statistics_dogs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amostra de cães para análise\n",
    "Esta análise não possui dados conclusivos, vendo que utiliza apenas um conjunto de dados pequeno, para aula de estatística."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports e conjunto de dados\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import log10, ceil\n",
    "%matplotlib inline\n",
    "\n",
    "df_dog = pd.read_excel('variaveis.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Raça</th>\n",
       "      <th>peso em Kg</th>\n",
       "      <th>idade</th>\n",
       "      <th>qtd vacinas</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>pinscher</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17 RAÇÃS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>rottweiller</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>pastor alemão</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pinscher</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>pug</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rottweiller</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>maltês</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pastor alemão</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sexo           Raça  peso em Kg  idade  qtd vacinas Unnamed: 5  Unnamed: 6  \\\n",
       "1    M       pinscher         7.5   12.0            1          F        24.0   \n",
       "2    M    rottweiller        12.0    7.0            0          M        26.0   \n",
       "3    F  pastor alemão         3.0   10.0            0        NaN         NaN   \n",
       "4    F            pug         2.0    1.0            3        NaN         NaN   \n",
       "5    F         maltês         1.5    0.6            1        NaN         NaN   \n",
       "\n",
       "      Unnamed: 7 Unnamed: 8  \n",
       "1            NaN   17 RAÇÃS  \n",
       "2            NaN        NaN  \n",
       "3       pinscher          3  \n",
       "4    rottweiller          4  \n",
       "5  pastor alemão          3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificação do conjunto de dados\n",
    "df_dog.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limpeza do conjunto de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dog.drop(['Unnamed: 5', 'Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Raça</th>\n",
       "      <th>peso em Kg</th>\n",
       "      <th>idade</th>\n",
       "      <th>qtd vacinas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>pinscher</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>rottweiller</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>pastor alemão</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>pug</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>maltês</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sexo           Raça  peso em Kg  idade  qtd vacinas\n",
       "1    M       pinscher         7.5   12.0            1\n",
       "2    M    rottweiller        12.0    7.0            0\n",
       "3    F  pastor alemão         3.0   10.0            0\n",
       "4    F            pug         2.0    1.0            3\n",
       "5    F         maltês         1.5    0.6            1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Corerção das colunas\n",
    "df_dog.rename(columns= lambda x: x.strip().lower().replace(' ', '_'), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sexo</th>\n",
       "      <th>raça</th>\n",
       "      <th>peso_em_kg</th>\n",
       "      <th>idade</th>\n",
       "      <th>qtd_vacinas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>pinscher</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>rottweiller</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>pastor alemão</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>pug</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>maltês</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sexo           raça  peso_em_kg  idade  qtd_vacinas\n",
       "1    M       pinscher         7.5   12.0            1\n",
       "2    M    rottweiller        12.0    7.0            0\n",
       "3    F  pastor alemão         3.0   10.0            0\n",
       "4    F            pug         2.0    1.0            3\n",
       "5    F         maltês         1.5    0.6            1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sexo           0\n",
       "raça           0\n",
       "peso_em_kg     0\n",
       "idade          0\n",
       "qtd_vacinas    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando valores vazios\n",
    "df_dog.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questões importantes para análise da amostra\n",
    "- Qual o sexo mais aparente na amostra?\n",
    "- Qual raça tem maior parcela na amostra?\n",
    "- Quais raças estão acima da idade mediana?\n",
    "- Qual porte mais aparente na amostra?\n",
    "- Quantos porcento da amostra de cães não está devidamente vacinados?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    26\n",
       "F    24\n",
       "Name: sexo, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valores de machos e femeas do conjunto\n",
    "sexo = df_dog['sexo'].value_counts()\n",
    "sexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "size_sexo = df_dog['sexo'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Macho', 'Femea']\n",
