Numpy_e_Pandas.ipynb

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      "name": "python3",
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Importando as bibliotecas"
      ],
      "metadata": {
        "id": "D9AV_8Lvu4o_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Documentações:**\n",
        "- Numpy: https://numpy.org/doc/stable/\n",
        "- Pandas: https://pandas.pydata.org/docs/index.html"
      ],
      "metadata": {
        "id": "QLGkhg3xX0dI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "zY3OTaVswDzo"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explorando o numpy"
      ],
      "metadata": {
        "id": "a3-0VockTlDU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Criando arrays"
      ],
      "metadata": {
        "id": "OGNDyEvPn7Y-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando um array com array()"
      ],
      "metadata": {
        "id": "4eiuqG0kWBOy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "array de uma dimensão"
      ],
      "metadata": {
        "id": "yigXx38aYtls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lista = [2, 4, 6, 8, 10]\n",
        "\n",
        "np.array(lista)"
      ],
      "metadata": {
        "id": "yoWyQ7q4WAnz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "916dbc7d-7bd7-4635-9d6a-32f2a1faabf9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  4,  6,  8, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "array1 = np.array(lista)"
      ],
      "metadata": {
        "id": "cb0fo2lQL8k1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(array1)"
      ],
      "metadata": {
        "id": "jtt8vuUMbBtE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52eb2b77-1917-48a8-e28b-8a1b0b8ac652"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "array de duas dimensões"
      ],
      "metadata": {
        "id": "GW52PI-AbW7r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "array2 = np.array([[1, 4, 6, 7, 10],[1,2,3,4,5]])"
      ],
      "metadata": {
        "id": "8U6kIR6ueOru"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "array2"
      ],
      "metadata": {
        "id": "ITXl8eAUeTM0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d0a3955-18aa-4c02-a6c0-899822a34065"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  4,  6,  7, 10],\n",
              "       [ 1,  2,  3,  4,  5]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando um array com arange()\n",
        "\n",
        "*sintaxe básica: numpy.arange(start, stop, step, dtype=None)*"
      ],
      "metadata": {
        "id": "zyktQfwhetFt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#com apenas um parâmetro\n",
        "array3 = np.arange(10)\n",
        "\n",
        "array3"
      ],
      "metadata": {
        "id": "zoRJyJYDexqE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eeff97ee-5d08-44ba-ebc5-a28328fd39b3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#completo\n",
        "array4 = np.arange(1, 20, 3)\n",
        "\n",
        "array4"
      ],
      "metadata": {
        "id": "Pi7eE6qKfUot",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31d27d05-6892-4a76-9dbb-9d27d0709cf1"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  4,  7, 10, 13, 16, 19])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usando o random.randint()\n",
        "\n",
        "*np.random.randint(low, high=None, size=None, dtype=int)*"
      ],
      "metadata": {
        "id": "1N0_0xUVeGT0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "array5 = np.random.randint(0, 40, 10)\n",
        "\n",
        "array5"
      ],
      "metadata": {
        "id": "K4NA7-ncg6KU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d9eca97-ec9c-4a10-b1a0-c0b2aa190355"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([25,  6, 28, 17, 30, 31, 37,  1, 36, 25])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Operações com arrays"
      ],
      "metadata": {
        "id": "6lKnjzfnn_rN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#máximo\n",
        "np.max(array1)"
      ],
      "metadata": {
        "id": "3kjjumNzpB1_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "174cfab9-58a9-4ba9-a9fc-ed33d5da29d2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#minimo\n",
        "np.min(array2)"
      ],
      "metadata": {
        "id": "EhShDv6PpVxt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72798b0f-3dc9-4c36-ae18-2ebb3cfb8558"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#média\n",
        "np.mean(array1)"
      ],
      "metadata": {
        "id": "sGYBgXNEqSZ2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "301f2669-e583-44d1-e4fc-3df7c5acb416"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.0"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mediana\n",
        "np.median(array5)"
      ],
      "metadata": {
        "id": "oOWphk6gM8Dr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32faec73-f4d4-4a17-ac3b-5f8860ac96cb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.5"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#média array bidimensional\n",
        "np.mean(array5)"
      ],
      "metadata": {
        "id": "R5Vs5ekZqw5o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09a058ab-dbc9-4dcb-c819-b37db2f977ce"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.6"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Explorando o pandas"
      ],
      "metadata": {
        "id": "YZLTt1aueBss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbXhPwoQHi8m",
        "outputId": "ebbfc5b1-9750-4290-b053-72ac86a695e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/dsday/Coronarianos.csv', sep= ';')"
      ],
      "metadata": {
        "id": "ovfdTYgzwHjv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados.head(10)"
      ],
      "metadata": {
        "id": "elzYwFMZwNFe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "outputId": "3d7365bd-3b78-4e30-da4c-76abcac45d0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  SEXO apHas apDm apAf  apTabagismo apDPOC apCreatinina apColesterol  \\\n",
              "0    M   Sim  Nao  Nao  Interropido    Nao          Nao          Nao   \n",
              "1    F   Sim  Sim  Nao          Nao    Nao          Nao          Sim   \n",
              "2    M   Sim  Sim  Nao          Nao    Nao          Nao          Nao   \n",
              "3    M   Sim  Nao  Nao  Interropido    Sim          Nao          Sim   \n",
              "4    M   Sim  Nao  Nao        Atual    Nao          Nao          Nao   \n",
              "5    M   Sim  Nao  Nao  Interropido    Nao          Sim          Sim   \n",
              "6    M   Sim  Sim  Nao  Interropido    Nao          Nao          Sim   \n",
              "7    F   Sim  Sim  Nao          Nao    Nao          Sim          Sim   \n",
              "8    M   Sim  Sim  Nao          Nao    Nao          Nao          Sim   \n",
              "9    M   Sim  Sim  Nao  Interropido    Nao          Nao          Nao   \n",
              "\n",
              "  apTriglicerides apVasculo  ...             dc_diag2        Lesao  \\\n",
              "0             Nao       Nao  ...      Angina Instavel  Triarterial   \n",
              "1             Sim       Nao  ...  Isquemia Silenciosa   Biarterial   \n",
              "2             Nao       Nao  ...      Angina Instavel   Biarterial   \n",
              "3             Sim       Nao  ...       Angina Estavel  Uniarterial   \n",
              "4             Nao       Nao  ...       Angina Estavel  Uniarterial   \n",
              "5             Nao       Nao  ...  Indicacao Anatomica       Tronco   \n",
