Aula07_Atividade_ExplorandoVariáveisQuantitativas_TV_vf.ipynb

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   "source": [
    "___\n",
    "# Atividade: <p> <center> Análise Exploratória -  Variáveis Quantitativas\n",
    "___\n",
    "\n",
    "## Aula 07\n",
    "\n",
    "**Objetivo da aula:** Ao final desta aula, o aluno deve ser capaz de:\n",
    " * Calcular medidas que representem os percentis (ou quantis) de uma particular amostra e interpreta-los;\n",
    " * Construir um HISTOGRAMA útil para visualização gráfica de variáveis quantitativas;\n",
    " * Explicar vantagens sobre o uso da densidade na construção de um histograma e saber interpretá-lo; e\n",
    " * Discutir alguns formatos de um histograma e suas relações com as medidas de posição: média, mediana e moda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"indice\"></div>\n",
    "\n",
    "## Índice\n",
    "\n",
    "- [Empresa de TV](#empresa-de-tv)\n",
    "\n",
    "\n",
    "- [Análise Exploratória - Medidas resumo](#medidasresumo)\n",
    "    - [Revisão do Exercício 2 da Aula 05](#ex2aula05)\n",
    "    - [Revisão Exercício 3 da Aula 05](#ex3aula05)   \n",
    "    - [Revisão Exercício 6 da Aula 05 - Relembrando uso de `describe()` e `groupby(by= )`](#ex6aula05)\n",
    "    \n",
    "- [Análise Exploratória - Boxplot](#boxplot)\n",
    "    - [Revisão Exercício 10 da Aula 05 - Relembrando uso do `plt.boxplot()`](#ex10aula05)    \n",
    "    \n",
    "- [Análise Exploratória - Histograma](#histograma)\n",
    "    - [Exercício 1](#ex1)\n",
    "    - [Exercício 2](#ex2) \n",
    "    - [Exercício 3](#ex3) \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from numpy import arange\n",
    "import numpy as np\n",
    "\n",
    "# Para ter melhor print\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esperamos trabalhar no diretório\n",
      "c:\\Users\\ribei\\OneDrive\\Área de Trabalho\\cdados\\cdados\n"
     ]
    }
   ],
   "source": [
    "print('Esperamos trabalhar no diretório')\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"empresa-de-tv\"></div>\n",
    "\n",
    "# Empresa de TV\n",
    "\n",
    "<img src=\"empresaTV.jpg\" height=\"400\">\n",
    "\n",
    "Fonte: extraído da internet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uma empresa de TV via satélite criou recentemente dois tipos de planos de canais (A e B).\n",
    "\n",
    "A empresa tem como objetivo estudar o perfil dos clientes que aderiram ao plano para enviar malas diretas aos potenciais clientes de cada tipo de plano.\n",
    "\n",
    "A base de dados apresenta algumas variáveis para uma amostra de 82 clientes selecionados aleatoriamente dentre aqueles que aderiram aos planos. As variáveis têm os seguintes significados:\n",
    "\n",
    "* CLIENTE: identificador do cliente.\n",
    "* PLANO: apresenta o plano adquirido pelo cliente – (1=A ou 2=B). \n",
    "* EC: apresenta estado civil do cliente no momento da adesão ao  plano – (1=Casado, 2=Solteiro e 3=Outros).\n",
    "* SATISFACAO: grau de satisfação do cliente pelo plano – \n",
    "\t(5=Muito satisfeito, 4=Satisfeito, 3=Indiferente, 2=Insatisfeito e 1=Muito insatisfeito).\n",
    "* RENDA: renda pessoal do cliente, em milhares de reais.\n",
    "\n",
    "O arquivo `EmpresaTV Cod.xlsx` contém as variáveis descritas acima.\n",
    "\n",
    "<br>\n",
    "\n",
    "**Leitura e Preparo da base de dados:**"
   ]
  },
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   "cell_type": "code",
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     "text": [
      "C:\\Users\\ribei\\AppData\\Local\\Temp\\ipykernel_15676\\2500623923.py:9: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n",
      "  dados.PLANO.cat.categories = (['A', 'B']) # Seguindo a ordem [1,2]\n",
