Mod18_tarefa01.ipynb

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    "<img src=\"https://raw.githubusercontent.com/rhatiro/Curso_EBAC-Profissao_Cientista_de_Dados/main/ebac-course-utils/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
    "\n",
    "---\n",
    "\n",
    "# **Profissão: Cientista de Dados**\n",
    "### **Módulo 18** | Exercício 1 - Regressão Logística I\n",
    "\n",
    "Aluna [Lays Félix](https://www.linkedin.com/in/laysfelixbusiness)<br>\n",
    "Data: 21 de agosto de 2024\n",
    "\n",
    "---"
   ]
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    "Neste exercício vamos trabalhar com a base \"Heart Disease Data Set\" disponível no site do [UCI](https://archive.ics.uci.edu/ml/datasets/heart+disease).\n",
    "\n",
    "Temos 303 indivíduos e vamos utilizar 13 variáveis disponíveis para prever a presença de doença cardíaca na variável ```num```(a 14$^a$ variável).\n",
    "\n",
    "A descrição das variáveis está recortada abaixo:\n",
    "- age: idade do paciente em anos\n",
    "- sex: sexo (1 = male; 0 = female)  \n",
    "- cp: tipo de dor no peito\n",
    "  - 1: angina típica\n",
    "  - 2: angina atípica\n",
    "  - 3: dor não-angina\n",
    "  - 4: assintomático\n",
    "- trestbps: pressão sanguínea em repouso (em mm Hg na admissão ao hospital\n",
    "- chol: colesterol sérico em mg/dl\n",
    "- fbs: (açúcar no sangue em jejum > 120 mg/dl) (1 = True; 0 = False)\n",
    "- restecg: resultados eletrocardiográficos em repouso\n",
    "  - 0: normal\n",
    "  - 1: tendo anormalidade da onda ST-T (Inversões de onda T e / ou ST com elevação ou depressão de > 0.05 mV)\n",
    "  - 2: mostrando hipertrofia ventricular esquerda provável ou definitiva pelos critérios de Estes\n",
    "- thalach: frequência cardíaca máxima alcançada\n",
    "- exang: angina induzida por exercício(1 = sim; 0 = não)\n",
    "- oldpeak = Depressão de ST induzida por exercício em relação ao repouso\n",
    "- slope: Depressão de ST induzida por exercício em relação ao repouso\n",
    "  - 1: inclinação ascendente\n",
    "  - 2: estável\n",
    "  - 3: inclinação descendente\n",
    "- ca: número de vasos principais (0-3) coloridos por fluorosopia\n",
    "- thal: 3 = normal; 6 = defeito corrigido; 7 = defeito reversível\n",
    "- num: diagnóstico de doença cardíaga (status de doença angiográfica)"
   ]
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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
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       "      <td>41.0</td>\n",
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       "      <td>172.0</td>\n",
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       "    age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0  63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1  67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2  67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3  37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4  41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "\n",
       "   slope   ca thal  num  \n",
       "0    3.0  0.0  6.0    0  \n",
       "1    2.0  3.0  3.0    2  \n",
       "2    2.0  2.0  7.0    1  \n",
       "3    3.0  0.0  3.0    0  \n",
       "4    1.0  0.0  3.0    0  "
      ]
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   "source": [
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data'\n",
    "\n",
    "df = pd.read_csv(url, \n",
    "                 names=['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg',\n",
    "                        'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num'])\n",
    "df.head()"
   ]
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       "      <td>45.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
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       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>?</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "      age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0    63.0  1.0  1.0     145.0  233.0  1.0      2.0    150.0    0.0      2.3   \n",
       "1    67.0  1.0  4.0     160.0  286.0  0.0      2.0    108.0    1.0      1.5   \n",
       "2    67.0  1.0  4.0     120.0  229.0  0.0      2.0    129.0    1.0      2.6   \n",
       "3    37.0  1.0  3.0     130.0  250.0  0.0      0.0    187.0    0.0      3.5   \n",
