ResumosEstatisticos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X0p4kq0U2Yi1"
   },
   "source": [
    "# Resumos Estatísticos dos Dados\n",
    "\n",
    "Fonte de dados: Painel de Precificação da ANS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "eHkNZ8WQ7uwd"
   },
   "outputs": [],
   "source": [
    "# Compartilhamento de arquivo no Drive\n",
    "id_link = \"XXXXXXXXXXXXXXXXXXXXXX\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWBeG5r9_WyH"
   },
   "source": [
    "## Bibliotecas utilizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JzqdNSNV2MVA",
    "outputId": "67e78381-3ea8-4410-f4ce-0eb962dd3089"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Mentoria DSA 2021 - Turma 43\n",
      "\n",
      "matplotlib: 3.2.2\n",
      "IPython   : 5.5.0\n",
      "numpy     : 1.19.5\n",
      "pandas    : 1.1.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# Versões dos pacotes usados neste jupyter notebook\n",
    "\n",
    "%reload_ext watermark\n",
    "%watermark -a \"Mentoria DSA 2021 - Turma 43\" --iversions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "QYocDMkU4Uvi"
   },
   "outputs": [],
   "source": [
    "!pip install -U -q PyDrive\n",
    "from pydrive.auth import GoogleAuth\n",
    "from pydrive.drive import GoogleDrive\n",
    "from google.colab import auth\n",
    "from oauth2client.client import GoogleCredentials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NfedQ6uU_dUK"
   },
   "source": [
    "## Carregamento dos dados do Google Drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Xs8VGif6_pq4"
   },
   "outputs": [],
   "source": [
    "# Authenticate and create the PyDrive client.\n",
    "auth.authenticate_user()\n",
    "gauth = GoogleAuth()\n",
    "gauth.credentials = GoogleCredentials.get_application_default()\n",
    "drive = GoogleDrive(gauth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "kj4mZHZz4Ywt"
   },
   "outputs": [],
   "source": [
    "downloaded = drive.CreateFile({'id':id_link}) \n",
    "downloaded.GetContentFile('dfPrecoMensal.csv')  \n",
    "dfDados = pd.read_csv('dfPrecoMensal.csv', sep = \",\", decimal='.', error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "Ly5gmbrA5yvS",
    "outputId": "e2a3b158-cf5b-45be-8d0a-c0fc24538e0d"
   },
   "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></th>\n",
       "      <th>contratacao</th>\n",
       "      <th>segmentacao</th>\n",
       "      <th>in_obstetricia</th>\n",
       "      <th>cobertura</th>\n",
       "      <th>abrangencia</th>\n",
       "      <th>fator</th>\n",
       "      <th>acomodacao</th>\n",
       "      <th>internacao</th>\n",
       "      <th>cd_faixa_etaria</th>\n",
       "      <th>nm_regiao</th>\n",
       "      <th>mensalidade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Individual ou familiar</td>\n",
       "      <td>Ambulatorial e Hospitalar</td>\n",
       "      <td>1</td>\n",
       "      <td>Médico-hospitalar</td>\n",
       "      <td>Nacional</td>\n",
       "      <td>Co-participação</td>\n",
       "      <td>Coletiva</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>1</td>\n",
       "      <td>Sul</td>\n",
       "      <td>221.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Individual ou familiar</td>\n",
       "      <td>Ambulatorial e Hospitalar</td>\n",
       "      <td>1</td>\n",
       "      <td>Médico-hospitalar</td>\n",
       "      <td>Nacional</td>\n",
       "      <td>Co-participação</td>\n",
       "      <td>Coletiva</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>2</td>\n",
       "      <td>Sul</td>\n",
       "      <td>232.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Individual ou familiar</td>\n",
       "      <td>Ambulatorial e Hospitalar</td>\n",
       "      <td>1</td>\n",
       "      <td>Médico-hospitalar</td>\n",
       "      <td>Nacional</td>\n",
       "      <td>Co-participação</td>\n",
       "      <td>Coletiva</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>3</td>\n",
       "      <td>Sul</td>\n",
       "      <td>255.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Individual ou familiar</td>\n",
       "      <td>Ambulatorial e Hospitalar</td>\n",
       "      <td>1</td>\n",
       "      <td>Médico-hospitalar</td>\n",
       "      <td>Nacional</td>\n",
       "      <td>Co-participação</td>\n",
       "      <td>Coletiva</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>4</td>\n",
       "      <td>Sul</td>\n",
       "      <td>288.393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Individual ou familiar</td>\n",
       "      <td>Ambulatorial e Hospitalar</td>\n",
       "      <td>1</td>\n",
       "      <td>Médico-hospitalar</td>\n",
       "      <td>Nacional</td>\n",
       "      <td>Co-participação</td>\n",
       "      <td>Coletiva</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>5</td>\n",
       "      <td>Sul</td>\n",
       "      <td>332.759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              contratacao                segmentacao  ...  nm_regiao mensalidade\n",
       "0  Individual ou familiar  Ambulatorial e Hospitalar  ...        Sul     221.837\n",
       "1  Individual ou familiar  Ambulatorial e Hospitalar  ...        Sul     232.932\n",
       "2  Individual ou familiar  Ambulatorial e Hospitalar  ...        Sul     255.115\n",
       "3  Individual ou familiar  Ambulatorial e Hospitalar  ...        Sul     288.393\n",
       "4  Individual ou familiar  Ambulatorial e Hospitalar  ...        Sul     332.759\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ng8RBM7Y_G9d",