    "sizes = [(sexo[0]/size_sexo)*100, (sexo[1]/size_sexo)*100]\n",
    "colors = ['Blue', 'Red']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Passando os paramêtros para geração do gráfico\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "\n",
    "plt.title('Porcentagem do sexo dos cachorros.')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "São mais aparentes cães machos na amostra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pug              9\n",
       "boxer            6\n",
       "poodlle          4\n",
       "rottweiller      4\n",
       "pinscher         3\n",
       "pequines         3\n",
       "bulldog          3\n",
       "pastor alemão    3\n",
       "fox p            3\n",
       "beagle           2\n",
       "yourkshire       2\n",
       "chihuauhua       2\n",
       "maltês           2\n",
       "shihtsu          1\n",
       "cocker           1\n",
       "salsicha         1\n",
       "cão pelado       1\n",
       "Name: raça, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantia por raça\n",
    "racas = df_dog['raça'].value_counts()\n",
    "# Valor total de registros em raças\n",
    "raca_size = df_dog['raça'].count()\n",
    "# Exibindo quantia por raça\n",
    "racas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando as labels para o gráfico\n",
    "labels = [raca.title() for raca in racas.index]\n",
    "# Determinando a porcentagem de cada um deles\n",
    "sizes = [(value/raca_size)*100 for value in racas.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gerando o gráfico\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.0f%%', shadow=True, startangle=180)\n",
    "ax1.axis('equal')\n",
    "plt.title('Porcentagem de registros de cães.')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A raça com maior parcela na amostra é o Pug, sendo 18% da amostra."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recebendo mediana\n",
    "mediana = df_dog['idade'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando apenas as raças com idade acima da mediana\n",
    "acima = df_dog.query('idade >= @mediana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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"
    }
   ],
   "source": [
    "# Gerando gráfico\n",
    "acima['raça'].value_counts().plot(kind='bar', title='Raças que estão acima da idade mediana')\n",
    "plt.ylabel('Quantidade');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estas são as raças e a quantidade de vezes em que aparecem acima da idade mediana."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "intevalos = [0, 6, 15, 25, 45, 60]\n",
    "rotulos = ['Mini', 'Pequeno', 'Médio', 'Grande', 'Gigante']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando uma nova coluna e inserido os rótulos de acordo com os intervalos\n",
    "df_dog['porte'] = pd.cut(df_dog['peso_em_kg'], intevalos, labels=rotulos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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"
    }
   ],
   "source": [
    "# Geração de um gráfico de barras com a quantidade por porte dos cães\n",
    "df_dog['porte'].value_counts().plot(kind='bar',title='Porte dos cães');\n",
    "plt.ylabel('Quantidade');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na maioria dos registros temos cães de porte Mini."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "total = df_dog['qtd_vacinas'].count()\n",
    "sem_vacina = df_dog['qtd_vacinas'].value_counts()[0]\n",
    "resto = total - sem_vacina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Não Vacinados', 'Vacinados']\n",
    "sizes = [(sem_vacina/total)*100, (resto/total)*100]\n",
    "explode = (0.1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gerando o gráfico\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.0f%%', shadow=True, startangle=90)\n",
    "ax1.axis('equal')\n",
    "\n",
    "plt.title('Gráfico de vacinas')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "20% dos cães não são devidamente vacidos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribuição de frequência\n",
    "### Tipos de frequência\n",
    "- Frequência absoluta $(f_i)$: número total de elementos em cada classe\n",
    "- Frequência relativa $(fr_i)$: razão entre cada valor da frequência absoluta e o total de observações \n",
    "$$\n",
    "fr_i = \\frac {f_i}{\\sum f_i}\n",
    "$$\n",
    "- Frequência acumulada $(F_i)$: soma das frequências simples de uma classe com a frequência simples da classe anterior\n",
    "- Frequência acumulada relativa $(Fr_i)$: frequência acumulada da classe dividida pelo total de observações\n",
    "$$\n",
    "Fr_i = \\frac {F_i}{\\sum f_i}\n",
    "$$\n",
    "### Critério de Sturges\n",
    "- Número de classes (linhas)\n",
    "\n",
    "$\n",
    "K = 1+3,33\\log^n\n",
    "$\n",
    "- Amplitude das classes\n",
    "\n",
    "$\n",
    "h = \\frac {Xmax-Xmin}{K}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descobrindo o número de classes (linhas)\n",
    "size_weight = df_dog['peso_em_kg'].shape[0]\n",
    "linhas = ceil(1+3.33*log10(size_weight))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descobrindo a amplitude\n",
