{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  **PARTE 3: Projeto 2 (Censo Americano)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importa as bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primeiro, é necessário carregar o dataset. Para baixar o dataset e mais informações, acesse: http://archive.ics.uci.edu/ml/datasets/Adult\n",
    "\n",
    "Este dataset contém informações do Censo Americano e o objetivo dele é predizer se uma determinada pessoa tem ganho maior que 50K em um ano. Essa predição é baseada nas informações sociais coletadas no Censo. Essa situação poderia ser utilizada para obter um empréstimo ou não (se ganha mais de 50K libera o empréstimo). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         workclass  fnlwgt  education  education-num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       marital-status         occupation   relationship   race     sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week native-country target  \n",
       "0          2174             0              40  United-States  <=50K  \n",
       "1             0             0              13  United-States  <=50K  \n",
       "2             0             0              40  United-States  <=50K  \n",
       "3             0             0              40  United-States  <=50K  \n",
       "4             0             0              40           Cuba  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importa o data set\n",
    "\n",
    "# para baixar o dataset, acesse: \n",
    "# Observação 1: Este dataset já é dividido em treino e teste previamente. Porém, vou usar somente o conjunto de \n",
    "# teste como sendo o dataset original, pois a maioria dos datasets não são previamentes divididos em conjuntos\n",
    "# de teste e conjunto de treino (isso é feito no processo de Aprendizado de Máquina)\n",
    "\n",
    "# do site eu recuperei o nome de cada coluna do data set\n",
    "columns = ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', \n",
    "           'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', \n",
    "           'target']\n",
    "\n",
    "# abro o dataset e nomeio cada coluna com meu vetor columns\n",
    "data = pd.read_csv('C:/Users/Mariana/Dropbox (Pessoal)/2º sem 2020/SME0123/Codigos/adult.data', names=columns)\n",
    "\n",
    "# Fazendo a mesma coisa do Google Colab\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "# data = pd.read_csv('/content/drive/My Drive/EABDA 2020/adult.data', names=columns)\n",
    "\n",
    "# elimina os espaços no começo e no fim das strings\n",
    "data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n",
    "\n",
    "# visualiza a tabela, o método head() mostra as 5 primeiras linhas da tabela\n",
    "# note que a coluna 'target' é a variável dependente ou resposta (classe).\n",
    "# o restante das colunas são as variáveis explicativas ou independentes ou preditoras (features, atributos), e cada linha é um indivíduo (instância ou exemplo).\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostra o número de linhas (unidades amostrais) e colunas (variáveis)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geralmente, os datasets vem com dados faltantes, no caso deste dataset os dados faltantes é simbolizado pelo caractere '?'. Existem diversas estratégias para trabalhar com dados faltantes. Algumas metodologias podem lidar com \"missings\" sem nenhum problema, mas outras necessitam de dados completos. Nesses casos, pode-se imputar dados. Há formas sofisticadas e simples de imputação, sendo uma delas a substituição pelo valor médio. Porém, no nosso caso, por questões didáticas e de simplificação, vou excluir todos os dados faltantes da nossa base de dados. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execute: preprocessing_remove_rows\n",
      "---Removed 0 rows in the table\n"
     ]
    }
   ],
   "source": [
    "# elimina as linhas com dados faltantes\n",
    "# data = pd.DataFrame\n",
    "# null_symbol = caractere que simboliza que há dados faltantes\n",
    "def remove_rows(data, null_symbol=np.nan):\n",
    "    # salva o total de exemplos\n",
    "    total = len(data)\n",
    "    # substitui o null_symbol por nulo\n",
    "    data = data.replace(null_symbol, np.nan)\n",
    "    # elimina as linhas que contém algum valor nulo\n",
    "    data.dropna(inplace=True)\n",
    "    print('Execute: preprocessing_remove_rows\\n---Removed %d rows in the table' % (total - len(data)))\n",
    "    return data\n",
    "\n",
    "data = remove_rows(data, '?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30162, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# o tamanho atualizado do conjunto de dados\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9782</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>35</td>\n",
       "      <td>36.883459</td>\n",
       "      <td>13.532427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>20380</td>\n",
       "      <td>17</td>\n",
       "      <td>90</td>\n",
       "      <td>38</td>\n",
       "      <td>39.184004</td>\n",
       "      <td>12.873243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age                                     \n",
       "        count min max median       mean        std\n",