       "4    41.0  0.0  2.0     130.0  204.0  0.0      2.0    172.0    0.0      1.4   \n",
       "..    ...  ...  ...       ...    ...  ...      ...      ...    ...      ...   \n",
       "298  45.0  1.0  1.0     110.0  264.0  0.0      0.0    132.0    0.0      1.2   \n",
       "299  68.0  1.0  4.0     144.0  193.0  1.0      0.0    141.0    0.0      3.4   \n",
       "300  57.0  1.0  4.0     130.0  131.0  0.0      0.0    115.0    1.0      1.2   \n",
       "301  57.0  0.0  2.0     130.0  236.0  0.0      2.0    174.0    0.0      0.0   \n",
       "302  38.0  1.0  3.0     138.0  175.0  0.0      0.0    173.0    0.0      0.0   \n",
       "\n",
       "     slope   ca thal  num  flag_doente  \n",
       "0      3.0  0.0  6.0    0            0  \n",
       "1      2.0  3.0  3.0    2            1  \n",
       "2      2.0  2.0  7.0    1            1  \n",
       "3      3.0  0.0  3.0    0            0  \n",
       "4      1.0  0.0  3.0    0            0  \n",
       "..     ...  ...  ...  ...          ...  \n",
       "298    2.0  0.0  7.0    1            1  \n",
       "299    2.0  2.0  7.0    2            1  \n",
       "300    2.0  1.0  7.0    3            1  \n",
       "301    2.0  1.0  3.0    1            1  \n",
       "302    1.0    ?  3.0    0            0  \n",
       "\n",
       "[303 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "df['flag_doente'] = (df['num'] != 0).astype('int64')\n",
    "df"
   ]
  },
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   "source": [
    "1. Monte uma bivariada para a variável ```sex``` com as seguintes estatísticas:\n",
    "\n",
    "- Número de *saudáveis*\n",
    "- Número de *doentes*\n",
    "- Total\n",
    "- Quantidade média de doentes\n",
    "- *Odds*\n",
    "- *Odds ratio* (Use o total como referência)\n",
    "- Logito (LOG(*Odds*))\n",
    "- O LOG do *Odds ratio* (com o total como referência) é chamado de *Weight of Evidence* (WOE). Calcule-o."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "      <th>flag_doente</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>doente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>doente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>feminino</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>45.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>doente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>68.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>4.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>doente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>57.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>doente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57.0</td>\n",
       "      <td>feminino</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>doente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>38.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>3.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>?</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age        sex   cp  trestbps   chol  fbs  restecg  thalach  exang  \\\n",
       "0    63.0  masculino  1.0     145.0  233.0  1.0      2.0    150.0    0.0   \n",
       "1    67.0  masculino  4.0     160.0  286.0  0.0      2.0    108.0    1.0   \n",
       "2    67.0  masculino  4.0     120.0  229.0  0.0      2.0    129.0    1.0   \n",
       "3    37.0  masculino  3.0     130.0  250.0  0.0      0.0    187.0    0.0   \n",
       "4    41.0   feminino  2.0     130.0  204.0  0.0      2.0    172.0    0.0   \n",
       "..    ...        ...  ...       ...    ...  ...      ...      ...    ...   \n",
       "298  45.0  masculino  1.0     110.0  264.0  0.0      0.0    132.0    0.0   \n",
       "299  68.0  masculino  4.0     144.0  193.0  1.0      0.0    141.0    0.0   \n",
       "300  57.0  masculino  4.0     130.0  131.0  0.0      0.0    115.0    1.0   \n",
       "301  57.0   feminino  2.0     130.0  236.0  0.0      2.0    174.0    0.0   \n",
       "302  38.0  masculino  3.0     138.0  175.0  0.0      0.0    173.0    0.0   \n",
       "\n",
       "     oldpeak  slope   ca thal  num flag_doente  \n",
       "0        2.3    3.0  0.0  6.0    0    saudável  \n",
       "1        1.5    2.0  3.0  3.0    2      doente  \n",
       "2        2.6    2.0  2.0  7.0    1      doente  \n",
       "3        3.5    3.0  0.0  3.0    0    saudável  \n",
       "4        1.4    1.0  0.0  3.0    0    saudável  \n",
