Data_Analysis.ipynb

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      "source": [
        "# Taller de Data Analysis - Academia Desafío Latam\n"
      ],
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        "#Importemos algunas de las librerías más clásicas para el manejo de datos en Python\n",
        "\n",
        "#Pandas es la librería básica para la manipulación y análisis de datos\n",
        "import pandas as pd\n",
        "\n",
        "#Numpy es la biblioteca para crear vectores y matrices, además de un conjunto grande de funciones matemáticas\n",
        "import numpy as np\n",
        "\n",
        "#Seaborn es una librería que usamos para graficar\n",
        "import seaborn as sns\n",
        "\n",
        "#Statsmodels es la biblioteca para realizar modelos\n",
        "import statsmodels.formula.api as smf"
      ],
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      "source": [
        "numero_pi = np.pi\n"
      ],
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              "3.141592653589793"
            ]
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        "Dentro de **pandas** existe una estructura de datos que facilita el trabajo de análisis de datos, se conocen como \"Dataframe\". Corresponden a una estructura ordenada por filas y columnas (como una pestaña de excel).\n",
        "\n",
        "Pandas tiene múltiples funciones, las que nos permitirán trabajar datos de una manera muy sencilla y amigable.\n",
        "\n",
        "## Ahora, ¡manos a la obra!"
      ],
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      "source": [
        "Trabajaremos con el dataset 'nations', el cual contiene información sobre diferentes atributos de desarrollo humano en 194 países, recolectados por las Naciones Unidas.\n",
        "\n",
        "* Contiene información a nivel mundial sobre demografía:\n",
        "    * `country`: País.\n",
        "    * `region`: Continente del país.\n",
        "    * `gdp`: Producto Interno Bruto per cápita, precios 2005.\n",
        "    * `school`: Promedio años de escolaridad.\n",
        "    * `adfert`: Fertilidad adolescente (Nacimientos 1:1000 en mujeres entre 15 y 19).\n",
        "    * `chldmort`: Probabilidad de muerte antes de los 5 años por cada 1000.\n",
        "    * `life`: Esperanza de vida al nacer.\n",
        "    * `pop`: Población total.\n",
        "    * `urban`: Porcentaje de población urbana.\n",
        "    * `femlab`: Tasa entre hombres y mujeres en el mercado laboral.\n",
        "    * `literacy`: Tasa de alfabetismo.\n",
        "    * `co2`: Toneladas de Co2 emitidas per cápita.\n",
        "    * `gini`: Coeficiente de desigualdad del ingreso.\n",
        "\n"
      ],
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      "cell_type": "code",
      "source": [
        "df_nations = pd.read_csv(\"https://raw.githubusercontent.com/DireccionAcademicaADL/Nations-DB/main/nations.csv\", encoding=\"ISO-8859-1\")"
      ],
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              "     Unnamed: 0          country   region           gdp     school  \\\n",
              "0             1          Algeria   Africa   7300.399902   6.716667   \n",
              "1             2            Benin   Africa   1338.800049   3.100000   \n",
              "2             3         Botswana   Africa  12307.400391   8.600000   \n",
              "3             4     Burkina Faso   Africa   1063.400024   1.300000   \n",
              "4             5          Burundi   Africa    349.200012   2.483333   \n",
              "..          ...              ...      ...           ...        ...   \n",
              "189         190            Samoa  Oceania   4012.600098  10.300000   \n",
              "190         191  Solomon Islands  Oceania   2249.199951   4.500000   \n",
              "191         192            Tonga  Oceania   4072.199951  10.133333   \n",
              "192         193           Tuvalu  Oceania           NaN        NaN   \n",
              "193         194          Vanuatu  Oceania   3809.800049   6.700000   \n",
              "\n",
              "         adfert  chldmort       life       pop      urban  femlab   literacy  \\\n",
              "0      7.300000     34.75  72.316666  34172236  64.933334  0.4522  72.599998   \n",
              "1    111.699997    122.75  54.733334   8237634  41.000000  0.8482  41.700001   \n",
              "2     52.099998     60.25  52.250000   1941233  59.250000  0.8870  84.099998   \n",
              "3    124.800003    170.50  53.783333  15308383  23.583334  0.8584  23.600000   \n",
              "4     18.600000    168.50  48.866665   7821783  10.250000  1.0344  66.599998   \n",
              "..          ...       ...        ...       ...        ...     ...        ...   \n",
              "189   28.299999     26.75  71.533333    181600  20.666668  0.5010  98.800003   \n",
              "190   70.300003     36.00  66.500000    503617  17.766666  0.4858        NaN   \n",
              "191   22.299999     19.25  71.833336    102550  23.266666  0.7150  99.000000   \n",
              "192   23.299999     36.50  66.033333      9767  49.233334     NaN        NaN   \n",
              "193   54.000000     17.75  69.966667    225317  24.500000  0.8988  82.000000   \n",
              "\n",
              "       co2       gini  \n",
              "0    15.00        NaN  \n",
              "1     1.20        NaN  \n",
              "2     9.20        NaN  \n",
              "3     0.20        NaN  \n",
              "4     0.10  33.299999  \n",
              "..     ...        ...  \n",
              "189   3.10        NaN  \n",
              "190   1.40        NaN  \n",
              "191   4.85        NaN  \n",
              "192    NaN        NaN  \n",
              "193   1.50        NaN  \n",
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              "      <td>8.600000</td>\n",
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              "      <td>349.200012</td>\n",
              "      <td>2.483333</td>\n",
              "      <td>18.600000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_nations",
              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1,\n        \"max\": 194,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          139,\n          17,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"Austria\",\n          \"Eritrea\",\n          \"Latvia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Americas\",\n          \"Oceania\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13942.341216777108,\n        \"min\": 279.799987792969,\n        \"max\": 74906.0,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          6441.25,\n          579.599975585938,\n          4245.60009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.95958880783582,\n        \"min\": 1.15000009536743,\n        \"max\": 12.6999998092651,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          12.1999998092651,\n          6.23333358764648,\n          11.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.06611951599424,\n        \"min\": 1.0,\n        \"max\": 207.100006103516,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          16.7999992370605,\n          18.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.80940042615551,\n        \"min\": 2.25,\n        \"max\": 209.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          49.5,\n          108.25,\n          38.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.055404060893808,\n        \"min\": 45.8499984741211,\n        \"max\": 82.7666625976562,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          57.3666687011719,\n          51.4333343505859,\n          80.0666656494141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131400365,\n        \"min\": 9767,\n        \"max\": 1324696064,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          8320033,\n          4871634,\n          2277500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.43910064890756,\n        \"min\": 10.25,\n        \"max\": 100.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          20.6666679382324,\n          78.466667175293,\n          84.8833312988281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18287073906229526,\n        \"min\": 0.19420000910759,\n        \"max\": 1.03439998626709,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.70059996843338,\n          0.723200023174286,\n          0.851000010967255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.46635915094258,\n        \"min\": 23.6000003814697,\n        \"max\": 100.0,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          56.9000015258789,\n          99.5,\n          66.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 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            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# El método 'head()' nos permite ver \"la cabecera\" del dataset\n",
        "\n",
        "df_nations.head()"
      ],
      "metadata": {
        "id": "qmV9WZjTtJHd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e80cc08f-6202-43dc-ed70-44a598c53dc2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0       country  region           gdp    school      adfert  \\\n",
              "0           1       Algeria  Africa   7300.399902  6.716667    7.300000   \n",
              "1           2         Benin  Africa   1338.800049  3.100000  111.699997   \n",
              "2           3      Botswana  Africa  12307.400391  8.600000   52.099998   \n",
              "3           4  Burkina Faso  Africa   1063.400024  1.300000  124.800003   \n",
              "4           5       Burundi  Africa    349.200012  2.483333   18.600000   \n",
              "\n",
              "   chldmort       life       pop      urban  femlab   literacy   co2  \\\n",
              "0     34.75  72.316666  34172236  64.933334  0.4522  72.599998  15.0   \n",
              "1    122.75  54.733334   8237634  41.000000  0.8482  41.700001   1.2   \n",
              "2     60.25  52.250000   1941233  59.250000  0.8870  84.099998   9.2   \n",
              "3    170.50  53.783333  15308383  23.583334  0.8584  23.600000   0.2   \n",
              "4    168.50  48.866665   7821783  10.250000  1.0344  66.599998   0.1   \n",
              "\n",
              "        gini  \n",
              "0        NaN  \n",
              "1        NaN  \n",
              "2        NaN  \n",
              "3        NaN  \n",
              "4  33.299999  "
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7f7728da-6ee5-4d94-8317-d0431aa48a2f');\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",
              "\n",
              "\n",
              "<div id=\"df-2cac911e-bc6c-4a96-9104-98022448e310\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cac911e-bc6c-4a96-9104-98022448e310')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2cac911e-bc6c-4a96-9104-98022448e310 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_nations",
              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 1,\n        \"max\": 194,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          139,\n          17,\n          156\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"Austria\",\n          \"Eritrea\",\n          \"Latvia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Americas\",\n          \"Oceania\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13942.341216777108,\n        \"min\": 279.799987792969,\n        \"max\": 74906.0,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          6441.25,\n          579.599975585938,\n          4245.60009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.95958880783582,\n        \"min\": 1.15000009536743,\n        \"max\": 12.6999998092651,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          12.1999998092651,\n          6.23333358764648,\n          11.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.06611951599424,\n        \"min\": 1.0,\n        \"max\": 207.100006103516,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          16.7999992370605,\n          18.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.80940042615551,\n        \"min\": 2.25,\n        \"max\": 209.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          49.5,\n          108.25,\n          38.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.055404060893808,\n        \"min\": 45.8499984741211,\n        \"max\": 82.7666625976562,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          57.3666687011719,\n          51.4333343505859,\n          80.0666656494141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131400365,\n        \"min\": 9767,\n        \"max\": 1324696064,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          8320033,\n          4871634,\n          2277500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.43910064890756,\n        \"min\": 10.25,\n        \"max\": 100.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          20.6666679382324,\n          78.466667175293,\n          84.8833312988281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18287073906229526,\n        \"min\": 0.19420000910759,\n        \"max\": 1.03439998626709,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.70059996843338,\n          0.723200023174286,\n          0.851000010967255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.46635915094258,\n        \"min\": 23.6000003814697,\n        \"max\": 100.0,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          56.9000015258789,\n          99.5,\n          66.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.670704032882238,\n        \"min\": 0.100000001490116,\n        \"max\": 210.649993896484,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          9.5,\n          70.5500030517578,\n          20.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gini\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.487873610842923,\n        \"min\": 19.0,\n        \"max\": 58.5,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          45.7999992370605,\n          33.2999992370605,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Con 'tail()' ¿qué se puede ver?"
      ],
      "metadata": {
        "id": "ayENhbjRtzXw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.tail(5)"
      ],
      "metadata": {
        "id": "25ebwJpytzaU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "02808146-70d3-4643-fca0-e5c186056300"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0          country   region          gdp     school     adfert  \\\n",
              "189         190            Samoa  Oceania  4012.600098  10.300000  28.299999   \n",
              "190         191  Solomon Islands  Oceania  2249.199951   4.500000  70.300003   \n",
              "191         192            Tonga  Oceania  4072.199951  10.133333  22.299999   \n",
              "192         193           Tuvalu  Oceania          NaN        NaN  23.299999   \n",
              "193         194          Vanuatu  Oceania  3809.800049   6.700000  54.000000   \n",
              "\n",
              "     chldmort       life     pop      urban  femlab   literacy   co2  gini  \n",
              "189     26.75  71.533333  181600  20.666668  0.5010  98.800003  3.10   NaN  \n",
              "190     36.00  66.500000  503617  17.766666  0.4858        NaN  1.40   NaN  \n",
              "191     19.25  71.833336  102550  23.266666  0.7150  99.000000  4.85   NaN  \n",
              "192     36.50  66.033333    9767  49.233334     NaN        NaN   NaN   NaN  \n",
              "193     17.75  69.966667  225317  24.500000  0.8988  82.000000  1.50   NaN  "
            ],
            "text/html": [
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              "      <th>country</th>\n",
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              "      <th>chldmort</th>\n",
              "      <th>life</th>\n",
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              "      <th>urban</th>\n",
              "      <th>femlab</th>\n",
              "      <th>literacy</th>\n",
              "      <th>co2</th>\n",
              "      <th>gini</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>190</td>\n",
              "      <td>Samoa</td>\n",
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              "      <td>191</td>\n",
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              "      <td>1.40</td>\n",
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              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>192</td>\n",
              "      <td>Tonga</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>4072.199951</td>\n",
              "      <td>10.133333</td>\n",
              "      <td>22.299999</td>\n",
              "      <td>19.25</td>\n",
              "      <td>71.833336</td>\n",
              "      <td>102550</td>\n",
              "      <td>23.266666</td>\n",
              "      <td>0.7150</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>4.85</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>193</td>\n",
              "      <td>Tuvalu</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.299999</td>\n",
              "      <td>36.50</td>\n",
              "      <td>66.033333</td>\n",
              "      <td>9767</td>\n",
              "      <td>49.233334</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>194</td>\n",
              "      <td>Vanuatu</td>\n",
              "      <td>Oceania</td>\n",
              "      <td>3809.800049</td>\n",
              "      <td>6.700000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>17.75</td>\n",
              "      <td>69.966667</td>\n",
              "      <td>225317</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>0.8988</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.50</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da5d7444-295f-4123-90c6-a42efa0bcbe6')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da5d7444-295f-4123-90c6-a42efa0bcbe6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-da5d7444-295f-4123-90c6-a42efa0bcbe6');\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",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 190,\n        \"max\": 194,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          191,\n          194,\n          192\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Solomon Islands\",\n          \"Vanuatu\",\n          \"Tonga\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Oceania\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 865.1548730583614,\n        \"min\": 2249.19995117188,\n        \"max\": 4072.19995117188,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2249.19995117188\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.813509126070425,\n        \"min\": 4.5,\n        \"max\": 10.3000001907349,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.462247411611347,\n        \"min\": 22.2999992370605,\n        \"max\": 70.3000030517578,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          70.3000030517578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.896979824637123,\n        \"min\": 17.75,\n        \"max\": 36.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7514043695371386,\n        \"min\": 66.033332824707,\n        \"max\": 71.8333358764648,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          66.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 186241,\n        \"min\": 9767,\n        \"max\": 503617,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          503617\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.647498670781847,\n        \"min\": 17.7666664123535,\n        \"max\": 49.2333335876465,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          17.7666664123535\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.196034866367667,\n        \"min\": 0.485799998044968,\n        \"max\": 0.898800015449524,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.485799998044968\n        ],\n        \"semantic_type\": \"\",\n     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\"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cuando necesites ver la documentación de un método, si trabajas en Colab de google basta con poner el cursor sobre el método, pero si usas Jupyter notebook, te debes ubicar sobre el texto y presionar *shift + tab*"
      ],
      "metadata": {
        "id": "Y9VuNBtfuGBP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si se fijaron en nuestro dataset, se puede ver que la columna \"Unnamed:  0\" parece no tener información relevante, por lo que la eliminaremos. Para ello, el método a usar es `drop()`"
      ],
      "metadata": {
        "id": "n_6hEoDEuh7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.drop(columns=[\"Unnamed: 0\"], inplace = True)"
      ],
      "metadata": {
        "id": "M9bC7WuItzdV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.head()"
      ],
      "metadata": {
        "id": "MRCrqefgtzfy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "fd4668ff-c56c-45f4-bdac-08f2091e6c2e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        country  region           gdp    school      adfert  chldmort  \\\n",
              "0       Algeria  Africa   7300.399902  6.716667    7.300000     34.75   \n",
              "1         Benin  Africa   1338.800049  3.100000  111.699997    122.75   \n",
              "2      Botswana  Africa  12307.400391  8.600000   52.099998     60.25   \n",
              "3  Burkina Faso  Africa   1063.400024  1.300000  124.800003    170.50   \n",
              "4       Burundi  Africa    349.200012  2.483333   18.600000    168.50   \n",
              "\n",
              "        life       pop      urban  femlab   literacy   co2       gini  \n",
              "0  72.316666  34172236  64.933334  0.4522  72.599998  15.0        NaN  \n",
              "1  54.733334   8237634  41.000000  0.8482  41.700001   1.2        NaN  \n",
              "2  52.250000   1941233  59.250000  0.8870  84.099998   9.2        NaN  \n",
              "3  53.783333  15308383  23.583334  0.8584  23.600000   0.2        NaN  \n",
              "4  48.866665   7821783  10.250000  1.0344  66.599998   0.1  33.299999  "
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              "      <td>349.200012</td>\n",
              "      <td>2.483333</td>\n",
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              "      <td>7821783</td>\n",
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              "            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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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-613a5cc5-fa52-4907-8667-79a36a20159b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_nations",
              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"Austria\",\n          \"Eritrea\",\n          \"Latvia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Americas\",\n          \"Oceania\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13942.341216777108,\n        \"min\": 279.799987792969,\n        \"max\": 74906.0,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          6441.25,\n          579.599975585938,\n          4245.60009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.95958880783582,\n        \"min\": 1.15000009536743,\n        \"max\": 12.6999998092651,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          12.1999998092651,\n          6.23333358764648,\n          11.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.06611951599424,\n        \"min\": 1.0,\n        \"max\": 207.100006103516,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          16.7999992370605,\n          18.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.80940042615551,\n        \"min\": 2.25,\n        \"max\": 209.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          49.5,\n          108.25,\n          38.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.055404060893808,\n        \"min\": 45.8499984741211,\n        \"max\": 82.7666625976562,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          57.3666687011719,\n          51.4333343505859,\n          80.0666656494141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131400365,\n        \"min\": 9767,\n        \"max\": 1324696064,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          8320033,\n          4871634,\n          2277500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.43910064890756,\n        \"min\": 10.25,\n        \"max\": 100.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          20.6666679382324,\n          78.466667175293,\n          84.8833312988281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18287073906229526,\n        \"min\": 0.19420000910759,\n        \"max\": 1.03439998626709,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.70059996843338,\n          0.723200023174286,\n          0.851000010967255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.46635915094258,\n        \"min\": 23.6000003814697,\n        \"max\": 100.0,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          56.9000015258789,\n          99.5,\n          66.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.670704032882238,\n        \"min\": 0.100000001490116,\n        \"max\": 210.649993896484,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          9.5,\n          70.5500030517578,\n          20.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gini\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.487873610842923,\n        \"min\": 19.0,\n        \"max\": 58.5,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          45.7999992370605,\n          33.2999992370605,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.columns"
      ],
      "metadata": {
        "id": "zjEzZfUetzia",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e41522fe-bfa9-4bb4-dc4d-4f4c2e24bef0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['country', 'region', 'gdp', 'school', 'adfert', 'chldmort', 'life',\n",
              "       'pop', 'urban', 'femlab', 'literacy', 'co2', 'gini'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si queremos observar solo una columna del dataset, la sintaxis es la siguiente:\n",
        "\n",
        "`df_nations[\"nombre_columna\"]`"
      ],
      "metadata": {
        "id": "KDCqnQc4vCD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations[\"region\"]"
      ],
      "metadata": {
        "id": "HaumE2_Etzlk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b404f6dc-a150-42bd-8363-4588b774377c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Africa\n",
              "1       Africa\n",
              "2       Africa\n",
              "3       Africa\n",
              "4       Africa\n",
              "        ...   \n",
              "189    Oceania\n",
              "190    Oceania\n",
              "191    Oceania\n",
              "192    Oceania\n",
              "193    Oceania\n",
              "Name: region, Length: 194, dtype: object"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Americas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Asia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Oceania</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ¿Cómo se debería hacer para observar la columna \"country\"?"