              "6             Nao       Nao  ...      Angina Instavel       Tronco   \n",
              "7             Nao       Nao  ...  Indicacao Anatomica  Triarterial   \n",
              "8             Nao       Nao  ...       Angina Estavel  Uniarterial   \n",
              "9             Nao       Nao  ...       Angina Estavel   Biarterial   \n",
              "\n",
              "  Pontes_Mamarias  QNP idadeCat    pesoCat      areaCat    alturaCat  \\\n",
              "0     Com mamaria  Nao  [71-95]  [62.5-70)  [1.66-1.78)  [1.62-1.67)   \n",
              "1     Com mamaria  Nao  [60-65)  [62.5-70)  [1.22-1.66)  [1.31-1.56)   \n",
              "2     Com mamaria  Nao  [65-71)    [70-76)  [1.78-1.88)  [1.62-1.67)   \n",
              "3     Com mamaria  Nao  [71-95]  [37-62.5)  [1.66-1.78)  [1.62-1.67)   \n",
              "4     Com mamaria  Nao  [18-54)   [85-148]     [2-11.9]  [1.72-71.8]   \n",
              "5     Sem mamaria  Nao  [71-95]    [76-85)     [1.88-2)  [1.72-71.8]   \n",
              "6     Sem mamaria  Nao  [71-95]    [76-85)     [1.88-2)  [1.67-1.72)   \n",
              "7     Sem mamaria  Nao  [71-95]  [62.5-70)  [1.66-1.78)  [1.56-1.62)   \n",
              "8     Sem mamaria  Nao  [65-71)   [85-148]     [2-11.9]  [1.67-1.72)   \n",
              "9     Com mamaria  Nao  [60-65)    [70-76)  [1.78-1.88)  [1.72-71.8]   \n",
              "\n",
              "  classCOMPLICACOES enf_Obito  \n",
              "0               Sim       Nao  \n",
              "1               Sim       Nao  \n",
              "2               Nao       Nao  \n",
              "3               Sim       Nao  \n",
              "4               Sim       Nao  \n",
              "5               Nao       Nao  \n",
              "6               Nao       Nao  \n",
              "7               Sim       Nao  \n",
              "8               Nao       Nao  \n",
              "9               Nao       Nao  \n",
              "\n",
              "[10 rows x 22 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEXO</th>\n",
              "      <th>apHas</th>\n",
              "      <th>apDm</th>\n",
              "      <th>apAf</th>\n",
              "      <th>apTabagismo</th>\n",
              "      <th>apDPOC</th>\n",
              "      <th>apCreatinina</th>\n",
              "      <th>apColesterol</th>\n",
              "      <th>apTriglicerides</th>\n",
              "      <th>apVasculo</th>\n",
              "      <th>...</th>\n",
              "      <th>dc_diag2</th>\n",
              "      <th>Lesao</th>\n",
              "      <th>Pontes_Mamarias</th>\n",
              "      <th>QNP</th>\n",
              "      <th>idadeCat</th>\n",
              "      <th>pesoCat</th>\n",
              "      <th>areaCat</th>\n",
              "      <th>alturaCat</th>\n",
              "      <th>classCOMPLICACOES</th>\n",
              "      <th>enf_Obito</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>Triarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.66-1.78)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Isquemia Silenciosa</td>\n",
              "      <td>Biarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[60-65)</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.22-1.66)</td>\n",
              "      <td>[1.31-1.56)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>Biarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.78-1.88)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>Uniarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[37-62.5)</td>\n",
              "      <td>[1.66-1.78)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Atual</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>Uniarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[18-54)</td>\n",
              "      <td>[85-148]</td>\n",
              "      <td>[2-11.9]</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Indicacao Anatomica</td>\n",
              "      <td>Tronco</td>\n",
              "      <td>Sem mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[76-85)</td>\n",
              "      <td>[1.88-2)</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>Tronco</td>\n",
              "      <td>Sem mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[76-85)</td>\n",
              "      <td>[1.88-2)</td>\n",
              "      <td>[1.67-1.72)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>F</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Indicacao Anatomica</td>\n",
              "      <td>Triarterial</td>\n",
              "      <td>Sem mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.66-1.78)</td>\n",
              "      <td>[1.56-1.62)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>Uniarterial</td>\n",
              "      <td>Sem mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[85-148]</td>\n",
              "      <td>[2-11.9]</td>\n",
              "      <td>[1.67-1.72)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>Biarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[60-65)</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.78-1.88)</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdc3d946-dfbd-4691-b195-ef36a3f83fc1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cdc3d946-dfbd-4691-b195-ef36a3f83fc1 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-cdc3d946-dfbd-4691-b195-ef36a3f83fc1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dicionário de dados"
      ],
      "metadata": {
        "id": "bhXlqQdRpyYS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pessoas que fizeram cirurgia coronariana"
      ],
      "metadata": {
        "id": "O8zVywOL4jrd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ap = Antecedentes pessoais do paciente**"
      ],
      "metadata": {
        "id": "4ImV0Ov8r2vQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|Atributo|Significado|\n",
        "|---|---|\n",
        "|apHAS|Hipertensão Arterial Sistêmica |\n",
        "|apDM|Diabete Melito|\n",
        "|apAf|Antecedentes familiares|\n",
        "|apTabagismo|Tabagista; ou interrompeu a mais de 1 ano ou não fuma|\n",
        "|apDPOC|Doença pulmonar obstrutiva crônica|\n",
        "|apCreatinina|Creatinina > 1,5 (quadro de Insuficiência renal)|\n",
        "|apColesterol|Colesterol > 200 mg/dl (Dislipidemia)|\n",
        "|apTriglicérides|Triglicérides > 200 mg/dl (Dislipidemia)|\n",
        "|apVasculo|Vasculopatia Arterial Periférica|\n",
        "|apIAMPrevio|Teve infarto agudo do miocárdio anteriormente|\n"
      ],
      "metadata": {
        "id": "Vvh6yYskp3hR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**eha = Estudo hemodinâmico atual**"
      ],
      "metadata": {
        "id": "EqkhOCOyuheP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|Atributo|Significado|\n",
        "|---|---|\n",
        "|eha_ventriculograma|Função ventricular esquerda|"
      ],
      "metadata": {
        "id": "3rBRHsitun5O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**dc = Diagnóstico Cardíaco**"
      ],
      "metadata": {
        "id": "Xa54pxUtsIEP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|Atributo|Significado|\n",
        "|---|---|\n",
        "|dc_diag2|Diagnóstico Clínico Atual|\n",
        "|lesão|Número de artérias acometidas (Uniarterial, biarterial ou triarterial), sendo o Tronco de coronária esquerda TCE referenciado a parte.|\n",
        "|Pontes_Mamárias|Revascularização utilizando enxertos com artéria mamária|\n",
        "|QNP|Quadro neurológico prévio|\n",
