PRUEBA2_GRUPO4.ipynb

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        "<a href=\"https://colab.research.google.com/github/JoseBelas/Taller1_Grupo4/blob/main/PRUEBA2_GRUPO4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# **EJERCICIO 1**"
      ],
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        "Construya un DataFrame a partir del siguiente diccionario\n",
        "\n",
        "`exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'], 'score': [12.5, 9, 16.5, 10, 9, 20, 14.5, 12, 8, 19], 'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1], 'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}`\n",
        "\n",
        "con las siguientes etiquetas para los índices\n",
        "\n",
        "`labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']`\n",
        "\n",
        "1. Seleccione solo las columnas *name* y *score*.\n",
        "  \n",
        "2. Filtre las filas donde el número de intentos (attemps) sea mayor que 2.\n",
        "\n",
        "3. Filtre las filas donde el *score* se encuentre entre 15 y 20 (incluidos)\n",
        "\n",
        "4. Cambie el *score* en la fila 'd' a 11.5\n",
        "\n",
        "5. Agregue una nueva fila con etiqueta 'k' al dataframe con un valor que usted desee para cada columna\n",
        "\n",
        "6. Borre la fila 'k' y entregue nuevamente el DataFrame original.\n",
        "\n",
        "7. Ordene el DataFrame por nombre en orden ascendente.\n",
        "\n",
        "8. Reemplace los valores de la columna *qualify* que contiene los valores *yes* y *no* por los valores booleanos True y False respectivamente."
      ]
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        "import pandas as pd\n",
        "\n",
        "#Diccionario\n",
        "exam_data = {'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
        "             'score': [12.5, 9, 16.5, 10, 9, 20, 14.5, 12, 8, 19],\n",
        "             'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "             'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
        "\n",
        "#Etiquetas de los indices\n",
        "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']\n",
        "\n",
        "#Crear dataframe a partir del diccionario con la respectiva etiqueta de los indices\n",
        "df = pd.DataFrame(exam_data, index = labels)\n",
        "df.head()"
      ],
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              "        name  score  attempts qualify\n",
              "a  Anastasia   12.5         1     yes\n",
              "b       Dima    9.0         3      no\n",
              "c  Katherine   16.5         2     yes\n",
              "d      James   10.0         3      no\n",
              "e      Emily    9.0         2      no"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#Punto 1:\n",
        "#Selecciona solo las columnas 'name' y 'score' del dataframe\n",
        "df[['name','score']]"
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        "outputId": "82260777-828e-40d2-f09c-204979e9a386"
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      "execution_count": 7,
      "outputs": [
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            "text/plain": [
              "        name  score\n",
              "a  Anastasia   12.5\n",
              "b       Dima    9.0\n",
              "c  Katherine   16.5\n",
              "d      James   10.0\n",
              "e      Emily    9.0\n",
              "f    Michael   20.0\n",
              "g    Matthew   14.5\n",
              "h      Laura   12.0\n",
              "i      Kevin    8.0\n",
              "j      Jonas   19.0"
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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",
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              "      display: none;\n",
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              "      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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-86d96aa3-f0cf-4ee8-a2d2-e1d5fb6980d8 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-86d96aa3-f0cf-4ee8-a2d2-e1d5fb6980d8');\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-c2ab5b0f-8769-4319-aa09-dad37476114d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2ab5b0f-8769-4319-aa09-dad37476114d')\"\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-c2ab5b0f-8769-4319-aa09-dad37476114d 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[['name','score']]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Kevin\",\n          \"Dima\",\n          \"Michael\"\n        ],\n        \"num_unique_values\": 10,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.3040678433314685,\n        \"min\": 8.0,\n        \"max\": 20.0,\n        \"samples\": [\n          8.0,\n          9.0,\n          14.5\n        ],\n        \"num_unique_values\": 9,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 2:\n",
        "#Filtra las filas donde el número de intentos (attemps) sea mayor que 2.\n",
        "\n",
        "df.loc[(df.attempts > 2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "9XdamdRrvMYS",
        "outputId": "393bd19b-a466-4818-dd24-307379a66dee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      name  score  attempts qualify\n",
              "b     Dima    9.0         3      no\n",
              "d    James   10.0         3      no\n",
              "f  Michael   20.0         3     yes"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83cfdb77-5adf-46a1-bd6e-29d7e719d4c7\" 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>name</th>\n",
              "      <th>score</th>\n",
              "      <th>attempts</th>\n",
              "      <th>qualify</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>Dima</td>\n",
              "      <td>9.0</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>James</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>Michael</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83cfdb77-5adf-46a1-bd6e-29d7e719d4c7')\"\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",
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              "      display: none;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-83cfdb77-5adf-46a1-bd6e-29d7e719d4c7 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-83cfdb77-5adf-46a1-bd6e-29d7e719d4c7');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9012d7fe-7037-43df-a187-c6f8c91f1b7c')\"\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",
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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",
              "    }\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-9012d7fe-7037-43df-a187-c6f8c91f1b7c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Dima\",\n          \"James\",\n          \"Michael\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.082762530298219,\n        \"min\": 9.0,\n        \"max\": 20.0,\n        \"samples\": [\n          9.0,\n          10.0,\n          20.0\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attempts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"samples\": [\n          3\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualify\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"yes\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 3:\n",
        "#Filtra las filas donde el 'score' se encuentre entre 15 y 20 (incluidos)\n",
        "\n",
        "df[df.score.between(15,20)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "S2YOT9-ovP6f",
        "outputId": "fe2ebb00-f8ac-46bf-ee4a-dc49b64e2625"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name  score  attempts qualify\n",
              "c  Katherine   16.5         2     yes\n",
              "f    Michael   20.0         3     yes\n",
              "j      Jonas   19.0         1     yes"
            ],
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Katherine\",\n          \"Michael\",\n          \"Jonas\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8027756377319946,\n        \"min\": 16.5,\n        \"max\": 20.0,\n        \"samples\": [\n          16.5,\n          20.0,\n          19.0\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attempts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"samples\": [\n          2,\n          3,\n          1\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualify\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"yes\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Punto 4: Cambie el score en la fila 'd' a 11.5\n",
        "df.loc['d','score'] = 11.5\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "k9cJRB8gvRlr",
        "outputId": "29834bdf-fc85-4583-e06c-b4f4918de4e9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name  score  attempts qualify\n",
              "a  Anastasia   12.5         1     yes\n",
              "b       Dima    9.0         3      no\n",