    "outputId": "2d846415-d5cd-45a5-dd2d-fbd44f72a732"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100886, 11)"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfDados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KtVfHy5p_vjV",
    "outputId": "9168ff48-72fa-4d34-dccd-e86e70f67b5d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100886 entries, 0 to 100885\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   contratacao      100886 non-null  object \n",
      " 1   segmentacao      100886 non-null  object \n",
      " 2   in_obstetricia   100886 non-null  int64  \n",
      " 3   cobertura        100886 non-null  object \n",
      " 4   abrangencia      100886 non-null  object \n",
      " 5   fator            100886 non-null  object \n",
      " 6   acomodacao       100886 non-null  object \n",
      " 7   internacao       100886 non-null  object \n",
      " 8   cd_faixa_etaria  100886 non-null  int64  \n",
      " 9   nm_regiao        100886 non-null  object \n",
      " 10  mensalidade      100886 non-null  float64\n",
      "dtypes: float64(1), int64(2), object(8)\n",
      "memory usage: 8.5  MB\n"
     ]
    }
   ],
   "source": [
    "dfDados.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "COHoebqWAMmN"
   },
   "source": [
    "## Análise Exploratória"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6dw3kv9QALps",
    "outputId": "0e036a37-bcb0-408f-f0f2-d6292141a814"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contratacao        0\n",
       "segmentacao        0\n",
       "in_obstetricia     0\n",
       "cobertura          0\n",
       "abrangencia        0\n",
       "fator              0\n",
       "acomodacao         0\n",
       "internacao         0\n",
       "cd_faixa_etaria    0\n",
       "nm_regiao          0\n",
       "mensalidade        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificação de valores missings (ausentes)\n",
    "dfDados.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "FsflEc5gA9e8"
   },
   "outputs": [],
   "source": [
    "# Conversão atributos em dados categóricos, com exceção do valor do plano\n",
    "for c in dfDados.columns:\n",
    "  if dfDados[c].dtype != \"float64\":\n",
    "    dfDados[c] = dfDados[c].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tuH-iQI2DyMW",
    "outputId": "d0f572de-ff4f-4504-95dc-87c416dd9fd4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100886 entries, 0 to 100885\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count   Dtype   \n",
      "---  ------           --------------   -----   \n",
      " 0   contratacao      100886 non-null  category\n",
      " 1   segmentacao      100886 non-null  category\n",
      " 2   in_obstetricia   100886 non-null  category\n",
      " 3   cobertura        100886 non-null  category\n",
      " 4   abrangencia      100886 non-null  category\n",
      " 5   fator            100886 non-null  category\n",
      " 6   acomodacao       100886 non-null  category\n",
      " 7   internacao       100886 non-null  category\n",
      " 8   cd_faixa_etaria  100886 non-null  category\n",
      " 9   nm_regiao        100886 non-null  category\n",
      " 10  mensalidade      100886 non-null  float64 \n",
      "dtypes: category(10), float64(1)\n",
      "memory usage: 1.7 MB\n"
     ]
    }
   ],
   "source": [
    "dfDados.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0KsvLUhiFeMB"
   },
   "source": [
    "### Variável Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gewKO_b_Ewee",
    "outputId": "05835ece-685a-4fa0-b24a-9e8e950fca0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100886.000000\n",
       "mean        501.571873\n",
       "std         563.642598\n",
       "min          11.950000\n",
       "25%         200.921000\n",
       "50%         339.076500\n",
       "75%         863.062000\n",
       "max       13739.635000\n",
       "Name: mensalidade, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dfDados['mensalidade'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "Exj3C855FiYR",
    "outputId": "d7848a0e-ef2d-46fe-d20d-fdc7ddab0cf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histograma - Mensalidade')"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmIAAAHnCAYAAAAbwzEQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy WH4yJAAAgAElEQVR4nO3de7jcVX3v8fdXAqigEIqmGGiDFduiVIQUqBfcoEIAa7SPx4JUQLBohR5UnlOjtkJFKpxWbVGrjSUFWzBYxEIFixTZ9WgLchG5aokQJCkXNVwMXjD6PX stWEymcmeZO/J2nvn/XqeeWZm/dbvMt/925lP1u yIzORJEnSpvek1hsgSZK0uTKISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMWkSRERGxGjr7ZCGKSJG6r5 alf7aERs0L2QNsXvTEQsj4jlw1yHNFEGMamqXwzr/TKp/7BnRMybxPXOq8s8Z7KWqckXEceM7SMR8ZX19JsXEb8YZH SpFmtN0CaIX4T FHrjdAmsQZ4aUT8emZ u8f0NwNR 20u/8YeBTy19UZI05EjYtIkyMxvZeZ3W2 HNokv1Oc3d0 IiC2ANwHXAvdvyo1qKTO/m5nfar0d0nRkEJMmQa/zXSLiaRHxZxFxS0Q8EhE/jIjvRMQFEbF37XMqcFed5eiOQ18ZEcd0LOtJEfHWiLg2IlZHxKP19R9FRM/f44g4MiJuiIgfR8QDEfGPEfGsXufzdJ77ExH7RMSlEbGq8zBsRBwQEYsj4rb6eX5cP9spEfHkHus/tc4/EhFHRMT1EfGjiPifiPhwRGxd x1Yt mRiHiwbucv9VjeBq1/iG4F/ovy89qya9phwLOAT61vARGxb0RcGBH3RcRjEXFPRPxdRDyrR9/RWsdZEfGeiLgjIn5a5zkzIrbqMc9LI JfI2JF7XtfRFwdEad09XtuRJwREddFxPdq37trnXcetCC99qnavlX9HfhOXfZdEfGBsZ99j/7Pioj3RcTXOmrzPxFxfkTs3meeiIgTI LWiPhJRKyMiI9FxHbjbPMREXFVRDxU57s9Iv6037ZJw7K5DJtLm1REBPBvwIsoX9p/TzlUtTNwAPD/gOuBUWB74CTgm8C/dCzmxo7X/wi8AbinLiuB1wJ/C7wEOLJr/X8CnAk8CJwLPAy8Evhafd3P7wDvBr4KLAF2BB6r094F/Abwn8ClwJOBFwOnAiMR8YrM/HmPZf4xcEj9bKPAQcA7gB0i4mJgaV3e4lqvP6jrPaRrORu7/mH4FKU C4ELO9r/EFgNfAY4pcd8RMSxlM/6U ASys90N8oI2 9GxH59RlfPB14KfBF4BDgU