              "      <td>233609905578</td>\n",
              "      <td>125166</td>\n",
              "      <td>467219811157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>2000</td>\n",
              "      <td>29199</td>\n",
              "      <td>54837530157</td>\n",
              "      <td>350398</td>\n",
              "      <td>658050361891</td>\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-09c74ec6-197e-484e-b771-4308931a10b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-09c74ec6-197e-484e-b771-4308931a10b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Se grafica el consumo total de energia por año en las regiones\n",
        "pos_for = 1\n",
        "data_grafica = Info_general_grafica[ (Info_general_grafica['Ano'] == anos_consulta['Ano'][pos_for]) ]\n",
        "\n",
        "plt.title(\"Consumo Por Region año \"+anos_consulta['Ano'][pos_for])\n",
        "\n",
        "x_data_axis = data_grafica[\"Region\"]\n",
        "y_data_axis = data_grafica[\"Uso de energia\"]\n",
        "z_data_axis = data_grafica[\"Consumo energia media por región\"]\n",
        "\n",
        "plt.bar(np.arange(len(x_data_axis)) - 0.2, y_data_axis, 0.4, label = \"Consumo de energia Total\")\n",
        "plt.bar(np.arange(len(x_data_axis)) + 0.2, z_data_axis, 0.4, label = \"consumo de energia medio\")\n",
        "plt.xticks(np.arange(len(x_data_axis)), x_data_axis)\n",
        "plt.xlabel(\"Región\")\n",
        "plt.ylabel(\"Consumo\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "stzxmXFdnEkn",
        "outputId": "2d8894a4-cbe8-428d-be24-b36e11d22982"
      },
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_grafica"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "13QJNprAnXFu",
        "outputId": "3ae7100e-dcef-442b-bd9b-55e8d5f3693f"
      },
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Region   Ano  Consumo energia media por región  \\\n",
              "1          Africa  2001                             17926   \n",
              "6            Asia  2001                            114260   \n",
              "11         Europa  2001                             65208   \n",
              "16   Las Americas  2001                            127997   \n",
              "21        Oceania  2001                             61435   \n",
              "25  Oriente Medio  2001                             31215   \n",
              "\n",