      ],
      "metadata": {
        "id": "of3n2cZ76LGM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations[\"country\"]"
      ],
      "metadata": {
        "id": "MsUID1jiIDEu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "41534044-173c-495c-9b21-85f6fc5951d8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              Algeria\n",
              "1                Benin\n",
              "2             Botswana\n",
              "3         Burkina Faso\n",
              "4              Burundi\n",
              "            ...       \n",
              "189              Samoa\n",
              "190    Solomon Islands\n",
              "191              Tonga\n",
              "192             Tuvalu\n",
              "193            Vanuatu\n",
              "Name: country, Length: 194, dtype: object"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Algeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Benin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Botswana</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Burkina Faso</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Burundi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cameroon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Cape Verde</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Central African Rep</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Chad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Comoros</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Congo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Congo (Dem Rep)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Côte d'Ivoire</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Djibouti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Egypt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Eritrea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Ethiopia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Gabon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Gambia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Ghana</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Guinea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Kenya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Lesotho</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Liberia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Libya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Madagascar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Malawi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Mali</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Mauritania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Mauritius</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Morocco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Mozambique</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Namibia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Niger</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Nigeria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Rwanda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Sao Tome/Principe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Senegal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Seychelles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Sierra Leone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Somalia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>South Africa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Sudan*</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Swaziland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Tanzania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Togo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Tunisia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Uganda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Zambia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Zimbabwe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Argentina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Bahamas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Barbados</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Belize</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Bolivia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Brazil</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Canada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Chile</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Colombia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Costa Rica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>Cuba</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Dominica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Dominican Republic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Ecuador</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>El Salvador</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Grenada</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Guatemala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>Guyana</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Haiti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>Honduras</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Jamaica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>Mexico</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Nicaragua</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>Panama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Paraguay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Peru</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>Saint Kitts/Nevis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Saint Lucia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>St Vincent/Grenadines</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>Suriname</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Trinidad/Tobago</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>United States</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Uruguay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Venezuela</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Afghanistan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Armenia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Azerbaijan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>Bahrain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Bangladesh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Bhutan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>Brunei Darussalam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Cambodia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>China</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Cyprus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Georgia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Hong Kong, China</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>India</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Indonesia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Iran</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Iraq</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>Israel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>Japan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Jordan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Kazakhstan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Kuwait</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Kyrgyzstan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Lao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Lebanon</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Malaysia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>Maldives</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Mongolia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>Myanmar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Nepal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>North Korea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Oman</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Pakistan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>Palestinian Territory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>Philippines</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Qatar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>Singapore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>South Korea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125</th>\n",
              "      <td>Sri Lanka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>Syria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>Tajikistan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Thailand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Timor-Leste</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Turkey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Turkmenistan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Uzbekistan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Viet Nam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Yemen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Albania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Andorra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Austria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Belarus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Belgium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Bosnia/Herzegovina</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Bulgaria</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Croatia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Czech Republic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Denmark</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Estonia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Finland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>France</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Germany</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Greece</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>Hungary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>Iceland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Ireland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Italy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Latvia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>Liechtenstein</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Lithuania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>Luxembourg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>Macedonia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>Malta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Moldova</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>Monaco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Montenegro</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>Netherlands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Norway</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Poland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Portugal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Romania</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>Russian Federation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>San Marino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>171</th>\n",
              "      <td>Serbia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Slovakia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>Slovenia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Spain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>Sweden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Switzerland</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Ukraine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>Angola</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>Australia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181</th>\n",
              "      <td>Fiji</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>Kiribati</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>Marshall Is</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>Micronesia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>Nauru</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>New Zealand</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>Palau</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>Papua New Guinea</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>Samoa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>Solomon Islands</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Tonga</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>Tuvalu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>Vanuatu</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si se quiere crear una nueva variable ( o atributo) en el dataset, no es necesario iniciar la variable primero, se puede crear directamente:\n",
        "\n",
        "Por ejemplo, el atributo \"gdp\" se mide en dólares corrientes del año 2005. Si quisieramos saber a cuántos pesos de la actualidad equivalen esos dólares, bastaría con hacer la conversión en una nueva variable, que llamaremos \"gdp_pesos2021\"\n",
        "Para simplificar el tipo de cambio, usaremos 1 USD = 850 pesos chilenos."
      ],
      "metadata": {
        "id": "wbzBbqdV9ik6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations[\"gdp_pesos2021\"] = df_nations[\"gdp\"]*850"
      ],
      "metadata": {
        "id": "t7tWmkEy87Vq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.head()"
      ],
      "metadata": {
        "id": "S5nxoyNLISwv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "ccbaa355-c555-4aba-c295-5a1698a9c0d6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        country  region           gdp    school      adfert  chldmort  \\\n",
              "0       Algeria  Africa   7300.399902  6.716667    7.300000     34.75   \n",
              "1         Benin  Africa   1338.800049  3.100000  111.699997    122.75   \n",
              "2      Botswana  Africa  12307.400391  8.600000   52.099998     60.25   \n",
              "3  Burkina Faso  Africa   1063.400024  1.300000  124.800003    170.50   \n",
              "4       Burundi  Africa    349.200012  2.483333   18.600000    168.50   \n",
              "\n",
              "        life       pop      urban  femlab   literacy   co2       gini  \\\n",
              "0  72.316666  34172236  64.933334  0.4522  72.599998  15.0        NaN   \n",
              "1  54.733334   8237634  41.000000  0.8482  41.700001   1.2        NaN   \n",
              "2  52.250000   1941233  59.250000  0.8870  84.099998   9.2        NaN   \n",
              "3  53.783333  15308383  23.583334  0.8584  23.600000   0.2        NaN   \n",
              "4  48.866665   7821783  10.250000  1.0344  66.599998   0.1  33.299999   \n",
              "\n",
              "   gdp_pesos2021  \n",
              "0   6.205340e+06  \n",
              "1   1.137980e+06  \n",
              "2   1.046129e+07  \n",
              "3   9.038900e+05  \n",
              "4   2.968200e+05  "
            ],
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              "      <td>Algeria</td>\n",
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0.851000010967255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.46635915094258,\n        \"min\": 23.6000003814697,\n        \"max\": 100.0,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          56.9000015258789,\n          99.5,\n          66.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.670704032882238,\n        \"min\": 0.100000001490116,\n        \"max\": 210.649993896484,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          9.5,\n          70.5500030517578,\n          20.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gini\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.487873610842923,\n        \"min\": 19.0,\n        \"max\": 58.5,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          45.7999992370605,\n          33.2999992370605,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_pesos2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11850990.034260543,\n        \"min\": 237829.98962402364,\n        \"max\": 63670100.0,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          5475062.5,\n          492659.9792480473,\n          3608760.0830078125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Para continuar con nuestro análisis de los datos, vamos a plantear una serie de interrogantes que guiarán el taller:\n",
        "1. ¿Qué tipos de atributos nos encontramos en el dataset?\n",
        "2. ¿Cuántos datos tenemos en cada región?\n",
        "3. ¿Cuántos países tienen índices de CO_2 mayores que el promedio?\n",
        "4. ¿Que se puede decir del alfabetismo en áfrica o europa?\n"
      ],
      "metadata": {
        "id": "sOLiFcaLvdiF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pregunta 1**\n",
        "Para saber los tipo de atributos contenidos en un dataset, podemos usar el método `info()` el cuál imprime un resumen conciso, informando el tipo de variables (categoricas, numéricas, string, etc) y además el número de valores no nulos de cada atributo y el uso de memoria."
      ],
      "metadata": {
        "id": "-IxorGlMyfS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.info()"
      ],
      "metadata": {
        "id": "Bdrxu1cjBUcJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa88bcb4-82fe-4435-fb0b-ec0bf5365b6f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 194 entries, 0 to 193\n",
            "Data columns (total 14 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   country        194 non-null    object \n",
            " 1   region         194 non-null    object \n",
            " 2   gdp            179 non-null    float64\n",
            " 3   school         188 non-null    float64\n",
            " 4   adfert         194 non-null    float64\n",
            " 5   chldmort       193 non-null    float64\n",
            " 6   life           194 non-null    float64\n",
            " 7   pop            194 non-null    int64  \n",
            " 8   urban          194 non-null    float64\n",
            " 9   femlab         177 non-null    float64\n",
            " 10  literacy       135 non-null    float64\n",
            " 11  co2            185 non-null    float64\n",
            " 12  gini           81 non-null     float64\n",
            " 13  gdp_pesos2021  179 non-null    float64\n",
            "dtypes: float64(11), int64(1), object(2)\n",
            "memory usage: 21.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Otro método muy útil cuando estamos explorando los datos, es `describe()`; el cuál calcula estadísticas básicas de los atributos numéricos (float o int), dejando fuera los atributos categóricos o cualitativos."
      ],
      "metadata": {
        "id": "17gUaIHgCn5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.describe()"
      ],
      "metadata": {
        "id": "SMRY5j7TtznY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "efeaeeb9-478d-4741-8edb-46a885a8a58e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                gdp      school      adfert    chldmort        life  \\\n",
              "count    179.000000  188.000000  194.000000  193.000000  194.000000   \n",
              "mean   12118.739193    7.459220   51.814433   47.650259   68.729295   \n",
              "std    13942.341217    2.959589   44.066120   52.809400   10.055404   \n",
              "min      279.799988    1.150000    1.000000    2.250000   45.849998   \n",
              "25%     2100.300049    5.225000   16.500000   10.250000   63.745833   \n",
              "50%     6932.799805    7.950000   39.350000   24.000000   72.183331   \n",
              "75%    17576.299805    9.850000   74.375002   69.750000   75.545835   \n",
              "max    74906.000000   12.700000  207.100006  209.000000   82.766663   \n",
              "\n",
              "                pop       urban      femlab    literacy         co2  \\\n",
              "count  1.940000e+02  194.000000  177.000000  135.000000  185.000000   \n",
              "mean   3.437752e+07   55.434880    0.691606   81.533148   17.885946   \n",
              "std    1.314004e+08   23.439101    0.182871   19.466359   25.670704   \n",
              "min    9.767000e+03   10.250000    0.194200   23.600000    0.100000   \n",
              "25%    1.483754e+06   36.325000    0.578200   70.099998    2.250000   \n",
              "50%    6.784909e+06   56.475000    0.730400   89.699997    8.450000   \n",
              "75%    2.225532e+07   73.483334    0.820600   97.699997   25.000000   \n",
              "max    1.324696e+09  100.000000    1.034400  100.000000  210.649994   \n",
              "\n",
              "            gini  gdp_pesos2021  \n",
              "count  81.000000   1.790000e+02  \n",
              "mean   40.477778   1.030093e+07  \n",
              "std     8.487874   1.185099e+07  \n",
              "min    19.000000   2.378300e+05  \n",
              "25%    34.000000   1.785255e+06  \n",
              "50%    39.700001   5.892880e+06  \n",
              "75%    46.200001   1.493985e+07  \n",
              "max    58.500000   6.367010e+07  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gdp</th>\n",
              "      <th>school</th>\n",
              "      <th>adfert</th>\n",
              "      <th>chldmort</th>\n",
              "      <th>life</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>179.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>194.000000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>194.000000</td>\n",
              "      <td>1.940000e+02</td>\n",
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              "      <td>177.000000</td>\n",
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              "      <td>185.000000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>7.459220</td>\n",
              "      <td>51.814433</td>\n",
              "      <td>47.650259</td>\n",
              "      <td>68.729295</td>\n",
              "      <td>3.437752e+07</td>\n",
              "      <td>55.434880</td>\n",
              "      <td>0.691606</td>\n",
              "      <td>81.533148</td>\n",
              "      <td>17.885946</td>\n",
              "      <td>40.477778</td>\n",
              "      <td>1.030093e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13942.341217</td>\n",
              "      <td>2.959589</td>\n",
              "      <td>44.066120</td>\n",
              "      <td>52.809400</td>\n",
              "      <td>10.055404</td>\n",
              "      <td>1.314004e+08</td>\n",
              "      <td>23.439101</td>\n",
              "      <td>0.182871</td>\n",
              "      <td>19.466359</td>\n",
              "      <td>25.670704</td>\n",
              "      <td>8.487874</td>\n",
              "      <td>1.185099e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>279.799988</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>45.849998</td>\n",
              "      <td>9.767000e+03</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>0.194200</td>\n",
              "      <td>23.600000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>2.378300e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2100.300049</td>\n",
              "      <td>5.225000</td>\n",
              "      <td>16.500000</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>63.745833</td>\n",
              "      <td>1.483754e+06</td>\n",
              "      <td>36.325000</td>\n",
              "      <td>0.578200</td>\n",
              "      <td>70.099998</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.785255e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6932.799805</td>\n",
              "      <td>7.950000</td>\n",
              "      <td>39.350000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>72.183331</td>\n",
              "      <td>6.784909e+06</td>\n",
              "      <td>56.475000</td>\n",
              "      <td>0.730400</td>\n",
              "      <td>89.699997</td>\n",
              "      <td>8.450000</td>\n",
              "      <td>39.700001</td>\n",
              "      <td>5.892880e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>17576.299805</td>\n",
              "      <td>9.850000</td>\n",
              "      <td>74.375002</td>\n",
              "      <td>69.750000</td>\n",
              "      <td>75.545835</td>\n",
              "      <td>2.225532e+07</td>\n",
              "      <td>73.483334</td>\n",
              "      <td>0.820600</td>\n",
              "      <td>97.699997</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>46.200001</td>\n",
              "      <td>1.493985e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>12.700000</td>\n",
              "      <td>207.100006</td>\n",
              "      <td>209.000000</td>\n",
              "      <td>82.766663</td>\n",
              "      <td>1.324696e+09</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.034400</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>210.649994</td>\n",
              "      <td>58.500000</td>\n",
              "      <td>6.367010e+07</td>\n",