    "amplitude = ceil((np.max(df_dog['peso_em_kg']) - np.min(df_dog['peso_em_kg'])) / linhas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pesos = ['{},5 → {},5'.format(peso, peso+amplitude) for peso in range(1, 58, amplitude)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inserindo os intervalos no dataframe\n",
    "df_frequencia_pesos = pd.DataFrame()\n",
    "df_frequencia_pesos['xi'] = pesos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a frequencia e inserindo no dataframe\n",
    "frequencias = []\n",
    "for valor in range(1, 58, amplitude):    \n",
    "    frequencia = sum((df_dog['peso_em_kg'] >= valor+0.5) & (df_dog['peso_em_kg'] < valor+amplitude+0.5))\n",
    "    frequencias.append(frequencia)\n",
    "df_frequencia_pesos['fi'] = frequencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando as frequencias em porcentagem e inserindo no dataframe\n",
    "df_frequencia_pesos['fri'] = [(fri/size_weight)*100 for fri in frequencias]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a frequencia relativa e inserindo no dataframe\n",
    "soma = 0\n",
    "FI = []\n",
    "for fi in frequencias:\n",
    "    soma=soma+fi\n",
    "    FI.append(soma)\n",
    "df_frequencia_pesos['Fi'] = FI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a frequencia relativa em porcentagem\n",
    "df_frequencia_pesos['Fri'] = [(fri/size_weight)*100 for fri in FI]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribuição de frequência dos pesos dos cães"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>xi</th>\n",
       "      <th>fi</th>\n",
       "      <th>fri</th>\n",
       "      <th>Fi</th>\n",
       "      <th>Fri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,5 → 10,5</td>\n",
       "      <td>41</td>\n",
       "      <td>82.0</td>\n",
       "      <td>41</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10,5 → 19,5</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19,5 → 28,5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28,5 → 37,5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37,5 → 46,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46,5 → 55,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55,5 → 64,5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            xi  fi   fri  Fi    Fri\n",
       "0   1,5 → 10,5  41  82.0  41   82.0\n",
       "1  10,5 → 19,5   4   8.0  45   90.0\n",
       "2  19,5 → 28,5   2   4.0  47   94.0\n",
       "3  28,5 → 37,5   2   4.0  49   98.0\n",
       "4  37,5 → 46,5   0   0.0  49   98.0\n",
       "5  46,5 → 55,5   0   0.0  49   98.0\n",
       "6  55,5 → 64,5   1   2.0  50  100.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frequencia_pesos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise\n",
    "- 4 cães ou seja 8%, apresentam peso maior de 10,5kg e menor que 19,5kg\n",
    "- 45 cães ou seja 90%, apresentam peso maior de 1,5kg e menor que 19,5kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descobrindo a amplitude\n",
    "amplitude = ceil((np.max(df_dog['idade']) - np.min(df_dog['idade'])) / linhas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "idades = ['{},6 → {},6'.format(idade, idade+amplitude) for idade in range(0, 14, amplitude)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_frequencia_idades = pd.DataFrame()\n",
    "df_frequencia_idades['xi'] = idades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a frequencia e inserindo no dataframe\n",
    "frequencias = []\n",
    "for valor in range(0, 14, amplitude):    \n",
    "    frequencia = sum((df_dog['idade'] >= valor+0.6) & (df_dog['idade'] < valor+amplitude+0.6))\n",
    "    frequencias.append(frequencia)\n",
    "df_frequencia_idades['fi'] = frequencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando as frequencias em porcentagem e inserindo no dataframe\n",
    "df_frequencia_idades['fri'] = [(fri/size_weight)*100 for fri in frequencias]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a frequencia relativa e inserindo no dataframe\n",
    "soma = 0\n",
    "FI = []\n",
    "for fi in frequencias:\n",
    "    soma=soma+fi\n",
    "    FI.append(soma)\n",
    "df_frequencia_idades['Fi'] = FI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a frequencia relativa em porcentagem\n",
    "df_frequencia_idades['Fri'] = [(fri/size_weight)*100 for fri in FI]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribuição de frequência das idades dos cães"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>xi</th>\n",
       "      <th>fi</th>\n",
       "      <th>fri</th>\n",
       "      <th>Fi</th>\n",
       "      <th>Fri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0,6 → 2,6</td>\n",
       "      <td>15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2,6 → 4,6</td>\n",
       "      <td>12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4,6 → 6,6</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6,6 → 8,6</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8,6 → 10,6</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10,6 → 12,6</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12,6 → 14,6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            xi  fi   fri  Fi    Fri\n",
       "0    0,6 → 2,6  15  30.0  15   30.0\n",
       "1    2,6 → 4,6  12  24.0  27   54.0\n",
       "2    4,6 → 6,6   5  10.0  32   64.0\n",
