Notebooks/2_analise_dos_dados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2_analise_dos_dados.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMOST7yu32quBwej+y4Lym6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EltonGS/covid-19_previsao_leito_UTI/blob/main/Notebooks/2_analise_dos_dados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_hxyDj3BvqY"
      },
      "source": [
        "# Introdução\n",
        "\n",
        "Neste notebook é feita a **análise exploratória e visualização dos dados** sobre pacientes com Covid-19, dados esses fornecidos pelo **Hospital Sírio Libanês** na plataforma **Kaggle**. Esses dados foram previamente tratados no notebook Tratamento de Dados. Esse é o segundo notebook, de 3, e faz parte do projeto final do **Bootcamp de Data Sciente Aplicada ministrado pela Alura Cursos Online**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kshIjISHmrZL"
      },
      "source": [
        "# Objetivo\n",
        "\n",
        "O objetivo da **análise dos dados** é identificar como está construída a base de dados e assim **extrair informações importantes**. Informações que são dispostas em **gráficos** que tornam mais simples a compreensão da informaçõa presente nos dados.\n",
        "\n",
        "As análises realizadas neste notebook buscam responder as questões abaixo:\n",
        "\n",
        "- **Questão 1**: Quantos pacientes foram admitidos na UTI?\n",
        "- **Questão 2**: Quantos pacientes eram do sexo masculino ou feminino?\n",
        "- **Questão 3**: Quantos pacientes são idosos ou não idosos?\n",
        "- **Questão 4**: Quantos pacientes idosos foram admitidos diretamente na UTI?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njHbl1nlnqXt"
      },
      "source": [
        "# Informações sobre os Dados\n",
        "\n",
        "## Informações compartilhadas pelo Sírio Libanês\n",
        "\n",
        "- Os dados são **anonimizados** e foram coletados nas unidades de São Paulo e Brasília. Os dados foram anonimizados utilizando as melhores práticas internacionais.\n",
        "\n",
        "- Os dados disponíveis nessa base são:\n",
        "> * Informação demográfica do paciente (03 colunas);\n",
        "> * Agrupamento de doenças já existentes no paciente (09 colunas);\n",
        "> * Resultados de exame de sangue (36 colunas);\n",
        "> * Sinais vitais (06 colunas);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEjVQ6lhkv6O"
      },
      "source": [
        "# Recursos utilizados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w60QgRopommJ"
      },
      "source": [
        "## Funções"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6RTaQqui2mY"
      },
      "source": [
        "def prepare_window(rows):\n",
        "  \"\"\"\n",
        "  Essa função identifica se o paciente em algum momento foi para a UTI,\n",
        "  e preenche essa informação na coluna ICU da janela 0-2.\n",
        "  Retorna somente a linha da janela 0-2\n",
        "  \"\"\"\n",
        "  if (np.any(rows['icu'])):\n",
        "    rows.loc[rows['window'] == '0-2', 'icu'] = 1\n",
        "  return rows.loc[rows['window'] == '0-2']"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "as_DgTFIkxyy"
      },
      "source": [
        "def dimensao_dados (dados):\n",
        "  \"\"\"\n",
        "  Essa função verifica as dimensões do dataframe\n",
        "  Retorna a quantidade de linhas e colunas\n",
        "  \"\"\"\n",
        "  # verifica as dimensões do dataframe\n",
        "  linha, coluna = dados.shape\n",
        "\n",
        "  # imprime as dimensões do dataframe\n",
        "  return print(f'O dataframe possui {linha} linhas e {coluna} colunas.')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6hS7RXyxZFx"
      },
      "source": [
        "## Dicionário de cores utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48iXIqNVxbwR"
      },
      "source": [
        "# paleta de cores para pacientes na UTI\n",
        "cor_uti = {\n",
        "    0 : 'cornflowerblue',\n",
        "    1 : 'maroon'\n",
        "}"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MB-G7m0k2GXS"
      },
      "source": [
        "# paleta de cores para o sexo dos pacientes\n",
        "cor_gender = {\n",
        "    0 : 'royalblue',\n",
        "    1 : 'palevioletred'\n",
        "}"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k37zHvX_4arE"
      },
      "source": [
        "# paleta de cores para a idade dos pacientes\n",
        "cor_idoso = {\n",
        "    0 : 'forestgreen',\n",
        "    1 : 'powderblue'\n",
        "}"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BhLHh_zYoqIK"
      },
      "source": [
        "## Bibliotecas utilizadas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8iTk0XBBu3Y"
      },
      "source": [
        "# biblioteca para manipulação dos dados\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# biblioteca para visualização dos dados\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G80kNel1oxdo"
      },
      "source": [
        "# Carregamento dos dados\n",
        "\n",
        "Carregamento e manipulação dos dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "al68WmMGm11d",
        "outputId": "10c6d27e-f997-4f66-a8df-441e2b8a43f1"
      },
      "source": [
        "# endereço dos dados\n",
        "url = 'https://github.com/EltonGS/covid_ML_previsao_UTI/blob/main/Dados/Dados%20Tratados/dados_eda.csv?raw=true'\n",
        "\n",
        "# carrega o dataframe\n",
        "dados = pd.read_csv(url)\n",
        "\n",
        "# visualiza os dados\n",
        "dados"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_visit_identifier</th>\n",
              "      <th>age_above65</th>\n",
              "      <th>age_percentil</th>\n",
              "      <th>gender</th>\n",
              "      <th>disease grouping 1</th>\n",
              "      <th>disease grouping 2</th>\n",
              "      <th>disease grouping 3</th>\n",
              "      <th>disease grouping 4</th>\n",
              "      <th>disease grouping 5</th>\n",
              "      <th>disease grouping 6</th>\n",
              "      <th>htn</th>\n",
              "      <th>immunocompromised</th>\n",
              "      <th>other</th>\n",
              "      <th>albumin_median</th>\n",
              "      <th>albumin_mean</th>\n",
              "      <th>albumin_min</th>\n",
              "      <th>albumin_max</th>\n",
              "      <th>albumin_diff</th>\n",
              "      <th>be_arterial_median</th>\n",
              "      <th>be_arterial_mean</th>\n",
              "      <th>be_arterial_min</th>\n",
              "      <th>be_arterial_max</th>\n",
              "      <th>be_arterial_diff</th>\n",
              "      <th>be_venous_median</th>\n",
              "      <th>be_venous_mean</th>\n",
              "      <th>be_venous_min</th>\n",
              "      <th>be_venous_max</th>\n",
              "      <th>be_venous_diff</th>\n",
              "      <th>bic_arterial_median</th>\n",
              "      <th>bic_arterial_mean</th>\n",
              "      <th>bic_arterial_min</th>\n",