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              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24682.394113284514,\n        \"min\": 179.0,\n        \"max\": 74906.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12118.739193367559,\n          6932.7998046875,\n          179.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.18515650326673,\n        \"min\": 1.15000009536743,\n        \"max\": 188.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.459219855196933,\n          7.950000047683715,\n          188.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        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            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Si queremos observar sólo 1 atributo\n",
        "\n",
        "df_nations[\"gini\"].describe()"
      ],
      "metadata": {
        "id": "sVuRbcZftzql",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "f68150c3-6ec8-48a4-811b-c906c2e40196"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    81.000000\n",
              "mean     40.477778\n",
              "std       8.487874\n",
              "min      19.000000\n",
              "25%      34.000000\n",
              "50%      39.700001\n",
              "75%      46.200001\n",
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              "Name: gini, dtype: float64"
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>81.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.477778</td>\n",
              "    </tr>\n",
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              "      <td>8.487874</td>\n",
              "    </tr>\n",
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              "      <th>min</th>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.700001</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>46.200001</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>58.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> float64</label>"
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          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations[\"gini\"].mean()"
      ],
      "metadata": {
        "id": "RGcUpxOiVb3z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d917a4a2-6271-4caa-aa66-9e1080f5c67f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.4777778107443"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations[\"gini\"].count()"
      ],
      "metadata": {
        "id": "lzugrvwUVl8s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a309529-3e66-4c35-fbc6-9931b38fe138"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# También se pueden usar métodos específicos como count(), mean(),  min() o max() para saber el valor específico del estadístico en una variable"
      ],
      "metadata": {
        "id": "9QQ1AqQyDV5N"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ¿cómo calculamos el valor promedio de gdp en el dataset?\n",
        "df_nations[\"gdp\"].mean()"
      ],
      "metadata": {
        "id": "XqR-3qa4DyuY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3315fe5d-a4e5-41ad-f34c-e281df798971"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12118.739193367559"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ¿Y cuánto es la tasa más baja de alfabetistmo en el dataset?\n",
        "df_nations[\"literacy\"].min()"
      ],
      "metadata": {
        "id": "SdXe5gMAX1kU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d95b601-f9c5-4fd7-f89f-0af0ae0b6bad"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.6000003814697"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pregunta 2** ¿Cuántos datos tenemos en cada región?\n",
        "\n",
        "\n",
        "Si queremos saber cuántos datos tiene cada región, se puede hacer a través de una tabla de frecuencias"
      ],
      "metadata": {
        "id": "fbQf_ENs0PSA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Para observar un atributo categórico usaremos una tabla de frecuencias\n",
        "df_nations[\"region\"].value_counts()"
      ],
      "metadata": {
        "id": "lH_Ekex1tzt2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f70d0e51-4227-48d9-8396-fba573073efe"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "region\n",
              "Africa      52\n",
              "Asia        49\n",
              "Europe      43\n",
              "Americas    35\n",
              "Oceania     15\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Africa</th>\n",
              "      <td>52</td>\n",
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              "    <tr>\n",
              "      <th>Asia</th>\n",
              "      <td>49</td>\n",
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              "    <tr>\n",
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              "      <td>43</td>\n",
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              "      <td>15</td>\n",
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              "</table><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 23
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    {
      "cell_type": "code",
      "source": [
        "# Otra forma de hacer este conteo, puede ser a través de una agrupación\n",
        "df_nations.groupby([\"region\"])[[\"country\"]].count()"
      ],
      "metadata": {
        "id": "7GiXPPEx_LlN",
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          "height": 238
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        "outputId": "e80381ef-61a2-4eb6-a6d1-8bee41d4361c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          country\n",
              "region           \n",
              "Africa         52\n",
              "Americas       35\n",
              "Asia           49\n",
              "Europe         43\n",
              "Oceania        15"
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
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              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fef2992a-4347-4c56-9e3e-0b5a0a54c7a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Americas\",\n          \"Oceania\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 15,\n        \"max\": 52,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          35,\n          15,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Siguiendo con la manipulación de los datos, un método que nos ayuda a recodificar una variable (o crear una nueva a partir de una condición), es el método `where()`; el cual proviene de la librería numpy y nos permite decidir, a través de una condición, qué va en el lugar de aquellas filas que cumplen la condición, y qué va en el lugar de las filas que no cumplen la condición. La sintáxis correcta de uso sería:\n",
        "\n",
        "df_nations[\"variable\"] = **np.where**(_Condición_, valor si verdadero, valor si falso)"
      ],
      "metadata": {
        "id": "-tZ4eguD_wIv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Usando where(), crearemos una variable nueva de co2, en dónde identifiquemos qué países están por debajo de la media, y qué países por sobre la media.\n",
        "df_nations[\"co2_recodificada\"] = np.where(df_nations[\"co2\"]> df_nations[\"co2\"].mean(), 1, 0)"
      ],
      "metadata": {
        "id": "AHTpIwVk_LiQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations.head()"
      ],
      "metadata": {
        "id": "W-kimvbI7rKY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "326732e9-8cb0-419c-8992-9d8803f80b2b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        country  region           gdp    school      adfert  chldmort  \\\n",
              "0       Algeria  Africa   7300.399902  6.716667    7.300000     34.75   \n",
              "1         Benin  Africa   1338.800049  3.100000  111.699997    122.75   \n",
              "2      Botswana  Africa  12307.400391  8.600000   52.099998     60.25   \n",
              "3  Burkina Faso  Africa   1063.400024  1.300000  124.800003    170.50   \n",
              "4       Burundi  Africa    349.200012  2.483333   18.600000    168.50   \n",
              "\n",
              "        life       pop      urban  femlab   literacy   co2       gini  \\\n",
              "0  72.316666  34172236  64.933334  0.4522  72.599998  15.0        NaN   \n",
              "1  54.733334   8237634  41.000000  0.8482  41.700001   1.2        NaN   \n",
              "2  52.250000   1941233  59.250000  0.8870  84.099998   9.2        NaN   \n",
              "3  53.783333  15308383  23.583334  0.8584  23.600000   0.2        NaN   \n",
              "4  48.866665   7821783  10.250000  1.0344  66.599998   0.1  33.299999   \n",
              "\n",
              "   gdp_pesos2021  co2_recodificada  \n",
              "0   6.205340e+06                 0  \n",
              "1   1.137980e+06                 0  \n",
              "2   1.046129e+07                 0  \n",
              "3   9.038900e+05                 0  \n",
              "4   2.968200e+05                 0  "
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>region</th>\n",
              "      <th>gdp</th>\n",
              "      <th>school</th>\n",
              "      <th>adfert</th>\n",
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              "      <th>life</th>\n",
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              "      <th>femlab</th>\n",
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              "      <th>co2</th>\n",
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              "      <th>gdp_pesos2021</th>\n",
              "      <th>co2_recodificada</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>7300.399902</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Benin</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>Africa</td>\n",
              "      <td>12307.400391</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>52.099998</td>\n",
              "      <td>60.25</td>\n",
              "      <td>52.250000</td>\n",
              "      <td>1941233</td>\n",
              "      <td>59.250000</td>\n",
              "      <td>0.8870</td>\n",
              "      <td>84.099998</td>\n",
              "      <td>9.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.046129e+07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1063.400024</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>124.800003</td>\n",
              "      <td>170.50</td>\n",
              "      <td>53.783333</td>\n",
              "      <td>15308383</td>\n",
              "      <td>23.583334</td>\n",
              "      <td>0.8584</td>\n",
              "      <td>23.600000</td>\n",
              "      <td>0.2</td>\n",
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              "      <td>9.038900e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>349.200012</td>\n",
              "      <td>2.483333</td>\n",
              "      <td>18.600000</td>\n",
              "      <td>168.50</td>\n",
              "      <td>48.866665</td>\n",
              "      <td>7821783</td>\n",
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              "      <td>1.0344</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
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              "        document.querySelector('#df-bd24e8a7-b6ca-4f27-b5aa-6ce03e009c62 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bd24e8a7-b6ca-4f27-b5aa-6ce03e009c62');\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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              "\n",
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              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-15528da6-83d7-477f-a7b1-a2e714134b98 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_nations",
              "summary": "{\n  \"name\": \"df_nations\",\n  \"rows\": 194,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 194,\n        \"samples\": [\n          \"Austria\",\n          \"Eritrea\",\n          \"Latvia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Americas\",\n          \"Oceania\",\n          \"Asia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13942.341216777108,\n        \"min\": 279.799987792969,\n        \"max\": 74906.0,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          6441.25,\n          579.599975585938,\n          4245.60009765625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.95958880783582,\n        \"min\": 1.15000009536743,\n        \"max\": 12.6999998092651,\n        \"num_unique_values\": 165,\n        \"samples\": [\n          12.1999998092651,\n          6.23333358764648,\n          11.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44.06611951599424,\n        \"min\": 1.0,\n        \"max\": 207.100006103516,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          16.7999992370605,\n          18.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.80940042615551,\n        \"min\": 2.25,\n        \"max\": 209.0,\n        \"num_unique_values\": 144,\n        \"samples\": [\n          49.5,\n          108.25,\n          38.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.055404060893808,\n        \"min\": 45.8499984741211,\n        \"max\": 82.7666625976562,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          57.3666687011719,\n          51.4333343505859,\n          80.0666656494141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131400365,\n        \"min\": 9767,\n        \"max\": 1324696064,\n        \"num_unique_values\": 194,\n        \"samples\": [\n          8320033,\n          4871634,\n          2277500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23.43910064890756,\n        \"min\": 10.25,\n        \"max\": 100.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          20.6666679382324,\n          78.466667175293,\n          84.8833312988281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18287073906229526,\n        \"min\": 0.19420000910759,\n        \"max\": 1.03439998626709,\n        \"num_unique_values\": 171,\n        \"samples\": [\n          0.70059996843338,\n          0.723200023174286,\n          0.851000010967255\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.46635915094258,\n        \"min\": 23.6000003814697,\n        \"max\": 100.0,\n        \"num_unique_values\": 112,\n        \"samples\": [\n          56.9000015258789,\n          99.5,\n          66.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.670704032882238,\n        \"min\": 0.100000001490116,\n        \"max\": 210.649993896484,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          9.5,\n          70.5500030517578,\n          20.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gini\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.487873610842923,\n        \"min\": 19.0,\n        \"max\": 58.5,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          45.7999992370605,\n          33.2999992370605,\n          36.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_pesos2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11850990.034260543,\n        \"min\": 237829.98962402364,\n        \"max\": 63670100.0,\n        \"num_unique_values\": 179,\n        \"samples\": [\n          5475062.5,\n          492659.9792480473,\n          3608760.0830078125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2_recodificada\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para observar los resultados, podemos hacer una tabla de frecuencias\n",
        "df_nations[\"co2_recodificada\"].value_counts()"
      ],
      "metadata": {
        "id": "xl3ffCN2EJ0k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "59427e56-882e-4553-8673-4c1370f67dc4"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "co2_recodificada\n",
              "0    130\n",
              "1     64\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>co2_recodificada</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con lo anterior podemos dar respuesta a la **Pregunta 3**\n",
        "¿Cuántos países tienen índices de _co2_ mayores a la media?"
      ],
      "metadata": {
        "id": "znOJQMMLEkBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations['co2_recodificada'].value_counts()[1]"
      ],
      "metadata": {
        "id": "HGjsg3Sy8EW1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f65846fd-d15f-497b-e1db-4e0becf41439"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Existen \" + str(df_nations['co2_recodificada'].value_counts()[1]) + \" países con índices de co2 mayores a la media\")"
      ],
      "metadata": {
        "id": "YbPyi2aF_LfF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c6c551c-020f-41f8-d757-d0b6f1a44d99"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Existen 64 países con índices de co2 mayores a la media\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Una variación del método value_counts() es que si pones un caracter dentro del paréntesis, te mostrará los datos en porcentaje:"
      ],
      "metadata": {
        "id": "NBGYIRUX_Lcg"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_nations[\"co2_recodificada\"].value_counts(\"%\")"
      ],
      "metadata": {
        "id": "9NENfrg4_LZX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "2f7e6c74-5b94-4fd2-aaf5-98acc3d60650"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "co2_recodificada\n",
              "0    0.670103\n",
              "1    0.329897\n",
              "Name: proportion, dtype: float64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>proportion</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>co2_recodificada</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.670103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.329897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pregunta 4** ¿Que se puede decir del alfabetismo en áfrica o europa?\n",
        "\n",
        "Para analizar un grupo específico de la muestra podemos utilizar filtros"
      ],
      "metadata": {
        "id": "JhqV3pjoHHVL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtro por variable \"Region\"\n",
        "df_nations[df_nations[\"region\"]==\"Africa\"]"
      ],
      "metadata": {
        "id": "bhHVToHL_LWt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "83b27c86-067c-439e-cc72-685454e68ac6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                country  region           gdp    school      adfert  chldmort  \\\n",
              "0               Algeria  Africa   7300.399902  6.716667    7.300000     34.75   \n",
              "1                 Benin  Africa   1338.800049  3.100000  111.699997    122.75   \n",
              "2              Botswana  Africa  12307.400391  8.600000   52.099998     60.25   \n",
              "3          Burkina Faso  Africa   1063.400024  1.300000  124.800003    170.50   \n",
              "4               Burundi  Africa    349.200012  2.483333   18.600000    168.50   \n",
              "5              Cameroon  Africa   1986.800049  5.650000  127.800003    155.00   \n",
              "6            Cape Verde  Africa   3052.199951  3.500000   81.599998     30.00   \n",
              "7   Central African Rep  Africa    677.000000  3.383333  106.599998    173.50   \n",
              "8                  Chad  Africa   1266.199951  1.500000  164.500000    209.00   \n",
              "9               Comoros  Africa   1099.000000  2.800000   58.000000    105.75   \n",
              "10                Congo  Africa   3628.000000  5.850000  118.699997    125.75   \n",
              "11      Congo (Dem Rep)  Africa    279.799988  3.416667  201.399994    199.00   \n",
              "12        Côte d'Ivoire  Africa   1539.199951  3.200000  129.399994    123.25   \n",
              "13             Djibouti  Africa   1972.199951  3.800000   22.900000     96.00   \n",
              "14                Egypt  Africa   4754.399902  5.950000   46.599998     24.75   \n",
              "15    Equatorial Guinea  Africa  27645.800781  5.400000  122.900002    149.50   \n",
              "16              Eritrea  Africa    579.599976  3.400000   66.599998     60.50   \n",
              "17             Ethiopia  Africa    741.400024  1.500000   72.400002    112.00   \n",
              "18                Gabon  Africa  13183.200195  7.183333   89.900002     72.50   \n",
              "19               Gambia  Africa   1218.400024  2.550000   76.599998    108.25   \n",
              "20                Ghana  Africa   1303.000000  6.783333   71.099998     75.25   \n",
              "21               Guinea  Africa    957.599976  1.600000  157.399994    149.50   \n",
              "22        Guinea-Bissau  Africa    967.599976  2.300000  111.099998    197.50   \n",
              "23                Kenya  Africa   1405.400024  6.750000  100.199997     87.75   \n",
              "24              Lesotho  Africa   1284.400024  5.583333   73.500000     98.25   \n",
              "25              Liberia  Africa    345.000000  3.650000  142.600006    125.50   \n",
              "26                Libya  Africa  14497.799805  6.816667    3.200000     19.75   \n",
              "27           Madagascar  Africa    919.799988  5.200000  134.300003     64.50   \n",
              "28               Malawi  Africa    660.400024  3.816667  119.199997    120.25   \n",
              "29                 Mali  Africa   1032.800049  1.833333  186.300003    196.00   \n",
              "30           Mauritania  Africa   1794.199951  3.533333   79.199997    118.00   \n",
              "31            Mauritius  Africa  10950.200195  6.966667   35.400002     16.25   \n",
              "32              Morocco  Africa   3802.800049  4.150000   15.100000     40.75   \n",
              "33           Mozambique  Africa    738.599976  1.150000  149.199997    150.75   \n",
              "34              Namibia  Africa   5740.200195  7.200000   74.400002     53.25   \n",
              "35                Niger  Africa    620.799988  1.350000  207.100006    171.75   \n",
              "36              Nigeria  Africa   1870.000000  5.000000  118.300003    147.00   \n",
              "37               Rwanda  Africa    954.000000  3.050000   38.700001    122.50   \n",
              "38    Sao Tome/Principe  Africa   1544.400024  4.200000   66.099998     79.25   \n",
              "39              Senegal  Africa   1635.599976  4.250000  105.900002     97.50   \n",
              "40           Seychelles  Africa  18625.800781  9.400000   51.299999     12.75   \n",
              "41         Sierra Leone  Africa    698.200012  2.733333  143.699997    202.50   \n",
              "42              Somalia  Africa           NaN       NaN   70.099998    180.00   \n",
              "43         South Africa  Africa   9175.400391  8.350000   59.200001     68.75   \n",
              "44               Sudan*  Africa   1838.800049  2.966667   61.900002    109.50   \n",
              "45            Swaziland  Africa   4468.799805  6.850000   83.900002     85.75   \n",
              "46             Tanzania  Africa   1151.800049  4.933333  130.399994    114.50   \n",
              "47                 Togo  Africa    775.000000  5.050000   65.300003    102.50   \n",
              "48              Tunisia  Africa   7032.000000  6.066667    5.700000     21.75   \n",
              "49               Uganda  Africa   1010.599976  4.500000  149.899994    132.50   \n",
              "50               Zambia  Africa   1211.599976  6.400000  146.800003    147.75   \n",
              "51             Zimbabwe  Africa           NaN  6.950000   64.599998     96.00   \n",
              "\n",
              "         life        pop      urban  femlab   literacy        co2       gini  \\\n",
              "0   72.316666   34172236  64.933334  0.4522  72.599998  15.000000        NaN   \n",
              "1   54.733334    8237634  41.000000  0.8482  41.700001   1.200000        NaN   \n",
              "2   52.250000    1941233  59.250000  0.8870  84.099998   9.200000        NaN   \n",
              "3   53.783333   15308383  23.583334  0.8584  23.600000   0.200000        NaN   \n",
              "4   48.866665    7821783  10.250000  1.0344  66.599998   0.100000  33.299999   \n",
              "5   50.116665   18564118  56.366665  0.6532        NaN   0.750000        NaN   \n",
              "6   73.333336     484867  59.283333  0.6358  84.800003   2.150000        NaN   \n",
              "7   45.933334    4203917  38.466667  0.8222  55.200001   0.200000        NaN   \n",
              "8   48.566666   10509983  26.400000  0.8006  33.599998   0.100000        NaN   \n",
              "9   59.783333     688300  28.000000  0.8554  74.199997   0.400000        NaN   \n",
              "10  56.016666    3785517  61.133331  0.7544        NaN   1.550000  47.299999   \n",
              "11  47.516666   61654348  33.633331  0.6552  66.800003   0.100000  44.400002   \n",
              "12  53.049999   18844750  48.666664  0.6132  55.299999   1.450000  46.099998   \n",
              "13  56.599998     848000  76.099998  0.7636        NaN   2.200000        NaN   \n",
              "14  72.333336   77645648  43.183334  0.3092  68.900002   8.450000  32.099998   \n",
              "15  50.066666     653533  39.266666  0.4140  93.300003  32.550003        NaN   \n",
              "16  60.016666    4871634  20.450001  0.7252  66.599998   0.500000        NaN   \n",
              "17  57.049999   78593232  16.283333  0.8700  29.799999   0.300000  29.799999   \n",
              "18  61.150002    1437467  84.883331  0.8404  87.699997   5.550000  41.500000   \n",
              "19  57.366669    1614767  56.049999  0.8264  46.500000   0.700000        NaN   \n",
              "20  62.516666   23000534  49.633331  0.9770  66.599998   1.350000  42.799999   \n",