        "|idadeCat||\n",
        "|pesoCat *||\n",
        "|areaCat|Área de superfície corpórea|\n",
        "|alturaCat *||"
      ],
      "metadata": {
        "id": "4PCUJQfhsVHn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**classCOMPLICACOES** = se o paciente teve complicação após a cirurgia\n",
        "\n",
        "**enf_Obito** = se o paciente veio a óbito após a cirurgia"
      ],
      "metadata": {
        "id": "Y6HA61mlbQMN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conhecendo a base de dados"
      ],
      "metadata": {
        "id": "yFkFcQx8gvNi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizando o describe()"
      ],
      "metadata": {
        "id": "mL3aVnmpOx_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.describe()"
      ],
      "metadata": {
        "id": "2o3gHGMKwRdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "226c1c74-d621-40a6-89fa-8e3182cd1ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        SEXO apHas  apDm  apAf apTabagismo apDPOC apCreatinina apColesterol  \\\n",
              "count   8704  8704  8704  8704        8704   8704         8704         8704   \n",
              "unique     2     2     2     2           3      2            2            2   \n",
              "top        M   Sim   Nao   Nao         Nao    Nao          Nao          Sim   \n",
              "freq    6097  7278  5085  7308        4020   8353         7964         4764   \n",
              "\n",
              "       apTriglicerides apVasculo  ...        dc_diag2        Lesao  \\\n",
              "count             8704      8704  ...            8689         8704   \n",
              "unique               2         2  ...               4            5   \n",
              "top                Nao       Nao  ...  Angina Estavel  Triarterial   \n",
              "freq              7208      7974  ...            4575         4472   \n",
              "\n",
              "       Pontes_Mamarias   QNP idadeCat  pesoCat   areaCat    alturaCat  \\\n",
              "count             8704  8704     8704     8704      8704         8704   \n",
              "unique               2     2        5        5         5            5   \n",
              "top        Com mamaria   Nao  [65-71)  [76-85)  [2-11.9]  [1.72-71.8]   \n",
              "freq              8027  8281     1880     1820      1744         1897   \n",
              "\n",
              "       classCOMPLICACOES enf_Obito  \n",
              "count               8704      8704  \n",
              "unique                 2         2  \n",
              "top                  Nao       Nao  \n",
              "freq                5082      8279  \n",
              "\n",
              "[4 rows x 22 columns]"
            ],
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              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEXO</th>\n",
              "      <th>apHas</th>\n",
              "      <th>apDm</th>\n",
              "      <th>apAf</th>\n",
              "      <th>apTabagismo</th>\n",
              "      <th>apDPOC</th>\n",
              "      <th>apCreatinina</th>\n",
              "      <th>apColesterol</th>\n",
              "      <th>apTriglicerides</th>\n",
              "      <th>apVasculo</th>\n",
              "      <th>...</th>\n",
              "      <th>dc_diag2</th>\n",
              "      <th>Lesao</th>\n",
              "      <th>Pontes_Mamarias</th>\n",
              "      <th>QNP</th>\n",
              "      <th>idadeCat</th>\n",
              "      <th>pesoCat</th>\n",
              "      <th>areaCat</th>\n",
              "      <th>alturaCat</th>\n",
              "      <th>classCOMPLICACOES</th>\n",
              "      <th>enf_Obito</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
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              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>...</td>\n",
              "      <td>8689</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "      <td>8704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
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              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>...</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>Triarterial</td>\n",
              "      <td>Com mamaria</td>\n",
              "      <td>Nao</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[76-85)</td>\n",
              "      <td>[2-11.9]</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>6097</td>\n",
              "      <td>7278</td>\n",
              "      <td>5085</td>\n",
              "      <td>7308</td>\n",
              "      <td>4020</td>\n",
              "      <td>8353</td>\n",
              "      <td>7964</td>\n",
              "      <td>4764</td>\n",
              "      <td>7208</td>\n",
              "      <td>7974</td>\n",
              "      <td>...</td>\n",
              "      <td>4575</td>\n",
              "      <td>4472</td>\n",
              "      <td>8027</td>\n",
              "      <td>8281</td>\n",
              "      <td>1880</td>\n",
              "      <td>1820</td>\n",
              "      <td>1744</td>\n",
              "      <td>1897</td>\n",
              "      <td>5082</td>\n",
              "      <td>8279</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0df94dfa-ebc8-4890-8dfa-e5f9ef896e6e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0df94dfa-ebc8-4890-8dfa-e5f9ef896e6e 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-0df94dfa-ebc8-4890-8dfa-e5f9ef896e6e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utilizando o info()"
      ],
      "metadata": {
        "id": "Pls9HflcO1ul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.info()"
      ],
      "metadata": {
        "id": "qi-atpIrg-PT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b26c4153-8520-4989-bfd8-af4b39330df3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8704 entries, 0 to 8703\n",
            "Data columns (total 22 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   SEXO                 8704 non-null   object\n",
            " 1   apHas                8704 non-null   object\n",
            " 2   apDm                 8704 non-null   object\n",
            " 3   apAf                 8704 non-null   object\n",
            " 4   apTabagismo          8704 non-null   object\n",
            " 5   apDPOC               8704 non-null   object\n",
            " 6   apCreatinina         8704 non-null   object\n",
            " 7   apColesterol         8704 non-null   object\n",
            " 8   apTriglicerides      8704 non-null   object\n",
            " 9   apVasculo            8704 non-null   object\n",
            " 10  apIAMPrevio          8704 non-null   object\n",
            " 11  eha_ventriculograma  8684 non-null   object\n",
            " 12  dc_diag2             8689 non-null   object\n",
            " 13  Lesao                8704 non-null   object\n",
            " 14  Pontes_Mamarias      8704 non-null   object\n",
            " 15  QNP                  8704 non-null   object\n",
            " 16  idadeCat             8704 non-null   object\n",
            " 17  pesoCat              8704 non-null   object\n",
            " 18  areaCat              8704 non-null   object\n",
            " 19  alturaCat            8704 non-null   object\n",
            " 20  classCOMPLICACOES    8704 non-null   object\n",
            " 21  enf_Obito            8704 non-null   object\n",
            "dtypes: object(22)\n",
            "memory usage: 1.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "type()"