       "sex                                               \n",
       "Female   9782  17  90     35  36.883459  13.532427\n",
       "Male    20380  17  90     38  39.184004  12.873243"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataF = data.loc[data.sex=='Female', \"age\"]\n",
    "dataM = data.loc[data.sex=='Male', \"age\"]\n",
    "\n",
    "data.groupby('sex').agg({'age': ['count',min, max, 'median','mean', 'std']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-14.039122252627893, pvalue=1.5227465818368628e-44)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "# Teste t de Student (bicaudal) para média de duas populações Normais com variâncias iguais\n",
    "stats.ttest_ind(dataF,dataM, equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=43.976656455846715, pvalue=3.3792425846568063e-11)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teste de Levene para igualdade de variâncias\n",
    "stats.levene(dataF,dataM)\n",
    "\n",
    "#stats.bartlett(dataF,dataM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.8989252152629614, pvalue=0.0037942674534447514)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecione uma amostra de 95% e outra de 0,5% e repare como o resultado do teste muda\n",
    "sampleF=dataF.sample(frac=0.09)\n",
    "sampleM=dataM.sample(frac=0.09)\n",
    "\n",
    "# Teste t de Student (bicaudal) para média de duas populações Normais com variâncias iguais\n",
    "stats.ttest_ind(sampleF,sampleM, equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "# Gráfico de probabilidade da Normal, para checar suposição de tal distribuição dos dados\n",
    "# para o teste t de Student\n",
    "stats.probplot(sampleF, plot=plt)\n",
    "stats.probplot(sampleM, plot=plt)\n",
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mariana\\Anaconda3\\lib\\site-packages\\scipy\\stats\\morestats.py:1653: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.975343644618988, 0.0)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teste de Shapiro-Wilks para normalidade\n",
    "stats.shapiro(dataM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.08001335891281569, pvalue=8.035805158495415e-55)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teste de Kolmogorov-Smirnov para normalidade\n",
    "mu = dataF.mean()\n",
    "sigma = dataF.std()\n",
    "stats.kstest(dataF,'norm',args=(mu,sigma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LeveneResult(statistic=1.5414156796712146, pvalue=0.21635809587326393)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Teste de Levene para igualdade de variâncias\n",
    "stats.levene(sampleF,sampleM)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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></th>\n",
       "      <th>counts</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>22654</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>7508</td>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       counts     %\n",
       "<=50K   22654  75.1\n",
       ">50K     7508  24.9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Há injustiça no valor salarial?\n",
    "c = data[\"target\"].value_counts(sort=False)\n",
    "p = data[\"target\"].value_counts(normalize=True,sort=False).round(3) * 100\n",
    "pd.concat([c,p], axis=1, keys=['counts', '%'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>target</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</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>Female</th>\n",
       "      <td>8670</td>\n",
       "      <td>1112</td>\n",
       "      <td>9782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>13984</td>\n",
       "      <td>6396</td>\n",
       "      <td>20380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>22654</td>\n",
       "      <td>7508</td>\n",
       "      <td>30162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target  <=50K  >50K    All\n",
       "sex                       \n",
       "Female   8670  1112   9782\n",
       "Male    13984  6396  20380\n",
       "All     22654  7508  30162"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabela de frequências cruzadas ou tabela de contingência\n",
    "pd.crosstab(data.sex, data.target,margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>target</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.886322</td>\n",
       "      <td>0.113678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.686163</td>\n",
       "      <td>0.313837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target     <=50K      >50K\n",
       "sex                       \n",
       "Female  0.886322  0.113678\n",
       "Male    0.686163  0.313837"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentual de linhas\n",
    "pd.crosstab(data.sex, data.target,normalize='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1415.2864042410245,\n",
       " 1.00155254124934e-309,\n",
       " 1,\n",
       " array([[ 7347.04024932,  2434.95975068],\n",
       "        [15306.95975068,  5073.04024932]]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# teste quiquadrado para independência/hamogeneidade de distribuição\n",
    "obs = pd.crosstab(data.sex, data.target)\n",
    "chi2_contingency(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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