              "c  Katherine   16.5         2     yes\n",
              "d      James   11.5         3      no\n",
              "e      Emily    9.0         2      no\n",
              "f    Michael   20.0         3     yes\n",
              "g    Matthew   14.5         1     yes\n",
              "h      Laura   12.0         1      no\n",
              "i      Kevin    8.0         2      no\n",
              "j      Jonas   19.0         1     yes"
            ],
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              "        const element = document.querySelector('#df-d0af8d88-94c7-4b99-b0e8-05d1ba55c842');\n",
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              "                                                    [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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Kevin\",\n          \"Dima\",\n          \"Michael\"\n        ],\n        \"num_unique_values\": 10,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.211096452627668,\n        \"min\": 8.0,\n        \"max\": 20.0,\n        \"samples\": [\n          8.0,\n          9.0,\n          14.5\n        ],\n        \"num_unique_values\": 9,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attempts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"samples\": [\n          1,\n          3,\n          2\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qualify\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Punto 5: Agregue una nueva fila con etiqueta 'k' al dataframe con un valor que usted desee para cada columna\n",
        "df['k'] = {'name': ['Randy'],\n",
        "             'score': [7],\n",
        "             'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "             'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
        "df\n",
        "\n",
        "\n",
        "exam_data = {'name': ['Anastasia', 'D ima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
        "             'score': [12.5, 9, 16.5, 10, 9, 20, 14.5, 12, 8, 19],\n",
        "             'attempts': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
        "             'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}"
      ],
      "metadata": {
        "id": "mRfagsHxvUSZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Punto 6: Borre la fila 'k' y entregue nuevamente el DataFrame original.\n",
        "df.drop(['k'], axis=0, inplace= True)\n",
        "df"
      ],
      "metadata": {
        "id": "TRAua14gvZAY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 7: Ordene el DataFrame por nombre en orden ascendente.\n",
        "df.sort_values(by='name', ascending=True, inplace= True)\n",
        "df"
      ],
      "metadata": {
        "id": "QwxbaG1JvZqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 8: Reemplace los valores de la columna qualify que contiene los valores yes y no por los valores booleanos True y False respectivamente.\n",
        "df['qualify'] = df['qualify'].map({'yes': True, 'no': False})\n",
        "df"
      ],
      "metadata": {
        "id": "JbVvm4Dtvb_t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**EJERCICIO 2**"
      ],
      "metadata": {
        "id": "GOUMVhTasbrW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importe el dataset athletes del repositorio del curso y realice las siguientes tareas:\n",
        "\n",
        "Importarlo como nombre athletes\n",
        "Filtre el dataframe tal que solo se quede con los atletas que hayan ganado al menos una medalla.\n",
        "Calcule la masa muscular por fila con una función como\n",
        "Im.m.=WeightHeight2\n",
        "\n",
        "Agrupe los datos por calculando la media por deporte para cada una del resto de características. ¿Cuál es el deporte en el que los jugadores tienen mayor peso promedio?\n",
        "\n",
        "¿Cuál es el pais con más participación femenina?"
      ],
      "metadata": {
        "id": "wDyoWihWsbZD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SOLUCIÓN**"
      ],
      "metadata": {
        "id": "FBni5qi6tKUm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Importar la libreria requerida\n",
        "import pandas as pd\n",
        "\n",
        "#Punto 1:\n",
        "#Importa el dataset 'athletes'\n",
        "\n",
        "athletes = pd.read_csv(\"https://raw.githubusercontent.com/tomasate/Datos_Clases/main/Datos_1/athletes.csv\")\n",
        "athletes.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0efsdm0kslPJ",
        "outputId": "addad4e3-5352-490a-f8d1-397521a20430"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id            name nationality     sex       dob  height  weight  \\\n",
              "0  736041664  A Jesus Garcia         ESP    male  10/17/69    1.72    64.0   \n",
              "1  532037425      A Lam Shin         KOR  female   9/23/86    1.68    56.0   \n",
              "2  435962603     Aaron Brown         CAN    male   5/27/92    1.98    79.0   \n",
              "3  521041435      Aaron Cook         MDA    male    1/2/91    1.83    80.0   \n",
              "4   33922579      Aaron Gate         NZL    male  11/26/90    1.81    71.0   \n",
              "\n",
              "       sport  gold  silver  bronze  \n",
              "0  athletics     0       0       0  \n",
              "1    fencing     0       0       0  \n",
              "2  athletics     0       0       1  \n",
              "3  taekwondo     0       0       0  \n",
              "4    cycling     0       0       0  "
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              "      background-color: #3B4455;\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "athletes",
              "summary": "{\n  \"name\": \"athletes\",\n  \"rows\": 11538,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 290864792,\n        \"min\": 18347,\n        \"max\": 999987786,\n        \"samples\": [\n          610541912,\n          620856226,\n          141698796\n        ],\n        \"num_unique_values\": 11538,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Apisai Domolailai\",\n          \"Darwin Espinal\",\n          \"Nina Rangelova\"\n        ],\n        \"num_unique_values\": 11517,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nationality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"SLE\",\n          \"QAT\",\n          \"IRL\"\n        ],\n        \"num_unique_values\": 207,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dob\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"min\": \"1974-01-07 00:00:00\",\n        \"max\": \"2073-11-09 00:00:00\",\n        \"samples\": [\n          \"4/25/91\",\n          \"12/15/84\"\n        ],\n        \"num_unique_values\": 5595,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11271869117624804,\n        \"min\": 1.21,\n        \"max\": 2.21,\n        \"samples\": [\n          1.89,\n          1.72\n        ],\n        \"num_unique_values\": 82,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.17733414697655,\n        \"min\": 31.0,\n        \"max\": 170.0,\n        \"samples\": [\n          75.0,\n          69.0\n        ],\n        \"num_unique_values\": 123,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sport\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"football\",\n          \"sailing\"\n        ],\n        \"num_unique_values\": 28,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 6,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"silver\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bronze\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 2:\n",
        "#Filtra el datafreme 'athletes' para que solo queden los atletas que han ganado al menos 1 medalla gold, silver o bronze\n",
        "\n",
        "athletes = athletes.loc[(athletes['gold'] >= 1) | (athletes['silver'] >= 1)| (athletes['bronze'] >= 1)]\n",
        "athletes.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yOIkRBrDssII",
        "outputId": "d5ac6b40-e699-43ba-8729-ebbbeb774de0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id                         name nationality     sex       dob  \\\n",
              "2   435962603                  Aaron Brown         CAN    male   5/27/92   \n",
              "6   266237702                Aaron Russell         USA    male    6/4/93   \n",
              "14  162792594               Abbey Weitzeil         USA  female   12/3/96   \n",
              "48  962468808  Abdoulrazak Issoufou Alfaga         NIG    male  12/26/94   \n",
              "55  969824503           Abdullah Alrashidi         IOA    male   8/21/63   \n",
              "\n",
              "    height  weight       sport  gold  silver  bronze  \n",
              "2     1.98    79.0   athletics     0       0       1  \n",
              "6     2.05    98.0  volleyball     0       0       1  \n",
              "14    1.78    68.0    aquatics     1       1       0  \n",
              "48    2.07    98.0   taekwondo     0       1       0  \n",