              "21  52.416668    9486467  34.166668  0.8850  39.500000   0.550000  39.400002   \n",
              "22  46.866669    1440083  29.766666  0.7114  52.200001   0.600000        NaN   \n",
              "23  54.816666   38011832  21.450001  0.8626  87.000000   1.150000  47.700001   \n",
              "24  45.849998    2117567  25.066666  0.8990  89.699997        NaN        NaN   \n",
              "25  54.233334    3576984  46.916668  0.8746  59.099998   0.800000  52.599998   \n",
              "26  74.016663    6075651  77.433334  0.3096  88.900002  33.849998        NaN   \n",
              "27  65.483337   19279600  29.316666  0.9466  64.500000   0.550000  47.200001   \n",
              "28  51.316666   13825916  18.549999  0.9520  73.699997   0.300000        NaN   \n",
              "29  50.000000   14254967  33.933334  0.5490  26.200001   0.200000  39.000000   \n",
              "30  57.700001    3253767  40.866669  0.7168  57.500000   2.000000        NaN   \n",
              "31  72.866669    1278950  41.983334  0.5426  87.900002  10.700001        NaN   \n",
              "32  71.150002   31168868  56.700001  0.3290  55.599998   5.550000  40.900002   \n",
              "33  48.866665   22075768  36.466667  0.9770  55.099998   0.350000  45.599998   \n",
              "34  60.583332    2180583  36.516666  0.8184  88.500000   5.000000        NaN   \n",
              "35  53.033333   14222333  16.850000  0.4378  28.700001   0.250000  34.000000   \n",
              "36  50.233334  148948448  48.000000  0.5282  60.799999   2.750000        NaN   \n",
              "37  53.849998    9882167  18.183332  1.0156  70.699997   0.300000  53.099998   \n",
              "38  63.833332     158800  60.166668  0.5782  88.800003   2.450000        NaN   \n",
              "39  58.183334   11640634  41.716667  0.7244  45.800003   1.500000  39.200001   \n",
              "40  72.983330      85167  54.066666     NaN  91.800003  30.799999  19.000000   \n",
              "41  46.000000    5529667  37.566666  0.9760  40.900002   0.650000        NaN   \n",
              "42  50.366669    8835650  36.299999  0.6706        NaN   0.100000        NaN   \n",
              "43  51.433334   49028316  60.483334  0.7406        NaN  31.400000        NaN   \n",
              "44  60.349998   40933000  38.283333  0.4116  70.199997   1.100000        NaN   \n",
              "45  47.116669    1143367  21.600000  0.6900  86.900002   3.300000        NaN   \n",
              "46  55.400002   41742736  25.266666  0.9544  72.900002   0.500000  37.599998   \n",
              "47  55.866669    5716200  41.650002  0.7318  56.900002   0.750000  34.400002   \n",
              "48  73.916664   10192267  66.300003  0.3568  77.599998   8.300000        NaN   \n",
              "49  52.049999   30878900  12.900000  0.8650  71.400002   0.300000  44.299999   \n",
              "50  46.599998   12243300  35.316666  0.7628  70.900002   0.800000        NaN   \n",
              "51  46.750000   12513100  37.066666  0.8120  91.900002   3.150000        NaN   \n",
              "\n",
              "    gdp_pesos2021  co2_recodificada  \n",
              "0    6.205340e+06                 0  \n",
              "1    1.137980e+06                 0  \n",
              "2    1.046129e+07                 0  \n",
              "3    9.038900e+05                 0  \n",
              "4    2.968200e+05                 0  \n",
              "5    1.688780e+06                 0  \n",
              "6    2.594370e+06                 0  \n",
              "7    5.754500e+05                 0  \n",
              "8    1.076270e+06                 0  \n",
              "9    9.341500e+05                 0  \n",
              "10   3.083800e+06                 0  \n",
              "11   2.378300e+05                 0  \n",
              "12   1.308320e+06                 0  \n",
              "13   1.676370e+06                 0  \n",
              "14   4.041240e+06                 0  \n",
              "15   2.349893e+07                 1  \n",
              "16   4.926600e+05                 0  \n",
              "17   6.301900e+05                 0  \n",
              "18   1.120572e+07                 0  \n",
              "19   1.035640e+06                 0  \n",
              "20   1.107550e+06                 0  \n",
              "21   8.139600e+05                 0  \n",
              "22   8.224600e+05                 0  \n",
              "23   1.194590e+06                 0  \n",
              "24   1.091740e+06                 0  \n",
              "25   2.932500e+05                 0  \n",
              "26   1.232313e+07                 1  \n",
              "27   7.818300e+05                 0  \n",
              "28   5.613400e+05                 0  \n",
              "29   8.778800e+05                 0  \n",
              "30   1.525070e+06                 0  \n",
              "31   9.307670e+06                 0  \n",
              "32   3.232380e+06                 0  \n",
              "33   6.278100e+05                 0  \n",
              "34   4.879170e+06                 0  \n",
              "35   5.276800e+05                 0  \n",
              "36   1.589500e+06                 0  \n",
              "37   8.109000e+05                 0  \n",
              "38   1.312740e+06                 0  \n",
              "39   1.390260e+06                 0  \n",
              "40   1.583193e+07                 1  \n",
              "41   5.934700e+05                 0  \n",
              "42            NaN                 0  \n",
              "43   7.799090e+06                 1  \n",
              "44   1.562980e+06                 0  \n",
              "45   3.798480e+06                 0  \n",
              "46   9.790300e+05                 0  \n",
              "47   6.587500e+05                 0  \n",
              "48   5.977200e+06                 0  \n",
              "49   8.590100e+05                 0  \n",
              "50   1.029860e+06                 0  \n",
              "51            NaN                 0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fde3678f-557b-4eb1-aa72-14af018a2ec9\" 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>country</th>\n",
              "      <th>region</th>\n",
              "      <th>gdp</th>\n",
              "      <th>school</th>\n",
              "      <th>adfert</th>\n",
              "      <th>chldmort</th>\n",
              "      <th>life</th>\n",
              "      <th>pop</th>\n",
              "      <th>urban</th>\n",
              "      <th>femlab</th>\n",
              "      <th>literacy</th>\n",
              "      <th>co2</th>\n",
              "      <th>gini</th>\n",
              "      <th>gdp_pesos2021</th>\n",
              "      <th>co2_recodificada</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Algeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>7300.399902</td>\n",
              "      <td>6.716667</td>\n",
              "      <td>7.300000</td>\n",
              "      <td>34.75</td>\n",
              "      <td>72.316666</td>\n",
              "      <td>34172236</td>\n",
              "      <td>64.933334</td>\n",
              "      <td>0.4522</td>\n",
              "      <td>72.599998</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.205340e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Benin</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1338.800049</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>111.699997</td>\n",
              "      <td>122.75</td>\n",
              "      <td>54.733334</td>\n",
              "      <td>8237634</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>0.8482</td>\n",
              "      <td>41.700001</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.137980e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>Africa</td>\n",
              "      <td>12307.400391</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>52.099998</td>\n",
              "      <td>60.25</td>\n",
              "      <td>52.250000</td>\n",
              "      <td>1941233</td>\n",
              "      <td>59.250000</td>\n",
              "      <td>0.8870</td>\n",
              "      <td>84.099998</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.046129e+07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1063.400024</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>124.800003</td>\n",
              "      <td>170.50</td>\n",
              "      <td>53.783333</td>\n",
              "      <td>15308383</td>\n",
              "      <td>23.583334</td>\n",
              "      <td>0.8584</td>\n",
              "      <td>23.600000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.038900e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>349.200012</td>\n",
              "      <td>2.483333</td>\n",
              "      <td>18.600000</td>\n",
              "      <td>168.50</td>\n",
              "      <td>48.866665</td>\n",
              "      <td>7821783</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>1.0344</td>\n",
              "      <td>66.599998</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>33.299999</td>\n",
              "      <td>2.968200e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cameroon</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1986.800049</td>\n",
              "      <td>5.650000</td>\n",
              "      <td>127.800003</td>\n",
              "      <td>155.00</td>\n",
              "      <td>50.116665</td>\n",
              "      <td>18564118</td>\n",
              "      <td>56.366665</td>\n",
              "      <td>0.6532</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.688780e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Cape Verde</td>\n",
              "      <td>Africa</td>\n",
              "      <td>3052.199951</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>81.599998</td>\n",
              "      <td>30.00</td>\n",
              "      <td>73.333336</td>\n",
              "      <td>484867</td>\n",
              "      <td>59.283333</td>\n",
              "      <td>0.6358</td>\n",
              "      <td>84.800003</td>\n",
              "      <td>2.150000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.594370e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Central African Rep</td>\n",
              "      <td>Africa</td>\n",
              "      <td>677.000000</td>\n",
              "      <td>3.383333</td>\n",
              "      <td>106.599998</td>\n",
              "      <td>173.50</td>\n",
              "      <td>45.933334</td>\n",
              "      <td>4203917</td>\n",
              "      <td>38.466667</td>\n",
              "      <td>0.8222</td>\n",
              "      <td>55.200001</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.754500e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Chad</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1266.199951</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>164.500000</td>\n",
              "      <td>209.00</td>\n",
              "      <td>48.566666</td>\n",
              "      <td>10509983</td>\n",
              "      <td>26.400000</td>\n",
              "      <td>0.8006</td>\n",
              "      <td>33.599998</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.076270e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Comoros</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1099.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>105.75</td>\n",
              "      <td>59.783333</td>\n",
              "      <td>688300</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.8554</td>\n",
              "      <td>74.199997</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.341500e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Congo</td>\n",
              "      <td>Africa</td>\n",
              "      <td>3628.000000</td>\n",
              "      <td>5.850000</td>\n",
              "      <td>118.699997</td>\n",
              "      <td>125.75</td>\n",
              "      <td>56.016666</td>\n",
              "      <td>3785517</td>\n",
              "      <td>61.133331</td>\n",
              "      <td>0.7544</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.550000</td>\n",
              "      <td>47.299999</td>\n",
              "      <td>3.083800e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Congo (Dem Rep)</td>\n",
              "      <td>Africa</td>\n",
              "      <td>279.799988</td>\n",
              "      <td>3.416667</td>\n",
              "      <td>201.399994</td>\n",
              "      <td>199.00</td>\n",
              "      <td>47.516666</td>\n",
              "      <td>61654348</td>\n",
              "      <td>33.633331</td>\n",
              "      <td>0.6552</td>\n",
              "      <td>66.800003</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>44.400002</td>\n",
              "      <td>2.378300e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Côte d'Ivoire</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1539.199951</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>129.399994</td>\n",
              "      <td>123.25</td>\n",
              "      <td>53.049999</td>\n",
              "      <td>18844750</td>\n",
              "      <td>48.666664</td>\n",
              "      <td>0.6132</td>\n",
              "      <td>55.299999</td>\n",
              "      <td>1.450000</td>\n",
              "      <td>46.099998</td>\n",
              "      <td>1.308320e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Djibouti</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1972.199951</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>22.900000</td>\n",
              "      <td>96.00</td>\n",
              "      <td>56.599998</td>\n",
              "      <td>848000</td>\n",
              "      <td>76.099998</td>\n",
              "      <td>0.7636</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.676370e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>Africa</td>\n",
              "      <td>4754.399902</td>\n",
              "      <td>5.950000</td>\n",
              "      <td>46.599998</td>\n",
              "      <td>24.75</td>\n",
              "      <td>72.333336</td>\n",
              "      <td>77645648</td>\n",
              "      <td>43.183334</td>\n",
              "      <td>0.3092</td>\n",
              "      <td>68.900002</td>\n",
              "      <td>8.450000</td>\n",
              "      <td>32.099998</td>\n",
              "      <td>4.041240e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Equatorial Guinea</td>\n",
              "      <td>Africa</td>\n",
              "      <td>27645.800781</td>\n",
              "      <td>5.400000</td>\n",
              "      <td>122.900002</td>\n",
              "      <td>149.50</td>\n",
              "      <td>50.066666</td>\n",
              "      <td>653533</td>\n",
              "      <td>39.266666</td>\n",
              "      <td>0.4140</td>\n",
              "      <td>93.300003</td>\n",
              "      <td>32.550003</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.349893e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Eritrea</td>\n",
              "      <td>Africa</td>\n",
              "      <td>579.599976</td>\n",
              "      <td>3.400000</td>\n",
              "      <td>66.599998</td>\n",
              "      <td>60.50</td>\n",
              "      <td>60.016666</td>\n",
              "      <td>4871634</td>\n",
              "      <td>20.450001</td>\n",
              "      <td>0.7252</td>\n",
              "      <td>66.599998</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.926600e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>741.400024</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>72.400002</td>\n",
              "      <td>112.00</td>\n",
              "      <td>57.049999</td>\n",
              "      <td>78593232</td>\n",
              "      <td>16.283333</td>\n",
              "      <td>0.8700</td>\n",
              "      <td>29.799999</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>29.799999</td>\n",
              "      <td>6.301900e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Gabon</td>\n",
              "      <td>Africa</td>\n",
              "      <td>13183.200195</td>\n",
              "      <td>7.183333</td>\n",
              "      <td>89.900002</td>\n",
              "      <td>72.50</td>\n",
              "      <td>61.150002</td>\n",
              "      <td>1437467</td>\n",
              "      <td>84.883331</td>\n",
              "      <td>0.8404</td>\n",
              "      <td>87.699997</td>\n",
              "      <td>5.550000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>1.120572e+07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Gambia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1218.400024</td>\n",
              "      <td>2.550000</td>\n",
              "      <td>76.599998</td>\n",
              "      <td>108.25</td>\n",
              "      <td>57.366669</td>\n",
              "      <td>1614767</td>\n",
              "      <td>56.049999</td>\n",
              "      <td>0.8264</td>\n",
              "      <td>46.500000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.035640e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1303.000000</td>\n",
              "      <td>6.783333</td>\n",
              "      <td>71.099998</td>\n",
              "      <td>75.25</td>\n",
              "      <td>62.516666</td>\n",
              "      <td>23000534</td>\n",
              "      <td>49.633331</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>66.599998</td>\n",
              "      <td>1.350000</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>1.107550e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Guinea</td>\n",
              "      <td>Africa</td>\n",
              "      <td>957.599976</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>157.399994</td>\n",
              "      <td>149.50</td>\n",
              "      <td>52.416668</td>\n",
              "      <td>9486467</td>\n",
              "      <td>34.166668</td>\n",
              "      <td>0.8850</td>\n",
              "      <td>39.500000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>39.400002</td>\n",
              "      <td>8.139600e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Guinea-Bissau</td>\n",
              "      <td>Africa</td>\n",
              "      <td>967.599976</td>\n",
              "      <td>2.300000</td>\n",
              "      <td>111.099998</td>\n",
              "      <td>197.50</td>\n",
              "      <td>46.866669</td>\n",
              "      <td>1440083</td>\n",
              "      <td>29.766666</td>\n",
              "      <td>0.7114</td>\n",
              "      <td>52.200001</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.224600e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1405.400024</td>\n",
              "      <td>6.750000</td>\n",
              "      <td>100.199997</td>\n",
              "      <td>87.75</td>\n",
              "      <td>54.816666</td>\n",
              "      <td>38011832</td>\n",
              "      <td>21.450001</td>\n",
              "      <td>0.8626</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>47.700001</td>\n",
              "      <td>1.194590e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Lesotho</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1284.400024</td>\n",
              "      <td>5.583333</td>\n",
              "      <td>73.500000</td>\n",
              "      <td>98.25</td>\n",
              "      <td>45.849998</td>\n",
              "      <td>2117567</td>\n",
              "      <td>25.066666</td>\n",
              "      <td>0.8990</td>\n",
              "      <td>89.699997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.091740e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Liberia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>345.000000</td>\n",
              "      <td>3.650000</td>\n",
              "      <td>142.600006</td>\n",
              "      <td>125.50</td>\n",
              "      <td>54.233334</td>\n",
              "      <td>3576984</td>\n",
              "      <td>46.916668</td>\n",
              "      <td>0.8746</td>\n",
              "      <td>59.099998</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>52.599998</td>\n",
              "      <td>2.932500e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Libya</td>\n",
              "      <td>Africa</td>\n",
              "      <td>14497.799805</td>\n",
              "      <td>6.816667</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>19.75</td>\n",
              "      <td>74.016663</td>\n",
              "      <td>6075651</td>\n",
              "      <td>77.433334</td>\n",
              "      <td>0.3096</td>\n",
              "      <td>88.900002</td>\n",
              "      <td>33.849998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.232313e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Madagascar</td>\n",
              "      <td>Africa</td>\n",
              "      <td>919.799988</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>134.300003</td>\n",
              "      <td>64.50</td>\n",
              "      <td>65.483337</td>\n",
              "      <td>19279600</td>\n",
              "      <td>29.316666</td>\n",
              "      <td>0.9466</td>\n",
              "      <td>64.500000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>47.200001</td>\n",
              "      <td>7.818300e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Malawi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>660.400024</td>\n",
              "      <td>3.816667</td>\n",
              "      <td>119.199997</td>\n",
              "      <td>120.25</td>\n",
              "      <td>51.316666</td>\n",
              "      <td>13825916</td>\n",
              "      <td>18.549999</td>\n",
              "      <td>0.9520</td>\n",
              "      <td>73.699997</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.613400e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Mali</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1032.800049</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>186.300003</td>\n",
              "      <td>196.00</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>14254967</td>\n",
              "      <td>33.933334</td>\n",
              "      <td>0.5490</td>\n",
              "      <td>26.200001</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>8.778800e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Mauritania</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1794.199951</td>\n",
              "      <td>3.533333</td>\n",
              "      <td>79.199997</td>\n",
              "      <td>118.00</td>\n",
              "      <td>57.700001</td>\n",
              "      <td>3253767</td>\n",
              "      <td>40.866669</td>\n",
              "      <td>0.7168</td>\n",
              "      <td>57.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.525070e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Mauritius</td>\n",
              "      <td>Africa</td>\n",
              "      <td>10950.200195</td>\n",
              "      <td>6.966667</td>\n",
              "      <td>35.400002</td>\n",
              "      <td>16.25</td>\n",
              "      <td>72.866669</td>\n",
              "      <td>1278950</td>\n",
              "      <td>41.983334</td>\n",
              "      <td>0.5426</td>\n",
              "      <td>87.900002</td>\n",
              "      <td>10.700001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.307670e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>Africa</td>\n",
              "      <td>3802.800049</td>\n",
              "      <td>4.150000</td>\n",
              "      <td>15.100000</td>\n",
              "      <td>40.75</td>\n",
              "      <td>71.150002</td>\n",
              "      <td>31168868</td>\n",
              "      <td>56.700001</td>\n",
              "      <td>0.3290</td>\n",
              "      <td>55.599998</td>\n",
              "      <td>5.550000</td>\n",
              "      <td>40.900002</td>\n",
              "      <td>3.232380e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Mozambique</td>\n",
              "      <td>Africa</td>\n",
              "      <td>738.599976</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>149.199997</td>\n",
              "      <td>150.75</td>\n",
              "      <td>48.866665</td>\n",
              "      <td>22075768</td>\n",
              "      <td>36.466667</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>55.099998</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>45.599998</td>\n",
              "      <td>6.278100e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>5740.200195</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>74.400002</td>\n",
              "      <td>53.25</td>\n",
              "      <td>60.583332</td>\n",
              "      <td>2180583</td>\n",
              "      <td>36.516666</td>\n",
              "      <td>0.8184</td>\n",
              "      <td>88.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.879170e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Niger</td>\n",
              "      <td>Africa</td>\n",