      ],
      "metadata": {
        "id": "MHWYeOKRO6dG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "type(dados)"
      ],
      "metadata": {
        "id": "s673revxh1vT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46b5f036-7638-4a42-8ff8-bc9db7cefd8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "shape"
      ],
      "metadata": {
        "id": "b_OLjhyqO-As"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.shape"
      ],
      "metadata": {
        "id": "IuBj_L9sizMD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15ca7150-71de-47c3-d974-c2e3ea0b86f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8704, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('A base de dados apresenta {} registros e {} variáveis'.format( dados.shape[0],dados.shape[1] ))"
      ],
      "metadata": {
        "id": "ivWfmPPjkSKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f5e4dee-2fe8-4079-e41d-fbdb1e16a18e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A base de dados apresenta 8704 registros e 22 variáveis\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando uma coluna"
      ],
      "metadata": {
        "id": "C86_yxEwPHrc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados['apColesterol']"
      ],
      "metadata": {
        "id": "f7OgnXwJqofT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32156ce2-fff6-41bf-fb42-a909eb9c51bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Nao\n",
              "1       Sim\n",
              "2       Nao\n",
              "3       Sim\n",
              "4       Nao\n",
              "       ... \n",
              "8699    Nao\n",
              "8700    Sim\n",
              "8701    Nao\n",
              "8702    Sim\n",
              "8703    Nao\n",
              "Name: apColesterol, Length: 8704, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(dados['apColesterol'])"
      ],
      "metadata": {
        "id": "URK5trJJQjmm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b14602f-e7b3-442c-c6b8-5b730afaf25d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Excluindo variávies"
      ],
      "metadata": {
        "id": "wfR7mHlpoTSr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Há diferentes formas de excluir uma variável, mas utilizaremos o drop()"
      ],
      "metadata": {
        "id": "cSP4j2HStmxc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "No caso do método drop(), precisaremos especificar o eixo em que se dará a exclusão, uma vez que ele pode atuar especificamente em linhas ou colunas. Então, escreveremos axis = 1, afinal o eixo 0 corresponde às linhas e o 1 corresponde às colunas."
      ],
      "metadata": {
        "id": "TVFlq90atqzr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.drop(['apCreatinina', 'apColesterol', 'apTriglicerides', 'apVasculo', 'eha_ventriculograma', 'Lesao', 'Pontes_Mamarias', 'QNP', 'areaCat'],axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "0d6w6jN7kVtT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados.shape"
      ],
      "metadata": {
        "id": "U2v8A2fZlV2T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66ce00f5-c25d-4f08-99e5-c4fdfe1078d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8704, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.head()"
      ],
      "metadata": {
        "id": "2O7LzwkCnksz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "d605cf86-2373-4c26-ca4f-7f3d9d2f3d1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  SEXO apHas apDm apAf  apTabagismo apDPOC apIAMPrevio             dc_diag2  \\\n",
              "0    M   Sim  Nao  Nao  Interropido    Nao         Sim      Angina Instavel   \n",
              "1    F   Sim  Sim  Nao          Nao    Nao         Nao  Isquemia Silenciosa   \n",
              "2    M   Sim  Sim  Nao          Nao    Nao         Sim      Angina Instavel   \n",
              "3    M   Sim  Nao  Nao  Interropido    Sim         Nao       Angina Estavel   \n",
              "4    M   Sim  Nao  Nao        Atual    Nao         Nao       Angina Estavel   \n",
              "\n",
              "  idadeCat    pesoCat    alturaCat classCOMPLICACOES enf_Obito  \n",
              "0  [71-95]  [62.5-70)  [1.62-1.67)               Sim       Nao  \n",
              "1  [60-65)  [62.5-70)  [1.31-1.56)               Sim       Nao  \n",
              "2  [65-71)    [70-76)  [1.62-1.67)               Nao       Nao  \n",
              "3  [71-95]  [37-62.5)  [1.62-1.67)               Sim       Nao  \n",
              "4  [18-54)   [85-148]  [1.72-71.8]               Sim       Nao  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e317baaa-ad3b-460d-a7c7-1d22a80c31bd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEXO</th>\n",
              "      <th>apHas</th>\n",
              "      <th>apDm</th>\n",
              "      <th>apAf</th>\n",
              "      <th>apTabagismo</th>\n",
              "      <th>apDPOC</th>\n",
              "      <th>apIAMPrevio</th>\n",
              "      <th>dc_diag2</th>\n",
              "      <th>idadeCat</th>\n",
              "      <th>pesoCat</th>\n",
              "      <th>alturaCat</th>\n",
              "      <th>classCOMPLICACOES</th>\n",
              "      <th>enf_Obito</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Isquemia Silenciosa</td>\n",
              "      <td>[60-65)</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.31-1.56)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[37-62.5)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Atual</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>[18-54)</td>\n",
              "      <td>[85-148]</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e317baaa-ad3b-460d-a7c7-1d22a80c31bd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e317baaa-ad3b-460d-a7c7-1d22a80c31bd 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-e317baaa-ad3b-460d-a7c7-1d22a80c31bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Renomeando colunas"
      ],
      "metadata": {
        "id": "RWUFhI1KvzJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.info()"
      ],
      "metadata": {
        "id": "4hHjd6YZwmUU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f0b11f1-4ae2-43b2-c41f-30c8ccf47342"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8704 entries, 0 to 8703\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype \n",
            "---  ------             --------------  ----- \n",
            " 0   SEXO               8704 non-null   object\n",
            " 1   apHas              8704 non-null   object\n",
            " 2   apDm               8704 non-null   object\n",
            " 3   apAf               8704 non-null   object\n",
            " 4   apTabagismo        8704 non-null   object\n",
            " 5   apDPOC             8704 non-null   object\n",
            " 6   apIAMPrevio        8704 non-null   object\n",
            " 7   dc_diag2           8689 non-null   object\n",
            " 8   idadeCat           8704 non-null   object\n",
            " 9   pesoCat            8704 non-null   object\n",
            " 10  alturaCat          8704 non-null   object\n",
            " 11  classCOMPLICACOES  8704 non-null   object\n",
            " 12  enf_Obito          8704 non-null   object\n",
            "dtypes: object(13)\n",
            "memory usage: 884.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para renomear uma coluna em um DataFrame Pandas, você pode usar o método rename() e passar um dicionário que mapeia o nome antigo da coluna para o nome novo da coluna."