BPgmZRRuLHlL6DU55G6/JXADpTD528D/rxjmb8HvBW4ilLXx4DndWzL/MxcOUhBenzOAD5LqdF3gI8BWwHHAnv0mW1/YFHdns9Rarkb8Drg1RHx4sz8Ztc8fw38b BeSl1/Vte5b13fY139iYgllJqtqOt5CNgPOA14eUS8MjPXbMznljZYZvrw4SMTSrhJyhd7v8dDtc 8HvOOdrzfo7Z9vsd6ngTM7ng/r/Y9p892HVGn3wBs29G DXBdnfaGjvZnU76Mvgfs0tEelICQ5Vd/rXWMdHz t/TZjmcD0aP9tDrf73e1n1rbHwZ s6N9a8qo0s BHwAv66rNFXW PSey/iHsH8fU9Xyg1v5h4PKO6XMpYftT9f2KHnV LiUYLAPmdk17ea3J57vaR t6rwd26Pr5L6vz/HJH  dq/xf0 Aw7dr2fC2zdo99Bdbmf6LOfnNprG7va3lD7/hfw5I72HSjBbK3fmTrtmcDTemzPCyih7Itd7S qy1nWVZsn1/UmsLzPz/Ei4Cl99tmThrkv fDR fDQpLSuU9bzWO/hjh5 3N2Qmb/IzAc3YBnH1udFmbm6YzmPUkaJYO3zld5AGe3 aGbe09E/KaMN6xs1ujEz/67XhMy8sy6j20fq88F9lnlWZt7esZyfAhdQQtelmfkfHdN AfxTffuCSVr/pKu1Px94ZTxxBe2xwBas/7DkHwFbUr7o1xppyswrKSNYvxsRT sx77syc1XXNpxHqeP8Hv177Xvf73q/sv48uvt9iRKWJ1LTsVG692TmTzqWvYoSnteRmQ9k5g97tH8T DJwQNfh4LF1nN5Vm59QRnZ7OYkSmI/NzO4anUb5z8GR68wlDYmHJqUumRn9pkW5J9GvDrCY2yiHFo IiF8FLqYc7rsuM9c5VDKOvYBfUEYduv0HJVi9sKNt7PVXuztn5t0RcQ9lFK6Xr/fbiIjYhvIl9lrKyM7TKKNsY b2mfW6Hm3/U5 v7zFtLKCsdY7SBNa/lui6B1Z1TmYuH2T Dp iHNY7rp57dRxwU2b2rSHl0C/AyyLit3tMfyYlzD2XdWvTq45jQXt2R9t5lEOO10TEBZTDfF/LzBXdM9fDh0dSRoleUJezRUeXDd1XO43tt vsh/Tel8e26TBKXedTDlF3f0/tSDkMObYOKL8H3b5K1386IuKplM/5feDt5eOv46eUw7jSJmEQk4YgM38eEQcC76Oc33JmnfTDiDgXeHfn6NY4tgNW9QpwmbkmIr5P QLv7A/9r9q7n/5B7L5ejXUU4svAPsAtlBGt71EOgUIZLex3knOvc9LWDDDt8ZGPCa6/W69zt0aB5QPOD0Bm3hARN1BGZa6mBPQ/Hme2sYsQ/s84/bbtsb6HevQbq9UWHf0uiohXASdTRuneAhAR11P2uys65v8w8HZKsLmcEoLHRomOYbD/dPQztt/ rMe0fvvZSZRzvh6kHKL LuW2MAm8hhKiOn/Offf1jt NTrMp4f0Z9DmHT9rUDGLSkNTDj 8A3hERzwFeRvlSPJFygv4bB1zUw5QT27fs/lKLiFmUEYJHOprHXs hHF7qNmd9m92nfSElBJ2TmW/qnBAROzH8L7VJW//6Rjw3wmLgk/XxY544rNrPWPDcLjMfWW/PCcjMS4FL6yjivsCrKIdFvxARL8zM2yLimZST3G8BXtR9SDAijpjgZvTdb4Ff7u5c9 VTKSFtr8y8t2v673TPwxP1nAPc2WN5O1LO1evu/43M3AtpCvAcMWkTyMxlmXk2JYytpgSLMWOHT7ZYZ8biG5Tf1f17TNu/zndDV38oV1OupR4m3WXwLX/cc rzRT2mvWwjljfd1t/P cCjlMOo/9xn1KrT1fX5pUPdqiozH83ML2fmO4G/oFxFOHY16rMp 9WXeoSwnev0ibihLn d/ZBy0n 3HSn/QfnPHiFsW544DNm9Dui9D7yErt pOgp9K/C8iNhhfRsvbSoGMWkIImLXiOj1RTabcmil8yThBykjUb/SZ3FL6vMH6zkuY t4KnBGfXt2R//zKYes/jgidunoH8AH6R/41md5fR7pbKyf8czuzkPQev091QCzgHLe2p8OMMvHKIdTPxIRz 2eWO 7NaGQFhH719GgbmMjoWN/AWJ5fX5JlBvRjs2/LeX8t4keMfmH nx6dNznrQagXrV6oG7b3nUbxvpvCfwNJah1O6c v7czWNX1fbDPdn2YEkiXRMT23RMjYnZEOFqmTcZDk9JwvAC4KCKuBW6nnJz DMpI2JZ0hIfMXB0R11D bM55wH9TRskuycybMvP8iFgIvB64NSL hSfOmdkVuCAzz tY3nci4n2UEZBv1hO2x 4jtgPlfmW/tYGf518ptwh4Z0TsQRl1 xXKIa9L6R8iJ0vr9feVmb1ORu/X91v1PmJLKD/Lf6P8vLekfIaXUs59 40JbNJZwNyI BolbD0G7A0cCNxNuW8bmXlfRCwFDgdujIgvUc65eiXwE8rFJntOYDs A/w 8GrglnrPuC0p50xeC/xaZ fM/EVEnEW5svfm2n8ryn33dqBcdHBA1zxfi4iPUs7NuyUiLuSJ 4g9yBMn9XfOsyTKDZXfBnwnIi6nnIu2A X3aX9KiHzrBD67NDBHxKThuI4yWrWGMmJyMuWQ0PXAoZn54a7 b6QEigWU851OY 1DMUcAJ1AurX8L5UviQcr5Zm/oXnlmfpDy9//uppxMfhwlEL6Y8h wDTo/qd4q4UDKaNvzKOcW/Vbdzj/YkGVtjNbrn0yZ U UYHQe5TOcSPkMz6HcHPZtE1zFXwD/zhM3Zn0rZTTsL4Df7rp1ynG1/SmU/etgyp9wehHrv/HvuOqtRv4XZX9 EuVzvpoScl7fZ7Y/o/yu/Jiyn/8e5XdpH0pY6uUkShB7uM5zBOXCg1fQ56rPzDwB F3KvcZeAbyzbtt2wF9SLhiQNonofVseSTNRRDydcoXZjZnZ6 RnSdIm5IiYNANFxDO6bnw5dhXZhyh3Hf98kw2TJK3FETFpBoqItwLvpxyiuody/sv lBuF3ki5XcE6d16XJG1anqwvzUzXUO4svj9P3ET0LuB04ExDmCRNDY6ISZIkNeI5YpIkSY1M20OTO 64Y86bN2 o63j00UfZZptthrqOmcA6jc8aDcY6DcY6DcY6DcY6DWaidbr  uu/n5nP6G6ftkFs3rx5XHfddUNdx 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 MGsYjYJSKuiojbIuLWiDiptp8aESsj4sb6OLRjnndHxLKI HZEHNzRvqC2LYuIRR3tu0bENbX9gojYarI/qCRJ0lQzyIjYGuDkzNwd2A84ISJ2r9M kpl71sdlAHXa4cDzgAXA30bEFhGxBfBx4BBgd CIjuWcWZf1HOBB4LhJ nySJElT1rhBLDPvzcwb6usfArcDc9czy0JgaWb NDPvApYB 9THssy8MzMfA5YCCyMigAOBC v85wKv2dgPJEmSNF1EZg7eOWIe8BXg cA7gWOAR4DrKKNmD0bEx4CrM/Of6jxnA1 si1iQmW u7W8E9gVOrf2fU9t3Ab6Ymc/vsf7jgeMB5syZs/fSpUs37NNuoNWrV7Ptttuu1XbzyofX6bfH3O2Guh1TXa86aW3WaDDWaTDWaTDWaTDWaTATrdMBBxxwfWbO726fNegCImJb4HPA2zPzkYj4BHAakPX5Q8CxG72FA8jMxcBigPnz5 fIyMgwV8fo6Cjd6zhm0aXr9Ft 5HC3Y6rrVSetzRoNxjoNxjoNxjoNxjoNZlh1GiiIRcSWlBB2XmZeBJCZ93dM/xTwhfp2JbBLx w71zb6tP8A2D4iZmXmmq7 kiRJM9YgV00GcDZwe2Z uKN9p45urwVuqa8vAQ6PiK0jYldgN DrwLXAbvUKya0oJ/RfkuXY6FXA6 r8RwMXT xjSZIkTX2DjIi9GHgjcHNE3Fjb3kO56nFPyqHJ5cBbADLz1oj4LHAb5YrLEzLz5wARcSJwObAFsCQzb63LexewNCI AHyDEvwkSZJmtHGDWGZ FYgeky5bzzynA6f3aL 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 z67tERFnRcSyiLgpIvbqWNbRtf8dEXF0R/veEXFzneesiIhhfFhJkqSpZJARsTXAyZm5O7AfcEJE7A4sAq7MzN2AK t7gEOA3erjeOATUIIbcAqwL7APcMpYeKt9/rBjvgUT/2iSJElT27hBLDPvzcwb6usfArcDc4GFwLm127nAa rrhcCns7ga2D4idgIOBq7IzFWZ SBwBbCgTnt6Zl6dmQl8umNZkiRJM9YGnSMWEfOAFwLXAHMy89466T5gTn09F7inY7YVtW197St6tEuSJM1oswbtGBHbAp8D3p6Zj3SexpWZGRE5hO3r3objKYc7mTNnDqOjo0Nd3 rVq9dZx8l7rFmn37C3Y6rrVSetzRoNxjoNxjoNxjoNxjoNZlh1GiiIRcSWlBB2XmZeVJvvj4idMvPeenjxgdq EtilY/ada9tKYKSrfbS279yj/zoyczGwGGD /Pk5MjLSq9ukGR0dpXsdxyy6dJ1 y48c7nZMdb3qpLVZo8FYp8FYp8FYp8FYp8EMq06DXDUZwNnA7Zn54Y5JlwBjVz4eDVzc0X5UvXpyP DhegjzcuCgiJhdT9I/CLi8TnskIvar6zqqY1mSJEkz1iAjYi8G3gjcHBE31rb3AGcAn42I44C7gdfXaZcBhwLLgB8BbwLIzFURcRpwbe33/sxcVV /DTgHeArwxfqQJEma0cYNYpn5VaDffb1e3qN/Aif0WdYSYEmP9uuA54 3LZIkSTOJd9aXJElqxCAmSZLUiEFMkiSpkYHvI6b 5vW6pcUZhzXYEkmSNJ04IiZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjYwbxCJiSUQ8EBG3dLSdGhErI LG ji0Y9q7I2JZRHw7Ig7uaF9Q25ZFxKKO9l0j4prafkFEbDWZH1CSJGmqGmRE7BxgQY/2j2TmnvVxGUBE7A4cDjyvzvO3EbFFRGwBfBw4BNgdOKL2BTizLus5wIPAcRP5QJIkSdPFuEEsM78CrBpweQuBpZn508y8C1gG7FMfyzLzzsx8DFgKLIyIAA4ELqzznwu8ZgM/gyRJ0rQ0kXPEToyIm qhy9m1bS5wT0efFbWtX/svAQ9l5pqudkmSpBlv1kbO9wngNCDr84eAYydro/qJiOOB4wHmzJnD6OjoUNe3evXqddZx8h5renfuMuxtm0p61Ulrs0aDsU6DsU6DsU6DsU6DGVadNiqIZeb9Y68j4lPAF rblcAuHV13rm30af8BsH1EzKqjYp39e613MbAYYP78 TkyMrIxmz w0dFRutdxzKJLB5p3 ZEj4/aZKXrVSWuzRoOxToOxToOxToOxToMZVp026tBkROzU8fa1wNgVlZcAh0fE1hGxK7Ab8HXgWmC3eoXkVpQT i/JzASuAl5X5z8auHhjtkmSJGm6GXdELCI A4wAO0bECuAUYCQi9qQcmlwOvAUgM2 NiM8CtwFrgBMy8 