x-001-amostras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O objetivo deste exercício é utilizar uma base de dados para testar as amostragens e comparar os resultados\n",
    "\n",
    "# carregue a base de dados credit_data.csv, que possui informações sobre empréstimos (se o cliente pagará ou não pagará o empréstimo)\n",
    "\n",
    "# Teste cada uma das técnicas de amostragem, selecionando 1000 registros\n",
    "\n",
    "# Para a amostragem estratificada, utilize o atributo 'c#default' para separar as categorias\n",
    "\n",
    "# No final, faça o comparativo da média utilizando os atributos 'age', 'income' e 'loan'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação das bibliotecas para analise de dados\n",
    "\n",
    "import pandas as pd                                                # Analise e tratamento de dados\n",
    "import numpy as np                                                 # Tratamento de dados numéricos\n",
    "import random                                                      # Gerador de números aleatórios\n",
    "import matplotlib.pyplot as plt                                    # Plotagem de gráficos\n",
    "from sklearn.model_selection import StratifiedShuffleSplit         # importação de um modulo para gerar amostras estratificadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregamento da base de dados em uma variável\n",
    "\n",
    "dados = pd.read_csv('../data/credit_data.csv')\n",
    "# 'credit_data.csv' é um arquivo com alguns dados que possui informações sobre empréstimos (se o cliente pagará ou não pagará o empréstimo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 5)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualização da quantidade de registros\n",
    "\n",
    "dados.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>i#clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>c#default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>66155.925095</td>\n",
       "      <td>59.017015</td>\n",
       "      <td>8106.532131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>34415.153966</td>\n",
       "      <td>48.117153</td>\n",
       "      <td>6564.745018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>57317.170063</td>\n",
       "      <td>63.108049</td>\n",
       "      <td>8020.953296</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42709.534201</td>\n",
       "      <td>45.751972</td>\n",
       "      <td>6103.642260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>66952.688845</td>\n",
       "      <td>18.584336</td>\n",
       "      <td>8770.099235</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   i#clientid        income        age         loan  c#default\n",
       "0           1  66155.925095  59.017015  8106.532131          0\n",
       "1           2  34415.153966  48.117153  6564.745018          0\n",
       "2           3  57317.170063  63.108049  8020.953296          0\n",
       "3           4  42709.534201  45.751972  6103.642260          0\n",
       "4           5  66952.688845  18.584336  8770.099235          1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualização dos primeiros registros do dataframe\n",
    "\n",
    "dados.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>i#clientid</th>\n",
       "      <th>income</th>\n",
       "      <th>age</th>\n",
       "      <th>loan</th>\n",
       "      <th>c#default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1996</td>\n",
       "      <td>59221.044874</td>\n",
       "      <td>48.518179</td>\n",
       "      <td>1926.729397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>1997</td>\n",
       "      <td>69516.127573</td>\n",
       "      <td>23.162104</td>\n",
       "      <td>3503.176156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>1998</td>\n",
       "      <td>44311.449262</td>\n",
       "      <td>28.017167</td>\n",
       "      <td>5522.786693</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>1999</td>\n",
       "      <td>43756.056605</td>\n",
       "      <td>63.971796</td>\n",
       "      <td>1622.722598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>2000</td>\n",
       "      <td>69436.579552</td>\n",
       "      <td>56.152617</td>\n",
       "      <td>7378.833599</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      i#clientid        income        age         loan  c#default\n",
       "1995        1996  59221.044874  48.518179  1926.729397          0\n",
       "1996        1997  69516.127573  23.162104  3503.176156          0\n",
       "1997        1998  44311.449262  28.017167  5522.786693          1\n",
       "1998        1999  43756.056605  63.971796  1622.722598          0\n",
       "1999        2000  69436.579552  56.152617  7378.833599          0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualização dos últimos registros do dataframe\n",
    "\n",
    "dados.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dados em % :\n",
      " 0    0.8585\n",
      "1    0.1415\n",
      "Name: c#default, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualização da variável 'c#default' graficamente e em percentual\n",
    "\n",
    "print('Dados em % :\\n', dados['c#default'].value_counts('1'))\n",
    "dados['c#default'].plot(kind='hist');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definindo as funções para retirar as amostras \n",
    "\n",
    "\n",
    "# função para gerar amostras sistemáticas com espaço de (quantidade de registros/ quantidade de amostras)\n",
    "def amostragem_sistematica(dados, amostras):\n",