      "C:\\Users\\ribei\\AppData\\Local\\Temp\\ipykernel_15676\\2500623923.py:10: FutureWarning: Setting categories in-place is deprecated and will raise in a future version. Use rename_categories instead.\n",
      "  dados.EC.cat.categories = (['Casado', 'Solteiro', 'Outros']) #Seguindo a ordem [1,2,3]\n"
     ]
    }
   ],
   "source": [
    "dados = pd.read_excel('EmpresaTV_Cod.xlsx')\n",
    "\n",
    "# Definições necessárias vistas na Aula 02\n",
    "dados.PLANO = dados.PLANO.astype('category')\n",
    "dados.EC = dados.EC.astype('category')\n",
    "dados.SATISFACAO = dados.SATISFACAO.astype('category')\n",
    "dados.RENDA = dados.RENDA.astype('float')\n",
    "\n",
    "dados.PLANO.cat.categories = (['A', 'B']) # Seguindo a ordem [1,2]\n",
    "dados.EC.cat.categories = (['Casado', 'Solteiro', 'Outros']) #Seguindo a ordem [1,2,3]\n",
    "\n",
    "# Pelo fato de SATISFACAO ser ordinal\n",
    "dados.SATISFACAO = pd.Categorical(dados.SATISFACAO, \n",
    "                                  categories = ['Muito Insatisfeito', 'Insatisfeito', 'Indiferente', \n",
    "                                                   'Satisfeito', 'Muito Satisfeito'], \n",
    "                                  ordered=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"medidasresumo\"></div>\n",
    "\n",
    "# Medidas resumo\n",
    "\n",
    "Para compreender as informações contidas em variáveis quantitativas, podemos recorrer ao cálculo de algumas medidas resumo como  por exemplo:\n",
    "  * média: [Series.mean()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mean.html)\n",
    "  * mediana [Series.median()](https://pandas.pydata.org/docs/reference/api/pandas.Series.median.html)\n",
    "  * percentis: [Series.quantile()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.quantile.html)\n",
    "  * desvio padrão: [Series.std()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html)\n",
    "  * variância: [Series.var()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.var.html)\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex2aula05\"></div>\n",
    "\n",
    "No **Exercicio 2** do notebook da **Aula05_Atividade**, foi pedido para segmentar a base de dados por plano e criar uma variável renda para cada plano.\n",
    "\n",
    "A seguir, temos as respostas para esse exercício:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cria dataframes apenas com clientes do plano colocado no filtro de linhas\n",
    "dados_a = dados.loc[dados['PLANO'] == 'A',:]\n",
    "dados_b = dados.loc[dados['PLANO'] == 'B',:]\n",
    "\n",
    "# Cria Series considerando apenas a coluna renda com clientes de um particular plano\n",
    "renda_a = dados.loc[dados['PLANO'] == 'A','RENDA']\n",
    "renda_b = dados.loc[dados['PLANO'] == 'B','RENDA']\n",
    "\n",
    "#ou\n",
    "renda_a = dados_a['RENDA']\n",
    "renda_b = dados_b['RENDA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex3aula05\"></div>\n",
    "\n",
    "Já no **Exercicio 3** do notebook da **Aula05_Atividade**, foi pedido para imprimir tamanho amostral, média, primeiro quartil (percentil de ordem 25%), segundo quartil (mediana), terceiro quartil (percentil de ordem 75%), mínimo, máximo, desvio padrão e variância das rendas do clientes para cada plano A e B, separadamente.\n",
    "\n",
    "A seguir, temos as respostas para esse exercício:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "Plano A\n",
      "----------------------------------------\n",
      "contagem               = 46\n",
      "média                  = 10.422\n",
      "mínimo                 = 0.700\n",
      "primeiro quartil (Q1)  = 7.475\n",
      "mediana          (Q2)  = 10.350\n",
      "terceiro quartil (Q3)  = 13.200\n",
      "máximo                 = 21.400\n",
      "variância amostral     = 19.941\n",
      "desvio padrão amostral = 4.466\n",
      "\n",
      "----------------------------------------\n",