              "      <td>620.799988</td>\n",
              "      <td>1.350000</td>\n",
              "      <td>207.100006</td>\n",
              "      <td>171.75</td>\n",
              "      <td>53.033333</td>\n",
              "      <td>14222333</td>\n",
              "      <td>16.850000</td>\n",
              "      <td>0.4378</td>\n",
              "      <td>28.700001</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>5.276800e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1870.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>118.300003</td>\n",
              "      <td>147.00</td>\n",
              "      <td>50.233334</td>\n",
              "      <td>148948448</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.5282</td>\n",
              "      <td>60.799999</td>\n",
              "      <td>2.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.589500e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Rwanda</td>\n",
              "      <td>Africa</td>\n",
              "      <td>954.000000</td>\n",
              "      <td>3.050000</td>\n",
              "      <td>38.700001</td>\n",
              "      <td>122.50</td>\n",
              "      <td>53.849998</td>\n",
              "      <td>9882167</td>\n",
              "      <td>18.183332</td>\n",
              "      <td>1.0156</td>\n",
              "      <td>70.699997</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>53.099998</td>\n",
              "      <td>8.109000e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Sao Tome/Principe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1544.400024</td>\n",
              "      <td>4.200000</td>\n",
              "      <td>66.099998</td>\n",
              "      <td>79.25</td>\n",
              "      <td>63.833332</td>\n",
              "      <td>158800</td>\n",
              "      <td>60.166668</td>\n",
              "      <td>0.5782</td>\n",
              "      <td>88.800003</td>\n",
              "      <td>2.450000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.312740e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Senegal</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1635.599976</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>105.900002</td>\n",
              "      <td>97.50</td>\n",
              "      <td>58.183334</td>\n",
              "      <td>11640634</td>\n",
              "      <td>41.716667</td>\n",
              "      <td>0.7244</td>\n",
              "      <td>45.800003</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>39.200001</td>\n",
              "      <td>1.390260e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Seychelles</td>\n",
              "      <td>Africa</td>\n",
              "      <td>18625.800781</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>51.299999</td>\n",
              "      <td>12.75</td>\n",
              "      <td>72.983330</td>\n",
              "      <td>85167</td>\n",
              "      <td>54.066666</td>\n",
              "      <td>NaN</td>\n",
              "      <td>91.800003</td>\n",
              "      <td>30.799999</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.583193e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Sierra Leone</td>\n",
              "      <td>Africa</td>\n",
              "      <td>698.200012</td>\n",
              "      <td>2.733333</td>\n",
              "      <td>143.699997</td>\n",
              "      <td>202.50</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>5529667</td>\n",
              "      <td>37.566666</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>40.900002</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.934700e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Somalia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.099998</td>\n",
              "      <td>180.00</td>\n",
              "      <td>50.366669</td>\n",
              "      <td>8835650</td>\n",
              "      <td>36.299999</td>\n",
              "      <td>0.6706</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>Africa</td>\n",
              "      <td>9175.400391</td>\n",
              "      <td>8.350000</td>\n",
              "      <td>59.200001</td>\n",
              "      <td>68.75</td>\n",
              "      <td>51.433334</td>\n",
              "      <td>49028316</td>\n",
              "      <td>60.483334</td>\n",
              "      <td>0.7406</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.400000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.799090e+06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Sudan*</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1838.800049</td>\n",
              "      <td>2.966667</td>\n",
              "      <td>61.900002</td>\n",
              "      <td>109.50</td>\n",
              "      <td>60.349998</td>\n",
              "      <td>40933000</td>\n",
              "      <td>38.283333</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>70.199997</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.562980e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Swaziland</td>\n",
              "      <td>Africa</td>\n",
              "      <td>4468.799805</td>\n",
              "      <td>6.850000</td>\n",
              "      <td>83.900002</td>\n",
              "      <td>85.75</td>\n",
              "      <td>47.116669</td>\n",
              "      <td>1143367</td>\n",
              "      <td>21.600000</td>\n",
              "      <td>0.6900</td>\n",
              "      <td>86.900002</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.798480e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Tanzania</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1151.800049</td>\n",
              "      <td>4.933333</td>\n",
              "      <td>130.399994</td>\n",
              "      <td>114.50</td>\n",
              "      <td>55.400002</td>\n",
              "      <td>41742736</td>\n",
              "      <td>25.266666</td>\n",
              "      <td>0.9544</td>\n",
              "      <td>72.900002</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>37.599998</td>\n",
              "      <td>9.790300e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Togo</td>\n",
              "      <td>Africa</td>\n",
              "      <td>775.000000</td>\n",
              "      <td>5.050000</td>\n",
              "      <td>65.300003</td>\n",
              "      <td>102.50</td>\n",
              "      <td>55.866669</td>\n",
              "      <td>5716200</td>\n",
              "      <td>41.650002</td>\n",
              "      <td>0.7318</td>\n",
              "      <td>56.900002</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>34.400002</td>\n",
              "      <td>6.587500e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>7032.000000</td>\n",
              "      <td>6.066667</td>\n",
              "      <td>5.700000</td>\n",
              "      <td>21.75</td>\n",
              "      <td>73.916664</td>\n",
              "      <td>10192267</td>\n",
              "      <td>66.300003</td>\n",
              "      <td>0.3568</td>\n",
              "      <td>77.599998</td>\n",
              "      <td>8.300000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.977200e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1010.599976</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>149.899994</td>\n",
              "      <td>132.50</td>\n",
              "      <td>52.049999</td>\n",
              "      <td>30878900</td>\n",
              "      <td>12.900000</td>\n",
              "      <td>0.8650</td>\n",
              "      <td>71.400002</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>44.299999</td>\n",
              "      <td>8.590100e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1211.599976</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>146.800003</td>\n",
              "      <td>147.75</td>\n",
              "      <td>46.599998</td>\n",
              "      <td>12243300</td>\n",
              "      <td>35.316666</td>\n",
              "      <td>0.7628</td>\n",
              "      <td>70.900002</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.029860e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>Africa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>64.599998</td>\n",
              "      <td>96.00</td>\n",
              "      <td>46.750000</td>\n",
              "      <td>12513100</td>\n",
              "      <td>37.066666</td>\n",
              "      <td>0.8120</td>\n",
              "      <td>91.900002</td>\n",
              "      <td>3.150000</td>\n",
              "      <td>NaN</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_nations[df_nations[\\\"region\\\"]==\\\"Africa\\\"]\",\n  \"rows\": 52,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 52,\n        \"samples\": [\n          \"Gambia\",\n          \"Sierra Leone\",\n          \"Togo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5406.009423468418,\n        \"min\": 279.799987792969,\n        \"max\": 27645.80078125,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          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53.88204255411929,\n        \"min\": 12.75,\n        \"max\": 209.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.551912059455901,\n        \"min\": 45.8499984741211,\n        \"max\": 74.0166625976562,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          60.3499984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26381035,\n        \"min\": 85167,\n        \"max\": 148948448,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1614767\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 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            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_africa = df_nations[df_nations[\"region\"]==\"Africa\"]"
      ],
      "metadata": {
        "id": "dQMQxNja_LUU"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_africa.head()"
      ],
      "metadata": {
        "id": "MEHFGJL29MZ3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "5526f11a-b0e6-4c70-8e68-b9453b0c3566"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        country  region           gdp    school      adfert  chldmort  \\\n",
              "0       Algeria  Africa   7300.399902  6.716667    7.300000     34.75   \n",
              "1         Benin  Africa   1338.800049  3.100000  111.699997    122.75   \n",
              "2      Botswana  Africa  12307.400391  8.600000   52.099998     60.25   \n",
              "3  Burkina Faso  Africa   1063.400024  1.300000  124.800003    170.50   \n",
              "4       Burundi  Africa    349.200012  2.483333   18.600000    168.50   \n",
              "\n",
              "        life       pop      urban  femlab   literacy   co2       gini  \\\n",
              "0  72.316666  34172236  64.933334  0.4522  72.599998  15.0        NaN   \n",
              "1  54.733334   8237634  41.000000  0.8482  41.700001   1.2        NaN   \n",
              "2  52.250000   1941233  59.250000  0.8870  84.099998   9.2        NaN   \n",
              "3  53.783333  15308383  23.583334  0.8584  23.600000   0.2        NaN   \n",
              "4  48.866665   7821783  10.250000  1.0344  66.599998   0.1  33.299999   \n",
              "\n",
              "   gdp_pesos2021  co2_recodificada  \n",
              "0   6.205340e+06                 0  \n",
              "1   1.137980e+06                 0  \n",
              "2   1.046129e+07                 0  \n",
              "3   9.038900e+05                 0  \n",
              "4   2.968200e+05                 0  "
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              "      <td>1.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.137980e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>Africa</td>\n",
              "      <td>12307.400391</td>\n",
              "      <td>8.600000</td>\n",
              "      <td>52.099998</td>\n",
              "      <td>60.25</td>\n",
              "      <td>52.250000</td>\n",
              "      <td>1941233</td>\n",
              "      <td>59.250000</td>\n",
              "      <td>0.8870</td>\n",
              "      <td>84.099998</td>\n",
              "      <td>9.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.046129e+07</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1063.400024</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>124.800003</td>\n",
              "      <td>170.50</td>\n",
              "      <td>53.783333</td>\n",
              "      <td>15308383</td>\n",
              "      <td>23.583334</td>\n",
              "      <td>0.8584</td>\n",
              "      <td>23.600000</td>\n",
              "      <td>0.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.038900e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>349.200012</td>\n",
              "      <td>2.483333</td>\n",
              "      <td>18.600000</td>\n",
              "      <td>168.50</td>\n",
              "      <td>48.866665</td>\n",
              "      <td>7821783</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>1.0344</td>\n",
              "      <td>66.599998</td>\n",
              "      <td>0.1</td>\n",
              "      <td>33.299999</td>\n",
              "      <td>2.968200e+05</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_africa",
              "summary": "{\n  \"name\": \"df_africa\",\n  \"rows\": 52,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 52,\n        \"samples\": [\n          \"Gambia\",\n          \"Sierra Leone\",\n          \"Togo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5406.009423468418,\n        \"min\": 279.799987792969,\n        \"max\": 27645.80078125,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          1972.19995117188\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.107357385262328,\n        \"min\": 1.15000009536743,\n        \"max\": 9.39999961853027,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          3.79999995231628\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.13865983507861,\n        \"min\": 3.20000004768372,\n        \"max\": 207.100006103516,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          76.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.88204255411929,\n        \"min\": 12.75,\n        \"max\": 209.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          96.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.551912059455901,\n        \"min\": 45.8499984741211,\n        \"max\": 74.0166625976562,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          60.3499984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26381035,\n        \"min\": 85167,\n        \"max\": 148948448,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          1614767\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.42884236400012,\n        \"min\": 10.25,\n        \"max\": 84.8833312988281,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          56.0499992370605\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19720575000158996,\n        \"min\": 0.30919998884201,\n        \"max\": 1.03439998626709,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          0.763599991798401\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.704853071159512,\n        \"min\": 23.6000003814697,\n        \"max\": 93.3000030517578,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          72.9000015258789\n        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237829.98962402364,\n        \"max\": 23498930.6640625,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          1676369.958496098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2_recodificada\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ¿Cómo creamos el filtro para los países de Europa?\n",
        "df_europa = df_nations[df_nations[\"region\"]==\"Europe\"]"
      ],
      "metadata": {
        "id": "X_Bq9uFt_LKB"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_europa.head()"
      ],
      "metadata": {
        "id": "TLZmbhBU-wJJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "1cc45904-6392-426a-f06b-75295dcc96d3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     country  region           gdp     school  adfert  chldmort       life  \\\n",
              "136  Albania  Europe   6832.600098  10.283333    17.9      17.0  76.400002   \n",
              "137  Andorra  Europe           NaN  10.400000     8.4       4.0  80.516663   \n",
              "138  Austria  Europe  34838.398438  10.366667    12.8       4.5  80.183334   \n",
              "139  Belarus  Europe  10391.799805   9.300000    22.1      13.0  69.466667   \n",
              "140  Belgium  Europe  32868.800781  10.733334    14.2       5.0  79.566666   \n",
              "\n",
              "          pop      urban  femlab   literacy        co2       gini  \\\n",
              "136   3174417  49.350002  0.6948  95.900002   5.150000  34.500000   \n",
              "137     81733  89.116669     NaN        NaN        NaN        NaN   \n",
              "138   8320033  67.016663  0.7714        NaN  31.900002        NaN   \n",
              "139   9707033  73.399994  0.8208  99.699997  25.000000  27.200001   \n",
              "140  10567200  97.350006  0.7590        NaN  38.150002        NaN   \n",
              "\n",
              "     gdp_pesos2021  co2_recodificada  \n",
              "136   5.807710e+06                 0  \n",
              "137            NaN                 0  \n",
              "138   2.961264e+07                 1  \n",
              "139   8.833030e+06                 1  \n",
              "140   2.793848e+07                 1  "
            ],
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              "      <th>136</th>\n",
              "      <td>Albania</td>\n",
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              "      <th>137</th>\n",
              "      <td>Andorra</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Austria</td>\n",
              "      <td>Europe</td>\n",
              "      <td>34838.398438</td>\n",
              "      <td>10.366667</td>\n",
              "      <td>12.8</td>\n",
              "      <td>4.5</td>\n",
              "      <td>80.183334</td>\n",
              "      <td>8320033</td>\n",
              "      <td>67.016663</td>\n",
              "      <td>0.7714</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31.900002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.961264e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>Belarus</td>\n",
              "      <td>Europe</td>\n",
              "      <td>10391.799805</td>\n",
              "      <td>9.300000</td>\n",
              "      <td>22.1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>69.466667</td>\n",
              "      <td>9707033</td>\n",
              "      <td>73.399994</td>\n",
              "      <td>0.8208</td>\n",
              "      <td>99.699997</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>27.200001</td>\n",
              "      <td>8.833030e+06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>Europe</td>\n",
              "      <td>32868.800781</td>\n",
              "      <td>10.733334</td>\n",
              "      <td>14.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>79.566666</td>\n",
              "      <td>10567200</td>\n",
              "      <td>97.350006</td>\n",
              "      <td>0.7590</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.150002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.793848e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a2a394f6-b001-490a-9d66-c3e4e413125f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
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              "        document.querySelector('#df-a2a394f6-b001-490a-9d66-c3e4e413125f 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-a2a394f6-b001-490a-9d66-c3e4e413125f');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7b12b4c9-8d48-4163-bdd8-bbe83d9bba0d')\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7b12b4c9-8d48-4163-bdd8-bbe83d9bba0d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_europa",
              "summary": "{\n  \"name\": \"df_europa\",\n  \"rows\": 43,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 43,\n        \"samples\": [\n          \"Slovenia\",\n          \"Malta\",\n          \"Moldova\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Europe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13820.650106238669,\n        \"min\": 2553.60009765625,\n        \"max\": 71141.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          25321.19921875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0822767448998345,\n        \"min\": 7.44999980926514,\n        \"max\": 12.6999998092651,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          11.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.283524083369558,\n        \"min\": 1.60000002384186,\n        \"max\": 42.7999992370605,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          17.2999992370605\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.362057664146333,\n        \"min\": 2.25,\n        \"max\": 18.0,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.080346082376956,\n        \"min\": 67.3999938964844,\n        \"max\": 81.783332824707,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          79.1999969482422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28240739,\n        \"min\": 31017,\n        \"max\": 143305536,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          2015533\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.645328875889884,\n        \"min\": 14.4499998092651,\n        \"max\": 100.0,\n        \"num_unique_values\": 42,\n        \"samples\": [\n          45.0666656494141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08231090742323934,\n        \"min\": 0.467599987983704,\n        \"max\": 0.905399978160858,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          0.624599993228912\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8644366062438165,\n        \"min\": 92.4000015258789,\n        \"max\": 99.8000030517578,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          95.9000015258789\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.331203491575144,\n        \"min\": 5.15000009536743,\n        \"max\": 90.4499969482422,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          29.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gini\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.662375511502076,\n        \"min\": 27.2000007629395,\n        \"max\": 45.2999992370605,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_pesos2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11747552.590302868,\n        \"min\": 2170560.0830078125,\n        \"max\": 60469850.0,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          21523019.3359375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2_recodificada\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para analizar el alfabestimo en áfrica, podemos usar el método mean() en la variable \"literacy\" para la muestra \"África\"\n",
        "df_africa[\"literacy\"].mean()"
      ],
      "metadata": {
        "id": "42kUCNxrJEqM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40d4a50e-8416-4953-f64d-638c1a25b55b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65.30851067887976"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Del mismo modo para Europa\n",
        "df_europa[\"literacy\"].mean()"
      ],
      "metadata": {
        "id": "Z55n-M3xJ0Pg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce673a73-b759-4f25-e043-873d2d936a3c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98.17727279663086"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "De lo anterior se desprende que Europa tiene una tasa de alfabetismo de 98,1% mientras que en África alcanza sólo al 65,3%."
      ],
      "metadata": {
        "id": "VLyH6ER1KHGL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A continuación, vamos con la sección de visualización de datos"
      ],
      "metadata": {
        "id": "eAgUea-zJuPD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La visualización gráfica de datos constituye una disciplina en si misma.\n",
        "Existen muchos tipos de datos y utilizar el gráfico correcto, según la necesidad de información y el objetivo a comunicar es clave en este proceso.\n",
        "\n",
        "Fuente: Analytics Vidhya\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "OK0WSyVlL792"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Comenzaremos con crear un histograma que nos muestre la distribución del índice gini"
      ],
      "metadata": {
        "id": "FzBrBVd9JEmc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df_nations[\"gini\"], kind=\"hist\")"
      ],
      "metadata": {
        "id": "C336ooNmzryl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "dbb8246f-3a94-40ca-ed6e-b80b63173a7c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ce742dc6fe0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Del gráfico anterior, se desprende que la mayor concentración del índice gini está entre los 30 y 50 puntos, pero sería valioso agregar el valor promedio del gini en nuestra muestra. ¿Cómo hacemos eso?"