    "    intervalo = len(dados) // amostras                                         # calcula o tamanho do intervalo\n",
    "    random.seed(1)                                                             # semente aleatória\n",
    "    inicio = random.randint(0, intervalo)                                      # sorteia um número aleatório entre 0 e o tamanho do intervalo   \n",
    "    indices = np.arange(inicio, len(dados), step = intervalo)                  # geração de números aleatórios que serão os índices dos registros\n",
    "    amostra_sistematica = dados.iloc[indices]                                  # seleção dos registros com base nos índices\n",
    "\n",
    "    return amostra_sistematica  \n",
    "\n",
    "\n",
    "# função para gerar amostras por grupos\n",
    "def amostragem_agrupamentos(dados, n_grupos):\n",
    "    intervalo = len(dados) // n_grupos                                         # calcula o tamanho do intervalo\n",
    "\n",
    "    grupos = []                                                                # lista vazia                      \n",
    "    id_grupo = 0                                                               # variavel auxiliar id do grupo\n",
    "    contagem = 0                                                               # variavel auxiliar contador de registros\n",
    "    for _ in dados.iterrows():                                                 # iteração sobre os registros da base de dados   \n",
    "        grupos.append(id_grupo)                                                # adiciona o id do grupo\n",
    "        contagem += 1                                                          # incrementa a contagem +1\n",
    "        if contagem > intervalo:                                               # se a contagem for maior que a divisão da quantidade de registros por 10\n",
    "            contagem = 0                                                       # zera a contagem para o próximo grupo\n",
    "            id_grupo += 1                                                      # incrementa o id do grupo +1\n",
    " \n",
    "    dados['grupo'] = grupos                                                    # adiciona o grupo ao dataframe\n",
    "    random.seed(1)                                                             # semente aleatória\n",
    "    grupo_selecionado = random.randint(0, n_grupos)                            # sorteia um número aleatório entre 0 e o número de grupos\n",
    "    return dados[dados['grupo'] == grupo_selecionado]                          # retorna os registros do grupo selecionado\n",
    "\n",
    "\n",
    "# função para gerar amostras estratificadas\n",
    "def amostragem_estratificada(dados, percentual):\n",
    "  split = StratifiedShuffleSplit(test_size=percentual, random_state=1)         # amostras estratificadas de 10% dos dados\n",
    "  for _, y in split.split(dados, dados['c#default']):                          # iteração sobre os dados para gerar as amostras\n",
    "    df_y = dados.iloc[y]                                                       # seleção dos registros df_y com a amostra estratificada de 50% dos dados\n",
    "  return df_y                                                                  # retorna os registros da amostra estratificada\n",
    "\n",
    "\n",
    "# função para gerar amostras de reservatório\n",
    "def amostragem_reservatorio(dados, amostras):\n",
    "    stream = []\n",
    "    for i in range(len(dados)):                                                # iteração sobre os registros da base de dados\n",
    "        stream.append(i)                                                       # adiciona um indice para cada registro\n",
    "    \n",
    "    i = 0                                                                      # variavel auxiliar para o indice\n",
    "    tamanho = len(stream)                                                      # variavel tamanho para ter a quantidade de registros\n",
    "\n",
    "    reservatorio = [0] * amostras                                              # onde serão armazenados os inidices dos registros sorteados\n",
    "    for i in range(amostras):                                                  # iteração para gerar os registros sorteados\n",
    "        reservatorio[i] = stream[i]                                            # adiciona os inidices dos registros sorteados\n",
    "                                            \n",
    "    while i < tamanho:                                                         # iteração para selecionar os registros \n",
    "        j = random.randrange(i + 1)                                            # sorteia um número aleatório entre i + 1\n",
    "        if j < amostras:\n",
    "            reservatorio[j] = stream[i]                                        # onde ficarão armazenados oa valores sorteados\n",
    "        i += 1                                                                 # incrementa o contador\n",
    "    \n",
    "    return dados.iloc[reservatorio]                                            # retorna os registros sorteados pelo algoritmo de amostragem de reservatório                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculo da quantidade de amostras estratificadas\n",
    "\n",
    "1000 / len(dados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Amostragem aleatória simples\n",
    "df_amostra_aleatoria_simples = dados.sample(n = 1000, random_state = 1)  # 1000 registros, random_state = 1(semente aleatória geradora) vai sortear os mesmos registros\n",