      ],
      "metadata": {
        "id": "YGArEKwhv2KN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.rename(columns={'SEXO':'sexo',\n",
        "              'apHas':'aHipertensao',\n",
        "              'apDm':'aDiabetes',\n",
        "              'apAf':'aFamiliar', \n",
        "              'apTabagismo':'aTabagismo',\n",
        "              'apDPOC':'aDPulmonar',\n",
        "              'apIAMPrevio':'aInfarto',\n",
        "              'dc_diag2':'diagAtual',\n",
        "              'classCOMPLICACOES': 'complicacoes',\n",
        "              'enf_Obito':'obito'}, inplace = True)"
      ],
      "metadata": {
        "id": "A99wQkAXu7-m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#verificando as mudanças\n",
        "\n",
        "dados.head()"
      ],
      "metadata": {
        "id": "ywn6rZlTySrV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "aaa13b0b-0914-4bc2-92ef-cce7dcf532dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  sexo aHipertensao aDiabetes aFamiliar   aTabagismo aDPulmonar aInfarto  \\\n",
              "0    M          Sim       Nao       Nao  Interropido        Nao      Sim   \n",
              "1    F          Sim       Sim       Nao          Nao        Nao      Nao   \n",
              "2    M          Sim       Sim       Nao          Nao        Nao      Sim   \n",
              "3    M          Sim       Nao       Nao  Interropido        Sim      Nao   \n",
              "4    M          Sim       Nao       Nao        Atual        Nao      Nao   \n",
              "\n",
              "             diagAtual idadeCat    pesoCat    alturaCat complicacoes obito  \n",
              "0      Angina Instavel  [71-95]  [62.5-70)  [1.62-1.67)          Sim   Nao  \n",
              "1  Isquemia Silenciosa  [60-65)  [62.5-70)  [1.31-1.56)          Sim   Nao  \n",
              "2      Angina Instavel  [65-71)    [70-76)  [1.62-1.67)          Nao   Nao  \n",
              "3       Angina Estavel  [71-95]  [37-62.5)  [1.62-1.67)          Sim   Nao  \n",
              "4       Angina Estavel  [18-54)   [85-148]  [1.72-71.8]          Sim   Nao  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sexo</th>\n",
              "      <th>aHipertensao</th>\n",
              "      <th>aDiabetes</th>\n",
              "      <th>aFamiliar</th>\n",
              "      <th>aTabagismo</th>\n",
              "      <th>aDPulmonar</th>\n",
              "      <th>aInfarto</th>\n",
              "      <th>diagAtual</th>\n",
              "      <th>idadeCat</th>\n",
              "      <th>pesoCat</th>\n",
              "      <th>alturaCat</th>\n",
              "      <th>complicacoes</th>\n",
              "      <th>obito</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
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              "      <td>Nao</td>\n",
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              "      <td>Angina Instavel</td>\n",
              "      <td>[71-95]</td>\n",
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              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
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              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Isquemia Silenciosa</td>\n",
              "      <td>[60-65)</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.31-1.56)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
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              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[37-62.5)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Atual</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Angina Estavel</td>\n",
              "      <td>[18-54)</td>\n",
              "      <td>[85-148]</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50f7a096-1a16-4cbb-8048-1e96aeeadbd9')\"\n",
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              "      \n",
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              "          const element = document.querySelector('#df-50f7a096-1a16-4cbb-8048-1e96aeeadbd9');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explorando a Base de Dados"
      ],
      "metadata": {
        "id": "xvoepSGXrv4u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando os dados existentes usando o unique()"
      ],
      "metadata": {
        "id": "AKd1uWhlsEY1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# da coluna 'diagAtual'\n",
        "\n",
        "dados['diagAtual'].unique()"
      ],
      "metadata": {
        "id": "jJFpVQ0gyT9E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d324a148-872c-47ca-9a3a-e3e1382a81b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Angina Instavel', 'Isquemia Silenciosa', 'Angina Estavel',\n",
              "       'Indicacao Anatomica', nan], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# da coluna 'idadeCat'\n",
        "dados['idadeCat'].unique()"
      ],
      "metadata": {
        "id": "7hVqNiapy1bl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d510c1d2-4dff-482b-e478-7892603880b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['[71-95]', '[60-65)', '[65-71)', '[18-54)', '[54-60)'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Tratamento de dados faltantes"
      ],
      "metadata": {
        "id": "luMD6BDsvfnV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Formas de verificar valores nulos:"
      ],
      "metadata": {
        "id": "_OPQ0liOvqN-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.isnull()"
      ],
      "metadata": {
        "id": "v7egHI1hteb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "4b827c5d-956e-4128-faae-25d0415b43ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sexo  aHipertensao  aDiabetes  aFamiliar  aTabagismo  aDPulmonar  \\\n",
              "0     False         False      False      False       False       False   \n",
              "1     False         False      False      False       False       False   \n",
              "2     False         False      False      False       False       False   \n",
              "3     False         False      False      False       False       False   \n",
              "4     False         False      False      False       False       False   \n",
              "...     ...           ...        ...        ...         ...         ...   \n",
              "8699  False         False      False      False       False       False   \n",
              "8700  False         False      False      False       False       False   \n",
              "8701  False         False      False      False       False       False   \n",
              "8702  False         False      False      False       False       False   \n",
              "8703  False         False      False      False       False       False   \n",
              "\n",
              "      aInfarto  diagAtual  idadeCat  pesoCat  alturaCat  complicacoes  obito  \n",
              "0        False      False     False    False      False         False  False  \n",
              "1        False      False     False    False      False         False  False  \n",
              "2        False      False     False    False      False         False  False  \n",
              "3        False      False     False    False      False         False  False  \n",
              "4        False      False     False    False      False         False  False  \n",
              "...        ...        ...       ...      ...        ...           ...    ...  \n",
              "8699     False      False     False    False      False         False  False  \n",
              "8700     False      False     False    False      False         False  False  \n",
              "8701     False      False     False    False      False         False  False  \n",
              "8702     False      False     False    False      False         False  False  \n",
              "8703     False      False     False    False      False         False  False  \n",
              "\n",
              "[8704 rows x 13 columns]"
            ],
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.notnull()"
      ],
      "metadata": {
        "id": "f73iIAXAvyXX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "1863c2e5-7457-4007-b991-a867aea6a2ed"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "      sexo  aHipertensao  aDiabetes  aFamiliar  aTabagismo  aDPulmonar  \\\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-224e6a53-5029-4962-86d4-308174f409d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.info()"
      ],
      "metadata": {
        "id": "wmcycz4Wv233",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d405437-a178-4a0e-eb89-cf34512676cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8704 entries, 0 to 8703\n",