              "      <th>bic_arterial_max</th>\n",
              "      <th>bic_arterial_diff</th>\n",
              "      <th>bic_venous_median</th>\n",
              "      <th>bic_venous_mean</th>\n",
              "      <th>bic_venous_min</th>\n",
              "      <th>bic_venous_max</th>\n",
              "      <th>bic_venous_diff</th>\n",
              "      <th>billirubin_median</th>\n",
              "      <th>billirubin_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>dimer_max</th>\n",
              "      <th>dimer_diff</th>\n",
              "      <th>bloodpressure_diastolic_mean</th>\n",
              "      <th>bloodpressure_sistolic_mean</th>\n",
              "      <th>heart_rate_mean</th>\n",
              "      <th>respiratory_rate_mean</th>\n",
              "      <th>temperature_mean</th>\n",
              "      <th>oxygen_saturation_mean</th>\n",
              "      <th>bloodpressure_diastolic_median</th>\n",
              "      <th>bloodpressure_sistolic_median</th>\n",
              "      <th>heart_rate_median</th>\n",
              "      <th>respiratory_rate_median</th>\n",
              "      <th>temperature_median</th>\n",
              "      <th>oxygen_saturation_median</th>\n",
              "      <th>bloodpressure_diastolic_min</th>\n",
              "      <th>bloodpressure_sistolic_min</th>\n",
              "      <th>heart_rate_min</th>\n",
              "      <th>respiratory_rate_min</th>\n",
              "      <th>temperature_min</th>\n",
              "      <th>oxygen_saturation_min</th>\n",
              "      <th>bloodpressure_diastolic_max</th>\n",
              "      <th>bloodpressure_sistolic_max</th>\n",
              "      <th>heart_rate_max</th>\n",
              "      <th>respiratory_rate_max</th>\n",
              "      <th>temperature_max</th>\n",
              "      <th>oxygen_saturation_max</th>\n",
              "      <th>bloodpressure_diastolic_diff</th>\n",
              "      <th>bloodpressure_sistolic_diff</th>\n",
              "      <th>heart_rate_diff</th>\n",
              "      <th>respiratory_rate_diff</th>\n",
              "      <th>temperature_diff</th>\n",
              "      <th>oxygen_saturation_diff</th>\n",
              "      <th>bloodpressure_diastolic_diff_rel</th>\n",
              "      <th>bloodpressure_sistolic_diff_rel</th>\n",
              "      <th>heart_rate_diff_rel</th>\n",
              "      <th>respiratory_rate_diff_rel</th>\n",
              "      <th>temperature_diff_rel</th>\n",
              "      <th>oxygen_saturation_diff_rel</th>\n",
              "      <th>window</th>\n",
              "      <th>icu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.283019</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.208791</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.432836</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.420290</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.838384</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.313433</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>0.246377</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.838384</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.313433</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>0.246377</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>-0.938950</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.994912</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.132075</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.443299</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.025641</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.313433</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-0.871658</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-0.863874</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-0.414634</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.996762</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.243021</td>\n",
              "      <td>-0.338537</td>\n",
              "      <td>-0.213031</td>\n",
              "      <td>-0.317859</td>\n",
              "      <td>0.033779</td>\n",
              "      <td>0.665932</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.376923</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>-0.340206</td>\n",
              "      <td>-0.4875</td>\n",
              "      <td>-0.572650</td>\n",
              "      <td>-0.857143</td>\n",
              "      <td>0.098901</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>0.286486</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.272727</td>\n",
              "      <td>0.362319</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.339130</td>\n",
              "      <td>0.325153</td>\n",
              "      <td>0.114504</td>\n",
              "      <td>0.176471</td>\n",
              "      <td>-0.238095</td>\n",
              "      <td>-0.818182</td>\n",
              "      <td>-0.389967</td>\n",
              "      <td>0.407558</td>\n",
              "      <td>-0.230462</td>\n",
              "      <td>0.096774</td>\n",
              "      <td>-0.242282</td>\n",
              "      <td>-0.814433</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1915</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.979571</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.292308</td>\n",
              "      <td>0.056604</td>\n",
              "      <td>-0.525424</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.012346</td>\n",
              "      <td>-0.292308</td>\n",
              "      <td>0.056604</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>0.535714</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.175258</td>\n",
              "      <td>-0.0500</td>\n",
              "      <td>0.145299</td>\n",
              "      <td>-0.428571</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.919192</td>\n",
              "      <td>-0.299145</td>\n",
              "      <td>-0.502703</td>\n",
              "      <td>-0.164179</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>0.246377</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1916</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.979571</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.384615</td>\n",
              "      <td>-0.113208</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.384615</td>\n",
              "      <td>-0.113208</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>-0.1250</td>\n",
              "      <td>-0.008547</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.472527</td>\n",
              "      <td>0.838384</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.567568</td>\n",
              "      <td>-0.298507</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.072464</td>\n",
              "      <td>0.578947</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1917</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.979571</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.169811</td>\n",
              "      <td>-0.593220</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.086420</td>\n",