d1OScClwNbAEsy89a6incBSyPiA8A3gLMn7dNJkiRNYeMGscw8okdz37CUmacDp/dovwy4rEf7nZSrKiVJkjYr3llfkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqZGN qPfm4ubVz488B/5liRJ2lCOiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKmRWa03YKaat jSddqWn3FYgy2RJElTlSNikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGhk3iEXEkoh4ICJu6WjbISKuiIg76vPs2h4RcVZELIuImyJir455jq7974iIozva946Im s8Z0VETPaHlCRJmooGGRE7B1jQ1bYIuDIzdwOurO8BDgF2q4/jgU9ACW7AKcC wD7AKWPhrfb5w475utclSZI0I40bxDLzK8CqruaFwLn19bnAazraP53F1cD2EbETcDBwRWauyswHgSuABXXa0zPz6sxM4NMdy5IkSZrRNvYcsTmZeW99fR8wp76eC9zT0W9FbVtf 4oe7ZIkSTPerIkuIDMzInIyNmY8EXE85ZAnc bMYXR0dKjrm/MUOHmPNZO2vGFvbyurV6 esZ9tslijwVinwVinwVinwVinwQyrThsbxO6PiJ0y8956ePGB2r4S2KWj3861bSUw0tU Wtt37tG/p8xcDCwGmD9/fo6MjPTrOik et7FfOjmCWfVxy0/cmTSljWVjI6OMuyfxXRnjQZjnQZjnQZjnQZjnQYzrDpt7KHJS4CxKx PBi7uaD qXj25H/BwPYR5OXBQRMyuJ kfBFxepz0SEfvVqyWP6liWJEnSjDbucE9EfIYymrVjRKygXP14BvDZiDgOuBt4fe1 GXAosAz4EfAmgMxcFRGnAdfWfu/PzLELAN5GuTLzKcAX60OSJGnGGzeIZeYRfSa9vEffBE7os5wlwJIe7dcBzx9vOyRJkmYa76wvSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSI7Nab8DmZN6iS9dpW37GYQ22RJIkTQWOiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRgxikiRJjRjEJEmSGjGISZIkNWIQkyRJasQgJkmS1IhBTJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpEYOYJElSIwYxSZKkRiYUxCJieUTcHBE3RsR1tW2HiLgiIu6oz7Nre0TEWRGxLCJuioi9OpZzdO1/R0QcPbGPJEmSND1MxojYAZm5Z2bOr 8XAVdm5m7AlfU9wCHAbvVxPPAJKMENOAXYF9gHOGUsvEmSJM1ks4awzIXASH19LjAKvKu2fzozE7g6IraPiJ1q3ysycxVARFwBLAA M4Rtm3LmLbq0Z/vyMw7bxFsiSZI2tSi5aCNnjrgLeBBI4O8yc3FEPJSZ29fpATyYmdtHxBeAMzLzq3XalZSANgI8OTM/UNv/DPhxZv5Vj/UdTxlNY86cOXsvXbp0o7d9EA sepj7fzzUVfS1x9zt2qx4I6xevZptt9229WZMadZoMNZpMNZpMNZpMNZpMBOt0wEHHHB9x9HDx010ROwlmbkyIp4JXBER3 qcmJkZERuf9Lpk5mJgMcD8 fNzZGRkshbd00fPu5gP3TyMQcPxLT9ypMl6N8bo6CjD/llMd9ZoMNZpMNZpMNZpMNZpMMOq04TOEcvMlfX5AeDzlHO87q HHKnPD9TuK4FdOmbfubb1a5ckSZrRNjqIRcQ2EfG0sdfAQcAtwCXA2JWPRwMX19eXAEfVqyf3Ax7OzHuBy4GDImJ2PUn/oNomSZI0o03kuNsc4PPlNDBmAedn5r9FxLXAZyPiOOBu4PW1/2XAocAy4EfAmwAyc1VEnAZcW/u9f zEfUmSpJlso4NYZt4JvKBH 8KlWl0AAAjkSURBVA Al/doT CEPstaAizZ2G2RJEmajryzviRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUiEFMkiSpkVmtN0C9zVt06Tpty884rMGWSJKkYXFETJIkqRGDmCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmNGMQkSZIaMYhJkiQ1YhCTJElqxCAmSZLUyKzWG6DBzVt06Tpty884rMGWSJKkyeCImCRJUiMGMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrE21dMc97SQpKk6csRMUmSpEYMYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIt6 YgbylhSRJ04MjYpIkSY0YxCRJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRb1 xmfCWFpIkTT2OiEmSJDViEJMkSWrEICZJktSI54htxjxvTJKkthwRkyRJasQRMa3FUTJJkjYdR8QkSZIacURM43KUTJKk4XBETJIkqRFHxLRROkfJTt5jDcf0GDUDR84kSVqfKTMiFhELIuLbEbEsIha13h5JkqRhmxIjYhGxBfBx4JXACuDaiLgkM29ru2WaqF7nl/XSa Ss37yOskmSZoopEcSAfYBlmXknQEQsBRYCBrHNxKCBbUP6GtgkSVPdVAlic4F7Ot6vAPZttC2aITYk3A3b s6jG89UGy30KlpJmjyRma23gYh4HbAgM99c378R2DczT zqdzxwfH3768C3h7xpOwLfH/I6ZgLrND5rNBjrNBjrNBjrNBjrNJiJ1ulXM/MZ3Y1TZURsJbBLx/uda9taMnMxsHhTbVREXJeZ8zfV qYr6zQ azQY6zQY6zQY6zQY6zSYYdVpqlw1eS2wW0TsGhFbAYcDlzTeJkmSpKGaEiNimbkmIk4ELge2AJZk5q2NN0uSJGmopkQQA8jMy4DLWm9Hl012GHSas07js0aDsU6DsU6DsU6DsU6DGUqdpsTJ pIkSZujqXKOmCRJ0mbHINbD5v7nliJil4i4KiJui4hbI Kk2r5DRFwREXfU59m1PSLirFqvmyJir45lHV373xERR7f6TMMSEVtExDci4gv1/a4RcU2txQX14hMiYuv6flmdPq9jGe u7d OiIPbfJLhiojtI LCiPhWRNweEb/j/rS2iHhH/X27JSI ExFPdn8qImJJRDwQEbd0tE3a/hMRe0fEzXWesyIiNu0nnLg NfrL jt3U0R8PiK275jWcz/p9/3Xb1 cbnrVqWPayRGREbFjfb9p9qXM9NHxoFws8B3g2cBWwDeB3Vtv1yauwU7AXvX104D/BnYH/i wqLYvAs6srw8FvggEsB9wTW3fAbizPs ur2e3/nyTXKt3AucDX6jvPwscXl9/Evij vptwCfr68OBC rr3es tjWwa933tmj9uYZQp3OBN9fXWwHbuz tVZ 5wF3AUzr2o2Pcnx6vz/7AXsAtHW2Ttv8AX699o857SOvPPEk1OgiYVV f2VGjnvsJ6/n 67cvTrdHrzrV9l0oFwzeDey4KfclR8TW9fifW8rMx4CxP7e02cjMezPzhvr6h8DtlC KhZQvVOrza rrhcCns7ga2D4idgIOBq7IzFWZ SBwBbBgE36UoYqInYHDgL v7wM4ELiwdumu0VjtLgReXvsvBJZm5k8z8y5gGWUfnDEiYjvKP35nA2TmY5n5EO5P3WYBT4mIWcBTgXtxfwIgM78CrOpqnpT9p057emZeneWb9NMdy5o2etUoM7 UmWvq26sp9 iE/vtJz  /cf5tm1b67EsAHwH BOg8cX6T7EsGsXX1 nNLcxttS3P1kMcLgWuAOZl5b510HzCnvu5Xs5ley7 m/OL or7/JeChjn/4Oj/v47Wo0x u/Wd6jaD8j/t7wD9EOYz79xGxDe5Pj8vMlcBfAd lBLCHgetxf1qfydp/5tbX3e0zzbGUERrY8Bqt79 2aS8iFgIrM/ObXZM2yb5kEFNfEbEt8Dng7Zn5SOe0mvY320tuI JVwAOZeX3rbZkGZlEOBXwiM18IPEo5lPQ496eYTfnf967As4BtmFmjfUO1ue8/44mI9wJrgPNab8tUExFPBd4DvK/VNhjE1jXQn1ua6SJiS0oIOy8zL6rN99ehV rzA7W9X81mci1fDLw6IpZThu8PBP6GMnQ9dn  zs/7eC3q9O2AHzCzazRmBbAiM6 p7y kBDP3pye8ArgrM7 XmT8DLqLsY 5P/U3W/rOSJw7ZdbbPCBFxDPAq4MgaWGHDa/QD u L092vUf4D9M367/nOwA0R8ctson3JILauzf7PLdXzAc4Gbs/MD3dMugQYuzrkaODijvaj6hUm wEP10MGlwMHRcTs j/ g2rbtJeZ787MnTNzHmUf XJmHglcBbyuduuu0VjtXlf7Z20/PMpVcLsCu1FO9pwxMvM 4J6I PXa9HLgNtyfOn0X2C8inlp//8Zq5P7U36TsP3XaIxGxX639UR3LmtYiYgHl9IlXZ aPOib12096fv/VfavfvjitZebNmfnMzJxX/z1fQblY7T421b406JUGm9ODcqXEf1OuHnlv6 1p8PlfQhnmvwm4sT4OpZwncCVwB/DvwA61fwAfr/W6GZjfsaxjKSeCLgPe1PqzDaleIzxx1eSzKf gLQP Gdi6tj 5vl9Wpz 7Y/731tp9m2l4tdaANdoTuK7uU/9CudLI/WntGv058C3gFuAfKVe0uT Vz/QZyrlzP6N8UR43mfsPML/W/TvAx6g3O59Ojz41WkY5l2ns3/FPjref0Of7r9  ON0everUNX05T1w1uUn2Je sL0mS1IiHJiVJkhoxiEmSJDViEJMkSWrEICZJktSIQUySJKkRg5gkSVIjBjFJkqRGDGKSJEmN/H9X6qxj1cu/VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfDados['mensalidade'].hist(bins=100, figsize=(10,8)).set_title(\"Histograma - Mensalidade\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 250
    },
    "id": "rc2Jx5zXIDpQ",
    "outputId": "c78be1c7-93f5-4cbc-e075-d1a29d1bde87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot - Mensalidade')"
      ]
     },