      "Plano B\n",
      "----------------------------------------\n",
      "contagem               = 36\n",
      "média                  = 5.689\n",
      "mínimo                 = 0.600\n",
      "primeiro quartil (Q1)  = 4.150\n",
      "mediana          (Q2)  = 5.150\n",
      "terceiro quartil (Q3)  = 6.375\n",
      "máximo                 = 19.200\n",
      "variância amostral     = 10.847\n",
      "desvio padrão amostral = 3.293\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for plano, var_quanti in (('Plano A', renda_a), ('Plano B', renda_b)):\n",
    "    print('-' * 40)\n",
    "    print(plano)\n",
    "    print('-' * 40)\n",
    "    print(f'contagem               = {len(var_quanti)}')\n",
    "    print(f'média                  = {var_quanti.mean():.3f}')\n",
    "    print(f'mínimo                 = {var_quanti.min():.3f}')\n",
    "    print(f'primeiro quartil (Q1)  = {var_quanti.quantile(q=0.25):.3f}')\n",
    "    print(f'mediana          (Q2)  = {var_quanti.median():.3f}')\n",
    "    print(f'terceiro quartil (Q3)  = {var_quanti.quantile(q=0.75):.3f}')\n",
    "    print(f'máximo                 = {var_quanti.max():.3f}')\n",
    "    print(f'variância amostral     = {var_quanti.var():.3f}')\n",
    "    print(f'desvio padrão amostral = {var_quanti.std():.3f}')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex6aula05\"></div>\n",
    "\n",
    "No **Exercicio 6** do notebook da **Aula05_Atividade**, foi pedido para obter as medidas-resumo da variável `RENDA` segmentadas por `PLANO`, fazendo uso dos comandos `describe()`  junto com `groupby(by= )`\n",
    "\n",
    "Relembrando uso de `describe()` e `groupby(by= )`. Vamos lembrar como fazê-lo? <br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PLANO</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>A</th>\n",
       "      <td>46.0</td>\n",
       "      <td>10.422</td>\n",
       "      <td>4.466</td>\n",
       "      <td>0.7</td>\n",
       "      <td>7.475</td>\n",
       "      <td>10.35</td>\n",
       "      <td>13.200</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>36.0</td>\n",
       "      <td>5.689</td>\n",
       "      <td>3.293</td>\n",
       "      <td>0.6</td>\n",
       "      <td>4.150</td>\n",
       "      <td>5.15</td>\n",
       "      <td>6.375</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count    mean    std  min    25%    50%     75%   max\n",
       "PLANO                                                       \n",
       "A       46.0  10.422  4.466  0.7  7.475  10.35  13.200  21.4\n",
       "B       36.0   5.689  3.293  0.6  4.150   5.15   6.375  19.2"
      ]
     },
     "execution_count": 449,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados.groupby('PLANO').RENDA.describe().round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"boxplot\"></div>\n",
    "\n",
    "# Análise Exploratória - Boxplot\n",
    "\n",
    "O boxplot usa os quartis, os quais segmentam os dados em quatro partes iguais em termos de concentração, ou seja, deixa 25% do tamanho da amostra entre os cinco pontos: **mínimo**, $Q1$, $Q2$, $Q3$ e **máximo**. <br>\n",
    "Ou seja, entre o mínimo e o primeiro quartil (Q1), estão os 25% menores valores observados para uma determinada variável quantitativa; já entre terceiro quartil (Q3) e o máximo, os 25% maiores valores observados. <br>\n",
    "Esse gráfico é uma ferramenta estatística muito importante para extrair informações de **variáveis quantitativas**, além de permitir, com facilidade, cruzar variável quantitativa segmentada por rótulos (níveis) de outra variável qualitativa. <br>\n",
    "Outra vantagem desse gráfico é que o mesmo permite identificar possíveis valores atípicos caso haja observação com valor fora dos limites inferior ($LI$) e superior ($LS$), os quais são calculados a partir dos quartis $Q1$ e $Q3$ da seguinte forma:\n",