              "    PIB media por región  Uso de energia             PIB  \n",
              "1            20392948198          466100    530216653172  \n",
              "6           286666237701         2970762   7453322180231  \n",
              "11          235342388214         2673534   9649037916781  \n",
              "16          563305812249         3071939  13519339493992  \n",
              "21          215973953142          122871    431947906285  \n",
              "25           55302865846          374587    663634390155  "
            ],
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              "      <th></th>\n",
              "      <th>Region</th>\n",
              "      <th>Ano</th>\n",
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              "      <th>PIB media por región</th>\n",
              "      <th>Uso de energia</th>\n",
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              "      <th>1</th>\n",
              "      <td>Africa</td>\n",
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              "    </tr>\n",
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              "      <th>6</th>\n",
              "      <td>Asia</td>\n",
              "      <td>2001</td>\n",
              "      <td>114260</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Europa</td>\n",
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              "      <td>65208</td>\n",
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              "      <th>16</th>\n",
              "      <td>Las Americas</td>\n",
              "      <td>2001</td>\n",
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              "      <td>13519339493992</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Oceania</td>\n",
              "      <td>2001</td>\n",
              "      <td>61435</td>\n",
              "      <td>215973953142</td>\n",
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              "      <td>431947906285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Oriente Medio</td>\n",
              "      <td>2001</td>\n",
              "      <td>31215</td>\n",
              "      <td>55302865846</td>\n",
              "      <td>374587</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Se grafica el consumo total de energia por año en las regiones\n",
        "len(anos_consulta)\n",
        "pos_for = 2\n",
        "data_grafica = Info_general_grafica[ (Info_general_grafica['Ano'] == anos_consulta['Ano'][pos_for]) ]\n",