              "55    1.83    84.0    shooting     0       0       1  "
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\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-3b746b2d-4a12-4967-83f7-2f980071121f 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": "athletes",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 3:\n",
        "#Función para calcular la masa muscular\n",
        "\n",
        "def calcular_masa_muscular(row):\n",
        "  return row.loc['weight'] / (row.loc['height']**2)\n",
        "\n",
        "#Aplica la función a cada fila del dataframe\n",
        "athletes['masa_muscular'] = athletes.apply(calcular_masa_muscular,\n",
        "         axis = 1)\n",
        "\n",
        "#Muestra los resultados\n",
        "athletes.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8DGVw9SksvD1",
        "outputId": "3cd3eabf-92f0-4cb9-e961-624eca69a20c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id                         name nationality     sex       dob  \\\n",
              "2   435962603                  Aaron Brown         CAN    male   5/27/92   \n",
              "6   266237702                Aaron Russell         USA    male    6/4/93   \n",
              "14  162792594               Abbey Weitzeil         USA  female   12/3/96   \n",
              "48  962468808  Abdoulrazak Issoufou Alfaga         NIG    male  12/26/94   \n",
              "55  969824503           Abdullah Alrashidi         IOA    male   8/21/63   \n",
              "\n",
              "    height  weight       sport  gold  silver  bronze  masa_muscular  \n",
              "2     1.98    79.0   athletics     0       0       1      20.151005  \n",
              "6     2.05    98.0  volleyball     0       0       1      23.319453  \n",
              "14    1.78    68.0    aquatics     1       1       0      21.461937  \n",
              "48    2.07    98.0   taekwondo     0       1       0      22.871012  \n",
              "55    1.83    84.0    shooting     0       0       1      25.082863  "
            ],
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              "      <td>266237702</td>\n",
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              "      <th>14</th>\n",
              "      <td>162792594</td>\n",
              "      <td>Abbey Weitzeil</td>\n",
              "      <td>USA</td>\n",
              "      <td>female</td>\n",
              "      <td>12/3/96</td>\n",
              "      <td>1.78</td>\n",
              "      <td>68.0</td>\n",
              "      <td>aquatics</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21.461937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>962468808</td>\n",
              "      <td>Abdoulrazak Issoufou Alfaga</td>\n",
              "      <td>NIG</td>\n",
              "      <td>male</td>\n",
              "      <td>12/26/94</td>\n",
              "      <td>2.07</td>\n",
              "      <td>98.0</td>\n",
              "      <td>taekwondo</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.871012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>969824503</td>\n",
              "      <td>Abdullah Alrashidi</td>\n",
              "      <td>IOA</td>\n",
              "      <td>male</td>\n",
              "      <td>8/21/63</td>\n",
              "      <td>1.83</td>\n",
              "      <td>84.0</td>\n",
              "      <td>shooting</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>25.082863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e115eef-2a5b-4f92-bbd1-e7983cc72f72')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-5e115eef-2a5b-4f92-bbd1-e7983cc72f72 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-5e115eef-2a5b-4f92-bbd1-e7983cc72f72');\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-7b42e6c6-00df-491a-beda-172a0e09a0b1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7b42e6c6-00df-491a-beda-172a0e09a0b1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\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",
              "    }\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-7b42e6c6-00df-491a-beda-172a0e09a0b1 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": "athletes",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 4:\n",
        "#Agrupa los datos por la columna 'sport' y calcula la media para el resto de características numericas\n",
        "stats = athletes[['height','weight','gold','silver','bronze','masa_muscular','sport']].groupby('sport').mean().reset_index()\n",
        "\n",
        "#Deporte en el que los jugadores tienen mayor peso promedio\n",
        "id_max = stats.weight.idxmax()\n",
        "stats.loc[id_max, 'sport']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4r4JSI4Qsz3Y",
        "outputId": "12007036-e315-4b44-a2c8-9d8d70857045"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'basketball'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Punto 5:\n",
        "#Agrupa los datos por nacionalidad y sexo y arroja el país con más cantidad de mujeres\n",
        "\n",
        "athletes.groupby(['nationality', 'sex']).sex.count().sort_values(ascending = False).index[0][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MU0pEkvbs0rY",
        "outputId": "a8eed90a-d4c5-4171-ad1e-98efd063850a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'USA'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EJERCICIO 3**"
      ],
      "metadata": {
        "id": "c0ugwrJHDBMY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importe el dataset de nba del repositorio de clase y responda (hint: Elimine las instancias con registros faltantes):\n",
        "\n",
        "Cuáles son los equipos que pagan los salarios más altos (presente el resultado como un dataFrame con el nombre del equipo y la cantidad de dinero de la nómina)\n",
        "Separe la variable Age en tres grupos y cree una nueva columna llamada Age-Group con esta información.<\n",
        "Construya una tabla dinámica que muestre el salario promedio de los jugadores por equipo y posición para para los cinco equipos que pagan mejores salarios."
      ],
      "metadata": {
        "id": "qmeeLLQnDCxr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SOLUCION**"
      ],
      "metadata": {
        "id": "nqLDC10PFaLZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "-rn8_W6mC4Eu"
      },
      "outputs": [],
      "source": [
        "# Iniciamos con la importación de las librerías a utilizar:\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importamos los datos desde el repositorio en GitHub:\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/tomasate/Datos_Clases/main/Datos_1/nba.csv\")"
      ],
      "metadata": {
        "id": "TsUkZK3CDKO8"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Damos un primer vistazo a la estructura de la Data\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "H48YLPPtFvRk",
        "outputId": "8740f535-f339-434c-fa79-4083852a9bdd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   Unnamed: 0           Name            Team  Number Position   Age Height  \\\n",
              "0           0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   \n",
              "1           1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   \n",
              "2           2   John Holland  Boston Celtics    30.0       SG  27.0    6-5   \n",
              "3           3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   \n",
              "4           4  Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   \n",
              "\n",
              "   Weight            College     Salary  \n",
              "0   180.0              Texas  7730337.0  \n",
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              "2   205.0  Boston University        NaN  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 458,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 132,\n        \"min\": 0,\n        \"max\": 457,\n        \"samples\": [\n          304,\n          39,\n          341\n        ],\n        \"num_unique_values\": 458,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Andre Miller\",\n          \"Kyle O'Quinn\",\n          \"Goran Dragic\"\n        ],\n        \"num_unique_values\": 457,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Oklahoma City Thunder\",\n          \"Dallas Mavericks\",\n          \"Orlando Magic\"\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.966090405679644,\n        \"min\": 0.0,\n        \"max\": 99.0,\n        \"samples\": [\n          10.0,\n          34.0,\n          26.0\n        ],\n        \"num_unique_values\": 53,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Position\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"SF\",\n          \"C\",\n          \"SG\"\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.404016424405833,\n        \"min\": 19.0,\n        \"max\": 40.0,\n        \"samples\": [\n          25.0,\n          34.0,\n          28.0\n        ],\n        \"num_unique_values\": 22,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"6-2\",\n          \"6-6\",\n          \"5-9\"\n        ],\n        \"num_unique_values\": 18,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.36834262669547,\n        \"min\": 161.0,\n        \"max\": 307.0,\n        \"samples\": [\n          169.0,\n          180.0,\n          254.0\n        ],\n        \"num_unique_values\": 87,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"College\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Connecticut\",\n          \"Florida State\",\n          \"LSU\"\n        ],\n        \"num_unique_values\": 118,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5229237.598910348,\n        \"min\": 30888.0,\n        \"max\": 25000000.0,\n        \"samples\": [\n          625093.0,\n          2569260.0,\n          245177.0\n        ],\n        \"num_unique_values\": 309,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 35