              "      <td>-0.230769</td>\n",
              "      <td>-0.169811</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>0.237113</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.059829</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>0.472527</td>\n",
              "      <td>0.898990</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.459459</td>\n",
              "      <td>-0.343284</td>\n",
              "      <td>-0.636364</td>\n",
              "      <td>-0.072464</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1918</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-0.717277</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>-0.982208</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.979571</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.209877</td>\n",
              "      <td>-0.384615</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.661017</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.473684</td>\n",
              "      <td>0.209877</td>\n",
              "      <td>-0.384615</td>\n",
              "      <td>-0.188679</td>\n",
              "      <td>-0.655172</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>0.473684</td>\n",
              "      <td>0.340206</td>\n",
              "      <td>-0.1250</td>\n",
              "      <td>-0.076923</td>\n",
              "      <td>-0.571429</td>\n",
              "      <td>0.560440</td>\n",
              "      <td>0.797980</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.567568</td>\n",
              "      <td>-0.358209</td>\n",
              "      <td>-0.696970</td>\n",
              "      <td>0.043478</td>\n",
              "      <td>0.473684</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1919</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.983255</td>\n",
              "      <td>-0.983255</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.888448</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.185185</td>\n",
              "      <td>-0.539103</td>\n",
              "      <td>-0.107704</td>\n",
              "      <td>-0.610169</td>\n",
              "      <td>0.050595</td>\n",
              "      <td>0.662281</td>\n",
              "      <td>-0.160494</td>\n",
              "      <td>-0.538462</td>\n",
              "      <td>-0.075472</td>\n",
              "      <td>-0.586207</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.631579</td>\n",
              "      <td>-0.175258</td>\n",
              "      <td>-0.3750</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.785714</td>\n",
              "      <td>0.186813</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.470270</td>\n",
              "      <td>-0.149254</td>\n",
              "      <td>-0.515152</td>\n",
              "      <td>0.101449</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.652174</td>\n",
              "      <td>-0.644172</td>\n",
              "      <td>-0.633588</td>\n",
              "      <td>-0.647059</td>\n",
              "      <td>-0.547619</td>\n",
              "      <td>-0.838384</td>\n",
              "      <td>-0.701863</td>\n",
              "      <td>-0.585967</td>\n",
              "      <td>-0.763868</td>\n",
              "      <td>-0.612903</td>\n",
              "      <td>-0.551337</td>\n",
              "      <td>-0.835052</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1920 rows × 231 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      patient_visit_identifier  age_above65  ...    window  icu\n",
              "0                            0            1  ...       0-2    0\n",
              "1                            0            1  ...       2-4    0\n",
              "2                            0            1  ...       4-6    0\n",
              "3                            0            1  ...      6-12    0\n",
              "4                            0            1  ...  ABOVE_12    1\n",
              "...                        ...          ...  ...       ...  ...\n",
              "1915                       384            0  ...       0-2    0\n",
              "1916                       384            0  ...       2-4    0\n",
              "1917                       384            0  ...       4-6    0\n",
              "1918                       384            0  ...      6-12    0\n",
              "1919                       384            0  ...  ABOVE_12    0\n",
              "\n",
              "[1920 rows x 231 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0FF7rNpde2e"
      },
      "source": [
        "As colunas que **interessam** para a **análise exploratória** são as com **dados categóricos**. Conforme visto no notebook **Tratamento dos Dados**, as colunas com dados contínuos são as com valores do tipo **float64**.\n",
        "\n",
        "Essas colunas podem ser excluídas da base ded dados para tornar mais simples a visualização."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXpJ26z0d0kW",
        "outputId": "e57d160d-f0e3-412f-e2a5-0cb2bb32b74b"
      },
      "source": [
        "# verifica as informações do dataset\n",
        "dados.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1920 entries, 0 to 1919\n",
            "Columns: 231 entries, patient_visit_identifier to icu\n",
            "dtypes: float64(225), int64(4), object(2)\n",
            "memory usage: 3.4+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "2iHjPrvBeWw-",
        "outputId": "d0d5e7b4-185d-4b0a-833c-471831431c62"
      },
      "source": [
        "# seleciona todos os tipos de dados exceto os float64\n",
        "dados_analise = dados.select_dtypes(exclude='float64')\n",
        "dados_analise"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_visit_identifier</th>\n",
              "      <th>age_above65</th>\n",
              "      <th>age_percentil</th>\n",
              "      <th>gender</th>\n",
              "      <th>window</th>\n",
              "      <th>icu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1915</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1916</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>2-4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1917</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>4-6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1918</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>6-12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1919</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>ABOVE_12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1920 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      patient_visit_identifier  age_above65  ...    window  icu\n",
              "0                            0            1  ...       0-2    0\n",
              "1                            0            1  ...       2-4    0\n",
              "2                            0            1  ...       4-6    0\n",
              "3                            0            1  ...      6-12    0\n",
              "4                            0            1  ...  ABOVE_12    1\n",