        "\n",
        "plt.title(\"Consumo Por Region año \"+anos_consulta['Ano'][pos_for])\n",
        "\n",
        "x_data_axis = data_grafica[\"Region\"]\n",
        "y_data_axis = data_grafica[\"Uso de energia\"]\n",
        "z_data_axis = data_grafica[\"Consumo energia media por región\"]\n",
        "\n",
        "plt.bar(np.arange(len(x_data_axis)) - 0.2, y_data_axis, 0.4, label = \"Consumo de energia Total\")\n",
        "plt.bar(np.arange(len(x_data_axis)) + 0.2, z_data_axis, 0.4, label = \"consumo de energia medio\")\n",
        "plt.xticks(np.arange(len(x_data_axis)), x_data_axis)\n",
        "plt.xlabel(\"Región\")\n",
        "plt.ylabel(\"Consumo\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wwbPlbqSnKs8",
        "outputId": "dfd0e8fd-61f0-4b29-adaf-2663b6881d81"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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jxgyefvppAI4//njWrFnDDz/8UKjcihUrOOOMM1i5ciW//PJLqW7/3FVJSwRmVgOYBuwd3mdyweezmtneRA//7kT0UO8z3D03WTGJVFaHt2jJGy89X+r59t577/zhqlWr5l8PnzVrFm+++SaTJ0/mnnvu4a233qJatWps27YNgG3btvHLL7/EradKlSr5r6tUqcKWLVt2drHyuTvnnnsuf/vb34ot06ZNG2bOnBl3el5MecuZN8+YMWPo3bv3DmWzs7PZb7/9ShXjp59+yp133sns2bPZf//9ycrKKtMfGA4fPpwrr7ySU089lezsbEaOHFlmdZckmW0EPwPHu3t7IAM4ycy6FihzPrDW3Q8H7gL+nsR4RCqtzO7H8MsvvzB5wvj8cQsWLGD69On06NGDSZMmsXXrVlavXs20adPIzMwssq7169ezbt06+vTpw1133cX8+fOB6A6enJwcAKZMmcLmzZtLFWO3bt148sknAZgwYQI9evQoVOaYY47hiSeeAOCVV15h7dq1AJxwwglMnjyZb775Boiuq3/22Y6dZR5xxBGsXr06PxFs3ryZRYsWFRtT7969uf/++/OX5eOPP+ann34qVK579+48/fTTbNu2jVWrVpGdnV2ozA8//MB+++1HnTp1WLVqFa+88kqx752nR48eTJgwAYgSUP369alduza1atXixx9/zC+3bt06Dj74YAAee+yxhOouK0k7I/CoFWh9eFk9/BV8LmY/YGQYngzcY2bmen6myA7MjLseepw7bv4jj953N7Vr7kvTpk0ZPXo0Rx99NDNnzqR9+/aYGbfffjsHHnggS5cujVvXjz/+SL9+/di0aRPuzqhRowC44IIL6NevH+3bt+ekk04q9RHzmDFjOO+887jjjjtIS0vj0UcfLVTmpptuYtCgQbRp04Zu3brRpEkTAFq3bs0tt9xCr1692LZtG9WrV+fee+/lkEMOyZ93r732YvLkyYwYMYJ169axZcsWrrjiCtq0aVNkTEOHDiU3N5eOHTvi7qSlpRVqtwD47W9/y5tvvknr1q1p3LgxHTt2pE6dOjuUad++PR06dKBly5Y0btyY7t2Lv9Mrz8iRIxkyZAjp6ensu++++Tv5U045hQEDBvD8888zZswYRo4cycCBA9l///05/vjj+fTTTxOqvywk9ZnFZlYVyAEOB+5192sLTF8InOTuK8LrT4Aj3f3bAuWGAcMAmjRp0qngkUIqU2Nx+ViyZAmtWrXaqXkXrCi5Mba00hvVLfM6U9369eupWbMma9asITMzk3feeYcDDzywosPaKfE+r2aW4+6d45VPamOxu28FMsysLvCsmbV194U7Uc9YYCxA586ddbYgImWub9++fP/99/zyyy/ccMMNlTYJ7IxyuWvI3b83s7eBk4DYRPAl0BhYYWbVgDpEjcYiIuUqXrtAqkhaY7GZpYUzAcxsH+BEoOBFyynAuWF4APCW2gdERMpXMs8IGgKPhXaCKsB/3P1FM/sLMMfdpwCPAI+b2XLgO+DMJMYjIiJxJPOuoQVAhzjjb4wZ3gQMTFYMIiJSMvU1JCKS4pQIRKTcZWdnJ9S1RUX66quvGDBgQIW9f2xnd3369EmoT6idpb6GRHbGyDollwmK74kmsmCofhtTEYrrgvqggw6K+xyBivDyyy8ntX6dEYhUEi9MfpIBJ3ZnYK+jOfvss4Giu37OyspixIgRdOvWjUMPPTR/h7Zy5UqOOeYYMjIyaNu2bX53zXldTgNMnjyZrKys/HouvvhiunbtyqGHHkp2djZDhgyhVatW+WUAJk6cSLt27Wjbti3XXrvD70bzvfrqq7Rs2ZKOHTvmdzwH8NNPPzFkyBAyMzPp0KEDzz8fv0+lO+64I7876bwuoHNzc2nVqhUXXHABbdq0oVevXmzcuBGATz75hJNOOolOnTrRo0eP/F9aZ2VlcdFFF3HkkUfyhz/8gU8++YSuXbvSrl07/vznP+evi9zcXNq2bZs/3KNHDzp27EjHjh3jPlAmNzeXli1bkpWVRYsWLRg8eDBvvPEG3bt3p3nz5syaNavY5d24cSNnnnkmrVq14rTTTstfDtjxAT6jRo2ibdu2tG3bltGjR8ddV6WlRCBSCSz/aAlj/3knD02awlNTZ3D33XcD5Hf9vGDBAgYPHsyIESPy51m5ciUzZszgxRdf5LrrrgPgiSeeoHfv3vndVmdkxO/aOtbatWuZOXMmd911F6eeeir/93//x6JFi/jwww+ZN28eX331Fddeey1vvfUW8+bNY/bs2YW6cdi0aRMXXHABL7zwAjk5OXz99df502699VaOP/54Zs2axdtvv80111xTqD+gqVOnsmzZMmbNmsW8efPIyclh2rRpACxbtoxLL72URYsWUbdu3fyePocNG8aYMWPIycnhzjvv5JJLLsmvb8WKFbz77ruMGjWKyy+/nMsvv5wPP/wwv6fWgn71q1/x+uuvM3fuXCZNmrTDet5hOy1fzlVXXcXSpUtZunQpTzzxBDNmzODOO+/kr3/9a7HLe//997PvvvuyZMkSbr755vx+n2Ll5OTw6KOP8v777/Pee+/x0EMP8cEHH5S0CUukS0MilcCsd6fT6+R+7F/vAADq1asHFN/1c//+/alSpQqtW7fOf8hJly5dGDJkCJs3b6Z///4JJYJTTjkFM6Ndu3Y0aNCAdu3aAdCmTRtyc3P57LPP6NmzJ2lpaQAMHjyYadOm0b9///w6li5dSrNmzWjevDkAv//97xk7diwQ7eSnTJnCnXfeCURJ4/PPP9+hi4SpU6cydepUOnSIbkRcv349y5Yto0mTJjRr1ix/OfK6oF6/fj3vvvsuAwduvynx559/zh8eOHAgVatWzV+HeYnrrLPOyn8+QKzNmzdz2WWXMW/ePKpWrVrkI0KbNWu2w/o54YQT8tddbNfY8ZZ32rRp+QkmPT09bvfWM2bM4LTTTsvvB+r0009n+vTp+etlZykRiOyhYruOzvud5jHHHMO0adN46aWXyMrK4sorr+Scc87Z4YHnBbtWju1yumB31Fu2bKF69eq7FKe78/TTT3PEEUcUW+b666/nwgsv3GF8bm5uoa62N27cyLZt26hbt26Rj4osbYd6d911Fw0aNGD+/Pls27aNGjVqxC2XSHfdiSxvedOlIZFKILNbD6a+9Dzfr/0OiLpphsS6fo712Wef0aBBAy644AKGDh3K3LlzAWjQoAFLlixh27ZtPPvss6WLLTOT//73v3z77bds3bqViRMncuyxx+5QpmXLluTm5uY/1nLixIn503r37s2YMWPyk1W8Sx29e/dm3LhxrF8fdWj85Zdf5ndZHU/t2rVp1qwZTz31FBDtfPO62y6oa9eu+ZeT8tZlQevWraNhw4ZUqVKFxx9/PO5jNBNV1PLGdtG9cOFCFixYUGjeHj168Nxzz7FhwwZ++uknnn322RK3eSKUCEQqgcOPaMUFw69iyICTGdjraK688kog6vr50UcfJT09nccffzy/7aAo2dnZ+d0pT5o0icsvvxyA2267jb59+9KtW7cdnvqViIYNG3Lbbbdx3HHH0b59ezp16kS/fv12KFOjRg3Gjh3LySefTMeOHXd4+tgNN9zA5s2bSU9Pp02bNtxwww2F3qNXr16cddZZHHXUUbRr144BAwbs0Jd/PBMmTOCRRx6hffv2tGnTpshG6NGjRzNq1CjS09NZvnx5oe6nAS655BIee+wx2rdvz9KlS0t9RhGrqOW9+OKLWb9+Pa1ateLGG2+kU6dOhebt2LEjWVlZZGZmcuSRRzJ06NBdviwESe6GOhk6d+7seffWirqhLi/qhnrPtWHDBvbZZx/MjCeffJKJEycWmTQqi92qG2oRkd1dTk4Ol112Ge5O3bp1GTduXEWHVO6UCEQkpfXo0aPI9oNUoTYCEZEUp0QgkqDK1p4mqWlnPqdKBCIJqFGjBmvWrFEykN2au7NmzZoif+dQFLURiCSgUaNGrFixgtWrV5d63lVrN5ZcqJSW/LhPmdcpe4YaNWoU2VVGUZQIZLeyu94OW716dZo1a7ZT8/5mN10mkTy6NCQikuKUCEREUpwSgYhIilMiEBFJcUoEIiIpTolARCTFKRGIiKS4pCUCM2tsZm+b2WIzW2Rml8cp09PM1pnZvPB3Y7LiERGR+JL5g7ItwFXuPtfMagE5Zva6uy8uUG66u/dNYhwiIlKMpJ0RuPtKd58bhn8ElgAHJ+v9RERk55RLG4GZNQU6AO/HmXyUmc03s1fMrE15xCMiItslva8hM6sJPA1c4e4/FJg8FzjE3debWR/gOaB5nDqGAcMAmjRpkuSIRURSS1LPCMysOlESmODuzxSc7u4/uPv6MPwyUN3M6scpN9bdO7t757S0tGSGLCKScpJ515ABjwBL3H1UEWUODOUws8wQz5pkxSQiIoUl89JQd+Bs4EMzmxfG/RFoAuDuDwADgIvNbAuwETjT9eQPEZFylbRE4O4zACuhzD3APcmKQURESqZfFouIpDglAhGRFKdEICKS4pQIRERSnBKBiEiKUyIQEUlxSgQiIilOiUBEJMUpEYiIpDglAhGRFKdEICKS4pQIRERSnBKBiEiKUyIQEUlxSgQiIilOiUBEJMUpEYiIpDglAhGRFKdEICKS4pQIRERSnBKBiEiKUyIQEUlxSgQiIilOiUBEJMUlLRGYWWMze9vMFpvZIjO7PE4ZM7N/mtlyM1tgZh2TFY+IiMRXLYl1bwGucve5ZlYLyDGz1919cUyZ3wDNw9+RwP3hv4iIlJOknRG4+0p3nxuGfwSWAAcXKNYP+JdH3gPqmlnDZMUkIiKFlUsbgZk1BToA7xeYdDDwRczrFRROFiIikkRJTwRmVhN4GrjC3X/YyTqGmdkcM5uzevXqsg1QRCTFJTURmFl1oiQwwd2fiVPkS6BxzOtGYdwO3H2su3d2985paWnJCVZEJEUl864hAx4Blrj7qCKKTQHOCXcPdQXWufvKZMUkIiKFJfOuoe7A2cCHZjYvjPsj0ATA3R8AXgb6AMuBDcB5SYxHRETiSCgRmFkjYAxwNODAdOByd19R1DzuPgOw4up1dwcuTThaEREpc4leGnqU6DJOQ+Ag4IUwTkREKrlEE0Gauz/q7lvC33hArbYiInuARBPBGjP7vZlVDX+/B9YkMzARESkfiSaCIcDvgK+BlcAA1LArIrJHSKix2N0/A05NciwiIlIBEr1rqBkwHGgaO4+7KzmIiFRyif6O4DmiH4e9AGxLXjgiIlLeEk0Em9z9n0mNREREKkSiieBuM7sJmAr8nDcyr5tpERGpvBJNBO2Iuos4nu2Xhjy8FhGRSizRRDAQONTdf0lmMCIiUv4S/R3BQqBuMgMREZGKkegZQV1gqZnNZsc2At0+KiJSySWaCG5KahQiIlJhEv1l8X+THYiIiFSMRH9Z/CPRXUIAewHVgZ/cvXayAhMRkfKR6BlBrbzh8AjKfkDXZAUlIiLlp9TPLPbIc0DvJMQjIiLlLNFLQ6fHvKwCdAY2JSUiEREpV4neNXRKzPAWIJfo8pCIiFRyibYR6CE0IiJ7qITaCMzsdjOrbWbVzexNM1sdHlcpIiKVXKKNxb3c/QegL9FlocOBa5IVlIiIlJ9EE0HeJaSTgafcfV2S4hERkXKWaGPxi2a2FNgIXGxmaeiuIRGRPUJCZwTufh3QDejs7puBnyjhriEzG2dm35jZwiKm9zSzdWY2L/zdWNrgRURk1yV6RgDQEmhqZrHz/KuY8uOBe0ooM93d+5YiBhERKWOJ/qDsceAwYB6wNYx2itnJu/s0M2u6i/GJiEiSJXpG0Blo7e5eYsnSOcrM5gNfAVe7+6J4hcxsGDAMoEmTJmUcgohIaivNE8oOLOP3ngsc4u7tgTHAc0UVdPex7t7Z3TunpaWVcRgiIqkt0TOC+sBiM5tFGT2hLPwuIW/4ZTO7z8zqu/u3O1uniIiUXqKJYGRZv7GZHQiscnc3s0yis5M1Zf0+IiJSvISfUGZmDYAuYdQsd/+muHnMbCLQE6hvZiuIHndZPdT3ADCA6DcJW4h+n3BmEtogRESkBIneNfQ74A4gGzBgjJld4+6Ti5rH3QcVV6e730N0e6mIiA7zGnYAABDMSURBVFSgRC8N/QnokncWEH5Z/AZQZCIQEZHKIdG7hqoUuBS0phTziojIbizRM4JXzew1YGJ4fQbwcnJCEhGR8lRsIjCzw4EG7n5NeFzl0WHSTGBCsoMTEZHkK+mMYDRwPYC7PwM8A2Bm7cK0U4qeVUREKoOSrvM3cPcPC44M45omJSIRESlXJSWCusVM26csAxERkYpRUiKYY2YXFBxpZkOBnOSEJCIi5amkNoIrgGfNbDDbd/ydgb2A05IZmIiIlI9iE4G7rwK6mdlxQNsw+iV3fyvpkYmISLlItK+ht4G3kxyLiIhUAP06WEQkxSkRiIikOCUCEZEUp0QgIpLilAhERFKcEoGISIpTIhARSXFKBCIiKU6JQEQkxSkRiIikOCUCEZEUp0QgIpLilAhERFJc0hKBmY0zs2/MbGER083M/mlmy81sgZl1TFYsIiJStGSeEYwHTipm+m+A5uFvGHB/EmMREZEiJC0RuPs04LtiivQD/uWR94C6ZtYwWfGIiEh8FdlGcDDwRczrFWGciIiUo0rRWGxmw8xsjpnNWb16dUWHIyKyR6nIRPAl0DjmdaMwrhB3H+vund29c1paWrkEJyKSKioyEUwBzgl3D3UF1rn7ygqMR0QkJSX08PqdYWYTgZ5AfTNbAdwEVAdw9weAl4E+wHJgA3BesmIREZGiJS0RuPugEqY7cGmy3l9ERBJTKRqLRUQkeZQIRERSnBKBiEiKUyIQEUlxSgQiIilOiUBEJMUpEYiIpDglAhGRFKdEICKS4pQIRERSnBKBiEiKUyIQEUlxSgQiIilOiUBEJMUpEYiIpDglAhGRFKdEICKS4pQIRERSnBKBiEiKUyIQEUlxSgQiIilOiUBEJMUpEYiIpDglAhGRFJfURGBmJ5nZR2a23MyuizM9y8xWm9m88Dc0mfGIiEhh1ZJVsZlVBe4FTgRWALPNbIq7Ly5QdJK7X5asOEREpHjJPCPIBJa7+//c/RfgSaBfEt9PRER2QjITwcHAFzGvV4RxBf3WzBaY2WQza5zEeEREJI6Kbix+AWjq7unA68Bj8QqZ2TAzm2Nmc1avXl2uAYqI7OmSmQi+BGKP8BuFcfncfY27/xxePgx0ileRu491987u3jktLS0pwYqIpKqkNRYDs4HmZtaMKAGcCZwVW8DMGrr7yvDyVGBJEuOh6XUvlXmdubedXOZ1ioiUp6QlAnffYmaXAa8BVYFx7r7IzP4CzHH3KcAIMzsV2AJ8B2QlKx4REYkvmWcEuPvLwMsFxt0YM3w9cH0yYxARkeJVdGOxiIhUMCUCEZEUl9RLQyIilUGq30iiMwIRkRSnRCAikuKUCEREUpwSgYhIilMiEBFJcUoEIiIpTolARCTFKRGIiKQ4JQIRkRSnRCAikuKUCEREUpwSgYhIilMiEBFJcUoEIiIpTolARCTFKRGIiKQ4JQIRkRSnJ5RJYSPrJKHOdWVfZyqrwG2U6k/z2hPpjEBEJMUpEYiIpDglAhGRFKc2Atnzqc1j96dtVKGSekZgZieZ2UdmttzMroszfW8zmxSmv29mTZMZj4iIFJa0MwIzqwrcC5wIrABmm9kUd18cU+x8YK27H25mZwJ/B85IVkwiIuWmEp3lJPPSUCaw3N3/B2BmTwL9gNhE0A8YGYYnA/eYmbm7JzGuslWJNraISDyWrH2umQ0ATnL3oeH12cCR7n5ZTJmFocyK8PqTUObbAnUNA4aFl0cAHyUl6J1TH/i2xFKVy562THva8sCet0x72vLA7rdMh7h7WrwJlaKx2N3HAmMrOo54zGyOu3eu6DjK0p62THva8sCet0x72vJA5VqmZDYWfwk0jnndKIyLW8bMqgF1gDVJjElERApIZiKYDTQ3s2ZmthdwJjClQJkpwLlheADwVqVqHxAR2QMk7dKQu28xs8uA14CqwDh3X2RmfwHmuPsU4BHgcTNbDnxHlCwqm93yktUu2tOWaU9bHtjzlmlPWx6oRMuUtMZiERGpHNTFhIhIilMiEBFJcUoEBZhZfzNzM2sZXqeF7i8+MLMecco/bGatyz/SnVNw+Yop97KZ1S2vuIpjZlvNbF7MX6HuSiogpvVJqDOhbbOL79HZzP5ZRnU1MrPnzWyZmX1iZneHG0OSyswOMrPJZRFXSXUlEMsVZrZvKecZb2YbzKxWzLjRYdvXL0U9WWZ2Txi+yMzOKU0csZQIChsEzAj/AU4APnT3Du4+PbagmVV196EFus3Y3RVcvrjcvY+7f18+IZVoo7tnxPzdluiM4bbkyiKhbbOzzKyau89x9xFlUJcBzwDPuXtzoAVQE7h1V+suibt/5e4DdjWusD6KrCtBVwClSgTBcqKeFTCzKsDxFL69PmHu/oC7/2tn58fd9Rf+iD4wXxJ9eD4CMoDPgdXAPGAfYD3wD2A+cDSQDXQO858EzA3T3gzjMoGZwAfAu8ARu8vyhXENgWlh+RYCPcL4XKB+GH4OyAEWAcMqIO71RYyPjbEzkB2GRwKPA+8AE4GmwFvAAuBNoEkoNx54AJgDfAz0DeObAtPDtpwLdEskJuAU4P2wrd8AGoTxx4b1Oy9Mq5XItgnjewL/BZ4H/gfcBgwGZgEfAoeFcmnA00S3bc8GuhexLnoCL8a856OhngXAb8P4+8M6WQTcHBPLbURdxCwA/gNMK7AMtYl+B7QfcGf4PC0AhofpncKy5BDdTdgwjL8gxDw/LMO+Mdvnn0Tfm/8