       "3    6,6 → 8,6   7  14.0  39   78.0\n",
       "4   8,6 → 10,6   7  14.0  46   92.0\n",
       "5  10,6 → 12,6   4   8.0  50  100.0\n",
       "6  12,6 → 14,6   0   0.0  50  100.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frequencia_idades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise\n",
    "- 12 cães ou seja 24%, tem sua idade maior de dois anos e seis meses e menor de 4 anos e seis meses\n",
    "- 27 cães ou seja 54%, tem sua idade maior de seis meses e menor de 4 anos e seis meses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Medidas estatísticas\n",
    "### Medidas de tendência central\n",
    "- Média aritimética (  $\\overline{x}$  )\n",
    "\n",
    "$$\n",
    "\\overline{x} = \\frac{\\sum x_i}{n}\n",
    "$$\n",
    "- Mediana - ($M_d$)\n",
    "Caso o número de elementos no conjunto seja impar, a mediana é o elemento central, \n",
    "caso seja par a mediana é a média dos dois elementos centrais\n",
    "\n",
    "$$\n",
    "M_d = \\frac{x_i + x_{i+1}}{2}\n",
    "$$\n",
    "- Moda - ($M_o$)\n",
    "Representa o valor mais frequente de um conjunto de dados, sendo assim, para defini-la basta observar a frequência com que os valores aparecem.\n",
    "Um conjunto de dados é chamado de bimodal quando apresenta duas modas, ou seja, dois valores são mais frequentes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medidas para peso em kilo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média = 8.844\n",
      "Mediana = 6.0\n",
      "Moda = 0    6.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "media = df_dog['peso_em_kg'].mean()\n",
    "mediana = df_dog['peso_em_kg'].median()\n",
    "moda = df_dog['peso_em_kg'].mode()\n",
    "print('Média = {}\\nMediana = {}\\nModa = {}'.format(media, mediana, moda))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medidas para idade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média = 5.106\n",
      "Mediana = 4.0\n",
      "Moda = 0    2.0\n",
      "1    3.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "media = df_dog['idade'].mean()\n",
    "mediana = df_dog['idade'].median()\n",
    "moda = df_dog['idade'].mode()\n",
    "print('Média = {}\\nMediana = {}\\nModa = {}'.format(media, mediana, moda))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Medidas de dispersão\n",
    "- Amplitude ($A$)\n",
    "\n",
    "$A = Xmax - Xmin$\n",
    "- Variância ($σ^2$ ou $S^2$)\n",
    "\n",
    "Populacional\n",
    "$$\n",
    "σ^2 = \\frac{\\sum(x_i - M)^2}{N} \n",
    "$$\n",
    "\n",
    "Amostral\n",
    "$$\n",
    "S^2 = \\frac{\\sum(x_i - \\overline{x})^2}{n-1}\n",
    "$$\n",
    "\n",
    "- Dados agrupados:\n",
    "\n",
    "Populacional\n",
    "$$\n",
    "σ^2 = \\frac{\\sum(x_i - M)^2f_i}{N} \n",
    "$$\n",
    "\n",
    "Amostral\n",
    "$$\n",
    "S^2 = \\frac{\\sum(x_i - \\overline{x})^2f_i}{n-1}\n",
    "$$\n",
    "\n",
    "\n",
    "- Desvio padrão ($σ$ ou $S$)\n",
    "- Coeficiente de variação ($CV$)\n",
    "- $x_if_i$ com Sturges\n",
    "$$\n",
    "\\overline{x} = \\frac{x_i + x_{i+1}}{2}\n",
    "$$\n",
    "\n",
    "$$\n",
    "x_if_i = \\overline{x}.f_i\n",
    "$$\n",
    "\n",
    "- Média frequência\n",
    "$$\n",
    "\\overline{x} = \\frac{\\sum x_if_i}{f_i}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando (amplitude) \n",
    "amplitude = np.max(df_dog['peso_em_kg']) - np.min(df_dog['peso_em_kg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formatando a coluna para fazer calculos\n",
    "df_frequencia_pesos['xi'] = df_frequencia_pesos['xi'].apply(lambda x: x.replace(',', '.').replace(' ', '').split('→'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando coluna ja sendo feito o calculo de (xifi com Sturges)\n",
    "df_frequencia_pesos['xifi'] = [( (float(x[0]) + float(x[1]) )/2)*df_frequencia_pesos['fi'][i] for i, x in enumerate(df_frequencia_pesos['xi'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.6"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a (média de frequência)\n",
    "media = sum(df_frequencia_pesos['xifi'])/sum(df_frequencia_pesos['fi'])\n",
    "media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a primeira parte da variancia\n",
    "df_frequencia_pesos['(xi - media)².fi'] = [( ((float(x[0]) + float(x[1]))/2) - media)**2 *df_frequencia_pesos['fi'][i] for i, x in enumerate(df_frequencia_pesos['xi'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medidas de dispersão para pesos em kilos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>xi</th>\n",
       "      <th>fi</th>\n",
       "      <th>fri</th>\n",
       "      <th>Fi</th>\n",
       "      <th>Fri</th>\n",
       "      <th>xifi</th>\n",
       "      <th>(xi - media)².fi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1.5, 10.5]</td>\n",
       "      <td>41</td>\n",
       "      <td>82.0</td>\n",
       "      <td>41</td>\n",
       "      <td>82.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>531.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[10.5, 19.5]</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45</td>\n",
       "      <td>90.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>116.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[19.5, 28.5]</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47</td>\n",