              "...                        ...          ...  ...       ...  ...\n",
              "1915                       384            0  ...       0-2    0\n",
              "1916                       384            0  ...       2-4    0\n",
              "1917                       384            0  ...       4-6    0\n",
              "1918                       384            0  ...      6-12    0\n",
              "1919                       384            0  ...  ABOVE_12    0\n",
              "\n",
              "[1920 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "saRRIkBRjETU"
      },
      "source": [
        "Agora com os **dados categóricos separados**, é possível aplicar a função **prepare_window** para que  cada paciente tenha somente uma linha no dataframe e indicando se o paciente foi para a UTI em algum momento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "KrsyaFJ-jXOS",
        "outputId": "8e8a4bdd-a267-416d-dacc-49adbd677280"
      },
      "source": [
        "# agrupa pela coluna patient_visit_identifier e aplica a função\n",
        "dados_pacientes = dados_analise.groupby('patient_visit_identifier').apply(prepare_window)\n",
        "\n",
        "# visualiza o dados_pacientes\n",
        "dados_pacientes"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>patient_visit_identifier</th>\n",
              "      <th>age_above65</th>\n",
              "      <th>age_percentil</th>\n",
              "      <th>gender</th>\n",
              "      <th>window</th>\n",
              "      <th>icu</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>patient_visit_identifier</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <th>10</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>0</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>15</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>40th</td>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <th>20</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10th</td>\n",
              "      <td>0</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>380</th>\n",
              "      <th>1895</th>\n",
              "      <td>380</td>\n",
              "      <td>0</td>\n",
              "      <td>40th</td>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>381</th>\n",
              "      <th>1900</th>\n",
              "      <td>381</td>\n",
              "      <td>1</td>\n",
              "      <td>Above 90th</td>\n",
              "      <td>0</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>382</th>\n",
              "      <th>1905</th>\n",
              "      <td>382</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>0</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <th>1910</th>\n",
              "      <td>383</td>\n",
              "      <td>0</td>\n",
              "      <td>40th</td>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>384</th>\n",
              "      <th>1915</th>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>50th</td>\n",
              "      <td>1</td>\n",
              "      <td>0-2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>384 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                               patient_visit_identifier  ...  icu\n",
              "patient_visit_identifier                                 ...     \n",
              "0                        0                            0  ...    1\n",
              "1                        5                            1  ...    1\n",
              "2                        10                           2  ...    1\n",
              "3                        15                           3  ...    0\n",
              "4                        20                           4  ...    0\n",
              "...                                                 ...  ...  ...\n",
              "380                      1895                       380  ...    1\n",
              "381                      1900                       381  ...    0\n",
              "382                      1905                       382  ...    1\n",
              "383                      1910                       383  ...    0\n",
              "384                      1915                       384  ...    0\n",
              "\n",
              "[384 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5riPD4clwwe",
        "outputId": "d32f9eda-b793-4f15-ce09-0a4fb5b03bc0"
      },
      "source": [
        "# verifica as dimensões do dataframe\n",
        "dimensao_dados(dados_pacientes)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O dataframe possui 384 linhas e 6 colunas.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQeN9a17l6up"
      },
      "source": [
        "Como **cada linha é um paciente**, o dataframe possui informação de **384 pacientes**. Com esse dataframe é possível verificar as questões enumeradas anteriormente.\n",
        "\n",
        "Com o dataframe nesse estado as questões levantadas anteriormente podem ser verificadas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcplvS8am3YZ"
      },
      "source": [
        "## Questão 1 - Quantos pacientes foram para a UTI?\n",
        "\n",
        "O dataframe possui 384 pacientes, quantos desses foram para a UTI em algum momento?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Jut7UyAAooSy",
        "outputId": "5ce8dd74-115e-4f67-dd1d-5a53cf9c4367"
      },
      "source": [
        "# define o estilo do plot\n",
        "sns.set_style('white')\n",
        "\n",
        "# cria a figura e define o tamanho\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "\n",
        "# cria o gráfico\n",
        "sns.countplot(data=dados_pacientes, x=dados_pacientes['icu'], palette=cor_uti, dodge=False, ax=ax)\n",
        "\n",
        "# define o título\n",
        "plt.title('Pacientes Admitidos na UTI', fontsize=25, fontweight='bold',\n",
        "          color='dimgrey')\n",
        "\n",
        "# parâmetros do eixo Y\n",
        "plt.ylim(0, 250)\n",
        "plt.ylabel('Número de Pacientes', fontsize=15, color='dimgrey',\n",
        "           fontweight='bold')\n",
        "plt.yticks(fontsize=8)\n",
        "\n",
        "# parâmetros do eixo X\n",
        "plt.xlabel('')\n",
        "plt.xticks(ticks=[0, 1], labels=['Não foram para a UTI', 'Foram para a UTI'],\n",
        "           fontsize=15, fontweight='bold', color='dimgrey')\n",
        "\n",
        "# parâmetros das linhas ao redor do gráfico\n",
        "ax.spines['bottom'].set_color('lightgrey')\n",
        "ax.spines['left'].set_color('lightgrey')\n",
        "sns.despine()\n",
        "\n",
        "total = []\n",
        "for p in ax.patches:\n",
        "  total.append(p.get_height())\n",
        "\n",
        "total = sum(total)\n",
        "\n",
        "for p in ax.patches:\n",
        "  ax.text(p.get_x() + .37, p.get_height() + 3, str(p.get_height()),\n",
        "          fontsize=13, color='dimgrey', fontweight='bold')\n",
        "\n",
        "# mostra o gráfico\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEQ1pI7p0Y2Y"
      },
      "source": [