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    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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        "id": "k-_WrPFSEtb2",
        "outputId": "11ff3916-2d82-444f-8f52-aec38cf73739"
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      "execution_count": 36,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0      Number         Age      Weight        Salary\n",
              "count  458.000000  457.000000  457.000000  457.000000  4.460000e+02\n",
              "mean   228.500000   17.678337   26.938731  221.522976  4.842684e+06\n",
              "std    132.357471   15.966090    4.404016   26.368343  5.229238e+06\n",
              "min      0.000000    0.000000   19.000000  161.000000  3.088800e+04\n",
              "25%    114.250000    5.000000   24.000000  200.000000  1.044792e+06\n",
              "50%    228.500000   13.000000   26.000000  220.000000  2.839073e+06\n",
              "75%    342.750000   25.000000   30.000000  240.000000  6.500000e+06\n",
              "max    457.000000   99.000000   40.000000  307.000000  2.500000e+07"
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              "      <th>mean</th>\n",
              "      <td>228.500000</td>\n",
              "      <td>17.678337</td>\n",
              "      <td>26.938731</td>\n",
              "      <td>221.522976</td>\n",
              "      <td>4.842684e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>132.357471</td>\n",
              "      <td>15.966090</td>\n",
              "      <td>4.404016</td>\n",
              "      <td>26.368343</td>\n",
              "      <td>5.229238e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>161.000000</td>\n",
              "      <td>3.088800e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>114.250000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>1.044792e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>228.500000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>220.000000</td>\n",
              "      <td>2.839073e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>342.750000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>6.500000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>457.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>307.000000</td>\n",
              "      <td>2.500000e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </style>\n",
              "\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\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",
              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\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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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-187d8e71-7872-4b9e-9d00-4edecf03c1a0 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\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 164.84678345532453,\n        \"min\": 0.0,\n        \"max\": 458.0,\n        \"samples\": [\n          458.0,\n          228.5,\n          342.75\n        ],\n        \"num_unique_values\": 7,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 155.8417549968162,\n        \"min\": 0.0,\n        \"max\": 457.0,\n        \"samples\": [\n          17.678336980306344,\n          13.0,\n          457.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 153.30279340498524,\n        \"min\": 4.404016424405833,\n        \"max\": 457.0,\n        \"samples\": [\n          26.938730853391686,\n          26.0,\n          457.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 122.38720471910906,\n        \"min\": 26.36834262669547,\n        \"max\": 457.0,\n        \"samples\": [\n          221.52297592997812,\n          220.0,\n          457.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8181552.631499937,\n        \"min\": 446.0,\n        \"max\": 25000000.0,\n        \"samples\": [\n          4842684.105381166,\n          2839073.0,\n          446.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfCX5K_PGqNb",
        "outputId": "6c711653-35fd-4df5-e406-ce3cbdc8ab29"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 458 entries, 0 to 457\n",
            "Data columns (total 10 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  458 non-null    int64  \n",
            " 1   Name        457 non-null    object \n",
            " 2   Team        457 non-null    object \n",
            " 3   Number      457 non-null    float64\n",
            " 4   Position    457 non-null    object \n",
            " 5   Age         457 non-null    float64\n",
            " 6   Height      457 non-null    object \n",
            " 7   Weight      457 non-null    float64\n",
            " 8   College     373 non-null    object \n",
            " 9   Salary      446 non-null    float64\n",
            "dtypes: float64(4), int64(1), object(5)\n",
            "memory usage: 35.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovnSBx9jFKD_",
        "outputId": "158d39ae-4ae3-4723-b7da-9b3aba5ab73d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(458, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Eliminación de instancias con registros faltantes:\n",
        "df = df.dropna()\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "s2PQYt89FXsB",
        "outputId": "77e20042-b20a-4511-ee08-6e830338b6e6"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Unnamed: 0           Name            Team  Number Position   Age Height  \\\n",
              "0             0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   \n",
              "1             1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   \n",
              "3             3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   \n",
              "6             6  Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   \n",
              "7             7   Kelly Olynyk  Boston Celtics    41.0        C  25.0    7-0   \n",
              "..          ...            ...             ...     ...      ...   ...    ...   \n",
              "449         449    Rodney Hood       Utah Jazz     5.0       SG  23.0    6-8   \n",
              "451         451  Chris Johnson       Utah Jazz    23.0       SF  26.0    6-6   \n",
              "452         452     Trey Lyles       Utah Jazz    41.0       PF  20.0   6-10   \n",
              "453         453   Shelvin Mack       Utah Jazz     8.0       PG  26.0    6-3   \n",
              "456         456    Jeff Withey       Utah Jazz    24.0        C  26.0    7-0   \n",
              "\n",
              "     Weight        College     Salary  \n",
              "0     180.0          Texas  7730337.0  \n",
              "1     235.0      Marquette  6796117.0  \n",
              "3     185.0  Georgia State  1148640.0  \n",
              "6     235.0            LSU  1170960.0  \n",
              "7     238.0        Gonzaga  2165160.0  \n",
              "..      ...            ...        ...  \n",
              "449   206.0           Duke  1348440.0  \n",
              "451   206.0         Dayton   981348.0  \n",
              "452   234.0       Kentucky  2239800.0  \n",
              "453   203.0         Butler  2433333.0  \n",
              "456   231.0         Kansas   947276.0  \n",
              "\n",
              "[364 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c3505e75-0b63-4e76-ab5c-4503033ea80e\" 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",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Name</th>\n",
              "      <th>Team</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Avery Bradley</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>0.0</td>\n",
              "      <td>PG</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6-2</td>\n",
              "      <td>180.0</td>\n",
              "      <td>Texas</td>\n",