       "..       ...    ...  ...  ...  ...         ...  \n",
       "298      1.2    2.0  0.0  7.0    1      doente  \n",
       "299      3.4    2.0  2.0  7.0    2      doente  \n",
       "300      1.2    2.0  1.0  7.0    3      doente  \n",
       "301      0.0    2.0  1.0  3.0    1      doente  \n",
       "302      0.0    1.0    ?  3.0    0    saudável  \n",
       "\n",
       "[303 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'] = df['sex'].replace({0: 'feminino', 1: 'masculino'})\n",
    "df['flag_doente'] = df['flag_doente'].replace({0: 'saudável', 1: 'doente'})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>flag_doente</th>\n",
       "      <th>doente</th>\n",
       "      <th>saudável</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>feminino</th>\n",
       "      <td>25</td>\n",
       "      <td>72</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>masculino</th>\n",
       "      <td>114</td>\n",
       "      <td>92</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>139</td>\n",
       "      <td>164</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "flag_doente  doente  saudável  All\n",
       "sex                               \n",
       "feminino         25        72   97\n",
       "masculino       114        92  206\n",
       "All             139       164  303"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_sexo = pd.crosstab(df['sex'], df['flag_doente'], margins= True)\n",
    "tab_sexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>flag_doente</th>\n",
       "      <th>doente</th>\n",
       "      <th>saudável</th>\n",
       "      <th>All</th>\n",
       "      <th>media_saudáveis</th>\n",
       "      <th>media_doentes</th>\n",
       "      <th>odds</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>LOG(Odds)</th>\n",
       "      <th>LOG(Odds_ratio)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></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>feminino</th>\n",
       "      <td>25</td>\n",
       "      <td>72</td>\n",
       "      <td>97</td>\n",
       "      <td>0.742268</td>\n",
       "      <td>0.257732</td>\n",
       "      <td>0.347222</td>\n",
       "      <td>0.409672</td>\n",
       "      <td>-1.057790</td>\n",
       "      <td>-0.892398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>masculino</th>\n",
       "      <td>114</td>\n",
       "      <td>92</td>\n",
       "      <td>206</td>\n",
       "      <td>0.446602</td>\n",
       "      <td>0.553398</td>\n",
       "      <td>1.239130</td>\n",
       "      <td>1.461996</td>\n",
       "      <td>0.214410</td>\n",
       "      <td>0.379802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>139</td>\n",
       "      <td>164</td>\n",
       "      <td>303</td>\n",
       "      <td>0.541254</td>\n",
       "      <td>0.458746</td>\n",
       "      <td>0.847561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.165392</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "flag_doente  doente  saudável  All  media_saudáveis  media_doentes      odds  \\\n",
       "sex                                                                            \n",
       "feminino         25        72   97         0.742268       0.257732  0.347222   \n",
       "masculino       114        92  206         0.446602       0.553398  1.239130   \n",
       "All             139       164  303         0.541254       0.458746  0.847561   \n",
       "\n",
       "flag_doente  odds_ratio  LOG(Odds)  LOG(Odds_ratio)  \n",
       "sex                                                  \n",
       "feminino       0.409672  -1.057790        -0.892398  \n",
       "masculino      1.461996   0.214410         0.379802  \n",
       "All            1.000000  -0.165392         0.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_sexo = pd.crosstab(df['sex'], df['flag_doente'], margins= True)\n",
    "\n",
    "tab_sexo['media_saudáveis'] = tab_sexo['saudável'] / tab_sexo['All']\n",
    "\n",
    "tab_sexo['media_doentes'] = tab_sexo['doente'] / tab_sexo['All']\n",
    "\n",
    "tab_sexo['odds'] = tab_sexo['doente'] / tab_sexo['saudável']\n",
    "\n",
    "tab_sexo['odds_ratio'] = tab_sexo['odds'] / tab_sexo.loc['All','odds']\n",
    "\n",
    "tab_sexo['LOG(Odds)'] = np.log(tab_sexo['odds'])\n",
    "\n",
    "tab_sexo['LOG(Odds_ratio)'] = np.log(tab_sexo['odds_ratio'])\n",