        "A imagem acima mostra que dos **384 pacientes**, **195 foram para a UTI** em algum momento após a chegada ao Hospital Sírio Libanês.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2e0Azfg1SSr"
      },
      "source": [
        "## Questão 2 - Quantos pacientes eram do sexo masculino ou feminino?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "-eMVtWnM1Z5K",
        "outputId": "b23a2386-f6e0-4f40-9e51-c8316755de89"
      },
      "source": [
        "# define o estilo do plot\n",
        "sns.set_style('white')\n",
        "\n",
        "# cria a figura e define o tamanho\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "\n",
        "# cria o gráfico\n",
        "sns.countplot(data=dados_pacientes, x=dados_pacientes['gender'], palette=cor_gender, dodge=False)\n",
        "\n",
        "# define o título\n",
        "plt.title('Gênero dos Pacientes', fontsize=25, fontweight='bold',\n",
        "          color='dimgrey')\n",
        "\n",
        "# parâmetros do eixo Y\n",
        "plt.ylim(0, 300)\n",
        "plt.ylabel('Número de Pacientes', fontsize=15, color='dimgrey',\n",
        "           fontweight='bold')\n",
        "plt.yticks(fontsize=8)\n",
        "\n",
        "# parâmetros do eixo X\n",
        "plt.xlabel('')\n",
        "plt.xticks(ticks=[0, 1], labels=['Masculino', 'Feminino'],\n",
        "           fontsize=15, fontweight='bold', color='dimgrey')\n",
        "\n",
        "# parâmetros das linhas ao redor do gráfico\n",
        "ax.spines['bottom'].set_color('lightgrey')\n",
        "ax.spines['left'].set_color('lightgrey')\n",
        "sns.despine()\n",
        "\n",
        "total = []\n",
        "for p in ax.patches:\n",
        "  total.append(p.get_height())\n",
        "\n",
        "total = sum(total)\n",
        "\n",
        "for p in ax.patches:\n",
        "  ax.text(p.get_x() + .37, p.get_height() + 3, str(p.get_height()),\n",
        "          fontsize=13, color='dimgrey', fontweight='bold')\n",
        "\n",
        "# mostra o gráfico\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Wn67c_W3xrg"
      },
      "source": [
        "A imagem acima mostra que dos **384 pacientes**, **242** são do **sexo masculino** e **142** são do **sexo feminino**.\n",
        "\n",
        "Com essa separação dos pacientes de acordo com o gênero, será que há alguma relação entre o gênero e se o paciente foi para a UTI?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6AyCVTq86ZM6",
        "outputId": "257c2a20-900b-4745-f0aa-f5230917290f"
      },
      "source": [
        "# define o estilo do plot\n",
        "sns.set_style('white')\n",
        "\n",
        "# cria a figura e define o tamanho\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "\n",
        "# cria o gráfico\n",
        "sns.countplot(data=dados_pacientes, x='gender', hue='icu', palette=cor_uti)\n",
        "\n",
        "# define o título\n",
        "plt.title('$\\\\bf{Pacientes\\ admitidos\\ na\\ UTI}$\\nDividido por gênero', fontsize=25,\n",
        "          color='dimgrey')\n",
        "\n",
        "# parâmetros do eixo Y\n",
        "plt.ylim(0, 200)\n",
        "plt.ylabel('Número de Pacientes', fontsize=15, color='dimgrey',\n",
        "           fontweight='bold')\n",
        "plt.yticks(fontsize=8)\n",
        "\n",
        "# parâmetros do eixo X\n",
        "plt.xlabel('')\n",
        "plt.xticks(ticks=[0, 1], labels=['Masculino', 'Feminino'],\n",
        "           fontsize=15, fontweight='bold', color='dimgrey')\n",
        "\n",
        "# parâmetros das linhas ao redor do gráfico\n",
        "ax.spines['bottom'].set_color('lightgrey')\n",
        "ax.spines['left'].set_color('lightgrey')\n",
        "sns.despine()\n",
        "\n",
        "total = []\n",
        "for p in ax.patches:\n",
        "  total.append(p.get_height())\n",
        "\n",
        "total = sum(total)\n",
        "\n",
        "for p in ax.patches:\n",
        "  ax.text(p.get_x() + .17, p.get_height() + 3, str(p.get_height()),\n",
        "          fontsize=13, color='dimgrey', fontweight='bold')\n",
        "\n",
        "# define a legenda\n",
        "plt.legend(labels=['Não Admitido na UTI', 'Admitido na UTI'], loc='upper left')\n",
        "\n",
        "# mostra o gráfico\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbY-G18k86bA"
      },
      "source": [
        "A imagem acima demonstra que o número de pacientes do **gênero masculino** que foram admitidos na **UTI (55%)** é, proporcionalmente, **maior** que o número de paciente do **gênero feminino admitidos na UTI (42%)**.\n",
        "\n",
        "Somente com essa informação **NÃO** é possível afirmar que o gênero do paciente é um fator determinante para a admissão na UTI."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-6piBok37f8"
      },
      "source": [
        "## Questão 3 - Quantos pacientes são idosos e não idosos?\n",
        "\n",
        "Para essa questão a idade que divide os pacientes em idosos ou não é 65 anos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "vJc2rQEZ4O8s",
        "outputId": "e35d11dd-2548-42d8-ef44-1265563b075f"
      },
      "source": [
        "# define o estilo do plot\n",
        "sns.set_style('white')\n",
        "\n",
        "# cria a figura e define o tamanho\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "\n",
        "# cria o gráfico\n",
        "sns.countplot(data=dados_pacientes, x=dados_pacientes['age_above65'],\n",
        "              palette=cor_idoso, dodge=False)\n",
        "\n",
        "# define o título\n",
        "plt.title('$\\\\bf{Idade\\ dos\\ Pacientes}$\\nIdoso x Não idoso', fontsize=25,\n",
        "          color='dimgrey')\n",
        "\n",
        "# parâmetros do eixo Y\n",
        "plt.ylim(0, 300)\n",
        "plt.ylabel('Número de Pacientes', fontsize=15, color='dimgrey',\n",
        "           fontweight='bold')\n",
        "plt.yticks(fontsize=8)\n",
        "\n",
        "# parâmetros do eixo X\n",
        "plt.xlabel('')\n",
        "plt.xticks(ticks=[0, 1], labels=['Não idoso (Menos de 65 anos)', 'Idoso (65 anos ou mais)'],\n",
        "           fontsize=15, fontweight='bold', color='dimgrey')\n",
        "\n",
        "# parâmetros das linhas ao redor do gráfico\n",
        "ax.spines['bottom'].set_color('lightgrey')\n",
        "ax.spines['left'].set_color('lightgrey')\n",
        "sns.despine()\n",
        "\n",
        "total = []\n",
        "for p in ax.patches:\n",
        "  total.append(p.get_height())\n",
        "\n",
        "total = sum(total)\n",
        "\n",
        "for p in ax.patches:\n",
        "  ax.text(p.get_x() + .37, p.get_height() + 3, str(p.get_height()),\n",
        "          fontsize=13, color='dimgrey', fontweight='bold')\n",
        "\n",
        "# mostra o gráfico\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bcI_A3j3YFuZ"
      },
      "source": [
        "A imagem acima mostra que a base de dados possui **180 pacientes idosos (47%)** e **204 pacientes não idosos (53%)**.\n",
        "\n",
        "Com essa informação é possível verificar se a idade é uma variável importante na admissão de pacientes na UTI."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "O9oUla6rY3vo",