    "\n",
    " * $LI = Q1 - 1,5 * (Q3 - Q1)$\n",
    " * $LS = Q3 + 1,5 * (Q3 - Q1)$\n",
    " \n",
    "A interpretação de um boxplot envolve observar a mediana, a dispersão dos dados (diferença entre $Q3$ e $Q1$) e a presença de valores atípicos. \n",
    "\n",
    "<br>\n",
    "\n",
    "**VALORES QUE SÃO CONSIDERADOS NA CONSTRUÇÃO DO BOXPLOT:**\n",
    "\n",
    "  * **Caixa do boxplot** é formada pelos quartis $Q1$ e $Q3$ e dentro é colocada uma linha de acordo com o valor obtido para a mediana ($Q2$). Esses valores são colocados de acordo com a escala definida no eixo y do gráfico.\n",
    "  * **Haste inferior** representada pelo menor valor numérico contido no conjunto de dados desde que esse seja maior que o limite inferior ($LI$). \n",
    "  * **Haste superior** representado pelo maior valor numérico contido no conjunto de dados desde que esse seja menor que o limite superior ($LS$). \n",
    "  * **Valores atípicos** representados pelos valores no conjunto de dados que são menores do que o $LI$ ou pelos valores no conjunto de dados que são maiores do que o $LS$. No boxplot, cada observação fora dos limites inferior e superior deve ser sinalizado por um ponto vazado como este $\\Large{\\circ}$ no gráfico, seguindo a grandeza da escala construída no eixo y.\n",
    "\n",
    "\n",
    "\n",
    "Para consultar uso de cores na edição de um boxplot, consulte [link1](https://www.geeksforgeeks.org/box-plot-in-python-using-matplotlib/) e [link2](https://stackoverflow.com/questions/41997493/python-matplotlib-boxplot-color), por exemplo.\n",
    "\n",
    "<br>\n",
    "\n",
    "A seguir, apresentamos o comando para gerar o **BOXPLOT** a partir do *matplotlib*. <br>\n",
    "Aproveite para entender o uso do comando `plt.subplot(linhas,colunas,posição)` útil para visualização da análise exploratória dos dados.<br>\n",
    "Note que é colocado apenas um `plt.show()` ao final de todo o código para fechar a janela gráfica, ou seja, nenhum código será adicionado na janela gráfica que o `plt.figure` abriu após executar o comando `plt.show()`."
   ]
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   "cell_type": "code",
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      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(dados.RENDA)\n",
    "plt.title('Boxplot sem nenhuma edição')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(dados.RENDA, labels = ['todos os clientes'],  \n",
    "            boxprops=dict(color='blue'),\n",
    "            medianprops=dict(color='deeppink'),\n",
    "            capprops=dict(color='springgreen'),\n",
    "            whiskerprops=dict(color='red'),\n",
    "            flierprops=dict(color='yellow', markeredgecolor='cyan')\n",
    "            )\n",
    "plt.title('RENDA dos clientes com adesão ao plano da empresa de TV')\n",
    "plt.ylabel('em mil reais')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex10aula05\"></div>\n",
    "\n",
    "No **Exercicio 10** do notebook da **Aula05_Atividade**, foi pedido para estudar o comportamento da variável quantitativa `RENDA` segmentado por `PLANO` utilizando a ferramenta gráfica Boxplot.\n",
    "\n",
    "Vamos lembrar como fazê-lo?\n",
    "\n",
    "Faça de duas formas diferentes:\n",
    "  1. Construção dos boxplots da renda para cada plano em gráficos separados. Aqui, podem usar o comando `plt.subplot()` para organizar o *output* adaptando o código acima.\n",
    "  1. Construção de um único gráfico (apenas um eixo y) contendo os dois boxplots da renda para cada plano.\n",
    "  1. Discuta prols e contras de cada caso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.boxplot(dados_a.RENDA, labels=[\"A\"])\n",
    "plt.title('Renda de cleintes do plano A')\n",
    "plt.ylabel('em mil reais')\n",