    "\n",
    "# uso da função para gerar amostras sistemáticas com 1000 amostras\n",
    "df_amostra_sistematica = amostragem_sistematica(dados, 1000)\n",
    "\n",
    "# uso da função para gerar amostras por grupos com 1000 grupos\n",
    "df_amostra_agrupamento = amostragem_agrupamentos(dados, 1000)\n",
    "\n",
    "# geração de amostras estratificadas\n",
    "df_amostra_estratificada = amostragem_estratificada(dados, 0.5)\n",
    "\n",
    "# uso da função para gerar amostras de reservatório com 1000 amostras\n",
    "df_amostragem_reservatorio = amostragem_reservatorio(dados, 1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# comparativo dos resultados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "amostras = {'media geral': dados.mean(),\n",
    "            'amostra simples': df_amostra_aleatoria_simples.mean(),\n",
    "            'amostra sistematica': df_amostra_sistematica.mean(),\n",
    "            'amostra agrupamento': df_amostra_agrupamento.mean(),\n",
    "            'amostra estratificada': df_amostra_estratificada.mean(),\n",
    "            'amostra reservatório': df_amostragem_reservatorio.mean()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média idade : 40.80755937840458 \n",
      "Média loan : 4444.369694688262 \n",
      "Média renda : 45331.60001779333\n"
     ]
    }
   ],
   "source": [
    "# média da variavel age(idade), loan, icome(renda) em todos os registros\n",
    "\n",
    "print('Média idade :', dados['age'].mean(), '\\nMédia loan :',dados['loan'].mean(), '\\nMédia renda :', dados['income'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média idade : 40.49552561124429 \n",
      "Média loan : 4449.4469004423645 \n",
      "Média renda : 45563.26865376901\n"
     ]
    }
   ],
   "source": [
    "# média da variavel age(idade), loan, icome(renda) usando a amostra simples\n",
    "\n",
    "print('Média idade :', df_amostra_aleatoria_simples['age'].mean(), '\\nMédia loan :',df_amostra_aleatoria_simples['loan'].mean(), '\\nMédia renda :', df_amostra_aleatoria_simples['income'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média idade : 40.91117381141754 \n",
      "Média loan : 4506.787976426329 \n",
      "Média renda : 45691.49875066951\n"
     ]
    }
   ],
   "source": [
    "# média da variavel age(idade), loan, icome(renda) usando a amostra sistematica\n",
    "\n",
    "print('Média idade :', df_amostra_sistematica['age'].mean(), '\\nMédia loan :',df_amostra_sistematica['loan'].mean(), '\\nMédia renda :', df_amostra_sistematica['income'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média idade : 43.83142139948904 \n",
      "Média loan : 4846.595734472186 \n",
      "Média renda : 39520.903173058\n"
     ]
    }
   ],
   "source": [
    "# média da variavel age(idade), loan, icome(renda) usando a amostra por agrupamento\n",
    "\n",
    "print('Média idade :', df_amostra_agrupamento['age'].mean(), '\\nMédia loan :',df_amostra_agrupamento['loan'].mean(), '\\nMédia renda :', df_amostra_agrupamento['income'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média idade : 40.53363707173919 \n",
      "Média loan : 4423.744651094196 \n",
      "Média renda : 45101.36675026739\n"
     ]
    }
   ],
   "source": [
    "# média da variavel age(idade), loan, icome(renda) usando a amostra estratificada\n",
    "\n",
    "print('Média idade :', df_amostra_estratificada['age'].mean(), '\\nMédia loan :',df_amostra_estratificada['loan'].mean(), '\\nMédia renda :', df_amostra_estratificada['income'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média idade : 40.57203708208336 \n",
      "Média loan : 4429.634971370371 \n",
      "Média renda : 45111.19429734541\n"
     ]
    }
   ],
   "source": [
    "# média da variavel age(idade), loan, icome(renda) usando a amostra por reservatório\n",
    "\n",
    "print('Média idade :', df_amostragem_reservatorio['age'].mean(), '\\nMédia loan :',df_amostragem_reservatorio['loan'].mean(), '\\nMédia renda :', df_amostragem_reservatorio['income'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# criação de um dataframe para armazenar os resultados\n",
    "\n",
    "resultados = pd.DataFrame(amostras)\n",
    "resultados = resultados.T                    # transposição do dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualização dos resultados graficamente idade\n",
    "# uso de linha vermelha para mostrar a média geral da variável loan\n",
    "\n",
    "resultados['age'].plot(kind='barh'),\n",
    "plt.axvline(x = dados['age'].mean(), color = \"red\")\n",
    "plt.title('Média da idade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualização dos resultados graficamente income\n",
    "# uso de linha vermelha para mostrar a média geral da variável loan\n",
    "\n",
    "resultados['income'].plot(kind='barh')\n",
    "plt.axvline(x = dados['income'].mean(), color = \"red\")\n",
    "plt.title('Média da renda')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualização dos resultados graficamente loan\n",
    "# uso de linha vermelha para mostrar a média geral da variável loan\n",
    "\n",
    "resultados['loan'].plot(kind='barh')\n",
    "plt.axvline(x = dados['loan'].mean(), color = \"red\")\n",
    "plt.title('Média de loan')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b3ba2566441a7c06988d0923437866b63cedc61552a5af99d1f4fb67d367b25f"
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  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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  "orig_nbformat": 4
 },
 "nbformat": 4,
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}