              "      <td>7730337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Jae Crowder</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>99.0</td>\n",
              "      <td>SF</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6-6</td>\n",
              "      <td>235.0</td>\n",
              "      <td>Marquette</td>\n",
              "      <td>6796117.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>R.J. Hunter</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>28.0</td>\n",
              "      <td>SG</td>\n",
              "      <td>22.0</td>\n",
              "      <td>6-5</td>\n",
              "      <td>185.0</td>\n",
              "      <td>Georgia State</td>\n",
              "      <td>1148640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>Jordan Mickey</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>55.0</td>\n",
              "      <td>PF</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6-8</td>\n",
              "      <td>235.0</td>\n",
              "      <td>LSU</td>\n",
              "      <td>1170960.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>Kelly Olynyk</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>41.0</td>\n",
              "      <td>C</td>\n",
              "      <td>25.0</td>\n",
              "      <td>7-0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>Gonzaga</td>\n",
              "      <td>2165160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>449</th>\n",
              "      <td>449</td>\n",
              "      <td>Rodney Hood</td>\n",
              "      <td>Utah Jazz</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SG</td>\n",
              "      <td>23.0</td>\n",
              "      <td>6-8</td>\n",
              "      <td>206.0</td>\n",
              "      <td>Duke</td>\n",
              "      <td>1348440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>451</td>\n",
              "      <td>Chris Johnson</td>\n",
              "      <td>Utah Jazz</td>\n",
              "      <td>23.0</td>\n",
              "      <td>SF</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6-6</td>\n",
              "      <td>206.0</td>\n",
              "      <td>Dayton</td>\n",
              "      <td>981348.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>452</td>\n",
              "      <td>Trey Lyles</td>\n",
              "      <td>Utah Jazz</td>\n",
              "      <td>41.0</td>\n",
              "      <td>PF</td>\n",
              "      <td>20.0</td>\n",
              "      <td>6-10</td>\n",
              "      <td>234.0</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>2239800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453</th>\n",
              "      <td>453</td>\n",
              "      <td>Shelvin Mack</td>\n",
              "      <td>Utah Jazz</td>\n",
              "      <td>8.0</td>\n",
              "      <td>PG</td>\n",
              "      <td>26.0</td>\n",
              "      <td>6-3</td>\n",
              "      <td>203.0</td>\n",
              "      <td>Butler</td>\n",
              "      <td>2433333.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>456</td>\n",
              "      <td>Jeff Withey</td>\n",
              "      <td>Utah Jazz</td>\n",
              "      <td>24.0</td>\n",
              "      <td>C</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7-0</td>\n",
              "      <td>231.0</td>\n",
              "      <td>Kansas</td>\n",
              "      <td>947276.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>364 rows × 10 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 364,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131,\n        \"min\": 0,\n        \"max\": 456,\n        \"samples\": [\n          232,\n          42,\n          19\n        ],\n        \"num_unique_values\": 364,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"David Lee\",\n          \"Lance Thomas\",\n          \"Jarrett Jack\"\n        ],\n        \"num_unique_values\": 364,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Oklahoma City Thunder\",\n          \"Dallas Mavericks\",\n          \"Orlando Magic\"\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.994162276377994,\n        \"min\": 0.0,\n        \"max\": 99.0,\n        \"samples\": [\n          26.0,\n          25.0,\n          27.0\n        ],\n        \"num_unique_values\": 51,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Position\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"SF\",\n          \"C\",\n          \"SG\"\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.23359053669338,\n        \"min\": 19.0,\n        \"max\": 40.0,\n        \"samples\": [\n          25.0,\n          31.0,\n          32.0\n        ],\n        \"num_unique_values\": 22,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"6-2\",\n          \"6-6\",\n          \"6-4\"\n        ],\n        \"num_unique_values\": 17,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.79309897509115,\n        \"min\": 161.0,\n        \"max\": 279.0,\n        \"samples\": [\n          190.0,\n          213.0,\n          219.0\n        ],\n        \"num_unique_values\": 76,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"College\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Blinn College\",\n          \"Gonzaga\",\n          \"Davidson\"\n        ],\n        \"num_unique_values\": 115,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5119715.580210538,\n        \"min\": 55722.0,\n        \"max\": 22875000.0,\n        \"samples\": [\n          2279040.0,\n          3431040.0,\n          2127840.0\n        ],\n        \"num_unique_values\": 256,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EJERCICIO 3 PUNTO 1**\n",
        "\n",
        "Cuáles son los equipos que pagan los salarios más altos (presente el resultado como un DataFrame con el nombre del equipo y la cantidad de dinero de la nómina)"
      ],
      "metadata": {
        "id": "wRQJAeqoIO2w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sa = df.groupby(['Team'])[['Salary']].sum().sort_values(by = 'Salary', ascending=False)"
      ],
      "metadata": {
        "id": "cvYZwQfTIXXz"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sa = sa.rename(columns={'Salary':'Total Payroll'})\n",
        "sa"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QffFB0Q5K-Qj",
        "outputId": "31ef33e9-fd23-4d39-b4f0-3f2571c3305c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Total Payroll\n",
              "Team                                 \n",
              "Los Angeles Clippers       93906914.0\n",
              "Oklahoma City Thunder      81515298.0\n",
              "Golden State Warriors      80535517.0\n",
              "Cleveland Cavaliers        74068189.0\n",
              "Chicago Bulls              73265800.0\n",
              "New Orleans Pelicans       68200408.0\n",
              "Detroit Pistons            67168263.0\n",
              "Milwaukee Bucks            66794557.0\n",
              "Miami Heat                 65838121.0\n",
              "Sacramento Kings           62786874.0\n",
              "San Antonio Spurs          59491233.0\n",
              "New York Knicks            57977202.0\n",
              "Toronto Raptors            57377129.0\n",
              "Dallas Mavericks           57140305.0\n",
              "Memphis Grizzlies          56862880.0\n",
              "Atlanta Hawks              56383300.0\n",
              "Utah Jazz                  52256491.0\n",
              "Washington Wizards         52111245.0\n",
              "Charlotte Hornets          51715614.0\n",
              "Houston Rockets            48445456.0\n",
              "Portland Trail Blazers     48301818.0\n",
              "Indiana Pacers             48057601.0\n",
              "Brooklyn Nets              47503125.0\n",
              "Phoenix Suns               44945135.0\n",
              "Boston Celtics             41541068.0\n",
              "Los Angeles Lakers         39245338.0\n",
              "Denver Nuggets             38167971.0\n",
              "Orlando Magic              37887288.0\n",
              "Philadelphia 76ers         30992894.0\n",
              "Minnesota Timberwolves     21310196.0"
            ],
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              "\n",
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              "type": "dataframe",
              "variable_name": "sa",
              "summary": "{\n  \"name\": \"sa\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"Total Payroll\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15815137.042049658,\n        \"min\": 21310196.0,\n        \"max\": 93906914.0,\n        \"samples\": [\n          37887288.0,\n          56383300.0,\n          44945135.0\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EJERCICIO 3 PUNTO 2**\n",
        "\n",
        "Separe la variable Age en tres grupos y cree una nueva columna llamada Age-Group con esta información."