    "plt.ylim(0, 22)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.boxplot(dados_b.RENDA, labels=[\"B\"])\n",
    "plt.title('Renda de cleintes do plano B')\n",
    "plt.ylabel('em mil reais')\n",
    "plt.ylim(0, 22)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "plt.boxplot([dados_a.RENDA, dados_b.RENDA], labels = [\"A\", \"B\"])\n",
    "plt.title('Renda de cleintes do plano A e B')\n",
    "plt.ylabel('em mil reais')\n",
    "plt.ylim(0, 22)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"histograma\"></div>\n",
    "\n",
    "# Análise Exploratória - Histograma\n",
    "\n",
    "O histograma é um gráfico adequado para representar as frequências (de contagem ou relativas) de variáveis OBRIGATORIAMENTE\n",
    "**quantitativas**. \n",
    "\n",
    "No **eixo x**, tem-se a representação da **grandeza da variável quantitativa** que está sendo estudada.\n",
    "\n",
    "No **eixo y**, o *default* considera a frequência absoluta (contagem). Entretanto, há dois importantes motivos que nos animam a utilizar a medida **densidade** alterando o argumento *density* para *True*, ou seja, `density=True`. Veja os motivos:\n",
    "  * Considerar a densidade no eixo y, permite considerar faixas com amplitudes diferentes sem distorção de interpretação.\n",
    "  * Considerar a densidade no eixo y, garante que a área do histograma seja igual a $1$ permitindo colocar curvas probabilísticas cuja área total também é $1$.\n",
    "\n",
    "Para construí-lo considerando a biblioteca `matplotlib`, consulte [aqui](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.hist.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex1\"></div>\n",
    "\n",
    "### EXERCÍCIO 1\n",
    "\n",
    "Execute a célula a seguir e veja algumas possibilidades de histograma com a variável `RENDA` considerando todos os clientes.\n",
    "\n",
    "Analise os quatro histogramas abaixo, observando:\n",
    " * o significado dos argumentos utilizados em cada caso;\n",
    " * o significado dos valores considerados no eixo x; e\n",
    " * o significado dos valores considerados no eixo y.\n",
    "\n",
    "Um dos quatro histogramas está seguramente errado. Identifique-o e descreva o motivo.\n",
    "\n",
    "<br> \n",
    "\n",
    "**Atenção:** \n",
    " * Aproveite para compreender o uso do comando `plt.subplot(Número de linhas, Número de colunas, Posição do gráfico)`. \n",
    " * Esse comando é útil para colocar vários gráficos dentro da mesma janela gráfica, cujo tamanho é dado em `plt.figure(figsize=(largura, comprimento)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 9))\n",
    "\n",
    "plt.subplot(221) # Janela gráfica com 2 linhas, 2 colunas e histograma a seguir é colocado na posição 1\n",
    "plt.hist(dados.RENDA, edgecolor='white')\n",
    "plt.title(\"Histograma 1\")\n",
    "plt.xlabel(\"em mil reais\")\n",
    "\n",
    "plt.subplot(222) # Janela gráfica com 2 linhas, 2 colunas e histograma a seguir é colocado na posição 2\n",
    "plt.hist(dados.RENDA, edgecolor='white', density=True)\n",
    "plt.title(\"Histograma 2\")\n",
    "plt.xlabel(\"em mil reais\")\n",
    "\n",
    "plt.subplot(223) # Janela gráfica com 2 linhas, 2 colunas e histograma a seguir é colocado na posição 3\n",
    "plt.hist(dados.RENDA, bins=[0.5,4,7.5,11,21.5], edgecolor='white')\n",
    "plt.title(\"Histograma 3\")\n",
    "plt.xlabel(\"em mil reais\")\n",
    "\n",
    "plt.subplot(224) # Janela gráfica com 2 linhas, 2 colunas e histograma a seguir é colocado na posição 4\n",
    "plt.hist(dados.RENDA, bins=[0.5,4,7.5,11,21.5], edgecolor='white', density=True)\n",
    "plt.title(\"Histograma 4\")\n",
    "plt.xlabel(\"em mil reais\")\\\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show() # Fecha a janela gráfica. Use apenas uma vez para cada plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coloque sua interpretação aqui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex2\"></div>\n",