        "outputId": "1dcf6f1c-086a-4894-fb4c-9957574bd8e2"
      },
      "source": [
        "# define o estilo do plot\n",
        "sns.set_style('white')\n",
        "\n",
        "# cria a figura e define o tamanho\n",
        "fig, ax = plt.subplots(figsize=(16, 8))\n",
        "\n",
        "# cria o gráfico\n",
        "sns.countplot(data=dados_pacientes, x=dados_pacientes['age_above65'], hue='icu',\n",
        "              palette=cor_uti)\n",
        "\n",
        "# define o título\n",
        "plt.title('$\\\\bf{Admissão\\ dos\\ Pacientes\\ na\\ UTI}$\\nIdoso x Não idoso', fontsize=25,\n",
        "          color='dimgrey')\n",
        "\n",
        "# parâmetros do eixo Y\n",
        "plt.ylim(0, 175)\n",
        "plt.ylabel('Número de Pacientes', fontsize=15, color='dimgrey',\n",
        "           fontweight='bold')\n",
        "plt.yticks(fontsize=8)\n",
        "\n",
        "# parâmetros do eixo X\n",
        "plt.xlabel('')\n",
        "plt.xticks(ticks=[0, 1], labels=['Não idoso (Menos de 65 anos)', 'Idoso (65 anos ou mais)'],\n",
        "           fontsize=15, fontweight='bold', color='dimgrey')\n",
        "\n",
        "# parâmetros das linhas ao redor do gráfico\n",
        "ax.spines['bottom'].set_color('lightgrey')\n",
        "ax.spines['left'].set_color('lightgrey')\n",
        "sns.despine()\n",
        "\n",
        "# adiciona o valor acima das colunas no gráfico\n",
        "total = []\n",
        "for p in ax.patches:\n",
        "  total.append(p.get_height())\n",
        "\n",
        "total = sum(total)\n",
        "\n",
        "for p in ax.patches:\n",
        "  ax.text(p.get_x() + .17, p.get_height() + 3, str(p.get_height()),\n",
        "          fontsize=13, color='dimgrey', fontweight='bold')\n",
        "  \n",
        "# define a legenda\n",
        "plt.legend(labels=['Não Admitido na UTI', 'Admitido na UTI'], loc='upper left')\n",
        "\n",
        "# mostra o gráfico\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzGX5GZjZ2G4"
      },
      "source": [
        "A imagem acima demonstra como a **idade É um fator importante** na evolução da doença Covid-19.\n",
        "\n",
        "Entre os pacientes **não idosos 37% foram admitidos na UTI**, para os pacientes **idosos 66% foram admitidos na UTI**.\n",
        "\n",
        "De acordo com a **Organização Mundial da Saúde (World Health Organization)**, pessoas idosas que são contaminadas pelo coronavírus tem um risco maior de desenvolver uma versão severa da doença devido as mudanças fisiológicas que acontecem com o envelhecimento e por possíveis doenças já existentes.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQzc_noDbepY"
      },
      "source": [
        "## Questão 4 - Entre os pacientes idosos, quantos foram internados diretamente na UTI?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "FyPkMqhIcbdI",
        "outputId": "785367b3-e734-4df4-ea58-2741011b445c"
      },
      "source": [
        "# selecionando paciente idosos e que foram para a UTI\n",
        "dados_idosos_uti = dados.query(\"age_above65 == 1 and window == '0-2' and icu == 1\")\n",
        "dados_idosos_uti.head()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>patient_visit_identifier</th>\n",
              "      <th>age_above65</th>\n",
              "      <th>age_percentil</th>\n",
              "      <th>gender</th>\n",
              "      <th>disease grouping 1</th>\n",
              "      <th>disease grouping 2</th>\n",
              "      <th>disease grouping 3</th>\n",
              "      <th>disease grouping 4</th>\n",
              "      <th>disease grouping 5</th>\n",
              "      <th>disease grouping 6</th>\n",
              "      <th>htn</th>\n",
              "      <th>immunocompromised</th>\n",
              "      <th>other</th>\n",
              "      <th>albumin_median</th>\n",
              "      <th>albumin_mean</th>\n",
              "      <th>albumin_min</th>\n",
              "      <th>albumin_max</th>\n",
              "      <th>albumin_diff</th>\n",
              "      <th>be_arterial_median</th>\n",
              "      <th>be_arterial_mean</th>\n",
              "      <th>be_arterial_min</th>\n",
              "      <th>be_arterial_max</th>\n",
              "      <th>be_arterial_diff</th>\n",
              "      <th>be_venous_median</th>\n",
              "      <th>be_venous_mean</th>\n",
              "      <th>be_venous_min</th>\n",
              "      <th>be_venous_max</th>\n",
              "      <th>be_venous_diff</th>\n",
              "      <th>bic_arterial_median</th>\n",
              "      <th>bic_arterial_mean</th>\n",
              "      <th>bic_arterial_min</th>\n",
              "      <th>bic_arterial_max</th>\n",
              "      <th>bic_arterial_diff</th>\n",
              "      <th>bic_venous_median</th>\n",
              "      <th>bic_venous_mean</th>\n",
              "      <th>bic_venous_min</th>\n",
              "      <th>bic_venous_max</th>\n",
              "      <th>bic_venous_diff</th>\n",
              "      <th>billirubin_median</th>\n",
              "      <th>billirubin_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>dimer_max</th>\n",
              "      <th>dimer_diff</th>\n",
              "      <th>bloodpressure_diastolic_mean</th>\n",
              "      <th>bloodpressure_sistolic_mean</th>\n",
              "      <th>heart_rate_mean</th>\n",
              "      <th>respiratory_rate_mean</th>\n",
              "      <th>temperature_mean</th>\n",
              "      <th>oxygen_saturation_mean</th>\n",
              "      <th>bloodpressure_diastolic_median</th>\n",
              "      <th>bloodpressure_sistolic_median</th>\n",
              "      <th>heart_rate_median</th>\n",
              "      <th>respiratory_rate_median</th>\n",
              "      <th>temperature_median</th>\n",
              "      <th>oxygen_saturation_median</th>\n",
              "      <th>bloodpressure_diastolic_min</th>\n",
              "      <th>bloodpressure_sistolic_min</th>\n",
              "      <th>heart_rate_min</th>\n",
              "      <th>respiratory_rate_min</th>\n",
              "      <th>temperature_min</th>\n",
              "      <th>oxygen_saturation_min</th>\n",
              "      <th>bloodpressure_diastolic_max</th>\n",
              "      <th>bloodpressure_sistolic_max</th>\n",
              "      <th>heart_rate_max</th>\n",
              "      <th>respiratory_rate_max</th>\n",
              "      <th>temperature_max</th>\n",
              "      <th>oxygen_saturation_max</th>\n",
              "      <th>bloodpressure_diastolic_diff</th>\n",
              "      <th>bloodpressure_sistolic_diff</th>\n",
              "      <th>heart_rate_diff</th>\n",
              "      <th>respiratory_rate_diff</th>\n",
              "      <th>temperature_diff</th>\n",
              "      <th>oxygen_saturation_diff</th>\n",
              "      <th>bloodpressure_diastolic_diff_rel</th>\n",
              "      <th>bloodpressure_sistolic_diff_rel</th>\n",
              "      <th>heart_rate_diff_rel</th>\n",