      ],
      "metadata": {
        "id": "xn3i4CmQONKj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "mUHweSubOw8G"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df_nations[\"gini\"], kind=\"hist\")\n",
        "plt.axvline(df_nations[\"gini\"].mean(), color = \"tomato\")"
      ],
      "metadata": {
        "id": "w6qAKkJJzr1n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "outputId": "d15eb08d-fd2b-4ce9-c3d7-1636a5b9d542"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7ce74222c310>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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RRUWF8vLytHTp0k4ZDAAAdDDUMTExzZZ79OihtLQ0LV++XDfddFOnDAYAADoY6meffbaz5wAAAC3oUKib5Ofnq7i4WJI0fPhwXX/99Z0yFAAAOKdDoa6srNRdd92lHTt2qG/fvpKkEydOaOrUqXrxxRd15ZVXduaMAAB0Wx161/eCBQt08uRJ/e1vf9Px48d1/PhxFRUVyefzaeHChZ09IwAA3VaHHlFv3rxZr7/+uoYOHRpYN2zYMK1evZo3kwEA0Ik69Ii6sbHxgu+glqTw8HA1NjZe9lAAAOCcDoX6i1/8oh544AF99NFHgXVHjhzRgw8+qGnTpnXacAAAdHcdCvXTTz8tn8+nlJQUDRkyREOGDFFqaqp8Pp9++ctfdvaMAAB0Wx16jTopKUkFBQV6/fXXtX//fknS0KFDlZGR0anDAQDQ3bXrEfW2bds0bNgw+Xw+eTwe/eu//qsWLFigBQsWaNy4cRo+fLjeeOONrpoVAIBup12hXrVqle655x5FR0dfsC0mJkbf+c53tHLlyk4bDgCA7q5dof7rX/+qL33pS61uv+mmm5Sfn3/ZQwEAgHPaFepjx461+LGsJmFhYfr4448veygAAHBOu0J91VVXqaioqNXt+/btU0JCwmUPBQAAzmlXqG+++WYtXbpUp0+fvmBbXV2dli1bpltvvbXThmtoaNDSpUuVmpqqyMhIDRkyRD/+8Y/lOE6nXQcAAJa16+NZP/rRj/TSSy/puuuu0/3336+0tDRJ0v79+7V69Wo1NDTohz/8YacN9/jjj2vNmjX67W9/q+HDhysvL09z585VTEwMf1McANAttCvU8fHxeuutt/Td735X2dnZgUe2Ho9H06dP1+rVqxUfH99pw7311luaNWuWbrnlFklSSkqKXnjhBb3zzjutHuP3++X3+wPLPp+v0+YBALc0faVwqImLi1NycrLbY4S0dv/Bk0GDBul//ud/9Mknn6i0tFSO4+jaa69Vv379On24z3/+81q3bp0OHDig6667Tn/961/15ptvtvkRsNzcXOXk5HT6LADghrrqv0vyaM6cOW6P0iGRkb21f38xsb4MHfrLZJLUr18/jRs3rjNnucBDDz0kn8+n9PR09ezZUw0NDXr00Uc1e/bsVo/Jzs5WVlZWYNnn8ykpKalL5wSArlJ/6qQkR2O+sURXpqa7PU67+CoOac8zOaqqqiLUl6HDoQ6GP/zhD3ruuef0/PPPa/jw4SosLNSiRYuUmJiozMzMFo/xer3yer1BnhQAutYVA5IVm5zm9hhwgelQL168WA899JDuuusuSdLIkSP14YcfKjc3t9VQAwDwWdKhb88KllOnTqlHj+Yj9uzZk++8BgB0G6YfUc+cOVOPPvqokpOTNXz4cL377rtauXKl5s2b5/ZoAAAEhelQ//KXv9TSpUt13333qbKyUomJifrOd76jhx9+2O3RAAAICtOhjoqK0qpVq7Rq1Sq3RwEAwBWmX6MGAKC7I9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDDzoT5y5IjmzJmj/v37KzIyUiNHjlReXp7bYwEAEBRhbg/Qlk8++USTJk3S1KlT9dprr+nKK6/UwYMH1a9fP7dHAwAgKEyH+vHHH1dSUpKeffbZwLrU1NQ2j/H7/fL7/YFln8/XZfMBAC6uuLjY7RE6LC4uTsnJya7OYDrUmzZt0vTp0/XVr35VO3fu1FVXXaX77rtP99xzT6vH5ObmKicnJ4hTAgBaUlf9d0kezZkzx+1ROiwysrf27y92NdamQ/3BBx9ozZo1ysrK0g9+8APt3btXCxcuVEREhDIzM1s8Jjs7W1lZWYFln8+npKSkYI0MAPiH+lMnJTka840lujI13e1x2s1XcUh7nslRVVUVoW5NY2Ojxo4dq8cee0ySdP3116uoqEhr165tNdRer1derzeYYwIA2nDFgGTFJqe5PUbIMv2u74SEBA0bNqzZuqFDh6q8vNyliQAACC7ToZ40aZJKSkqarTtw4IAGDRrk0kQAAASX6VA/+OCDevvtt/XYY4+ptLRUzz//vNatW6f58+e7PRoAAEFhOtTjxo3Thg0b9MILL2jEiBH68Y9/rFWrVmn27NlujwYAQFCYfjOZJN1666269dZb3R4DAABXmH5EDQBAd0eoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhIRXqFStWyOPxaNGiRW6PAgBAUIRMqPfu3atf/epXGjVqlNujAAAQNCER6pqaGs2ePVu//vWv1a9fvzb39fv98vl8zS4AAISqkAj1/PnzdcsttygjI+Oi++bm5iomJiZwSUpKCsKEAAB0DfOhfvHFF1VQUKDc3NxL2j87O1vV1dWBy+HDh7t4QgAAuk6Y2wO05fDhw3rggQe0ZcsW9erV65KO8Xq98nq9XTwZAADBYTrU+fn5qqys1Oc+97nAuoaGBu3atUtPP/20/H6/evbs6eKEAAB0LdOhnjZtmt57771m6+bOnav09HQtWbKESAMAPvNMhzoqKkojRoxotq5Pnz7q37//BesBAPgsMv9mMgAAujPTj6hbsmPHDrdHAAAgaHhEDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgWMh9HzUuT3l5uaqqqtweo92Ki4vdHgEAXEGou5Hy8nKlpw9VXd0pt0fpsHr/GbdHAICgItTdSFVVlerqTmnCvGWKTkhxe5x2qXhvt4o2rdPZs2fdHgUAgopQd0PRCSmKTU5ze4x28VUccnsEAHAFbyYDAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMPOhzs3N1bhx4xQVFaUBAwbotttuU0lJidtjAQAQFOZDvXPnTs2fP19vv/22tmzZovr6et10002qra11ezQAALpcmNsDXMzmzZubLa9fv14DBgxQfn6+brzxRpemAgAgOMyH+nzV1dWSpNjY2Ba3+/1++f3+wLLP5+vU6y8vL1dVVVWn/s5gKS4udnsEAEA7hVSoGxsbtWjRIk2aNEkjRoxocZ/c3Fzl5OR0yfWXl5crPX2o6upOdcnvD5Z6/xm3RwAAXKKQCvX8+fNVVFSkN998s9V9srOzlZWVFVj2+XxKSkrqlOuvqqpSXd0pTZi3TNEJKZ3yO4Op4r3dKtq0TmfPnnV7FADAJQqZUN9///169dVXtWvXLl199dWt7uf1euX1ert0luiEFMUmp3XpdXQFX8Uht0cAALST+VA7jqMFCxZow4YN2rFjh1JTU90eCQCAoDEf6vnz5+v555/Xyy+/rKioKB09elSSFBMTo8jISJenAwCga5n/HPWaNWtUXV2tKVOmKCEhIXD5/e9/7/ZoAAB0OfOPqB3HcXsEAABcY/4RNQAA3RmhBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGEWoAAAwj1AAAGEaoAQAwjFADAGAYoQYAwDBCDQCAYYQaAADDCDUAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGgAAwwg1AACGhUSoV69erZSUFPXq1UsTJkzQO++84/ZIAAAEhflQ//73v1dWVpaWLVumgoICjR49WtOnT1dlZaXbowEA0OXMh3rlypW65557NHfuXA0bNkxr165V79699cwzz7g9GgAAXS7M7QHacubMGeXn5ys7OzuwrkePHsrIyNDu3btbPMbv98vv9weWq6urJUk+n++y56mpqZEkHf+wRGf9dZf9+4LNV/GhJKn6yEGFh3lcnqZ9Qnl2KbTnv9zZvY1n5TtzVpJUWbpP/h7B/c9OKJ97KbTnD+XZJcl3tFzSuf/2d0ZDJCkqKkoeTzvPhWPYkSNHHEnOW2+91Wz94sWLnfHjx7d4zLJlyxxJXLhw4cKFi7lLdXV1u1to+hF1R2RnZysrKyuw3NjYqOPHj6t///7t/38xHeTz+ZSUlKTDhw8rOjo6KNfZmZjfXczvvlC/Dczvrrbmj4qKavfvMx3quLg49ezZU8eOHWu2/tixYxo4cGCLx3i9Xnm93mbr+vbt21Ujtik6Ojok/5E1YX53Mb/7Qv02ML+7Omt+028mi4iI0A033KCtW7cG1jU2Nmrr1q2aOHGii5MBABAcph9RS1JWVpYyMzM1duxYjR8/XqtWrVJtba3mzp3r9mgAAHQ586G+88479fHHH+vhhx/W0aNHNWbMGG3evFnx8fFuj9Yqr9erZcuWXfAUfKhgfncxv/tC/TYwv7s6e36P4zhOp/wmAADQ6Uy/Rg0AQHdHqAEAMIxQAwBgGKEGAMAwQt1Bubm5GjdunKKiojRgwADddtttKikpabbP6dOnNX/+fPXv319XXHGFvvKVr1zwx1vccinzT5kyRR6Pp9nl3nvvdWni5tasWaNRo0YF/qDAxIkT9dprrwW2Wz73TS52Gyyf//OtWLFCHo9HixYtCqwLhfvg01q6DZbvg0ceeeSC2dLT0wPbrZ//i81v+dw3OXLkiObMmaP+/fsrMjJSI0eOVF5eXmC74zh6+OGHlZCQoMjISGVkZOjgwYPtvh5C3UE7d+7U/Pnz9fbbb2vLli2qr6/XTTfdpNra2sA+Dz74oF555RX98Y9/1M6dO/XRRx/pjjvucHHqf7qU+SXpnnvuUUVFReDyxBNPuDRxc1dffbVWrFih/Px85eXl6Ytf/KJmzZqlv/3tb5Jsn/smF7sNkt3z/2l79+7Vr371K40aNarZ+lC4D5q0dhsk2/fB8OHDm8325ptvBraFwvlva37J9rn/5JNPNGnSJIWHh+u1117T//3f/+nJJ59Uv379Avs88cQTeuqpp7R27Vrt2bNHffr00fTp03X69On2XVn7vyoDLamsrHQkOTt37nQcx3FOnDjhhIeHO3/84x8D+xQXFzuSnN27d7s1ZqvOn99xHOcLX/iC88ADD7g3VDv169fP+c1vfhNy5/7Tmm6D44TG+T958qRz7bXXOlu2bGk2byjdB63dBsexfR8sW7bMGT16dIvbQuH8tzW/49g+947jOEuWLHEmT57c6vbGxkZn4MCBzk9/+tPAuhMnTjher9d54YUX2nVdPKLuJE1fpxkbGytJys/PV319vTIyMgL7pKenKzk5udWv6HTT+fM3ee655xQXF6cRI0YoOztbp06dcmO8NjU0NOjFF19UbW2tJk6cGHLnXrrwNjSxfv7nz5+vW265pdm5lkLr339rt6GJ5fvg4MGDSkxM1ODBgzV79myVl5/7WsZQOf+tzd/E8rnftGmTxo4dq69+9asaMGCArr/+ev36178ObC8rK9PRo0eb3QcxMTGaMGFCu+8D83+ZLBQ0NjZq0aJFmjRpkkaMGCFJOnr0qCIiIi74QpD4+HgdPXrUhSlb19L8kvSNb3xDgwYNUmJiovbt26clS5aopKREL730kovT/tN7772niRMn6vTp07riiiu0YcMGDRs2TIWFhSFz7lu7DZL98//iiy+qoKBAe/fuvWBbqPz7b+s2SLbvgwkTJmj9+vVKS0tTRUWFcnJy9C//8i8qKioKifPf1vxRUVGmz70kffDBB1qzZo2ysrL0gx/8QHv37tXChQsVERGhzMzMwHk+/69oduQ+INSdYP78+SoqKrrg9ZVQ0dr83/72twM/jxw5UgkJCZo2bZref/99DRkyJNhjXiAtLU2FhYWqrq7Wn/70J2VmZmrnzp1uj9Uurd2GYcOGmT7/hw8f1gMPPKAtW7aoV69ers7SUZdyGyzfBzNmzAj8PGrUKE2YMEGDBg3SH/7wB0VGRro42aVpa/5vfvObps+9dO4BztixY/XYY49Jkq6//noVFRVp7dq1yszM7NTr4qnvy3T//ffr1Vdf1fbt23X11VcH1g8cOFBnzpzRiRMnmu3f1ld0uqG1+VsyYcIESVJpaWkwRruoiIgIXXPNNbrhhhuUm5ur0aNH6xe/+EXInHup9dvQEkvnPz8/X5WVlfrc5z6nsLAwhYWFaefOnXrqqacUFham+Ph48/fBxW5DQ0PDBcdYug/O17dvX1133XUqLS0Nqf8NNPn0/C2xdu4TEhICz341GTp0aODp+6bz3J6vaW4Noe4gx3F0//33a8OGDdq2bZtSU1Obbb/hhhsUHh7e7Cs6S0pKVF5ebuIrOi82f0sKCwslnfsHalFjY6P8fr/5c9+WptvQEkvnf9q0aXrvvfdUWFgYuIwdO1azZ88O/Gz9PrjYbejZs+cFx1i6D85XU1Oj999/XwkJCSH5v4FPz98Sa+d+0qRJF3yk9cCBAxo0aJAkKTU1VQMHDmx2H/h8Pu3Zs6f990EH3/DW7X33u991YmJinB07djgVFRWBy6lTpwL73HvvvU5ycrKzbds2Jy8vz5k4caIzceJEF6f+p4vNX1pa6ixfvtzJy8tzysrKnJdfftkZPHiwc+ONN7o8+TkPPfSQs3PnTqesrMzZt2+f89BDDzkej8f53//9X8dxbJ/7Jm3dBuvnvyXnv0s3FO6D8336Nli/D773ve85O3bscMrKypy//OUvTkZGhhMXF+dUVlY6jmP//Lc1v/Vz7ziO88477zhhYWHOo48+6hw8eNB57rnnnN69ezu/+93vAvusWLHC6du3r/Pyyy87+/btc2bNmuWkpqY6dXV17bouQt1Bklq8PPvss4F96urqnPvuu8/p16+f07t3b+f22293Kioq3Bv6Uy42f3l5uXPjjTc6sbGxjtfrda655hpn8eLFTnV1tbuD/8O8efOcQYMGOREREc6VV17pTJs2LRBpx7F97pu0dRusn/+WnB/qULgPzvfp22D9PrjzzjudhIQEJyIiwrnqqqucO++80yktLQ1st37+25rf+rlv8sorrzgjRoxwvF6vk56e7qxbt67Z9sbGRmfp0qVOfHy84/V6nWnTpjklJSXtvh6+5hIAAMN4jRoAAMMINQAAhhFqAAAMI9QAABhGqAEAMIxQAwBgGKEGAMAwQg0AgGGEGkCrUlJStGrVqkvef8eOHfJ4PBd8GQSAjuMvkwFo1ccff6w+ffqod+/el7T/mTNndPz4ccXHx8vj8XTxdED3QKgBADCMp76BbuzkyZOaPXu2+vTpo4SEBP385z/XlClTtGjRIkkXPvXt8Xj0m9/8Rrfffrt69+6ta6+9Vps2bQps56lvoPMRaqAby8rK0l/+8hdt2rRJW7Zs0RtvvKGCgoI2j8nJydHXvvY17du3TzfffLNmz56t48ePB2lioPsh1EA3dfLkSf32t7/Vz372M02bNk0jRozQs88+q4aGhjaP+4//+A99/etf1zXXXKPHHntMNTU1euedd4I0NdD9EGqgm/rggw9UX1+v8ePHB9bFxMQoLS2tzeNGjRoV+LlPnz6Kjo5WZWVll80JdHeEGkC7hIeHN1v2eDxqbGx0aRrgs49QA93U4MGDFR4err179wbWVVdX68CBAy5OBeB8YW4PAMAdUVFRyszM1OLFixUbG6sBAwZo2bJl6tGjB5+BBgzhETXQja1cuVITJ07UrbfeqoyMDE2aNElDhw5Vr1693B4NwD/wB08ABNTW1uqqq67Sk08+qW9+85tujwNAPPUNdGvvvvuu9u/fr/Hjx6u6ulrLly+XJM2aNcvlyQA0IdRAN/ezn/1MJSUlioiI0A033KA33nhDcXFxbo8F4B946hsAAMN4MxkAAIYRagAADCPUAAAYRqgBADCMUAMAYBihBgDAMEINAIBhhBoAAMP+H123obh3WWZWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Graficos de barras"
      ],
      "metadata": {
        "id": "_6yIE6rfPRVL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora utilizaremos gráficos de barras para observar comparaciones de comportamiento de regiones versus el resto del mundo.\n",
        "\n",
        "Ya habíamos creado los filtros de df_africa y df_europa, con esto vamos a comparar la mortalidad infantil."