     "execution_count": 93,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAADXCAYAAAB1XpKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1 /AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy WH4yJAAAbEElEQVR4nO3debhkVXnv8e9PRgEDYosDSFoNomAUkShGVNSIRFG8ahQxUUCv0cSAGs11iEFvws2oJg4xQcOggpogRtSocMHGeQBExSkqgkBAJQgOaCPy5o 1jl0Wdbqrh rdfer7eZ791Km1p7d2rVOn3rOGnapCkiRJkrTx3WLoACRJkiRpXpmQSZIkSdJATMgkSZIkaSAmZJIkSZI0EBMySZIkSRqICZkkSZIkDcSETJLmUJLlSSrJSUPHorWT5MD 3r1irHxFkrW6l00/zooNGd Ec1yS5JJZnkOSNmcmZJK0lvqX2PFlZf/ieXKSewwd48aS5Ij  o/YyOerJB9ZzXbLk9y0sO3GiE2SpHWx5dABSNJm7JUjP 8I3A94GvCEJAdU1YXDhDUXbgQelGTPqvrahPXPBNK3m5e/dU8Dths6CEnS2pmXP1KStMFV1SvGy5K8Dngu8DzgiI0c0jx5H/A4WuL1otEVSbYAjgQ C9wR2HWjRzeAqvr20DFIktaeXRYlacM6sz/ednxFkm2SvDjJF5Ncn QHST6a5Elj2z2 d7X7VJKtxtbds /7X0l2GSm/pC87Jnl9kiuS/DTJl5McnSTTvoAkd0jyhn68G5J8L8npSe47tt0K4MT 9MSxLpzLpz3fOvoS8Eng6ePXCHg0LRF70 oOkOT SU5LclV/nZcl eckd5yw7Yr urZM8tIkX /dVC9L8tdJtp6wz4OSvDfJ5X3bq/p7euzYdndL8ldJzuvXemWSS5Mcn2S3aS/IYmPIkmyd5OVJvtmP/a0kf5Fkm0WOc8ckf5bk4yPX5r SnJpkr0X2SZLnJvlSr3dX9Hq44xpifkqSDye5tu/3lSR/ulhskrQU2UImSRvWb/XH80YL xf2DwEPAb4KvIHWveyJwDuT7FNVLwWoqtOTvAH4Q A44E/6MbYD/hXYBnhqVX137NxbA/8f2Al4R3/ BOAfgD378VYryZ2Bj9ESmnOAtwN3An4HeHSSJ1TV /rmJwHXAocC7wFGu2heu6ZzbQBvAk7o5z9tpPx/Az ixX7shP1IchRwPLASOAO4DNiD1uL2mCT7L9LidCrwIOADwA AR9Hen11orXILxz8YeH/f5gzgCmBn4B7AH/DL3V0fDzwb DDwCeAGYO RWParqiumuSATXmdodeZQ4JvA62n14ijg1xfZ7cHAi3s876Jdyz1odfWxSR5YVZ8f2 fvgaOBK2nX9Wf9nPfv57thQmwn0K7Z5f081wL7A38OPDzJI6rqxnV53ZK0WakqFxcXF5e1WIDqyytGllcDHwVuAt4L3Gpsn5f0ff4D2HKkfBfgkr7uN0fKtwEu6Mc7uJed2Ld75YSYFo7xMWCbkfKdaV/EC3jwSPnyXnbS2HE 1MtfNlb m7TxWP8N7DBSfkTf/oiNdO0XzvcXwPbAdcCHRtbv2uN8U39 eftT90vHuBstQfgGsOvYuocDPwfePVa op/3fGDnkfLt 3F Dtx pPxdfft7T3gNy8ae7zr6no2UH9SP 8ax8gMX6t kGMfKDu/bfhLYdpF6sWJsn13G628vvzctOfvAhLpR/TqMXptt 3kLuGSR9/F04JZj617R1x2zMeqUi4uLy9CLXRYlad0dO7I8HzgA Arw9qr64di2R9G ZL6gRv7rX62V68/702eOlK8Engz8GHhLkhfSvsR BPi/q4npJX3fheNcM3L8Iyfv0vTucQcB3wb ZnRdVX2C1uK0M61FZ3BV9WNai9UjRrpIHgVsweq7Kz4H2Ir2hf XWp6q6mxai9Zjktxqwr7/p1/T0RhOoQ0B2G/C9j ZEPfVY8 vGH3PRsrPpHXNfORqXsuaLLznL62qn44ce7RejJ/3uxPqL9Vaxc4BHjrWTXThHMeNXZuf0v4RMckxtMT5qKoav0Z/Tkv8n7roq5KkJcQui5K0jqrqF OykmxP62b2V8ApSfauqpf1dbcCfg24oqq OuFQ5/TH 4wd/ tJng28Dfhb4Grg8Kr6 SIh3Ujr8jZuxaTjT7Cw/qNV9bNF4vzdvt1b1nCs1crYPbS6k6rqkrU81Jto3f2e0cdmPQP4QlV9ZjX7PKA/PiTJb0xYvwstqbsbrUVs1Hk335zL uOtR8pOoSWun07yTlr3v49X1eXjO/duhU lJdz37sfZYmSTm3X3Wwv70lpZPzZh3YrFdkryaNp13Q9Yxs2/LyyjdU9cOAfAuRMO9TFaK9/osbejvc6rgectMrxxJa17pyQteSZkkrQB9JaSzyR5PK2b3J8k aequow2JT6s gI7bqF8pwnrzqSNQ/oV4N/GW3TGXL1IsnZVf1ztBAvrGefamjS2awWt6 XUquqCJBfQWmk Bfwq8Edr2O02/fFFq90Kdphwvklj4xZaPLcY2e70JIcAf0xrtft9gCTn01oxzxrZ/9W0WTmvpHUZvYJVLWtH0F7TutoRuGaRBPuqCWUkOYY2Juz7wFm0FtPraS28j6MlU6OTbizUm  MH6uqbkxy9VjxrWm3JLgti4zxk6R5YkImSRtQVV2b5Gu0VoN9aa0n1/XVt19ktzv0x tGC3vLyVtoydjVwLOSvKOqFrsh8rIkW0xIyhbOe934DmPWKc51Mdq6uAEcD/xTX35Ca1FcnYX4d6yqH2zAOH5JVb0feH9vPb0/cAitu T7ktynqr6cNlPm0cBFtDGEv9RVMMlT1jOM64Cdk2w1ISm72fucZEvaGK6rgH2r6sqx9Q8Y34dV1/N2wMUTjreM9k K8e0/V1X7IklzzjFkkrThLXRduwVA/5L9TWDXJHtM2P6h/fGCsfIXAQfTur89jDZz3alJbsNkW9ImWBh3YH/83BriXlh/QP8iPU2cC8nfFgznVNpYu91orYhrmuHxU/3xQTONqquqH1fVOVX1AuD/0WYd/O2  i60enLmhGRst75 fVzQj3/AhHUHTihbRmsB/cSEZGwHVnVPHD8HtBlExx3AWN2oqh/RxsbtnWTn1QUvSfPAhEySNqAkjwPuTEueRsdznUDrpvW3aTcuXth GfDykW0WyvenTXn/DeA5VfVF2sQhuwInZ5GBN8Bfjt7DqX/h/dP 9MTJuzR9fNNZtBkYnzf2uu5Pm7Hv 8C7R1b9d3/cfXXHnqWeyBwM/C9WvdbVeT3t/XlNkruNr z37VqvZC3JgxdJam/XH6/vj5f0xwPG6sUOtPFx69uTZeE9Py7JtiPHH60Xo77bY7tvj2Fh 61ot09YNmGfk/rjy0YTrH6 v1wkrlfTEtMTktysC2ySWyex9UzSXLDLoiSto7GJKbYH9mJVy8dLq2p0TM3f9XWHAp9P8h 0 5D9Dm0Sib pqo/14 5Em9HwJuCwhZaTqvqnJA n3Q/qBcCrxkK6kja256IkZ9BmEnwiravhP66mq OoZwMfpyWOB9EmsVi4D9lNwJFjLTmfpH2Bf15vuVsYl/S6qlrvro3TWrh2U2771X4fshOALyX5IPCftOu1O63l7HvA3dcjpNfSWkQ/Tku6bgDuS2vpvJR2nziq6qok7wAOAy5MciZtTNYjgJ/S7u22z3rE8XbabJ2PpdWL97CqXnwWuOvoxlV1U5LX0u5D9sW /da01tGdaZOTPHRsn48neR1t7N5FSU5j1X3Ivs EMYlVdULajcb/APhmkg/RxqrtTPuHxoNpyeSz1 O1S9LmYeh5911cXFw2t4VV9yEbXW6kffF8D/CIRfbbFngpbbzQT4Af0mahe8rYdgv3sHr hGPsSBuncwNwv5HyS/qyI 2m01fQZqr7Cm2MUsaOs5wJ9yHr63YF3khLHG6gjV/7d A3FnldB9MSsx NXI/lM7r2R/Tj/8WU29/sPmQj636d1rpzab9W1/T35p Bh41tu2I1x1mI6YiRsifRkqGv9 vyg37s44Dbju2/HataQ39KG3f4BtrkIzc7L2txH7JevjXwZ73erOz15Dha8j7pPmRb0hL 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 vOnAl YTUiSJEmSNB mTciOBJ4DHNOffwR440wikiRJkqQ5Me209z8FXtMXafNx7rnt8cADBw1jbnidtam48ELYZ5 ho5AkaY2mSsiS7AH8JbAXbcZFAKrqLjOKS5KkdbfPPnD44UNHIUnSGk3bZfFE4FhaC9lDaV0Yp50QRBrOZjjTjiRJkubHtEnVLavqbCBVdWlVvQJ49OzCkiRJkqSlb9oWspVJbgF8Pclzafcg22F2YUmSJEnS0jdtC9kxwHbA0cB9gd8Dnj6roCRJkiRpHkw7y Jn 48/oo0fkyRJkiStp9UmZEneCyw6K0JVPXaDRyRJkiRJc2JNLWR/1x8fD9weeFt//hTgO7MKSpIkSZLmwWoTsqo6FyDJq6pqv5FV701y3kwjkyRJkqQlbtpJPbZP8oubQCe5M7D9bEKSJEmSpPkw7bT3zwdWJLkYCPCrwO/PLCpJkiRJmgPTzrL4wSR7AHfvRV tqpWzC0uSJEmSlr41zbL4sKo6J8njx1bdNQlVdfoMY5MkSZKkJW1NLWQPAc4BHjNhXQEmZJIkSZK0jtY0y Kx/dGbQUuSJEnSBramLosvWN36qnr1hg1HkiRJkubHmros3mqjRCFJkiRJc2hNXRZfubECkSRJkqR5M9W090m2BZ4B7A1su1BeVUfNKC5JkiRJWvJuMeV2bwVuDzwSOBfYDfjhrIKSJEmSpHkwbUL2a1X1cuDHVXUy8Gjg/rMLS5IkSZKWvmkTsp/1x2uT3BPYEdhlNiFJkiRJ0nyYagwZcHySWwN/CpwB7AD82cyikiRJkqQ5MFVCVlVv7j9 BLjL7MKRJEmSpPkxVZfFJMck ZU0b05yQZKDZh2cJEmSJC1l044hO6qqfgAcBNwG D3gr2YWlSRJkiTNgWkTsvTHRwFvqaovjZRJkiRJktbBtAnZ UnOpCVkH0pyK Cm2YUlSZIkSUvftLMsPgPYB7i4qq5PchvgyNmFJUmSJElL37SzLN6U5DvAXkmmTeIkSZIkSasxVXKV5K BJwNfBn7ei4s2Db4kSZIkaR1M29r1OGDPqlo5y2AkSZIkaZ5MO6nHxcBWswxEkiRJkubNtC1k1wMXJjkb EUrWVUdPZOoJEmSJGkOTJuQndEXSZIkSdIGMu0siycnuSWwe1V9bcYxSZIkSdJcmGoMWZLHABcCH zP90lii5kkSZIkrYdpJ/V4BXA/4FqAqroQuMuMYpIkSZKkuTBtQvazqrpurOymDR2MJEmSJM2TaSf1 FKSw4EtkuwBHA18YnZhSZIkSdLSN20L2R8Be9OmvD8VuA44ZlZBSZIkSdI8mDYh26svWwLbAocCn51VUJIkSZI0D6btsngK8ELgIhw7JkmSJEkbxLQJ2feq6r0zjUSSJEmS5sy0CdmxSd4MnE0bRwZAVZ0 k6gkSZIkaQ5Mm5AdCdwd2IpVXRYLMCGTJEmSpHU0bUL2G1W150wjkSRJkqQ5M 0si59IstdMI5EkSZKkOTNtC9n 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 /fMi4tWEl2SnJaUm muQrSR7g58F8SfL8/vfgoiRvT7KtnwfzIckJSb6b5KKRsrX /U/y9L7915M8fYjXonWzSB342/434QtJ3p1kp5F1L l14GtJHjlSvlFziSWXkCXZAngD8NvAXsBTkuw1bFSaoRuBP66qvYD9gT/s7/eLgbOrag/g7P4cWr3Yoy/PAt648UPWjBwDfGXk V8Dr6mqXwO Dzyjlz8D H4vf03fTkvHPwAfrKq7A/em1Qk/D ZEkl2Bo4H9quqewBbAYfh5MC9OAg4eK1ur3/8kOwPHAvcH7gccu5DEabNwEjevA2cB96yqewH/CbwEoH9fPAzYu /zj/2fuxs9l1hyCRntl cbVXVxVd0AvAM4dOCYNCNVdWVVXdB//iHty9eutPf85L7ZycDj s HAm p5lPATknusJHD1gaWZDfg0cCb /MADwNO65uM14GFunEa8PC vTZzSXYEHgz8C0BV3VBV1 LnwbzZErhlki2B7YAr8fNgLlTVR4BrxorX9vf/kcBZVXVNVX2f9mV /Au NlGT6kBVnVlVN/annwJ26z8fCryjqlZW1beAb9DyiI2eSyzFhGxX4LKR55f3Mi1xvavJfYBPA7erqiv7qquA2/WfrR9L098DfwLc1J/fBrh25AN49H3 RR3o66/r22vzd2fge8CJvfvqm5Nsj58Hc6OqrgD Dvg2LRG7DjgfPw/m2dr /vu5sLQdBXyg/7zJ1IGlmJBpDiXZAXgX8Lyq sHoumpTiTqd6BKV5BDgu1V1/tCxaHBbAvsCb6yq wA/ZlX3JMDPg6Wudy07lJac3xHYHls31Pn7P9 SvIw21OWUoWMZtxQTsiuAO408362XaYlKshUtGTulqk7vxd9Z6HrUH7/by60fS88DgccmuYTWreBhtHFEO/UuS/DL7/Mv6kBfvyPw3xszYM3M5cDlVfXp/vw0WoLm58H8 C3gW1X1var6GXA67TPCz4P5tba//34uLEFJjgAOAZ5aq 75tcnUgaWYkH0W2KPPqLQ1bbDeGQPHpBnpff3/BfhKVb16ZNUZwMLMSE8H3jNS/rQ u9L wHUjXRm0Gaqql1TVblW1nPb7fk5VPRX4MPDEvtl4HVioG0/s2/sf0yWgqq4CLkuyZy96OPBl/DyYJ98G9k yXf/7sFAH/DyYX2v7 /8h4KAkt 4trgf1Mm2mkhxMG9bw2Kq6fmTVGcBhfbbVO9MmePkMA QSS/LG0EkeRRtTsgVwQlUdN3BImpEkBwAfBb7IqvFDL6WNI/tXYHfgUuBJVXVN/wP9eloXluuBI6vqvI0euGYiyYHAC6vqkCR3obWY7Qx8DvjdqlqZZFvgrbTxhtcAh1XVxUPFrA0ryT60yV22Bi4GjqT989HPgzmR5JXAk2ldkz4HPJM2/sPPgyUuyduBA4FlwHdosyX O2v5 5/kKNp3CYDjqurEjfk6tO4WqQMvAbZhVev3p6rq2X37l9HGld1IG/bygV6 UXOJJZmQSZIkSdLmYCl2WZQkSZKkzYIJmSRJkiQNxIRMkiRJkgZiQiZJkiRJAzEhkyRJkqSBmJBJkiRJ0kBMyCRJkiRpICZkkiRJkjSQ/wH1n9kCgbxfJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxprops = dict(linestyle='-', linewidth=1.5, color = 'r')\n",
    "dfDados.boxplot(column=['mensalidade'], \n",
    "                vert=False, \n",
    "                grid=False,\n",
    "                showfliers=False, \n",
    "                rot=90, \n",
    "                fontsize=10, \n",
    "                figsize=(15,3),\n",
    "                color=dict(boxes='r', whiskers='r', medians='r', caps='r'),\n",
    "                boxprops=boxprops).set_title(\"Boxplot - Mensalidade\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FOXIzSPuXGH8"
   },
   "source": [
    "## Variáveis categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "id": "NoH3hcDWXC_f",
    "outputId": "13e69d66-9f3d-4dd7-db8a-be5e02d73782"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abrangencia</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Estadual</th>\n",
       "      <td>9038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grupo de estados</th>\n",
       "      <td>2324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grupo de municípios</th>\n",
       "      <td>44554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Municipal</th>\n",
       "      <td>9482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nacional</th>\n",
       "      <td>35488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                count\n",
       "abrangencia               \n",
       "Estadual              9038\n",
       "Grupo de estados      2324\n",
       "Grupo de municípios  44554\n",
       "Municipal             9482\n",
       "Nacional             35488"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['abrangencia'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "KzG67PhvYbow",
    "outputId": "e84fb24b-0266-48f9-d035-643807204bca"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acomodacao</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Coletiva</th>\n",
       "      <td>50616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual</th>\n",
       "      <td>44486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não identificado</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não se aplica</th>\n",
       "      <td>5754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0             count\n",
       "acomodacao             \n",
       "Coletiva          50616\n",
       "Individual        44486\n",
       "Não identificado     30\n",
       "Não se aplica      5754"
      ]
     },
     "execution_count": 98,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['acomodacao'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 388
    },
    "id": "sw8JP4XOl0UG",
    "outputId": "92d2dad1-d09f-457d-b22e-9fef498c1733"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd_faixa_etaria</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0            count\n",
       "cd_faixa_etaria       \n",
       "1                10058\n",
       "2                10070\n",
       "3                10074\n",
       "4                10081\n",
       "5                10088\n",
       "6                10097\n",
       "7                10101\n",
       "8                10103\n",
       "9                10102\n",
       "10               10112"
      ]
     },
     "execution_count": 113,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['cd_faixa_etaria'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "id": "DStU2zZ2YjtS",
    "outputId": "20845a76-865c-4f3b-8548-bd75ccce8148"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cobertura</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Médico-hospitalar</th>\n",
       "      <td>100886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0               count\n",
       "cobertura                \n",
       "Médico-hospitalar  100886"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['cobertura'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "p61_6CIWZBCO",