            "Data columns (total 13 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   sexo          8704 non-null   object\n",
            " 1   aHipertensao  8704 non-null   object\n",
            " 2   aDiabetes     8704 non-null   object\n",
            " 3   aFamiliar     8704 non-null   object\n",
            " 4   aTabagismo    8704 non-null   object\n",
            " 5   aDPulmonar    8704 non-null   object\n",
            " 6   aInfarto      8704 non-null   object\n",
            " 7   diagAtual     8689 non-null   object\n",
            " 8   idadeCat      8704 non-null   object\n",
            " 9   pesoCat       8704 non-null   object\n",
            " 10  alturaCat     8704 non-null   object\n",
            " 11  complicacoes  8704 non-null   object\n",
            " 12  obito         8704 non-null   object\n",
            "dtypes: object(13)\n",
            "memory usage: 884.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Retornando um dataframe de dados faltantes em 'diagAtual'"
      ],
      "metadata": {
        "id": "ReUpllhAz-w-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados[dados['diagAtual'].isnull()]"
      ],
      "metadata": {
        "id": "IGBp1-_NwNqn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "86a1c104-74e0-45b3-db29-47b880f5e207"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sexo aHipertensao aDiabetes aFamiliar   aTabagismo aDPulmonar aInfarto  \\\n",
              "87      M          Sim       Nao       Nao          Nao        Nao      Sim   \n",
              "110     M          Sim       Nao       Nao  Interropido        Nao      Sim   \n",
              "250     M          Sim       Sim       Nao          Nao        Nao      Nao   \n",
              "252     M          Sim       Nao       Nao          Nao        Nao      Nao   \n",
              "910     M          Sim       Nao       Nao  Interropido        Nao      Nao   \n",
              "1353    M          Sim       Nao       Sim          Nao        Nao      Nao   \n",
              "3256    M          Sim       Nao       Nao        Atual        Nao      Sim   \n",
              "4833    M          Sim       Sim       Sim          Nao        Nao      Sim   \n",
              "5632    M          Sim       Nao       Nao        Atual        Nao      Nao   \n",
              "5763    M          Sim       Nao       Nao          Nao        Nao      Nao   \n",
              "6748    M          Sim       Nao       Nao          Nao        Nao      Nao   \n",
              "6902    F          Sim       Nao       Nao        Atual        Nao      Nao   \n",
              "6946    M          Sim       Sim       Sim          Nao        Nao      Nao   \n",
              "7371    M          Sim       Nao       Nao          Nao        Nao      Sim   \n",
              "7608    F          Sim       Sim       Nao          Nao        Nao      Sim   \n",
              "\n",
              "     diagAtual idadeCat    pesoCat    alturaCat complicacoes obito  \n",
              "87         NaN  [60-65)   [85-148]  [1.72-71.8]          Sim   Nao  \n",
              "110        NaN  [65-71)  [62.5-70)  [1.62-1.67)          Nao   Nao  \n",
              "250        NaN  [65-71)    [70-76)  [1.62-1.67)          Nao   Nao  \n",
              "252        NaN  [18-54)   [85-148]  [1.72-71.8]          Nao   Nao  \n",
              "910        NaN  [65-71)    [70-76)  [1.67-1.72)          Nao   Nao  \n",
              "1353       NaN  [60-65)    [70-76)  [1.67-1.72)          Sim   Nao  \n",
              "3256       NaN  [71-95]    [70-76)  [1.56-1.62)          Sim   Nao  \n",
              "4833       NaN  [60-65)  [37-62.5)  [1.56-1.62)          Nao   Nao  \n",
              "5632       NaN  [65-71)  [62.5-70)  [1.62-1.67)          Sim   Nao  \n",
              "5763       NaN  [71-95]    [70-76)  [1.72-71.8]          Sim   Nao  \n",
              "6748       NaN  [65-71)    [70-76)  [1.62-1.67)          Nao   Nao  \n",
              "6902       NaN  [54-60)  [62.5-70)  [1.31-1.56)          Sim   Nao  \n",
              "6946       NaN  [65-71)   [85-148]  [1.67-1.72)          Sim   Nao  \n",
              "7371       NaN  [71-95]    [76-85)  [1.67-1.72)          Nao   Nao  \n",
              "7608       NaN  [71-95]   [85-148]  [1.31-1.56)          Sim   Nao  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-357097a7-721c-4040-b534-12ada516a89a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sexo</th>\n",
              "      <th>aHipertensao</th>\n",
              "      <th>aDiabetes</th>\n",
              "      <th>aFamiliar</th>\n",
              "      <th>aTabagismo</th>\n",
              "      <th>aDPulmonar</th>\n",
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              "      <th>diagAtual</th>\n",
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              "  </thead>\n",
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              "      <th>87</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
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              "      <td>Sim</td>\n",
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              "      <td>[60-65)</td>\n",
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              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
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              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
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              "      <td>Sim</td>\n",
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              "      <td>[65-71)</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[18-54)</td>\n",
              "      <td>[85-148]</td>\n",
              "      <td>[1.72-71.8]</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>910</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Interropido</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[65-71)</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.67-1.72)</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1353</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[60-65)</td>\n",
              "      <td>[70-76)</td>\n",
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              "      <td>Sim</td>\n",
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              "      <td>Nao</td>\n",
              "      <td>Atual</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[71-95]</td>\n",
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              "      <td>Sim</td>\n",
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              "      <th>4833</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
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              "      <th>5632</th>\n",
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              "      <th>5763</th>\n",
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              "      <td>Nao</td>\n",
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              "    <tr>\n",
              "      <th>6902</th>\n",
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              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
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              "      <td>Nao</td>\n",
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              "      <td>[54-60)</td>\n",
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              "      <th>6946</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
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              "      <td>[1.67-1.72)</td>\n",
              "      <td>Sim</td>\n",
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              "    <tr>\n",
              "      <th>7371</th>\n",
              "      <td>M</td>\n",
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              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
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              "    <tr>\n",
              "      <th>7608</th>\n",
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              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
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              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
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              "          if (!dataTable) return;\n",
              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Eliminando valores nulos no dataset, utilizando o dropna()\n",
        "\n",
        "Utilizando apenas a coluna 'diagAtual' como objetivo, mas se não especificarmos nada em dropna(), ele elimina todas as linhas que contém valores nulos."