       "      <td>94.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>414.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[28.5, 37.5]</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1095.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[37.5, 46.5]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[46.5, 55.5]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[55.5, 64.5]</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>50</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2540.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             xi  fi   fri  Fi    Fri   xifi  (xi - media)².fi\n",
       "0   [1.5, 10.5]  41  82.0  41   82.0  246.0            531.36\n",
       "1  [10.5, 19.5]   4   8.0  45   90.0   60.0            116.64\n",
       "2  [19.5, 28.5]   2   4.0  47   94.0   48.0            414.72\n",
       "3  [28.5, 37.5]   2   4.0  49   98.0   66.0           1095.12\n",
       "4  [37.5, 46.5]   0   0.0  49   98.0    0.0              0.00\n",
       "5  [46.5, 55.5]   0   0.0  49   98.0    0.0              0.00\n",
       "6  [55.5, 64.5]   1   2.0  50  100.0   60.0           2540.16"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frequencia_pesos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variância: 95.88\n"
     ]
    }
   ],
   "source": [
    "variancia = sum(df_frequencia_pesos['(xi - media)².fi'])/(sum(df_frequencia_pesos['fi'])-1)\n",
    "print('Variância: {:.2f}'.format(variancia))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando (amplitude) \n",
    "amplitude = np.max(df_dog['idade']) - np.min(df_dog['idade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formatando a coluna para fazer calculos\n",
    "df_frequencia_idades['xi'] = df_frequencia_idades['xi'].apply(lambda x: x.replace(',', '.').replace(' ', '').split('→'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando coluna ja sendo feito o calculo de (xifi com Sturges)\n",
    "df_frequencia_idades['xifi'] = [( (float(x[0]) + float(x[1]) )/2)*df_frequencia_idades['fi'][i] for i, x in enumerate(df_frequencia_idades['xi'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.239999999999998"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando a (média de frequência)\n",
    "media = sum(df_frequencia_idades['xifi'])/sum(df_frequencia_idades['fi'])\n",
    "media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculando a primeira parte da variancia\n",
    "df_frequencia_idades['(xi - media)².fi'] = [( ((float(x[0]) + float(x[1]))/2) - media)**2 *df_frequencia_idades['fi'][i] for i, x in enumerate(df_frequencia_idades['xi'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Medidas de dispersão para idades "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>xi</th>\n",
       "      <th>fi</th>\n",
       "      <th>fri</th>\n",
       "      <th>Fi</th>\n",
       "      <th>Fri</th>\n",
       "      <th>xifi</th>\n",
       "      <th>(xi - media)².fi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.6, 2.6]</td>\n",
       "      <td>15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>198.7440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[2.6, 4.6]</td>\n",
       "      <td>12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>27</td>\n",
       "      <td>54.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>32.2752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[4.6, 6.6]</td>\n",
       "      <td>5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32</td>\n",
       "      <td>64.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.6480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[6.6, 8.6]</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39</td>\n",
       "      <td>78.0</td>\n",
       "      <td>53.2</td>\n",
       "      <td>38.9872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[8.6, 10.6]</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>46</td>\n",
       "      <td>92.0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>133.0672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[10.6, 12.6]</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>50</td>\n",
       "      <td>100.0</td>\n",
       "      <td>46.4</td>\n",
       "      <td>161.7984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[12.6, 14.6]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             xi  fi   fri  Fi    Fri  xifi  (xi - media)².fi\n",
       "0    [0.6, 2.6]  15  30.0  15   30.0  24.0          198.7440\n",
       "1    [2.6, 4.6]  12  24.0  27   54.0  43.2           32.2752\n",
       "2    [4.6, 6.6]   5  10.0  32   64.0  28.0            0.6480\n",
       "3    [6.6, 8.6]   7  14.0  39   78.0  53.2           38.9872\n",
       "4   [8.6, 10.6]   7  14.0  46   92.0  67.2          133.0672\n",
       "5  [10.6, 12.6]   4   8.0  50  100.0  46.4          161.7984\n",
       "6  [12.6, 14.6]   0   0.0  50  100.0   0.0            0.0000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_frequencia_idades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variância: 11.54\n"
     ]
    }
   ],
   "source": [
    "variancia = sum(df_frequencia_idades['(xi - media)².fi'])/(sum(df_frequencia_idades['fi'])-1)\n",
    "print('Variância: {:.2f}'.format(variancia))"
   ]
  }
 ],
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