      ],
      "metadata": {
        "id": "KYaxUbPGLQqJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age Group'] = pd.cut(df['Age'], bins = 3, labels = ['Juniors', 'Pro', 'Master'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "tz3jMLb7LhqO",
        "outputId": "94fd639b-e0ed-40a1-d5eb-369ab8939fc8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-a79a012528b3>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Age Group'] = pd.cut(df['Age'], bins = 3, labels = ['Juniors', 'Pro', 'Master'])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0           Name            Team  Number Position   Age Height  \\\n",
              "0           0  Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   \n",
              "1           1    Jae Crowder  Boston Celtics    99.0       SF  25.0    6-6   \n",
              "3           3    R.J. Hunter  Boston Celtics    28.0       SG  22.0    6-5   \n",
              "6           6  Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   \n",
              "7           7   Kelly Olynyk  Boston Celtics    41.0        C  25.0    7-0   \n",
              "\n",
              "   Weight        College     Salary Age Group  \n",
              "0   180.0          Texas  7730337.0   Juniors  \n",
              "1   235.0      Marquette  6796117.0   Juniors  \n",
              "3   185.0  Georgia State  1148640.0   Juniors  \n",
              "6   235.0            LSU  1170960.0   Juniors  \n",
              "7   238.0        Gonzaga  2165160.0   Juniors  "
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              "      <td>SF</td>\n",
              "      <td>25.0</td>\n",
              "      <td>6-6</td>\n",
              "      <td>235.0</td>\n",
              "      <td>Marquette</td>\n",
              "      <td>6796117.0</td>\n",
              "      <td>Juniors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>R.J. Hunter</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>28.0</td>\n",
              "      <td>SG</td>\n",
              "      <td>22.0</td>\n",
              "      <td>6-5</td>\n",
              "      <td>185.0</td>\n",
              "      <td>Georgia State</td>\n",
              "      <td>1148640.0</td>\n",
              "      <td>Juniors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>Jordan Mickey</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>55.0</td>\n",
              "      <td>PF</td>\n",
              "      <td>21.0</td>\n",
              "      <td>6-8</td>\n",
              "      <td>235.0</td>\n",
              "      <td>LSU</td>\n",
              "      <td>1170960.0</td>\n",
              "      <td>Juniors</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>Kelly Olynyk</td>\n",
              "      <td>Boston Celtics</td>\n",
              "      <td>41.0</td>\n",
              "      <td>C</td>\n",
              "      <td>25.0</td>\n",
              "      <td>7-0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>Gonzaga</td>\n",
              "      <td>2165160.0</td>\n",
              "      <td>Juniors</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca42f072-6cc4-4615-9ab4-d58e987dad51')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ca42f072-6cc4-4615-9ab4-d58e987dad51 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-ca42f072-6cc4-4615-9ab4-d58e987dad51');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6011ed61-640a-4f68-ab54-a3862f8e150b\">\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\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",
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              "      --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-6011ed61-640a-4f68-ab54-a3862f8e150b 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 364,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 131,\n        \"min\": 0,\n        \"max\": 456,\n        \"samples\": [\n          232,\n          42,\n          19\n        ],\n        \"num_unique_values\": 364,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"David Lee\",\n          \"Lance Thomas\",\n          \"Jarrett Jack\"\n        ],\n        \"num_unique_values\": 364,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Team\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Oklahoma City Thunder\",\n          \"Dallas Mavericks\",\n          \"Orlando Magic\"\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.994162276377994,\n        \"min\": 0.0,\n        \"max\": 99.0,\n        \"samples\": [\n          26.0,\n          25.0,\n          27.0\n        ],\n        \"num_unique_values\": 51,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Position\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"SF\",\n          \"C\",\n          \"SG\"\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.23359053669338,\n        \"min\": 19.0,\n        \"max\": 40.0,\n        \"samples\": [\n          25.0,\n          31.0,\n          32.0\n        ],\n        \"num_unique_values\": 22,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"6-2\",\n          \"6-6\",\n          \"6-4\"\n        ],\n        \"num_unique_values\": 17,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.79309897509115,\n        \"min\": 161.0,\n        \"max\": 279.0,\n        \"samples\": [\n          190.0,\n          213.0,\n          219.0\n        ],\n        \"num_unique_values\": 76,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"College\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Blinn College\",\n          \"Gonzaga\",\n          \"Davidson\"\n        ],\n        \"num_unique_values\": 115,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5119715.580210538,\n        \"min\": 55722.0,\n        \"max\": 22875000.0,\n        \"samples\": [\n          2279040.0,\n          3431040.0,\n          2127840.0\n        ],\n        \"num_unique_values\": 256,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"Juniors\",\n          \"Pro\",\n          \"Master\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Age Group'])[['Name']].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "j5EhyOA5Mxc2",
        "outputId": "01cd29a7-f005-4b1e-e02f-53871f3ee4f4"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Name\n",
              "Age Group      \n",
              "Juniors     201\n",
              "Pro         133\n",
              "Master       30"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-daf37013-ade6-4ded-8c7c-e84e1b85ea27\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age Group</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>201</td>\n",
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              "  </svg>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-daf37013-ade6-4ded-8c7c-e84e1b85ea27');\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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              "  </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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              "\n",
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              "      try {\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
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              "  </div>\n"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 86,\n        \"min\": 30,\n        \"max\": 201,\n        \"samples\": [\n          201,\n          133,\n          30\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EJERCICIO 3 PUNTO 3**\n",
        "\n",
        "Construya una tabla dinámica que muestre el salario promedio de los jugadores por equipo y posición para para los cinco equipos que pagan mejores salarios."
      ],
      "metadata": {
        "id": "nRApxgf4PWLA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Primero, se identifican los 5 equipos con mayor gasto en salarios.\n",
        "sa.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "7Y-xDcICrGtg",
        "outputId": "678d92a5-2851-4369-ef67-e16a4e70ca07"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Total Payroll\n",
              "Team                                \n",
              "Los Angeles Clippers      93906914.0\n",
              "Oklahoma City Thunder     81515298.0\n",
              "Golden State Warriors     80535517.0\n",
              "Cleveland Cavaliers       74068189.0\n",
              "Chicago Bulls             73265800.0"
            ],
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sa",
              "summary": "{\n  \"name\": \"sa\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"Total Payroll\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15815137.042049658,\n        \"min\": 21310196.0,\n        \"max\": 93906914.0,\n        \"samples\": [\n          37887288.0,\n          56383300.0,\n          44945135.0\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ahora, guardamos los 5 equipos con mayor gasto de nómina en una lista.\n",
        "sa_2 = sa.reset_index().head()\n",
        "teams = sa_2['Team']\n",
        "teams"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIl0NPb4XXO-",
        "outputId": "d12a09d6-5d5c-498d-9f61-f3594441fc3b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     Los Angeles Clippers\n",