    "\n",
    "tab_sexo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Monte uma bivariada para a variável ```age``` com as seguintes estatísticas:\n",
    "- Categorize a variável em 5 grupos\n",
    "- Calcule as mesmas quantidades calculadas no execício anterior\n",
    "- Calcule também o valor médio da variável ```age```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>num</th>\n",
       "      <th>flag_doente</th>\n",
       "      <th>age_grupo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "      <td>59 a 69 anos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>doente</td>\n",
       "      <td>59 a 69 anos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
       "      <td>doente</td>\n",
       "      <td>59 a 69 anos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>masculino</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "      <td>29 a 39 anos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>feminino</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>saudável</td>\n",
       "      <td>39 a 49 anos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age        sex   cp  trestbps   chol  fbs  restecg  thalach  exang  \\\n",
       "0  63.0  masculino  1.0     145.0  233.0  1.0      2.0    150.0    0.0   \n",
       "1  67.0  masculino  4.0     160.0  286.0  0.0      2.0    108.0    1.0   \n",
       "2  67.0  masculino  4.0     120.0  229.0  0.0      2.0    129.0    1.0   \n",
       "3  37.0  masculino  3.0     130.0  250.0  0.0      0.0    187.0    0.0   \n",
       "4  41.0   feminino  2.0     130.0  204.0  0.0      2.0    172.0    0.0   \n",
       "\n",
       "   oldpeak  slope   ca thal  num flag_doente     age_grupo  \n",
       "0      2.3    3.0  0.0  6.0    0    saudável  59 a 69 anos  \n",
       "1      1.5    2.0  3.0  3.0    2      doente  59 a 69 anos  \n",
       "2      2.6    2.0  2.0  7.0    1      doente  59 a 69 anos  \n",
       "3      3.5    3.0  0.0  3.0    0    saudável  29 a 39 anos  \n",
       "4      1.4    1.0  0.0  3.0    0    saudável  39 a 49 anos  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print(df['age'].min())\n",
    "#print(df['age'].max())\n",
    "\n",
    "bins = [29, 39, 49, 59, 69, 77]\n",
    "labels = ['29 a 39 anos',\n",
    "          '39 a 49 anos',\n",
    "          '49 a 59 anos',\n",
    "          '59 a 69 anos',\n",
    "          '69 a 77 anos']\n",
    "df['age_grupo'] = pd.cut(df['age'], bins=bins, labels=labels, right=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>flag_doente</th>\n",
       "      <th>doente</th>\n",
       "      <th>saudável</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_grupo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29 a 39 anos</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39 a 49 anos</th>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49 a 59 anos</th>\n",
       "      <td>53</td>\n",
       "      <td>63</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59 a 69 anos</th>\n",
       "      <td>57</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69 a 77 anos</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>138</td>\n",
       "      <td>164</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "flag_doente   doente  saudável  All\n",
       "age_grupo                          \n",
       "29 a 39 anos       3         8   11\n",
       "39 a 49 anos      21        50   71\n",
       "49 a 59 anos      53        63  116\n",
       "59 a 69 anos      57        35   92\n",
       "69 a 77 anos       4         8   12\n",
       "All              138       164  302"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_age = pd.crosstab(df['age_grupo'], df['flag_doente'], margins= True)\n",
    "tab_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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>flag_doente</th>\n",
       "      <th>doente</th>\n",
       "      <th>saudável</th>\n",
       "      <th>All</th>\n",
       "      <th>media_saudáveis</th>\n",
       "      <th>media_doentes</th>\n",
       "      <th>odds</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>LOG(Odds)</th>\n",
       "      <th>LOG(Odds_ratio)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_grupo</th>\n",
       "      <th></th>\n",