      ],
      "metadata": {
        "id": "2Av36IE5QG1K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Graficamos\n",
        "df_euafr = df_nations.loc[df_nations[\"region\"].isin([\"Europe\",\"Africa\"])]\n",
        "sns.barplot(data=df_euafr, x=\"region\", y=\"chldmort\")"
      ],
      "metadata": {
        "id": "KDdVbl8lSNFt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "d254d91e-83a7-4822-ffdc-527fbb7be3a5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='region', ylabel='chldmort'>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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J0rhx45SSkqKQkBCPDggAAFAfbt9n57bbbtOECRM8OQsAAIDHuR07p0+f1q5du1RaWqra2lqXdVOmTKn3YAAAAJ7gVuysWrVKTz75pAIDA9WyZUvZbDbnOpvNRuwAAACf4VbsZGZmatasWcrIyJCfn1sf6AIAAGgUbt9nZ+zYsYQOAADweW7VSmpqqt577z1PzwIAAOBxbp3GysrK0k9+8hPl5OSoW7duCggIcFm/YMECjwwHAABQX27HzpYtW9SxY0dJuu4CZQAAAF/hVuy89tprWrFihR577DEPjwMAAOBZbl2zExQUpIEDB3p6FgAAAI9zK3aeeeYZLVmyxNOzAAAAeJxbp7H27dunbdu2afPmzeratet1Fyh/8MEHHhkOAACgvtyKnYiICD344IOengUAAMDj3IqdlStXenoOAACABsEtkAEAgNFu+chOz549b/keOgUFBW4PBAAA4Em3HDujR492fn3x4kW9+eab6tKlixISEiRJe/bs0eHDh/X00097fEgAAAB33XLsvPDCC86vf/azn2nKlCn65S9/ed02RUVFnpsOAACgnty6Zue9997To48+et3yhx9+WO+//369hwIAAPAUt2InJCREeXl51y3Py8tTcHBwvYcCAADwFLdiJz09XU899ZSmTJmit99+W2+//bYmT56sSZMm6dlnn/XogH/729/08MMPq2XLlgoJCVG3bt20f/9+53rLsjRr1ixFR0crJCREiYmJOn78uEdnAAAATZdb99mZOXOm7rzzTr3++ut6++23JUmdO3fWypUrNWbMGI8Nd+7cOQ0cOFBDhw7V73//e7Vu3VrHjx9XixYtnNvMmzdPixcv1urVqxUXF6fMzEwlJSXpyy+/5CgTAACQzbIsy9tD1GXmzJnKy8vTp59+esP1lmUpJiZGzz33nH7xi19IksrLy+VwOLRq1SqNHTv2hq+rrq5WdXW183lFRYViY2NVXl6u8PBwz78RSfHT1jTIfoGm7sCvrr/+DwBuRUVFhex2+7/899unbyr44Ycfqnfv3vrP//xPtWnTRj179tRvfvMb5/rCwkIVFxcrMTHRucxut6tfv37avXt3nfvNysqS3W53PmJjYxv0fQAAAO+55dhp0aKFIiMjb+nhKX/5y1+0bNkydejQQVu2bHFeJ7R69WpJUnFxsSTJ4XC4vM7hcDjX3UhGRobKy8udDz4uDwCAuW75mp1FixY14Bg3Vltbq969e+vVV1+VdPUuzl988YWys7M1fvx4t/cbFBSkoKAgT40JAAB82C3HTn3iwl3R0dHq0qWLy7LOnTs77+UTFRUlSSopKVF0dLRzm5KSEvXo0aPR5gQAAL7LrU9jSVePupw4cUKlpaWqra11WTd48OB6DyZJAwcO1NGjR12WHTt2THfccYckKS4uTlFRUcrNzXXGTUVFhfbu3aunnnrKIzMAAICmza3Y2bNnj/7rv/5LX3/9tf75w1w2m01XrlzxyHDPPvusBgwYoFdffVVjxozRvn379Otf/1q//vWvnd8rPT1ds2fPVocOHZwfPY+JiXH5XV4AAOCHy63Y+fnPf67evXvr448/VnR09C3/NvTvq0+fPtqwYYMyMjL08ssvKy4uTosWLVJKSopzm+nTp6uqqkoTJ05UWVmZBg0apJycHO6xAwAAJLl5n53Q0FAdOnRI7du3b4iZGt2tfk6/PrjPDnBj3GcHgLsa9D47/fr104kTJ9weDgAAoLHc8mmszz//3Pn15MmT9dxzz6m4uFjdunVTQECAy7bdu3f33IQAAAD1cMux06NHD9lsNpcLkp944gnn19fWefICZQAAgPq65dgpLCxsyDkAAAAaxC3HzrV720hXf7eUw+FwObIjSStWrNA333yjGTNmeG5CAACAenDrAuW33npLnTp1um55165dlZ2dXe+hAAAAPMWt2CkuLnb59QzXtG7dWmfOnKn3UAAAAJ7iVuzExsYqLy/vuuV5eXmKiYmp91AAAACe4tYdlCdMmKD09HTV1NTo3nvvlSTl5uZq+vTpeu655zw6IAAAQH24FTvTpk3Tt99+q6efflqXLl2SJAUHB2vGjBnKyMjw6IAAAAD14Vbs2Gw2zZ07V5mZmTpy5IhCQkLUoUMHBQUFeXo+AACAenErdq4JCwtTnz59PDULAACAx7l1gTIAAEBTQewAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAozWp2JkzZ45sNpvS09Odyy5evKhJkyapZcuWCgsLU3JyskpKSrw3JAAA8ClNJnby8/P11ltvqXv37i7Ln332WX300Ud67733tGPHDp0+fVoPPvigl6YEAAC+pknETmVlpVJSUvSb3/xGLVq0cC4vLy/X8uXLtWDBAt17772Kj4/XypUr9ac//Ul79uypc3/V1dWqqKhweQAAADM1idiZNGmSHnjgASUmJrosP3DggGpqalyWd+rUSW3bttXu3bvr3F9WVpbsdrvzERsb22CzAwAA7/L52Hn33XdVUFCgrKys69YVFxcrMDBQERERLssdDoeKi4vr3GdGRobKy8udj6KiIk+PDQAAfIS/twe4maKiIj3zzDPaunWrgoODPbbfoKAgBQUFeWx/AADAd/n0kZ0DBw6otLRUvXr1kr+/v/z9/bVjxw4tXrxY/v7+cjgcunTpksrKylxeV1JSoqioKO8MDQAAfIpPH9m577779Oc//9ll2eOPP65OnTppxowZio2NVUBAgHJzc5WcnCxJOnr0qE6dOqWEhARvjAwAAHyMT8dO8+bNddddd7ksCw0NVcuWLZ3LU1NTNXXqVEVGRio8PFyTJ09WQkKC+vfv742RAQCAj/Hp2LkVCxculJ+fn5KTk1VdXa2kpCS9+eab3h4LAAD4iCYXO5988onL8+DgYC1dulRLly71zkAAAMCn+fQFygAAAPVF7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACM5tOxk5WVpT59+qh58+Zq06aNRo8eraNHj7psc/HiRU2aNEktW7ZUWFiYkpOTVVJS4qWJAQCAr/Hp2NmxY4cmTZqkPXv2aOvWraqpqdH999+vqqoq5zbPPvusPvroI7333nvasWOHTp8+rQcffNCLUwMAAF/i7+0BbiYnJ8fl+apVq9SmTRsdOHBAgwcPVnl5uZYvX65169bp3nvvlSStXLlSnTt31p49e9S/f39vjA0AAHyITx/Z+Wfl5eWSpMjISEnSgQMHVFNTo8TEROc2nTp1Utu2bbV79+4691NdXa2KigqXBwAAMFOTiZ3a2lqlp6dr4MCBuuuuuyRJxcXFCgwMVEREhMu2DodDxcXFde4rKytLdrvd+YiNjW3I0QEAgBc1mdiZNGmSvvjiC7377rv13ldGRobKy8udj6KiIg9MCAAAfJFPX7NzTVpamjZv3qydO3fq9ttvdy6PiorSpUuXVFZW5nJ0p6SkRFFRUXXuLygoSEFBQQ05MgAA8BE+fWTHsiylpaVpw4YN2rZtm+Li4lzWx8fHKyAgQLm5uc5lR48e1alTp5SQkNDY4wIAAB/k00d2Jk2apHXr1mnTpk1q3ry58zocu92ukJAQ2e12paamaurUqYqMjFR4eLgmT56shIQEPokFAAAk+XjsLFu2TJI0ZMgQl+UrV67UY489JklauHCh/Pz8lJycrOrqaiUlJenNN99s5EkBAICv8unYsSzrX24THByspUuXaunSpY0wEQAAaGp8+podAACA+iJ2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGM3f2wMAAOAJlmWpqqrK+Tw0NFQ2m82LE8FXEDsAACNUVVVp1KhRzuebNm1SWFiYFyeCr+A0FgAAMBqxAwAAjEbsAAAAoxE7AADAaFygDAD1FD9tjbdHgCTb5Uuy/8PzIZnvyvIP9No8kA786lFvjyCJIzsAAMBwxA4AADAasQMAAIxG7AAAAKNxgTIAwAhWswCVdx/n8hyQiB0AgClsNj59hRviNBYAADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwmjGxs3TpUrVr107BwcHq16+f9u3b5+2RAACADzAidtavX6+pU6fqhRdeUEFBge6++24lJSWptLTU26MBAAAvMyJ2FixYoAkTJujxxx9Xly5dlJ2drdtuu00rVqzw9mgAAMDLmvwvAr106ZIOHDigjIwM5zI/Pz8lJiZq9+7dN3xNdXW1qqurnc/Ly8slSRUVFQ0255Xq7xps30BT1pA/d42Fn2/gxhr65/va/i3Luul2TT52/v73v+vKlStyOBwuyx0Oh7766qsbviYrK0svvfTSdctjY2MbZEYAdbMv+bm3RwDQQBrr5/v8+fOy2+11rm/yseOOjIwMTZ061fm8trZWZ8+eVcuWLWWz2bw4GRpDRUWFYmNjVVRUpPDwcG+PA8CD+Pn+YbEsS+fPn1dMTMxNt2vysdOqVSs1a9ZMJSUlLstLSkoUFRV1w9cEBQUpKCjIZVlERERDjQgfFR4ezl+GgKH4+f7huNkRnWua/AXKgYGBio+PV25urnNZbW2tcnNzlZCQ4MXJAACAL2jyR3YkaerUqRo/frx69+6tvn37atGiRaqqqtLjjz/u7dEAAICXGRE7Dz30kL755hvNmjVLxcXF6tGjh3Jycq67aBmQrp7GfOGFF647lQmg6ePnGzdis/7V57UAAACasCZ/zQ4AAMDNEDsAAMBoxA4AADAasQNjWJaliRMnKjIyUjabTZ999lmd29psNm3cuLHRZgMAeA+xgyZn9+7datasmR544AGX5Tk5OVq1apU2b96sM2fO6K677qpzH2fOnNHw4cMbelQAdXjsscdks9muewwbNszbo8FARnz0HD8sy5cv1+TJk7V8+XKdPn3aeZvwkydPKjo6WgMGDKjztZcuXVJgYGCdd9cG0HiGDRumlStXuixz9yPjlmXpypUr8vfnnzVcjyM7aFIqKyu1fv16PfXUU3rggQe0atUqSVf/lzh58mSdOnVKNptN7dq1kyQNGTJEaWlpSk9PV6tWrZSUlCTp+tNY//d//6dx48YpMjJSoaGh6t27t/bu3SvpakSNGjVKDodDYWFh6tOnj/74xz825tsGjBQUFKSoqCiXR4sWLfTXv/71ulPRZWVlstls+uSTTyRJn3zyiWw2m37/+98rPj5eQUFB2rVrl6qrqzVlyhS1adNGwcHBGjRokPLz8537ufa6jz/+WN27d1dwcLD69++vL774wmW2Xbt26cc//rFCQkIUGxurKVOmqKqqqjH+WNAAiB00Kb/73e/UqVMndezYUQ8//LBWrFghy7L0+uuv6+WXX9btt9+uM2fOuPzltnr1agUGBiovL0/Z2dnX7bOyslL33HOP/va3v+nDDz/UoUOHNH36dNXW1jrXjxgxQrm5uTp48KCGDRumkSNH6tSpU432vgHc2MyZMzVnzhwdOXJE3bt31/Tp0/X+++9r9erVKigoUPv27ZWUlKSzZ8+6vG7atGl67bXXlJ+fr9atW2vkyJGqqamRdPU/OMOGDVNycrI+//xzrV+/Xrt27VJaWpo33iI8wQKakAEDBliLFi2yLMuyampqrFatWlnbt2+3LMuyFi5caN1xxx0u299zzz1Wz549r9uPJGvDhg2WZVnWW2+9ZTVv3tz69ttvb3mOrl27WkuWLHHrPQCwrPHjx1vNmjWzQkNDXR6vvPKKVVhYaEmyDh486Nz+3LlzliTnz/v27dstSdbGjRud21RWVloBAQHW2rVrncsuXbpkxcTEWPPmzXN53bvvvuvc5ttvv7VCQkKs9evXW5ZlWampqdbEiRNd5v30008tPz8/67vvvvP0HwUaASc30WQcPXpU+/bt04YNGyRJ/v7+euihh7R8+XINGTKkztfFx8ffdL+fffaZevbsqcjIyBuur6ys1IsvvqiPP/5YZ86c0eXLl/Xdd99xZAeop6FDh2rZsmUuyyIjI1VRUXHL++jdu7fz65MnT6qmpkYDBw50LgsICFDfvn115MgRl9f94y+KjoyMVMeOHZ3bHDp0SJ9//rnWrl3r3MayLNXW1qqwsFCdO3e+5fngG4gdNBnLly/X5cuXnRckS1f/AgoKCtIbb7xR5+tCQ0Nvut+QkJCbrv/FL36hrVu3av78+Wrfvr1CQkL005/+VJcuXfp+bwCAi9DQULVv3/665ZWVlZKu/nxfc+0U04324WmVlZV68sknNWXKlOvWtW3b1uPfDw2P2EGTcPnyZa1Zs0avvfaa7r//fpd1o0eP1jvvvOP2vrt3767f/va3Onv27A2P7uTl5emxxx7Tf/zHf0i6+hfhX//6V7e/H4Cba926taSrt4jo2bOnJN30vlnX/OhHP3Jen3fHHXdIuhpJ+fn5Sk9Pd9l2z549znA5d+6cjh075jxi06tXL3355Zc3DDE0TcQOmoTNmzfr3LlzSk1Nld1ud1mXnJys5cuXKyUlxa19jxs3Tq+++qpGjx6trKwsRUdH6+DBg4qJiVFCQoI6dOigDz74QCNHjpTNZlNmZqbz4mUA7quurlZxcbHLMn9/f7Vq1Ur9+/fXnDlzFBcXp9LSUj3//PP/cn+hoaF66qmnNG3aNEVGRqpt27aaN2+eLly4oNTUVJdtX375ZbVs2VIOh0P//d//rVatWmn06NGSpBkzZqh///5KS0vTz372M4WGhurLL7/U1q1bb3oUGb6LT2OhSVi+fLkSExOvCx3pauzs37//e53n/0eBgYH6wx/+oDZt2mjEiBHq1q2b5syZo2bNmkmSFixYoBYtWmjAgAEaOXKkkpKS1KtXr3q9HwBXbwQaHR3t8hg0aJAkacWKFbp8+bLi4+OVnp6u2bNn39I+58yZo+TkZD3yyCPq1auXTpw4oS1btqhFixbXbffMM88oPj5excXF+uijjxQYGCjp6tHeHTt26NixY/rxj3+snj17atasWS6n0NG02Kx/PCkKAIDBPvnkEw0dOlTnzp1TRESEt8dBI+HIDgAAMBqxAwAAjMZpLAAAYDSO7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AH4Q2rVrp0WLFnl7DABewEfPAfwgfPPNNwoNDdVtt93m7VEANDJiB4BPu3TpkvN3FgGAOziNBcCnDBkyRGlpaUpPT1erVq2UlJSkL774QsOHD1dYWJgcDoceeeQR/f3vf3e+5vz580pJSVFoaKiio6O1cOFCDRkyROnp6c5t/vk01qlTpzRq1CiFhYUpPDxcY8aMUUlJiXP9iy++qB49euh//ud/1K5dO9ntdo0dO1bnz59vjD8GAB5E7ADwOatXr1ZgYKDy8vI0Z84c3XvvverZs6f279+vnJwclZSUaMyYMc7tp06dqry8PH344YfaunWrPv30UxUUFNS5/9raWo0aNUpnz57Vjh07tHXrVv3lL3/RQw895LLdyZMntXHjRm3evFmbN2/Wjh07NGfOnAZ73wAahr+3BwCAf9ahQwfNmzdPkjR79mz17NlTr776qnP9ihUrFBsbq2PHjik6OlqrV6/WunXrdN9990mSVq5cqZiYmDr3n5ubqz//+c8qLCxUbGysJGnNmjXq2rWr8vPz1adPH0lXo2jVqlVq3ry5JOmRRx5Rbm6uXnnllQZ53wAaBrEDwOfEx8c7vz506JC2b9+usLCw67Y7efKkvvvuO9XU1Khv377O5Xa7XR07dqxz/0eOHFFsbKwzdCSpS5cuioiI0JEjR5yx065dO2foSFJ0dLRKS0vr9d4AND5iB4DPCQ0NdX5dWVmpkSNHau7cuddtFx0drRMnTjTYHAEBAS7PbTabamtrG+z7AWgYXLMDwKf16tVLhw8fVrt27dS+fXuXR2hoqO68804FBAQoPz/f+Zry8nIdO3aszn127txZRUVFKioqci778ssvVVZWpi5dujTo+wHQ+IgdAD5t0qRJOnv2rMaNG6f8/HydPHlSW7Zs0eOPP64rV66oefPmGj9+vKZNm6bt27fr8OHDSk1NlZ+fn2w22w33mZiYqG7duiklJUUFBQXat2+fHn30Ud1zzz3q3bt3I79DAA2N2AHg02JiYpSXl6crV67o/vvvV7du3ZSenq6IiAj5+V39K2zBggVKSEjQT37yEyUmJmrgwIHq3LmzgoODb7hPm82mTZs2qUWLFho8eLASExN15513av369Y351gA0Em4qCMA4VVVV+rd/+ze99tprSk1N9fY4ALyMC5QBNHkHDx7UV199pb59+6q8vFwvv/yyJGnUqFFengyALyB2ABhh/vz5Onr0qAIDAxUfH69PP/1UrVq18vZYAHwAp7EAAIDRuEAZAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYLT/B1AYGZ/59/rRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Otro gráfico de barras: Alfabetismo promedio entre Americas y el resto del mundo\n",
        "\n",
        "# Filtrar los datos para las Americas y el resto del mundo\n",
        "df_americas = df_nations[df_nations['region'] == 'Americas']\n",
        "df_resto_mundo = df_nations[df_nations['region'] != 'Americas']"
      ],
      "metadata": {
        "id": "DsBIjBsIPRaO"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcular el alfabetismo promedio para cada grupo\n",
        "alfabetismo_promedio_americas = df_americas['literacy'].mean()\n",
        "alfabetismo_promedio_resto_mundo = df_resto_mundo['literacy'].mean()"
      ],
      "metadata": {
        "id": "UH4Gg1oj-OD9"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear el gráfico de barras utilizando sns.barplot\n",
        "sns.barplot(x=['Americas', 'Rest of the world'], y=[alfabetismo_promedio_americas, alfabetismo_promedio_resto_mundo])"
      ],
      "metadata": {
        "id": "SPZndsuiiXiv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "55a6f447-1ad6-4503-e003-01b3021ec6c2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ahora usando gráfico Boxplot para observar la distribución de la escolaridad por región\n",
        "\n",
        "sns.boxplot(x=df_nations[\"region\"], y=df_nations[\"school\"])"
      ],
      "metadata": {
        "id": "W1w4bAv9PRdp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "d12045c4-14a1-42db-89d1-754fa485a7e1"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='region', ylabel='school'>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Otra forma de observar la información es a traves de los diagramas de dispersión, pero para usar estos necesitamos eliminar datos perdidos en el dataset.\n",
        "df_limpia = df_nations.dropna()"
      ],
      "metadata": {
        "id": "3rlt4VEhUJF9"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Teniendo data adecuada, procedemos a observar la dispersión de los años de escolaridad sobre el grado de alfabetismo\n"
      ],
      "metadata": {
        "id": "8nIbCRfZlTvb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_limpia"
      ],
      "metadata": {
        "id": "O_XhyauqFpTM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "8500a4a5-cc55-4e46-9766-e1d9f1472cd0"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                country  region           gdp     school      adfert  \\\n",
              "4               Burundi  Africa    349.200012   2.483333   18.600000   \n",
              "11      Congo (Dem Rep)  Africa    279.799988   3.416667  201.399994   \n",
              "12        Côte d'Ivoire  Africa   1539.199951   3.200000  129.399994   \n",
              "14                Egypt  Africa   4754.399902   5.950000   46.599998   \n",
              "17             Ethiopia  Africa    741.400024   1.500000   72.400002   \n",
              "..                  ...     ...           ...        ...         ...   \n",
              "161             Moldova  Europe   2553.600098   9.533333   33.799999   \n",
              "166              Poland  Europe  15446.400391   9.816667   14.800000   \n",
              "168             Romania  Europe  10560.400391  10.283333   32.000000   \n",
              "169  Russian Federation  Europe  13424.799805   9.783334   30.000000   \n",
              "177             Ukraine  Europe   6124.000000  11.200000   30.799999   \n",
              "\n",
              "     chldmort       life        pop      urban  femlab   literacy        co2  \\\n",
              "4      168.50  48.866665    7821783  10.250000  1.0344  66.599998   0.100000   \n",
              "11     199.00  47.516666   61654348  33.633331  0.6552  66.800003   0.100000   \n",
              "12     123.25  53.049999   18844750  48.666664  0.6132  55.299999   1.450000   \n",
              "14      24.75  72.333336   77645648  43.183334  0.3092  68.900002   8.450000   \n",
              "17     112.00  57.049999   78593232  16.283333  0.8700  29.799999   0.300000   \n",
              "..        ...        ...        ...        ...     ...        ...        ...   \n",
              "161     18.00  68.366669    3660717  45.066666  0.9054  98.500000   7.600000   \n",
              "166      7.25  75.550003   38211584  61.183334  0.7490  99.500000  29.850000   \n",
              "168     14.50  73.116669   21622482  56.049999  0.7584  97.699997  16.200001   \n",
              "169     14.25  67.399994  143305536  73.016663  0.8206  99.599998  39.349998   \n",
              "177     16.00  67.733330   46158800  68.166672  0.7940  99.699997  25.350000   \n",
              "\n",
              "          gini  gdp_pesos2021  co2_recodificada  \n",
              "4    33.299999   2.968200e+05                 0  \n",
              "11   44.400002   2.378300e+05                 0  \n",
              "12   46.099998   1.308320e+06                 0  \n",
              "14   32.099998   4.041240e+06                 0  \n",
              "17   29.799999   6.301900e+05                 0  \n",
              "..         ...            ...               ...  \n",
              "161  38.000000   2.170560e+06                 0  \n",
              "166  34.200001   1.312944e+07                 1  \n",
              "168  31.200001   8.976340e+06                 0  \n",
              "169  42.299999   1.141108e+07                 1  \n",
              "177  27.500000   5.205400e+06                 1  \n",
              "\n",
              "[71 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a5f2c08-bd14-4174-bf67-c36752b2cfb0\" 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>country</th>\n",
              "      <th>region</th>\n",
              "      <th>gdp</th>\n",
              "      <th>school</th>\n",
              "      <th>adfert</th>\n",
              "      <th>chldmort</th>\n",
              "      <th>life</th>\n",
              "      <th>pop</th>\n",
              "      <th>urban</th>\n",
              "      <th>femlab</th>\n",
              "      <th>literacy</th>\n",
              "      <th>co2</th>\n",
              "      <th>gini</th>\n",
              "      <th>gdp_pesos2021</th>\n",
              "      <th>co2_recodificada</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Burundi</td>\n",
              "      <td>Africa</td>\n",
              "      <td>349.200012</td>\n",
              "      <td>2.483333</td>\n",
              "      <td>18.600000</td>\n",
              "      <td>168.50</td>\n",
              "      <td>48.866665</td>\n",
              "      <td>7821783</td>\n",
              "      <td>10.250000</td>\n",
              "      <td>1.0344</td>\n",
              "      <td>66.599998</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>33.299999</td>\n",
              "      <td>2.968200e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Congo (Dem Rep)</td>\n",
              "      <td>Africa</td>\n",
              "      <td>279.799988</td>\n",
              "      <td>3.416667</td>\n",
              "      <td>201.399994</td>\n",
              "      <td>199.00</td>\n",
              "      <td>47.516666</td>\n",
              "      <td>61654348</td>\n",
              "      <td>33.633331</td>\n",
              "      <td>0.6552</td>\n",
              "      <td>66.800003</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>44.400002</td>\n",
              "      <td>2.378300e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Côte d'Ivoire</td>\n",
              "      <td>Africa</td>\n",
              "      <td>1539.199951</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>129.399994</td>\n",
              "      <td>123.25</td>\n",
              "      <td>53.049999</td>\n",
              "      <td>18844750</td>\n",
              "      <td>48.666664</td>\n",
              "      <td>0.6132</td>\n",
              "      <td>55.299999</td>\n",
              "      <td>1.450000</td>\n",
              "      <td>46.099998</td>\n",