BA2K2z8IittXwYuLaF8gium39rRBLbF1VgTtCLAuAC2O2QTZRNzhLgQmAASOAX8L6ezuU7UX0fZ8LPAXUjLOtxwN/BF4Ir48P6zyX7Z/n34dtPA94EKgaxp9H9FmdBTwE3BOzna8OwxnAe2EZngX2L/E7VlE7pd3xj+gL9kgYfjd8cLPyVnYY78DvYl5nE+2E0oAvgGZhfL2YD2G1MPxr4OndbPmuAv4U80WoFYZjP5R5y7IP0Zf7gHKOeyvbd6TzgDPixFgwEeQA+4TXLwDnhuEhREeLeV/IV4nOjJsTdY5Yg2iHUSOUaU50u3PBmOIlgv3ZfifeUOAfMe+ft2Oumfd5KGnbhOGewPdECXtvomRxc5h2OTA6DD8BHB2GmwBLilgXPdmeCP6eN39e/AW2d9Xw+U4HDiA6OMpbvmuBu+Isxwchrsls/9zXA6qH5UoL484guqWc2M8TcAvbE8d4op1pFaA1Ud9lsOPOu+C2+ryYuNKJvs8rYpYxtq5hwJ/D8N5EybBZWGfriH4UW4VoR5+3rnPZ/hmsT3RQtV/MOroxTizjiX439R7RZ+YhooOF3FBHK6LPTPVQ/j7gHKLPwOdE+5q9iJJ7vESwADg2DP8ldhsX9VeZTpvLwyDg7jD8ZHi9sECZrURHLQV1JToS+RTA3b8L4+sAj5lZc6IkUr2sgy6FeMs3BRhnZtWJdpDz4sw3wsxOC8ONib5w5fkL8I3unlHKeaa4+8YwfBRwehh+HLg9ptx/3H0bsMzM/ge0BD4l6gAxg2h7t0jwPRsBk8ysIdEX9dMw/h1glJlNAJ7x0LdWAfG2TU54PdvdV0J+f1xTw/gPgePC8K+B1tGVEQBqm1nNMBy7LmL9mpjf7rj72jD4u9C/VzWinU9rojOBTcAjZvYi0ZFwUXoC97n7llDvd2bWFmgLvB5irAqsDOXbmtktQF2iRPlaTF3Phe2z2MwaxHmv6uy4rQ4sJq48r8d8P2P1AtJDP2kQfXebEy3rLN/eJ9o8ogQyo8D8XYnW1TthGfciShpFeYZo/R8JXBgz/gSig7TZoZ59gG9CuWx3Xx3imESBz6aZ1QHquvt/w6jHiJJpsZQIAjOrR3SK1s7MnOiD6kSnx7E2ufvWUlT9/4hOG08Lz1v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          },
          "metadata": {
            "needs_background": "light"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_grafica"
      ],
      "metadata": {
        "id": "XDEUyv-mwG8L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "e8f7b3f0-7836-4867-dd74-03c37d03c3dd"
      },
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Region   Ano  Consumo energia media por región  \\\n",
              "2          Africa  2002                             18290   \n",
              "7            Asia  2002                            118697   \n",
              "12         Europa  2002                             65126   \n",
              "17   Las Americas  2002                            129428   \n",
              "22        Oceania  2002                             63290   \n",
              "26  Oriente Medio  2002                             33005   \n",
              "\n",
              "    PIB media por región  Uso de energia             PIB  \n",
              "2            20607796202          475563    535802701253  \n",
              "7           291415084000         3086123   7576792184024  \n",
              "12          257670235745         2670205  10564479665585  \n",
              "17          568067069429         3106287  13633609666309  \n",