    "outputId": "eeb87924-08e9-4b14-d7ab-6eb5987b2fa4"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contratacao</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Coletivo por adesão</th>\n",
       "      <td>62551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual ou familiar</th>\n",
       "      <td>38335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                   count\n",
       "contratacao                  \n",
       "Coletivo por adesão     62551\n",
       "Individual ou familiar  38335"
      ]
     },
     "execution_count": 100,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['contratacao'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "X0fmIJerZRhJ",
    "outputId": "2d4453ff-ac00-4628-b148-2958089d5200"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fator</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ausente</th>\n",
       "      <td>40715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Co-participação</th>\n",
       "      <td>52233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Franquia</th>\n",
       "      <td>1340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Franquia   Co-participação</th>\n",
       "      <td>6598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                       count\n",
       "fator                            \n",
       "Ausente                     40715\n",
       "Co-participação             52233\n",
       "Franquia                     1340\n",
       "Franquia   Co-participação   6598"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['fator'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "_BV-kZgPZfnc",
    "outputId": "64cce1bd-836f-461a-8713-c90b20d5bd42"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in_obstetricia</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0           count\n",
       "in_obstetricia       \n",
       "0               10536\n",
       "1               90350"
      ]
     },
     "execution_count": 103,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['in_obstetricia'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "xwVwTvcUZuAm",
    "outputId": "cdb28b9e-2fc6-4cf0-f25d-676fd48887c8"
   },
   "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>cd_faixa_etaria</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in_obstetricia</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1048</td>\n",
       "      <td>1050</td>\n",
       "      <td>1051</td>\n",
       "      <td>1050</td>\n",
       "      <td>1054</td>\n",
       "      <td>1054</td>\n",
       "      <td>1053</td>\n",
       "      <td>1055</td>\n",
       "      <td>1060</td>\n",
       "      <td>1061</td>\n",
       "      <td>10536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9010</td>\n",
       "      <td>9020</td>\n",
       "      <td>9023</td>\n",
       "      <td>9031</td>\n",
       "      <td>9034</td>\n",
       "      <td>9043</td>\n",
       "      <td>9048</td>\n",
       "      <td>9048</td>\n",
       "      <td>9042</td>\n",
       "      <td>9051</td>\n",
       "      <td>90350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>10058</td>\n",
       "      <td>10070</td>\n",
       "      <td>10074</td>\n",
       "      <td>10081</td>\n",
       "      <td>10088</td>\n",
       "      <td>10097</td>\n",
       "      <td>10101</td>\n",
       "      <td>10103</td>\n",
       "      <td>10102</td>\n",
       "      <td>10112</td>\n",
       "      <td>100886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cd_faixa_etaria      1      2      3      4  ...      8      9     10   Total\n",
       "in_obstetricia                               ...                             \n",
       "0                 1048   1050   1051   1050  ...   1055   1060   1061   10536\n",
       "1                 9010   9020   9023   9031  ...   9048   9042   9051   90350\n",
       "Total            10058  10070  10074  10081  ...  10103  10102  10112  100886\n",
       "\n",
       "[3 rows x 11 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['in_obstetricia'], columns=dfDados['cd_faixa_etaria'], margins=True, margins_name=\"Total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "SKlH9sQihUNF",
    "outputId": "194ce778-ef95-4797-e5bd-77b9d72f11d5"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internacao</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ausente</th>\n",
       "      <td>79396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parcial com internação</th>\n",
       "      <td>19341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parcial sem internação</th>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                   count\n",
       "internacao                   \n",
       "Ausente                 79396\n",
       "Parcial com internação  19341\n",
       "Parcial sem internação   1760\n",
       "Total                     389"
      ]
     },
     "execution_count": 107,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['internacao'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "id": "vytYg6pohoDv",
    "outputId": "98119b09-fa85-454c-f2b1-31cb5dc0abb5"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nm_regiao</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Centro-Oeste</th>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordeste</th>\n",
       "      <td>16764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norte</th>\n",
       "      <td>7933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudeste</th>\n",