              "      <th>respiratory_rate_diff_rel</th>\n",
              "      <th>temperature_diff_rel</th>\n",
              "      <th>oxygen_saturation_diff_rel</th>\n",
              "      <th>window</th>\n",
              "      <th>icu</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>90th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-0.210526</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>-0.967556</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.046154</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.830508</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.283951</td>\n",
              "      <td>-0.046154</td>\n",
              "      <td>0.188679</td>\n",
              "      <td>0.862069</td>\n",
              "      <td>-0.107143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.072165</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.264957</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.318681</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.504274</td>\n",
              "      <td>-0.329730</td>\n",
              "      <td>-0.059701</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>-0.275362</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.679144</td>\n",
              "      <td>-0.679144</td>\n",
              "      <td>-0.679144</td>\n",
              "      <td>-0.679144</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-0.219512</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.966510</td>\n",
              "      <td>-0.966510</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.967082</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.209877</td>\n",
              "      <td>0.030769</td>\n",
              "      <td>-0.056604</td>\n",
              "      <td>-0.525424</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.209877</td>\n",
              "      <td>0.030769</td>\n",
              "      <td>-0.056604</td>\n",
              "      <td>-0.517241</td>\n",
              "      <td>0.214286</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>0.340206</td>\n",
              "      <td>0.2125</td>\n",
              "      <td>0.042735</td>\n",
              "      <td>-0.428571</td>\n",
              "      <td>0.516484</td>\n",
              "      <td>0.878788</td>\n",
              "      <td>-0.162393</td>\n",
              "      <td>-0.275676</td>\n",
              "      <td>-0.253731</td>\n",
              "      <td>-0.575758</td>\n",
              "      <td>-0.014493</td>\n",
              "      <td>0.684211</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>70th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.73822</td>\n",
              "      <td>-0.73822</td>\n",
              "      <td>-0.73822</td>\n",
              "      <td>-0.73822</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-0.170732</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.976975</td>\n",
              "      <td>-0.976975</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.524265</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.104056</td>\n",
              "      <td>-0.059341</td>\n",
              "      <td>-0.832884</td>\n",
              "      <td>-0.118644</td>\n",
              "      <td>-0.214286</td>\n",
              "      <td>0.834586</td>\n",
              "      <td>-0.061728</td>\n",
              "      <td>-0.138462</td>\n",
              "      <td>-0.849057</td>\n",
              "      <td>-0.103448</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>0.736842</td>\n",
              "      <td>-0.154639</td>\n",
              "      <td>-0.1750</td>\n",
              "      <td>-0.675214</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.010989</td>\n",
              "      <td>0.858586</td>\n",
              "      <td>-0.196581</td>\n",
              "      <td>-0.048649</td>\n",
              "      <td>-0.791045</td>\n",
              "      <td>-0.212121</td>\n",
              "      <td>-0.304348</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.617391</td>\n",
              "      <td>-0.361963</td>\n",
              "      <td>-0.908397</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.666667</td>\n",
              "      <td>-0.858586</td>\n",
              "      <td>-0.689777</td>\n",
              "      <td>-0.399526</td>\n",
              "      <td>-0.891572</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.663881</td>\n",
              "      <td>-0.858709</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>270</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>60th</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-0.463415</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.609756</td>\n",
              "      <td>-0.609756</td>\n",
              "      <td>-0.609756</td>\n",
              "      <td>-0.609756</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>-0.979069</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.190123</td>\n",
              "      <td>-0.550769</td>\n",
              "      <td>-0.207547</td>\n",
              "      <td>-0.254237</td>\n",
              "      <td>0.678571</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.234568</td>\n",
              "      <td>-0.553846</td>\n",
              "      <td>-0.207547</td>\n",
              "      <td>-0.241379</td>\n",
              "      <td>0.678571</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.051546</td>\n",
              "      <td>-0.3250</td>\n",
              "      <td>-0.094017</td>\n",
              "      <td>-0.142857</td>\n",
              "      <td>0.802198</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>-0.367521</td>\n",
              "      <td>-0.621622</td>\n",
              "      <td>-0.373134</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.362319</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>-0.878261</td>\n",
              "      <td>-0.865031</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.890980</td>\n",
              "      <td>-0.841512</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>70th</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>0.605263</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.00000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-0.317073</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.365854</td>\n",
              "      <td>-0.365854</td>\n",
              "      <td>-0.365854</td>\n",
              "      <td>-0.365854</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.936159</td>\n",
              "      <td>-0.936159</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.978029</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.530864</td>\n",
              "      <td>-0.784615</td>\n",
              "      <td>-0.377358</td>\n",
              "      <td>-0.389831</td>\n",
              "      <td>0.321429</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.530864</td>\n",
              "      <td>-0.784615</td>\n",
              "      <td>-0.377358</td>\n",
              "      <td>-0.379310</td>\n",