              "1    Oklahoma City Thunder\n",
              "2    Golden State Warriors\n",
              "3      Cleveland Cavaliers\n",
              "4            Chicago Bulls\n",
              "Name: Team, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Luego, filtramos el DataFrame original de manera que solo nos quede la información de los equipos en cuestión:\n",
        "df_3 = df[df.Team.isin(teams)]\n",
        "df_3['Team'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f11FEtE-rqle",
        "outputId": "12bdf08c-5ade-4b84-db12-ecae98d379e4"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Golden State Warriors', 'Los Angeles Clippers', 'Chicago Bulls',\n",
              "       'Cleveland Cavaliers', 'Oklahoma City Thunder'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ahora, creamos la tabla dinámica:\n",
        "df_3.pivot_table('Salary', index = 'Team', columns = 'Position', aggfunc = 'mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "-AHKkPaEsZT5",
        "outputId": "12e418db-ad9c-4f75-99cf-c58ea70c1e02"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Position                          C            PF            PG            SF  \\\n",
              "Team                                                                            \n",
              "Chicago Bulls          1.340000e+07  3.578740e+06  1.117153e+07  1.958160e+06   \n",
              "Cleveland Cavaliers    7.768435e+06  1.394101e+07  6.551592e+06  9.472760e+05   \n",
              "Golden State Warriors  6.541249e+06  1.426087e+07  1.137079e+07  3.766367e+06   \n",
              "Los Angeles Clippers   1.039480e+07  6.655482e+06  1.228975e+07  3.662924e+06   \n",
              "Oklahoma City Thunder  6.303143e+06  2.606520e+06  9.382869e+06  8.599621e+06   \n",
              "\n",
              "Position                         SG  \n",
              "Team                                 \n",
              "Chicago Bulls          5.717549e+06  \n",
              "Cleveland Cavaliers    3.349079e+06  \n",
              "Golden State Warriors  8.224138e+06  \n",
              "Los Angeles Clippers   4.639893e+06  \n",
              "Oklahoma City Thunder  3.207058e+06  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Chicago Bulls</th>\n",
              "      <td>1.340000e+07</td>\n",
              "      <td>3.578740e+06</td>\n",
              "      <td>1.117153e+07</td>\n",
              "      <td>1.958160e+06</td>\n",
              "      <td>5.717549e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cleveland Cavaliers</th>\n",
              "      <td>7.768435e+06</td>\n",
              "      <td>1.394101e+07</td>\n",
              "      <td>6.551592e+06</td>\n",
              "      <td>9.472760e+05</td>\n",
              "      <td>3.349079e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Golden State Warriors</th>\n",
              "      <td>6.541249e+06</td>\n",
              "      <td>1.426087e+07</td>\n",
              "      <td>1.137079e+07</td>\n",
              "      <td>3.766367e+06</td>\n",
              "      <td>8.224138e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Los Angeles Clippers</th>\n",
              "      <td>1.039480e+07</td>\n",
              "      <td>6.655482e+06</td>\n",
              "      <td>1.228975e+07</td>\n",
              "      <td>3.662924e+06</td>\n",
              "      <td>4.639893e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oklahoma City Thunder</th>\n",
              "      <td>6.303143e+06</td>\n",
              "      <td>2.606520e+06</td>\n",
              "      <td>9.382869e+06</td>\n",
              "      <td>8.599621e+06</td>\n",
              "      <td>3.207058e+06</td>\n",
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          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Team']).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hRl3rsHr0I-7",
        "outputId": "5eb1d617-83b8-4abe-f6f1-6074f1ff3775"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-48-42e336151de8>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df.groupby(['Team']).sum()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Unnamed: 0  Number    Age  Weight      Salary\n",
              "Team                                                                 \n",
              "Atlanta Hawks                 3459   210.0  314.0  2422.0  56383300.0\n",
              "Boston Celtics                  94   350.0  286.0  2616.0  41541068.0\n",
              "Brooklyn Nets                  295   220.0  335.0  2810.0  47503125.0\n",
              "Charlotte Hornets             4311   227.0  334.0  2817.0  51715614.0\n",
              "Chicago Bulls                 1899   222.0  328.0  2539.0  73265800.0\n",
              "Cleveland Cavaliers           1901   139.0  318.0  2443.0  74068189.0\n",
              "Dallas Mavericks              2802   182.0  348.0  2640.0  57140305.0\n",
              "Denver Nuggets                3496   100.0  250.0  1878.0  38167971.0\n",
              "Detroit Pistons               2820   259.0  393.0  3333.0  67168263.0\n",
              "Golden State Warriors          995   242.0  319.0  2710.0  80535517.0\n",
              "Houston Rockets               2729   168.0  296.0  2353.0  48445456.0\n",
              "Indiana Pacers                2438   245.0  308.0  2668.0  48057601.0\n",
              "Los Angeles Clippers          1368   284.0  403.0  3111.0  93906914.0\n",
              "Los Angeles Lakers            1356   185.0  314.0  2819.0  39245338.0\n",
              "Memphis Grizzlies             3438   189.0  375.0  2849.0  56862880.0\n",
              "Miami Heat                    3456   108.0  288.0  2266.0  65838121.0\n",
              "Milwaukee Bucks               3068   269.0  351.0  3117.0  66794557.0\n",
              "Minnesota Timberwolves        3236   128.0  192.0  1740.0  21310196.0\n",
              "New Orleans Pelicans          4552   273.0  423.0  3426.0  68200408.0\n",
              "New York Knicks                410   141.0  297.0  2460.0  57977202.0\n",
              "Oklahoma City Thunder         5868   201.0  380.0  3196.0  81515298.0\n",
              "Orlando Magic                 3618   135.0  252.0  2160.0  37887288.0\n",
              "Philadelphia 76ers             749   229.0  332.0  3078.0  30992894.0\n",
              "Phoenix Suns                  1664   193.0  325.0  2797.0  44945135.0\n",
              "Portland Trail Blazers        6510   240.0  376.0  3279.0  48301818.0\n",
              "Sacramento Kings              1867   233.0  345.0  2885.0  62786874.0\n",
              "San Antonio Spurs             3311   167.0  341.0  2429.0  59491233.0\n",
              "Toronto Raptors                683   196.0  266.0  2162.0  57377129.0\n",
              "Utah Jazz                     4484   182.0  246.0  2204.0  52256491.0\n",
              "Washington Wizards            4877   209.0  353.0  2795.0  52111245.0"
            ],
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              "    <tr>\n",
              "      <th>Indiana Pacers</th>\n",
              "      <td>2438</td>\n",
              "      <td>245.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>2668.0</td>\n",
              "      <td>48057601.0</td>\n",
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              "    <tr>\n",
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              "      <td>1368</td>\n",
              "      <td>284.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>3111.0</td>\n",
              "      <td>93906914.0</td>\n",
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              "    <tr>\n",
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              "      <td>1356</td>\n",
              "      <td>185.0</td>\n",
              "      <td>314.0</td>\n",
              "      <td>2819.0</td>\n",
              "      <td>39245338.0</td>\n",
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              "    <tr>\n",
              "      <th>Memphis Grizzlies</th>\n",
              "      <td>3438</td>\n",
              "      <td>189.0</td>\n",
              "      <td>375.0</td>\n",
              "      <td>2849.0</td>\n",
              "      <td>56862880.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miami Heat</th>\n",
              "      <td>3456</td>\n",
              "      <td>108.0</td>\n",
              "      <td>288.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>65838121.0</td>\n",
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              "    <tr>\n",
              "      <th>Milwaukee Bucks</th>\n",
              "      <td>3068</td>\n",
              "      <td>269.0</td>\n",
              "      <td>351.0</td>\n",
              "      <td>3117.0</td>\n",
              "      <td>66794557.0</td>\n",
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              "    <tr>\n",
              "      <th>Minnesota Timberwolves</th>\n",
              "      <td>3236</td>\n",
              "      <td>128.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>1740.0</td>\n",