       "      <th></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>29 a 39 anos</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.445652</td>\n",
       "      <td>-0.980829</td>\n",
       "      <td>-0.808217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39 a 49 anos</th>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>71</td>\n",
       "      <td>0.704225</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.499130</td>\n",
       "      <td>-0.867501</td>\n",
       "      <td>-0.694888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49 a 59 anos</th>\n",
       "      <td>53</td>\n",
       "      <td>63</td>\n",
       "      <td>116</td>\n",
       "      <td>0.543103</td>\n",
       "      <td>0.456897</td>\n",
       "      <td>0.841270</td>\n",
       "      <td>0.999770</td>\n",
       "      <td>-0.172843</td>\n",
       "      <td>-0.000230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59 a 69 anos</th>\n",
       "      <td>57</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>0.619565</td>\n",
       "      <td>1.628571</td>\n",
       "      <td>1.935404</td>\n",
       "      <td>0.487703</td>\n",
       "      <td>0.660316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69 a 77 anos</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.594203</td>\n",
       "      <td>-0.693147</td>\n",
       "      <td>-0.520534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>138</td>\n",
       "      <td>164</td>\n",
       "      <td>302</td>\n",
       "      <td>0.543046</td>\n",
       "      <td>0.456954</td>\n",
       "      <td>0.841463</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.172613</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "flag_doente   doente  saudável  All  media_saudáveis  media_doentes      odds  \\\n",
       "age_grupo                                                                       \n",
       "29 a 39 anos       3         8   11         0.727273       0.272727  0.375000   \n",
       "39 a 49 anos      21        50   71         0.704225       0.295775  0.420000   \n",
       "49 a 59 anos      53        63  116         0.543103       0.456897  0.841270   \n",
       "59 a 69 anos      57        35   92         0.380435       0.619565  1.628571   \n",
       "69 a 77 anos       4         8   12         0.666667       0.333333  0.500000   \n",
       "All              138       164  302         0.543046       0.456954  0.841463   \n",
       "\n",
       "flag_doente   odds_ratio  LOG(Odds)  LOG(Odds_ratio)  \n",
       "age_grupo                                             \n",
       "29 a 39 anos    0.445652  -0.980829        -0.808217  \n",
       "39 a 49 anos    0.499130  -0.867501        -0.694888  \n",
       "49 a 59 anos    0.999770  -0.172843        -0.000230  \n",
       "59 a 69 anos    1.935404   0.487703         0.660316  \n",
       "69 a 77 anos    0.594203  -0.693147        -0.520534  \n",
       "All             1.000000  -0.172613         0.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tab_age = pd.crosstab(df['age_grupo'], df['flag_doente'], margins= True)\n",
    "\n",
    "tab_age['media_saudáveis'] = tab_age['saudável'] / tab_age['All']\n",
    "\n",
    "tab_age['media_doentes'] = tab_age['doente'] / tab_age['All']\n",
    "\n",
    "tab_age['odds'] = tab_age['doente'] / tab_age['saudável']\n",
    "\n",
    "tab_age['odds_ratio'] = tab_age['odds'] / tab_age.loc['All','odds']\n",
    "\n",
    "tab_age['LOG(Odds)'] = np.log(tab_age['odds'])\n",
    "\n",
    "tab_age['LOG(Odds_ratio)'] = np.log(tab_age['odds_ratio'])\n",
    "\n",
    "tab_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.43894389438944\n"
     ]
    }
   ],
   "source": [
    "print(df['age'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Visualize a informação da bivariada construída acima através de um gráfico da sua escolha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age_grupo'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(tab_age[['doente', 'saudável']]\n",
    " .drop('All')\n",
    " .plot.bar(rot=0, figsize=(10, 5), zorder=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(tab_age[['media_saudáveis', 'media_doentes', 'odds', 'odds_ratio', 'LOG(Odds)', 'LOG(Odds_ratio)']]\n",
    " .drop('All')\n",
    " .plot.bar(rot=0, figsize=(10, 5), zorder=2)\n",
    " .grid(zorder=1, color='grey', axis='y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}