       "      <td>46134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sul</th>\n",
       "      <td>19227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0         count\n",
       "nm_regiao          \n",
       "Centro-Oeste  10828\n",
       "Nordeste      16764\n",
       "Norte          7933\n",
       "Sudeste       46134\n",
       "Sul           19227"
      ]
     },
     "execution_count": 108,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['nm_regiao'], columns='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "dbwj_oRKh68V",
    "outputId": "68918d99-1c41-4622-811e-c2443fa955a3"
   },
   "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>col_0</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segmentacao</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ambulatorial</th>\n",
       "      <td>5754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ambulatorial e Hospitalar</th>\n",
       "      <td>90712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hospitalar</th>\n",
       "      <td>4420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0                      count\n",
       "segmentacao                     \n",
       "Ambulatorial                5754\n",
       "Ambulatorial e Hospitalar  90712\n",
       "Hospitalar                  4420"
      ]
     },
     "execution_count": 109,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['segmentacao'], columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "utIUGIRwiXuM",
    "outputId": "18d18f4e-438d-4013-ce4c-34c205c41dd1"
   },
   "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>nm_regiao</th>\n",
       "      <th>Centro-Oeste</th>\n",
       "      <th>Nordeste</th>\n",
       "      <th>Norte</th>\n",
       "      <th>Sudeste</th>\n",
       "      <th>Sul</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segmentacao</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>Ambulatorial</th>\n",
       "      <td>280</td>\n",
       "      <td>939</td>\n",
       "      <td>230</td>\n",
       "      <td>2775</td>\n",
       "      <td>1530</td>\n",
       "      <td>5754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ambulatorial e Hospitalar</th>\n",
       "      <td>9978</td>\n",
       "      <td>15105</td>\n",
       "      <td>7223</td>\n",
       "      <td>41479</td>\n",
       "      <td>16927</td>\n",
       "      <td>90712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hospitalar</th>\n",
       "      <td>570</td>\n",
       "      <td>720</td>\n",
       "      <td>480</td>\n",
       "      <td>1880</td>\n",
       "      <td>770</td>\n",
       "      <td>4420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>10828</td>\n",
       "      <td>16764</td>\n",
       "      <td>7933</td>\n",
       "      <td>46134</td>\n",
       "      <td>19227</td>\n",
       "      <td>100886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "nm_regiao                  Centro-Oeste  Nordeste  ...    Sul   Total\n",
       "segmentacao                                        ...               \n",
       "Ambulatorial                        280       939  ...   1530    5754\n",
       "Ambulatorial e Hospitalar          9978     15105  ...  16927   90712\n",
       "Hospitalar                          570       720  ...    770    4420\n",
       "Total                             10828     16764  ...  19227  100886\n",
       "\n",
       "[4 rows x 6 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['segmentacao'], columns=dfDados['nm_regiao'], margins=True, margins_name=\"Total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "dL8270vDjkzM",
    "outputId": "de2de1f3-c720-47df-be08-9cadb301e8e8"
   },
   "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>nm_regiao</th>\n",
       "      <th>Centro-Oeste</th>\n",
       "      <th>Nordeste</th>\n",
       "      <th>Norte</th>\n",
       "      <th>Sudeste</th>\n",
       "      <th>Sul</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contratacao</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>Coletivo por adesão</th>\n",
       "      <td>7890</td>\n",
       "      <td>11127</td>\n",
       "      <td>6203</td>\n",
       "      <td>26085</td>\n",
       "      <td>11246</td>\n",
       "      <td>62551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual ou familiar</th>\n",
       "      <td>2938</td>\n",
       "      <td>5637</td>\n",
       "      <td>1730</td>\n",
       "      <td>20049</td>\n",
       "      <td>7981</td>\n",
       "      <td>38335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>10828</td>\n",
       "      <td>16764</td>\n",
       "      <td>7933</td>\n",
       "      <td>46134</td>\n",
       "      <td>19227</td>\n",
       "      <td>100886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "nm_regiao               Centro-Oeste  Nordeste  Norte  Sudeste    Sul   Total\n",
       "contratacao                                                                  \n",
       "Coletivo por adesão             7890     11127   6203    26085  11246   62551\n",
       "Individual ou familiar          2938      5637   1730    20049   7981   38335\n",
       "Total                          10828     16764   7933    46134  19227  100886"
      ]
     },
     "execution_count": 112,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=dfDados['contratacao'], columns=dfDados['nm_regiao'], margins=True, margins_name=\"Total\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "ResumosEstatisticos.ipynb",
   "provenance": []
  },
  "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}