              "      <td>1.308320e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>Africa</td>\n",
              "      <td>4754.399902</td>\n",
              "      <td>5.950000</td>\n",
              "      <td>46.599998</td>\n",
              "      <td>24.75</td>\n",
              "      <td>72.333336</td>\n",
              "      <td>77645648</td>\n",
              "      <td>43.183334</td>\n",
              "      <td>0.3092</td>\n",
              "      <td>68.900002</td>\n",
              "      <td>8.450000</td>\n",
              "      <td>32.099998</td>\n",
              "      <td>4.041240e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Ethiopia</td>\n",
              "      <td>Africa</td>\n",
              "      <td>741.400024</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>72.400002</td>\n",
              "      <td>112.00</td>\n",
              "      <td>57.049999</td>\n",
              "      <td>78593232</td>\n",
              "      <td>16.283333</td>\n",
              "      <td>0.8700</td>\n",
              "      <td>29.799999</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>29.799999</td>\n",
              "      <td>6.301900e+05</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>Moldova</td>\n",
              "      <td>Europe</td>\n",
              "      <td>2553.600098</td>\n",
              "      <td>9.533333</td>\n",
              "      <td>33.799999</td>\n",
              "      <td>18.00</td>\n",
              "      <td>68.366669</td>\n",
              "      <td>3660717</td>\n",
              "      <td>45.066666</td>\n",
              "      <td>0.9054</td>\n",
              "      <td>98.500000</td>\n",
              "      <td>7.600000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>2.170560e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>Poland</td>\n",
              "      <td>Europe</td>\n",
              "      <td>15446.400391</td>\n",
              "      <td>9.816667</td>\n",
              "      <td>14.800000</td>\n",
              "      <td>7.25</td>\n",
              "      <td>75.550003</td>\n",
              "      <td>38211584</td>\n",
              "      <td>61.183334</td>\n",
              "      <td>0.7490</td>\n",
              "      <td>99.500000</td>\n",
              "      <td>29.850000</td>\n",
              "      <td>34.200001</td>\n",
              "      <td>1.312944e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>Romania</td>\n",
              "      <td>Europe</td>\n",
              "      <td>10560.400391</td>\n",
              "      <td>10.283333</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>14.50</td>\n",
              "      <td>73.116669</td>\n",
              "      <td>21622482</td>\n",
              "      <td>56.049999</td>\n",
              "      <td>0.7584</td>\n",
              "      <td>97.699997</td>\n",
              "      <td>16.200001</td>\n",
              "      <td>31.200001</td>\n",
              "      <td>8.976340e+06</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>Russian Federation</td>\n",
              "      <td>Europe</td>\n",
              "      <td>13424.799805</td>\n",
              "      <td>9.783334</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>14.25</td>\n",
              "      <td>67.399994</td>\n",
              "      <td>143305536</td>\n",
              "      <td>73.016663</td>\n",
              "      <td>0.8206</td>\n",
              "      <td>99.599998</td>\n",
              "      <td>39.349998</td>\n",
              "      <td>42.299999</td>\n",
              "      <td>1.141108e+07</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>Europe</td>\n",
              "      <td>6124.000000</td>\n",
              "      <td>11.200000</td>\n",
              "      <td>30.799999</td>\n",
              "      <td>16.00</td>\n",
              "      <td>67.733330</td>\n",
              "      <td>46158800</td>\n",
              "      <td>68.166672</td>\n",
              "      <td>0.7940</td>\n",
              "      <td>99.699997</td>\n",
              "      <td>25.350000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>5.205400e+06</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71 rows × 15 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "df_limpia",
              "summary": "{\n  \"name\": \"df_limpia\",\n  \"rows\": 71,\n  \"fields\": [\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"Brazil\",\n          \"Burundi\",\n          \"Philippines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Americas\",\n          \"Europe\",\n          \"Africa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9524.122994034686,\n        \"min\": 279.799987792969,\n        \"max\": 74906.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          9081.2001953125,\n          349.200012207031,\n          3118.19995117188\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"school\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8447757971264647,\n        \"min\": 1.15000009536743,\n        \"max\": 12.1000003814697,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          7.31666660308838,\n          4.25,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adfert\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47.94730934375676,\n        \"min\": 8.39999961853027,\n        \"max\": 207.100006103516,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          75.5999984741211,\n          18.6000003814697,\n          54.0999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chldmort\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.61884137962557,\n        \"min\": 6.0,\n        \"max\": 199.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          17.0,\n          18.0,\n          168.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"life\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.545295818309375,\n        \"min\": 47.5166664123535,\n        \"max\": 78.8833312988281,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          64.6166687011719,\n          55.4000015258789,\n          57.0499992370605\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 208907272,\n        \"min\": 1288367,\n        \"max\": 1324696064,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          190579936,\n          7821783,\n          89408720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"urban\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.771894327491523,\n        \"min\": 10.25,\n        \"max\": 95.6333312988281,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          85.3999938964844,\n          10.25,\n          48.4500007629395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"femlab\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19256548125421702,\n        \"min\": 0.246800005435944,\n        \"max\": 1.03439998626709,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          0.554000020027161,\n          1.03439998626709,\n          0.515600025653839\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"literacy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.80377807787837,\n        \"min\": 26.2000007629395,\n        \"max\": 99.8000030517578,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          89.033332824707,\n          98.8000030517578,\n          66.5999984741211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.856486388021462,\n        \"min\": 0.100000001490116,\n        \"max\": 210.649993896484,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          25.0,\n          19.9500007629395,\n          0.100000001490116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gini\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.902953499548177,\n        \"min\": 22.6000003814697,\n        \"max\": 58.5,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          34.5,\n          35.7000007629395,\n          33.2999992370605\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gdp_pesos2021\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8095504.544929482,\n        \"min\": 237829.98962402364,\n        \"max\": 63670100.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          7719020.166015625,\n          296820.0103759764,\n          2650469.958496098\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"co2_recodificada\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=df_limpia[\"school\"], y=df_limpia[\"literacy\"])"
      ],
      "metadata": {
        "id": "RC_pvvXXk0cu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "e3800987-11bb-4adb-8950-284192894ca1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='school', ylabel='literacy'>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ¿Cómo se podría crear un diagrama de dispersión de la tasa de escolaridad (\"school\") versus el pib (\"gdp\")?\n",
        "sns.scatterplot(x=df_limpia[\"school\"], y=df_limpia[\"gdp\"])"
      ],
      "metadata": {
        "id": "UBeqhR_-lXkh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "b2ec833d-6401-4ab4-d5a5-d6d99e1166d6"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='school', ylabel='gdp'>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El último tipo de gráfico que revisaremos en este taller, corresponde al mapa de calor o `heatmap`. Este tipo de gráfico es una representación visual de los datos en que se muestra concentración o intensidad.\n",
        "\n",
        "En este caso lo utilizaremos para buscar correlación entre las distintas variables de nuestro dataset.\n",
        "\n",
        "**¿Qué es correlación?**\n",
        "\n",
        "Correlación es una medida estadística que expresa hasta qué punto dos variables están relacionadas linealmente. Es una herramienta común para describir relaciones simples sin hacer afirmaciones sobre causa y efecto."
      ],
      "metadata": {
        "id": "ZahvEsHeTNN7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df_nations.corr(numeric_only=True)"
      ],
      "metadata": {
        "id": "-x5OeyTzzr42"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams[\"figure.figsize\"] =(8,8)\n",
        "sns.heatmap(corr, cmap=\"Greens\", annot=True)"
      ],
      "metadata": {
        "id": "EwwlCIgUmDBh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "2c04dc29-d434-4bb1-ef53-5503b5cce751"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dentro de las principales correlaciones existentes, nos encontramos con la correlación positiva entre mortalidad infantil y tasa de fertilidad adolecente, también la correlación positiva entre el promedio de años de escolaridad y tasa de alfabetización. Correlación entre PIB per capita y porcentaje de población urbana."
      ],
      "metadata": {
        "id": "EW7VXH0hWJUf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Por último, sección de modelación"
      ],
      "metadata": {
        "id": "lskyHRSxWroW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La regresión lineal es un modelo matemático usado para aproximar la relación de dependencia entre una variable dependiente $Y$, $m$ variables independientes $X_{i}$ y un término aleatorio (de error) $e$.\n",
        "\n",
        "$$\n",
        "y_i = \\beta_{0} + \\beta_1 x_{i1} + ... +\\beta_{m} x_{im} + \\epsilon_i\n",
        "$$\n",
        "\n",
        "La principal motivación es mostrar cuánto de la variabilidad de la variable dependiente $Y$ es explicado por la variabilidad de las variables independientes $X_{i}$.\n"
      ],
      "metadata": {
        "id": "88s_0ERpW1Xn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para trabajar el modelo de regresión lineal es fundamental usar datos limpios de valores perdidos, por lo que utilizaremos el dataset que llamamos data_limpia.\n",
        "\n",
        "Y los pasos a seguir para implementar el modelo de regresión lineal más simple:\n",
        "  1. Plantear el modelo con su formula\n",
        "  2. Ajustar el modelo utilizando el método `.fit()`\n",
        "  3. Mostrar los resultados del modelo"
      ],
      "metadata": {
        "id": "btC9zBTlYb7u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ¿Cual es la relación CO2 y el GDP?"
      ],
      "metadata": {
        "id": "5FqDEgUOWyrL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo1 = smf.ols(\"gdp ~ co2\", data=df_limpia)"
      ],
      "metadata": {
        "id": "UB13iYaOWzM_"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo1 = modelo1.fit()"
      ],
      "metadata": {
        "id": "PYatpO-baEdX"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo1.summary()"
      ],
      "metadata": {
        "id": "aS26HmcnoeGb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "1bb66931-c48a-4308-c277-b49581aff409"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                    gdp   R-squared:                       0.872\n",
              "Model:                            OLS   Adj. R-squared:                  0.870\n",
              "Method:                 Least Squares   F-statistic:                     470.4\n",
              "Date:                Wed, 31 Jul 2024   Prob (F-statistic):           1.59e-32\n",
              "Time:                        14:37:11   Log-Likelihood:                -677.72\n",
              "No. Observations:                  71   AIC:                             1359.\n",
              "Df Residuals:                      69   BIC:                             1364.\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept   2791.2666    448.368      6.225      0.000    1896.797    3685.737\n",
              "co2          343.9784     15.860     21.688      0.000     312.338     375.619\n",
              "==============================================================================\n",
              "Omnibus:                        7.687   Durbin-Watson:                   1.154\n",
              "Prob(Omnibus):                  0.021   Jarque-Bera (JB):                7.423\n",
              "Skew:                           0.785   Prob(JB):                       0.0244\n",
              "Kurtosis:                       3.217   Cond. No.                         31.1\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>gdp</td>       <th>  R-squared:         </th> <td>   0.872</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.870</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   470.4</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Wed, 31 Jul 2024</td> <th>  Prob (F-statistic):</th> <td>1.59e-32</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>14:37:11</td>     <th>  Log-Likelihood:    </th> <td> -677.72</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    71</td>      <th>  AIC:               </th> <td>   1359.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    69</td>      <th>  BIC:               </th> <td>   1364.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td> 2791.2666</td> <td>  448.368</td> <td>    6.225</td> <td> 0.000</td> <td> 1896.797</td> <td> 3685.737</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>co2</th>       <td>  343.9784</td> <td>   15.860</td> <td>   21.688</td> <td> 0.000</td> <td>  312.338</td> <td>  375.619</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 7.687</td> <th>  Durbin-Watson:     </th> <td>   1.154</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.021</td> <th>  Jarque-Bera (JB):  </th> <td>   7.423</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.785</td> <th>  Prob(JB):          </th> <td>  0.0244</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.217</td> <th>  Cond. No.          </th> <td>    31.1</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}    &       gdp        & \\textbf{  R-squared:         } &     0.872   \\\\\n\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.870   \\\\\n\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     470.4   \\\\\n\\textbf{Date:}             & Wed, 31 Jul 2024 & \\textbf{  Prob (F-statistic):} &  1.59e-32   \\\\\n\\textbf{Time:}             &     14:37:11     & \\textbf{  Log-Likelihood:    } &   -677.72   \\\\\n\\textbf{No. Observations:} &          71      & \\textbf{  AIC:               } &     1359.   \\\\\n\\textbf{Df Residuals:}     &          69      & \\textbf{  BIC:               } &     1364.   \\\\\n\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{Intercept} &    2791.2666  &      448.368     &     6.225  &         0.000        &     1896.797    &     3685.737     \\\\\n\\textbf{co2}       &     343.9784  &       15.860     &    21.688  &         0.000        &      312.338    &      375.619     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Omnibus:}       &  7.687 & \\textbf{  Durbin-Watson:     } &    1.154  \\\\\n\\textbf{Prob(Omnibus):} &  0.021 & \\textbf{  Jarque-Bera (JB):  } &    7.423  \\\\\n\\textbf{Skew:}          &  0.785 & \\textbf{  Prob(JB):          } &   0.0244  \\\\\n\\textbf{Kurtosis:}      &  3.217 & \\textbf{  Cond. No.          } &     31.1  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{OLS Regression Results}\n\\end{center}\n\nNotes: \\newline\n [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    }
  ]
}