              "22          230231720313          126580    460463440627  \n",
              "26           56329575160          396067    675954901929  "
            ],
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              "          const element = document.querySelector('#df-305a3f8e-36e3-438f-a5b3-b5704de82bbd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Se grafica el consumo PIB por año en las regiones\n",
        "len(anos_consulta)\n",
        "pos_for = 0\n",
        "data_grafica = Info_general_grafica[ (Info_general_grafica['Ano'] == anos_consulta['Ano'][pos_for]) ]\n",
        "\n",
        "plt.title(\"PIB Region año \"+anos_consulta['Ano'][pos_for])\n",
        "\n",
        "x_data_axis = data_grafica[\"Region\"]\n",
        "y_data_axis = data_grafica[\"PIB\"]\n",
        "z_data_axis = data_grafica[\"PIB media por región\"]\n",
        "\n",
        "plt.bar(np.arange(len(x_data_axis)) - 0.2, y_data_axis, 0.4, label = \"PIB Total\")\n",
        "plt.bar(np.arange(len(x_data_axis)) + 0.2, z_data_axis, 0.4, label = \"PIB medio\")\n",
        "plt.xticks(np.arange(len(x_data_axis)), x_data_axis)\n",
        "plt.xlabel(\"Región\")\n",
        "plt.ylabel(\"PIB\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "JjopSvNKnkNJ",
        "outputId": "6d5c8bfc-2dac-4410-f753-be975e1d0895"
      },
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #Se grafica el consumo PIB por año en las regiones\n",
        "len(anos_consulta)\n",
        "pos_for = 1\n",
        "data_grafica = Info_general_grafica[ (Info_general_grafica['Ano'] == anos_consulta['Ano'][pos_for]) ]\n",
        "\n",
        "plt.title(\"PIB Region año \"+anos_consulta['Ano'][pos_for])\n",
        "\n",
        "x_data_axis = data_grafica[\"Region\"]\n",
        "y_data_axis = data_grafica[\"PIB\"]\n",
        "z_data_axis = data_grafica[\"PIB media por región\"]\n",
        "\n",
        "plt.bar(np.arange(len(x_data_axis)) - 0.2, y_data_axis, 0.4, label = \"PIB Total\")\n",
        "plt.bar(np.arange(len(x_data_axis)) + 0.2, z_data_axis, 0.4, label = \"PIB medio\")\n",
        "plt.xticks(np.arange(len(x_data_axis)), x_data_axis)\n",
        "plt.xlabel(\"Región\")\n",
        "plt.ylabel(\"PIB\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1LTY5kRYn9vC",
        "outputId": "cf3a7e64-f82a-43b2-80bd-3345da0eaece",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}