      ],
      "metadata": {
        "id": "Bs29GGmb0ZJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.dropna(subset=['diagAtual'], inplace = True)"
      ],
      "metadata": {
        "id": "MuA4hqmD0G-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados[dados['diagAtual'].isnull()]"
      ],
      "metadata": {
        "id": "yye6H7D83TxY",
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          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "outputId": "c4d7fbb1-7bf7-48cd-ac20-eb2cf02c0993"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [sexo, aHipertensao, aDiabetes, aFamiliar, aTabagismo, aDPulmonar, aInfarto, diagAtual, idadeCat, pesoCat, alturaCat, complicacoes, obito]\n",
              "Index: []"
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              "      <th></th>\n",
              "      <th>sexo</th>\n",
              "      <th>aHipertensao</th>\n",
              "      <th>aDiabetes</th>\n",
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              "      <th>aDPulmonar</th>\n",
              "      <th>aInfarto</th>\n",
              "      <th>diagAtual</th>\n",
              "      <th>idadeCat</th>\n",
              "      <th>pesoCat</th>\n",
              "      <th>alturaCat</th>\n",
              "      <th>complicacoes</th>\n",
              "      <th>obito</th>\n",
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              "  </tbody>\n",
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              "  </style>\n",
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              "\n",
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              "          const element = document.querySelector('#df-e92a6235-a71c-4d29-bba5-6824119b3b66');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Temos também o método fillna() que preenche os valores ausentes num dataframe ou series.\n",
        "\n",
        "exemplo:"
      ],
      "metadata": {
        "id": "0sbYQrdz3713"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({'A': [1, 2, np.nan, 4],\n",
        "                   'B': [5, np.nan, 7, np.nan],\n",
        "                   'C': [8, 9, 10, 11],\n",
        "                   'D': [np.nan, np.nan, np.nan, np.nan]})\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "poL-meqq3wCI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "568bd777-00fd-4f4d-a477-1c83d3314213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     A    B   C   D\n",
              "0  1.0  5.0   8 NaN\n",
              "1  2.0  NaN   9 NaN\n",
              "2  NaN  7.0  10 NaN\n",
              "3  4.0  NaN  11 NaN"
            ],
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>A</th>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f15da7c1-5a95-40fc-a62b-c02ddc37424b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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",
              "    [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-f15da7c1-5a95-40fc-a62b-c02ddc37424b 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-f15da7c1-5a95-40fc-a62b-c02ddc37424b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna(1, inplace = True)\n",
        "df"
      ],
      "metadata": {
        "id": "Fw30ePXh4qmn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "9a528de8-0b1f-4a50-82c5-d13fa11bd4cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     A    B   C    D\n",
              "0  1.0  5.0   8  1.0\n",
              "1  2.0  1.0   9  1.0\n",
              "2  1.0  7.0  10  1.0\n",
              "3  4.0  1.0  11  1.0"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>A</th>\n",
              "      <th>B</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0464e522-2bdc-4643-bd39-163797a727f5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0464e522-2bdc-4643-bd39-163797a727f5 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-0464e522-2bdc-4643-bd39-163797a727f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Substituindo o dataset anterior pelo atual"
      ],
      "metadata": {
        "id": "Ls3-MN7Wgd9K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.to_csv('/content/drive/MyDrive/Colab Notebooks/dsday/NewCoronarianos.csv', sep= ';', index = False)"
      ],
      "metadata": {
        "id": "ZqeaLGKegg99"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Realizando Seleções"
      ],
      "metadata": {
        "id": "t35Ofp9V7Epg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando um DataFrame com as colunas referentes a antecedentes pessoais do paciente"
      ],
      "metadata": {
        "id": "iP7SbWnz8zlY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "antecedentesp = dados[['aHipertensao','aDiabetes','aFamiliar','aTabagismo','aDPulmonar','aInfarto']]\n",
        "antecedentesp"
      ],
      "metadata": {
        "id": "t20POD-a7KIo",
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        "outputId": "103c327e-ac4e-4541-cf3b-726a9610e598"
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      "execution_count": null,
      "outputs": [
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              "     aHipertensao aDiabetes aFamiliar   aTabagismo aDPulmonar aInfarto\n",
              "0             Sim       Nao       Nao  Interropido        Nao      Sim\n",
              "1             Sim       Sim       Nao          Nao        Nao      Nao\n",
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              "...           ...       ...       ...          ...        ...      ...\n",
              "8699          Sim       Sim       Nao          Nao        Nao      Sim\n",
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              "8702          Sim       Sim       Sim          Nao        Nao      Nao\n",
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              "      <td>Sim</td>\n",
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              "      <td>Nao</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecionando uma Series com os óbitos iguais a 'Sim'"
      ],
      "metadata": {
        "id": "Xf3Sdj9i-wlo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selecao = dados['obito'] == 'Sim'\n",
        "selecao"
      ],
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        "id": "UagKajw48lzJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "8699    False\n",
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              "8702    False\n",
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              "Name: obito, Length: 8689, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 51
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    {
      "cell_type": "markdown",
      "source": [
        "Transformando em um dataframe, temos:"
      ],
      "metadata": {
        "id": "LzT2bZ5ZASpp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "obitos = dados[selecao]\n",
        "obitos"
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        "id": "RLIP57gP-hPp",