              "      <td>21310196.0</td>\n",
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              "    <tr>\n",
              "      <th>New Orleans Pelicans</th>\n",
              "      <td>4552</td>\n",
              "      <td>273.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>3426.0</td>\n",
              "      <td>68200408.0</td>\n",
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              "      <td>410</td>\n",
              "      <td>141.0</td>\n",
              "      <td>297.0</td>\n",
              "      <td>2460.0</td>\n",
              "      <td>57977202.0</td>\n",
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              "      <td>5868</td>\n",
              "      <td>201.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>3196.0</td>\n",
              "      <td>81515298.0</td>\n",
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              "    <tr>\n",
              "      <th>Orlando Magic</th>\n",
              "      <td>3618</td>\n",
              "      <td>135.0</td>\n",
              "      <td>252.0</td>\n",
              "      <td>2160.0</td>\n",
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              "      <td>749</td>\n",
              "      <td>229.0</td>\n",
              "      <td>332.0</td>\n",
              "      <td>3078.0</td>\n",
              "      <td>30992894.0</td>\n",
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              "    <tr>\n",
              "      <th>Phoenix Suns</th>\n",
              "      <td>1664</td>\n",
              "      <td>193.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>2797.0</td>\n",
              "      <td>44945135.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portland Trail Blazers</th>\n",
              "      <td>6510</td>\n",
              "      <td>240.0</td>\n",
              "      <td>376.0</td>\n",
              "      <td>3279.0</td>\n",
              "      <td>48301818.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sacramento Kings</th>\n",
              "      <td>1867</td>\n",
              "      <td>233.0</td>\n",
              "      <td>345.0</td>\n",
              "      <td>2885.0</td>\n",
              "      <td>62786874.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>San Antonio Spurs</th>\n",
              "      <td>3311</td>\n",
              "      <td>167.0</td>\n",
              "      <td>341.0</td>\n",
              "      <td>2429.0</td>\n",
              "      <td>59491233.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toronto Raptors</th>\n",
              "      <td>683</td>\n",
              "      <td>196.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>2162.0</td>\n",
              "      <td>57377129.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Utah Jazz</th>\n",
              "      <td>4484</td>\n",
              "      <td>182.0</td>\n",
              "      <td>246.0</td>\n",
              "      <td>2204.0</td>\n",
              "      <td>52256491.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Washington Wizards</th>\n",
              "      <td>4877</td>\n",
              "      <td>209.0</td>\n",
              "      <td>353.0</td>\n",
              "      <td>2795.0</td>\n",
              "      <td>52111245.0</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1637,\n        \"min\": 94,\n        \"max\": 6510,\n        \"samples\": [\n          683,\n          3456,\n          1664\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.69584891360303,\n        \"min\": 100.0,\n        \"max\": 350.0,\n        \"samples\": [\n          196.0,\n          269.0,\n          284.0\n        ],\n        \"num_unique_values\": 29,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.74950880233385,\n        \"min\": 192.0,\n        \"max\": 423.0,\n        \"samples\": [\n          246.0,\n          192.0,\n          403.0\n        ],\n        \"num_unique_values\": 29,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 420.2425353361403,\n        \"min\": 1740.0,\n        \"max\": 3426.0,\n        \"samples\": [\n          2162.0,\n          2266.0,\n          2797.0\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15815137.042049656,\n        \"min\": 21310196.0,\n        \"max\": 93906914.0,\n        \"samples\": [\n          57377129.0,\n          65838121.0,\n          44945135.0\n        ],\n        \"num_unique_values\": 30,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_4 = df_3.pivot_table('Salary', index = 'Team', columns = 'Position', aggfunc = 'mean')\n",
        "df_4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "RkkZBNgZx8tf",
        "outputId": "583d1f4e-b4c5-446d-efc3-95a4785c334c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Position                          C            PF            PG            SF  \\\n",
              "Team                                                                            \n",
              "Chicago Bulls          1.340000e+07  3.578740e+06  1.117153e+07  1.958160e+06   \n",
              "Cleveland Cavaliers    7.768435e+06  1.394101e+07  6.551592e+06  9.472760e+05   \n",
              "Golden State Warriors  6.541249e+06  1.426087e+07  1.137079e+07  3.766367e+06   \n",
              "Los Angeles Clippers   1.039480e+07  6.655482e+06  1.228975e+07  3.662924e+06   \n",
              "Oklahoma City Thunder  6.303143e+06  2.606520e+06  9.382869e+06  8.599621e+06   \n",
              "\n",
              "Position                         SG  \n",
              "Team                                 \n",
              "Chicago Bulls          5.717549e+06  \n",
              "Cleveland Cavaliers    3.349079e+06  \n",
              "Golden State Warriors  8.224138e+06  \n",
              "Los Angeles Clippers   4.639893e+06  \n",
              "Oklahoma City Thunder  3.207058e+06  "
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            "text/html": [
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              "      <th>Position</th>\n",
              "      <th>C</th>\n",
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              "      <th>Golden State Warriors</th>\n",
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              "      <td>1.137079e+07</td>\n",
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              "      <td>8.224138e+06</td>\n",
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              "      <th>Los Angeles Clippers</th>\n",
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              "      <th>Oklahoma City Thunder</th>\n",
              "      <td>6.303143e+06</td>\n",
              "      <td>2.606520e+06</td>\n",
              "      <td>9.382869e+06</td>\n",
              "      <td>8.599621e+06</td>\n",
              "      <td>3.207058e+06</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-9abb55f4-ceeb-48be-beb5-3e094f27ef3d')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9abb55f4-ceeb-48be-beb5-3e094f27ef3d 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-9abb55f4-ceeb-48be-beb5-3e094f27ef3d');\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-de6e9b17-99c5-4c4d-b3c7-2625c7fe2984\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de6e9b17-99c5-4c4d-b3c7-2625c7fe2984')\"\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-de6e9b17-99c5-4c4d-b3c7-2625c7fe2984 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a2606efa-20c3-4c10-aad6-230f365121e8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_4')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a2606efa-20c3-4c10-aad6-230f365121e8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_4');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_4",
              "summary": "{\n  \"name\": \"df_4\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"C\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3002937.5442529917,\n        \"min\": 6303142.666666667,\n        \"max\": 13400000.0,\n        \"samples\": [\n          7768435.0,\n          6303142.666666667,\n          6541249.333333333\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5583941.947210264,\n        \"min\": 2606520.0,\n        \"max\": 14260870.0,\n        \"samples\": [\n          13941014.5,\n          2606520.0,\n          14260870.0\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2272076.6922714864,\n        \"min\": 6551592.333333333,\n        \"max\": 12289745.5,\n        \"samples\": [\n          6551592.333333333,\n          9382869.0,\n          11370786.0\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2940485.7744057914,\n        \"min\": 947276.0,\n        \"max\": 8599620.666666666,\n        \"samples\": [\n          947276.0,\n          8599620.666666666,\n          3766367.4\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SG\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2060097.6516985926,\n        \"min\": 3207057.5,\n        \"max\": 8224138.0,\n        \"samples\": [\n          3349079.0,\n          3207057.5,\n          8224138.0\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    }
  ]
}