    "\n",
    "### EXERCÍCIO 2\n",
    "\n",
    "Faça dois histogramas com as seguintes restrições:\n",
    "  * crie uma janela gráfica de largura igual a 15 e altura igual a 10\n",
    "  * use plt.subplot() com 2 linhas e 1 coluna\n",
    "  \n",
    "  \n",
    "  * no gráfico da posição 1 do subplot criado acima:\n",
    "    * usar variável renda com todos os clientes\n",
    "    * usar densidade no eixo y\n",
    "    * faixas com amplitudes iguais de tamanho $3.5$ \n",
    "    * iniciar faixa em $0.5$\n",
    "    * use o comando `arange` da biblioteca `numpy` para construir essas faixas. Busque a sintaxe desse comando no seu navegador\n",
    "    \n",
    "    \n",
    "  * no gráfico da posição 2 do subplot criado acima:\n",
    "    * usar variável renda com todos os clientes\n",
    "    * usar densidade no eixo y\n",
    "    * faixas com amplitudes desiguais dadas pela lista `[ 0.5, 4, 7.5, 11, 21.5]`\n",
    "\n",
    "\n",
    "  * use as cores descritas [aqui](https://matplotlib.org/stable/gallery/color/named_colors.html), caso queira incrementar a visualização dos histogramas. Leia [aqui](https://marketingpordados.com/marketing/data-visualization-x-dashboards-entenda-qual-e-diferenca-2/) e [aqui](https://aodigital.com.br/data-visualization/) para saber a importância do DataViz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# COLOQUE SEU CÓDIGO AQUI\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.subplot(211)\n",
    "faixas = np.arange(0.5, dados.RENDA.describe()['max'], 3.5)\n",
    "plt.hist(dados.RENDA, bins=faixas, density=True, edgecolor=\"black\")\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.hist(dados.RENDA, bins=[0.5,4,7.5,11,21.5], density=True, edgecolor=\"black\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"ex3\"></div>\n",
    "\n",
    "### EXERCÍCIO 3\n",
    "\n",
    "Faça dois histogramas com as seguintes restrições:\n",
    "  * crie uma janela gráfica de largura igual a 15 e altura igual a 5\n",
    "  * use plt.subplot() com 1 linha e 2 colunas\n",
    "  * usar variável renda segmentada por plano (um histograma para cada plano)\n",
    "  * usar densidade no eixo y\n",
    "  * faixas com amplitudes iguais de tamanho $3$ \n",
    "  * iniciar faixa em $0.5$\n",
    "  * use o comando `arange` da biblioteca `numpy` para construir essas faixas. Busque a sintaxe desse comando no seu navegador\n",
    "  * identificar o plano no título do gráfico\n",
    "  * identificar rótulos (label) com significado dos eixos x e y\n",
    "  * manter eixo y dos dois gráficos na mesma escala\n",
    "  * use as cores descritas [aqui](https://matplotlib.org/stable/gallery/color/named_colors.html), caso queira incrementar a visualização dos histogramas. Leia [aqui](https://marketingpordados.com/marketing/data-visualization-x-dashboards-entenda-qual-e-diferenca-2/) e [aqui](https://aodigital.com.br/data-visualization/) para saber a importância do DataViz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# COLOQUE SEU CÓDIGO AQUI\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(121)\n",
    "faixas_a = np.arange(0.5, dados_a.RENDA.describe()['max'], 3)\n",
    "plt.hist(dados_a.RENDA, bins=faixas_a, density=True, edgecolor=\"black\")\n",
    "plt.ylim(0,0.25)\n",
    "plt.xlabel(\"em milhares de reais\")\n",
    "plt.ylabel(\"Densidade\")\n",
    "\n",
    "plt.subplot(122)\n",
    "faixas_b = np.arange(0.5, dados_b.RENDA.describe()['max'], 3)\n",
    "plt.hist(dados_b.RENDA, bins=faixas_b, density=True, edgecolor=\"black\")\n",
    "plt.ylim(0,0.25)\n",
    "plt.xlabel(\"em milhares de reais\")\n",
    "plt.ylabel(\"Densidade\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volta ao Índice](#indice)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}