              "      <td>0.321429</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-0.278351</td>\n",
              "      <td>-0.4500</td>\n",
              "      <td>-0.247863</td>\n",
              "      <td>-0.285714</td>\n",
              "      <td>0.582418</td>\n",
              "      <td>0.979798</td>\n",
              "      <td>-0.675214</td>\n",
              "      <td>-0.848649</td>\n",
              "      <td>-0.507463</td>\n",
              "      <td>-0.454545</td>\n",
              "      <td>0.072464</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0-2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 231 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     patient_visit_identifier  age_above65  ... window  icu\n",
              "5                           1            1  ...    0-2    1\n",
              "135                        27            1  ...    0-2    1\n",
              "205                        41            1  ...    0-2    1\n",
              "270                        54            1  ...    0-2    1\n",
              "310                        62            1  ...    0-2    1\n",
              "\n",
              "[5 rows x 231 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-7BiENmeIrI",
        "outputId": "ad10eb98-73ec-48d4-e8d8-9c5fd3a9adf7"
      },
      "source": [
        "dimensao_dados(dados_idosos_uti)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O dataframe possui 19 linhas e 231 colunas.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWp76iWWeMpB"
      },
      "source": [
        "São **19 pacientes idosos** que foram admitidos no Hospital Sírio Libanês diretamente na UTI."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mUdD1-tnfBGu"
      },
      "source": [
        "# Conclusões\n",
        "\n",
        "Ao analisar, e visualizar, informações da base de dados do Hospital Sírio Libanês é possível tirar algumas conclusões:\n",
        "\n",
        "- A base de dados possui informações sobre 384 pacientes;\n",
        "> - 242 do sexo masculino;\n",
        "> - 142 do sexo feminino;\n",
        "> - 204 não são idosos;\n",
        "> - 180 são idosos;\n",
        "\n",
        "- Sobre a admissão de pacientes na UTI:\n",
        "> - É possível verificar nos dados a relação entre a idade do paciente e a necessidade de admissão na UTI. Conforme comprovado pela Organização Mundial de Saúde, pacientes idosos tem probabilidade maior de necessitarem de um leito de UTI ao contrair Covid-19.\n",
        "> - Não é possível apontar uma relação entre o gênero do paciente e a necessidade de um leito de UTI.\n",
        "\n",
        "A análise exploratória e visualização dos dados se encerra aqui. O próximo passo é o desenvolvimento de modelos de Machine Learning que predizem se um paciente necessitará de UTI ou não.\n",
        "\n",
        "As outras partes desse projeto podem ser vistas acessando os links abaixo:\n",
        "\n",
        "- Parte 1 - Tratamento dos dados\n",
        "- Parte 3 - Modelos de Machine Learning\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9iRxIGSrPtL"
      },
      "source": [
        "## Referências utilizadas durante o projeto\n",
        "\n",
        "1. [Página do Kaggle com informações sobre a base de dados](https://www.kaggle.com/S%C3%ADrio-Libanes/covid19)\n",
        "\n",
        "2. [Text classification with extremely small datasets](https://towardsdatascience.com/text-classification-with-extremely-small-datasets-333d322caee2)\n",
        "\n",
        "3. [5 Feature Selection Method From SciKit Learn You Should Know](https://towardsdatascience.com/5-feature-selection-method-from-scikit-learn-you-should-know-ed4d116e4172)\n",
        "\n",
        "4. [Como Selecionar As Melhores Features Para Seu Modelo de Machine Learning](https://paulovasconcellos.com.br/como-selecionar-as-melhores-features-para-seu-modelo-de-machine-learning-2e9df83d062a)\n",
        "\n",
        "5. [Machine Learning Map](https://scikit-learn.org/stable/tutorial/machine_learning_map/)\n",
        "\n",
        "6. [Primeiro caso de Covid-19 pode ter atingindo a Chine em outubro](https://www.cnnbrasil.com.br/saude/primeiro-caso-covid-19-pode-ter-atingido-a-china-em-outubro-de-2019-diz-estudo/)\n",
        "\n",
        "7. [Evolução dos Sintomas do Covid-19](https://saude.abril.com.br/medicina/casos-sem-sintomas-evolucoes-coronavirus/)\n",
        "\n",
        "8. [Covid Impact to Global Healthcare](https://edition.cnn.com/videos/world/2021/04/14/covid-impact-global-healthcare-lon-orig.cnn)\n",
        "\n",
        "9. [Observatório Covid-19 aponta maior colapso sanitário e hospitalar da história do Brasil](https://portal.fiocruz.br/noticia/observatorio-covid-19-aponta-maior-colapso-sanitario-e-hospitalar-da-historia-do-brasil)\n",
        "\n",
        "10. [Primeiro caso Covid-19 no Brasil](https://saude.abril.com.br/medicina/coronavirus-primeiro-caso-brasil/)\n",
        "\n",
        "11. [Overfitting e Underfitting em Machine Learning](https://abracd.org/overfitting-e-underfitting-em-machine-learning/#:~:text=Underfitting%20%C3%A9%20uma%20tradu%C3%A7%C3%A3o%20para,Overfitting%20%C3%A9%20o%20oposto)\n",
        "\n",
        "12. [WHO delivers advice and support for older people during Covid-19](https://www.who.int/news-room/feature-stories/detail/who-delivers-advice-and-support-for-older-people-during-covid-19#:~:text=The%20COVID%2D19%20pandemic,potential%20underlying%20health%20conditions)\n",
        "\n",
        "13. [Machine Learning in Healthcare](https://healthinformatics.uic.edu/blog/machine-learning-in-healthcare/)\n",
        "\n",
        "14. [Machine Learning](https://www.ibm.com/cloud/learn/machine-learning)\n",
        "\n",
        "15. [Hiperparameter Tuning](https://towardsdatascience.com/hyperparameter-tuning-c5619e7e6624)\n",
        "\n",
        "16. [SKLearn Model Selection - RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n",
        "\n",
        "17. [Entenda o que é AUC e ROC nos modelos de Machine Learning](https://medium.com/bio-data-blog/entenda-o-que-%C3%A9-auc-e-roc-nos-modelos-de-machine-learning-8191fb4df772)\n",
        "\n",
        "18. [Understanding Confusion Matrix](https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62)\n",
        "\n",
        "19. [Understanding a Classification Report For Your Machine Learning Model](https://medium.com/@kohlishivam5522/understanding-a-classification-report-for-your-machine-learning-model-88815e2ce397)\n",
        "\n",
        "20. [Understanding AUC ROC Curve](https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5)\n",
        "\n",
        "21. [Regressão Logística](https://pt.wikipedia.org/wiki/Regress%C3%A3o_log%C3%ADstica)\n",
        "\n",
        "22. [SciKit Learn - RandomForestClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)\n",
        "\n",
        "23. [SciKit Learn - Logistic Regression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n",
        "\n",
        "24. [SciKit Learn - SVC](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)\n",
        "\n",
        "25. [SciKit Learn - LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html)"
      ]
    }
  ]
}