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      },
      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sexo aHipertensao aDiabetes aFamiliar   aTabagismo aDPulmonar aInfarto  \\\n",
              "11      M          Sim       Nao       Nao          Nao        Nao      Sim   \n",
              "59      M          Sim       Nao       Nao        Atual        Nao      Sim   \n",
              "60      M          Sim       Nao       Nao          Nao        Nao      Nao   \n",
              "103     M          Sim       Nao       Nao  Interropido        Nao      Nao   \n",
              "112     M          Nao       Nao       Nao  Interropido        Nao      Sim   \n",
              "...   ...          ...       ...       ...          ...        ...      ...   \n",
              "8588    M          Sim       Nao       Nao  Interropido        Nao      Sim   \n",
              "8603    M          Sim       Sim       Nao  Interropido        Nao      Sim   \n",
              "8616    M          Sim       Nao       Nao          Nao        Nao      Sim   \n",
              "8618    M          Sim       Sim       Nao          Nao        Nao      Nao   \n",
              "8637    M          Sim       Sim       Nao          Nao        Nao      Sim   \n",
              "\n",
              "                diagAtual idadeCat    pesoCat    alturaCat complicacoes obito  \n",
              "11         Angina Estavel  [71-95]  [37-62.5)  [1.62-1.67)          Sim   Sim  \n",
              "59    Indicacao Anatomica  [18-54)    [76-85)  [1.72-71.8]          Sim   Sim  \n",
              "60         Angina Estavel  [54-60)    [76-85)  [1.72-71.8]          Sim   Sim  \n",
              "103   Isquemia Silenciosa  [71-95]   [85-148]  [1.72-71.8]          Sim   Sim  \n",
              "112        Angina Estavel  [71-95]  [37-62.5)  [1.31-1.56)          Sim   Sim  \n",
              "...                   ...      ...        ...          ...          ...   ...  \n",
              "8588  Indicacao Anatomica  [71-95]   [85-148]  [1.72-71.8]          Sim   Sim  \n",
              "8603  Indicacao Anatomica  [18-54)   [85-148]  [1.67-1.72)          Sim   Sim  \n",
              "8616  Indicacao Anatomica  [71-95]  [62.5-70)  [1.62-1.67)          Sim   Sim  \n",
              "8618      Angina Instavel  [71-95]    [70-76)  [1.62-1.67)          Sim   Sim  \n",
              "8637      Angina Instavel  [71-95]  [62.5-70)  [1.67-1.72)          Sim   Sim  \n",
              "\n",
              "[425 rows x 13 columns]"
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              "      <td>Nao</td>\n",
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              "      <td>Indicacao Anatomica</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8618</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[70-76)</td>\n",
              "      <td>[1.62-1.67)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8637</th>\n",
              "      <td>M</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Nao</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Angina Instavel</td>\n",
              "      <td>[71-95]</td>\n",
              "      <td>[62.5-70)</td>\n",
              "      <td>[1.67-1.72)</td>\n",
              "      <td>Sim</td>\n",
              "      <td>Sim</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>425 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a964a01f-d1c5-4c4b-968c-ff0d68f257f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a964a01f-d1c5-4c4b-968c-ff0d68f257f8 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-a964a01f-d1c5-4c4b-968c-ff0d68f257f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('De {} pacientes que fizeram cirurgia coronariana, {} vieram a obito após complicações.'.format(dados.shape[0], obitos.shape[0]))"
      ],
      "metadata": {
        "id": "YFwqRhtK-pR4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91153348-4562-4ffd-c012-a9724d1f9654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "De 8689 pacientes que fizeram cirurgia coronariana, 425 vieram a obito após complicações.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotando gráficos"
      ],
      "metadata": {
        "id": "s0tPBFF5Wnpx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rc('figure', figsize = (20, 10))"
      ],
      "metadata": {
        "id": "ChE3H31FA12J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando um gráfico para visualizar a porcentagem de óbitos por gênero (M e F)"
      ],
      "metadata": {
        "id": "3Dn09FOCYz66"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentual_obito = obitos['sexo'].value_counts(normalize=True) * 100\n",
        "percentual_obito"
      ],
      "metadata": {
        "id": "YUCUI2QOZf7r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97cf7446-3482-4aad-fc83-52fae6a84647"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    57.176471\n",
              "F    42.823529\n",
              "Name: sexo, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pizza = percentual_obito.plot.pie(labels=['Masculino', 'Feminino'],autopct='%1.1f%%')\n",
        "pizza.set_title('Porcentagem de óbitos/sexo')"
      ],
      "metadata": {
        "id": "RW3eDh49aYzs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "cacda813-84d3-4f4d-ee1c-2b0cfdc6ed18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Porcentagem de óbitos/sexo')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando um gráfico sobre antecedentes familiares (dos pacientes)"
      ],
      "metadata": {
        "id": "RlUqkgfjbsJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "porcentagem_familiar = dados['aFamiliar'].value_counts(normalize=True) * 100"
      ],
      "metadata": {
        "id": "2-XpPjQ1bKt6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pizza2 = porcentagem_familiar.plot.pie(autopct='%1.1f%%')\n",
        "pizza2.set_title('Porcentagem dos pacientes que tem antecedentes familiares')"
      ],
      "metadata": {
        "id": "Feioe00icKhL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "outputId": "abb436dc-32bf-47f5-a8ae-56dc274a9d4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Porcentagem dos pacientes que tem antecedentes familiares')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando um gráfico para visualizar os diferentes diagnósticos"
      ],
      "metadata": {
        "id": "LX_gn-ngdg3I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diagnosticos = dados['diagAtual'].value_counts()"
      ],
      "metadata": {
        "id": "ZfJc38POcdqa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "barra1 = diagnosticos.plot.bar(color='purple')"
      ],
      "metadata": {
        "id": "OoSM7HVad3nk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "outputId": "21cc9b1a-489d-4f57-a9df-85a6a9a05945"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-LhdD-IEd9xL"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}