{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estatística Descritiva com Pandas\n",
    "\n",
    "A estatística descritiva é usada para descrever as características básicas dos dados em um estudo. Elas fornecem resumos simples sobre a amostra e as medidas. Junto com a análise gráfica simples, elas formam a base de praticamente todas as análises quantitativas de dados.\n",
    "\n",
    "Para começar, precisamos coletar os dados para o nosso **DataFrame**. Para este exemplo, eu coletei os seguintes dados sobre estudantes:\n",
    "\n",
    "| Nome  | Idade | Pontuação |\n",
    "|---|---|---|\n",
    "| Rafael  | 20  | 70  |\n",
    "| Miguel  | 22  | 80  |\n",
    "| Gabriel  | 27  | 87  |\n",
    "| Emanuel  | 19  | 92  |\n",
    "| Maria  | 25  | 77  |\n",
    "| Sofia  | 30  | 98  |\n",
    "| Luana  | 18  | 100  |\n",
    "| Cassandra  | 17  |   |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de iniciarmos, é necessário importar a biblioteca [pandas](https://pandas.pydata.org/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Criando um DataFrame\n",
    "\n",
    "Em seguida, precisamos criar o DataFrame com base nos dados coletados.\n",
    "\n",
    "Para nosso exemplo, o código para criar o DataFrame é:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>nome</th>\n",
       "      <th>idade</th>\n",
       "      <th>pontuação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rafael</td>\n",
       "      <td>20</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miguel</td>\n",
       "      <td>22</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gabriel</td>\n",
       "      <td>27</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Emanuel</td>\n",
       "      <td>19</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maria</td>\n",
       "      <td>25</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sofia</td>\n",
       "      <td>30</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Luana</td>\n",
       "      <td>18</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cassandra</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        nome  idade  pontuação\n",
       "0     Rafael     20       70.0\n",
       "1     Miguel     22       80.0\n",
       "2    Gabriel     27       87.0\n",
       "3    Emanuel     19       92.0\n",
       "4      Maria     25       80.0\n",
       "5      Sofia     30       98.0\n",
       "6      Luana     18      100.0\n",
       "7  Cassandra     17        NaN"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados = {\n",
    "    'nome': ['Rafael', 'Miguel', 'Gabriel', 'Emanuel','Maria', 'Sofia', 'Luana', 'Cassandra'], \n",
    "    'idade': [20, 22, 27, 19, 25, 30, 18, 17], \n",
    "    'pontuação': [70, 80, 87, 92, 80, 98, 100, None],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(dados)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Digamos que você deseja obter as estatísticas descritivas para o campo '**Idade**', que contém dados numéricos. \n",
    "\n",
    "Nesse caso, a sintaxe que precisamos aplicar é:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     8.000000\n",
       "mean     22.250000\n",
       "std       4.652188\n",
       "min      17.000000\n",
       "25%      18.750000\n",
       "50%      21.000000\n",
       "75%      25.500000\n",
       "max      30.000000\n",
       "Name: idade, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['idade'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para saber as dimensões de nossos dados (quantas linhas e colunas existem), podemos acessar o atributo **shape**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 3)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O atributo **columns** nos informa todas as colunas de nosso DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['nome', 'idade', 'pontuação'], dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O atributo **dtypes** nos informa os tipos de dados de cada coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nome          object\n",
       "idade          int64\n",
       "pontuação    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O método **sum** nos permite obter a soma dos dados de uma coluna numérica:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['idade'].sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos ainda dividir as estatísticas descritivas no seguinte:\n",
    "\n",
    "#### Count (Contagem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].count() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean (Média)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.71428571428571"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mode (Moda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Median (Mediana)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].median() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.25 Quantile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].quantile(q=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.50 Quantile (Median):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].quantile(q=0.50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.75 Quantile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].quantile(q=0.75)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standard Deviation (Desvio Padrão)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.812250547631695"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Var (Variância)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116.90476190476188"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].var() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CumSum (Soma Cumulativa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     70.0\n",
       "1    150.0\n",
       "2    237.0\n",
       "3    329.0\n",
       "4    409.0\n",
       "5    507.0\n",
       "6    607.0\n",
       "7      NaN\n",
       "Name: pontuação, dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].cumsum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Min (Menor Valor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].min() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Max (maior Valor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].max() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Podemos contar quantos valores nulos existem em nosso conjunto de dados usando o método **isna** combinado com **sum**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Também podemos ordenar os valores de uma coluna com o método **sort_values**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7    Cassandra\n",
       "3      Emanuel\n",
       "2      Gabriel\n",
       "6        Luana\n",
       "4        Maria\n",
       "1       Miguel\n",
       "0       Rafael\n",
       "5        Sofia\n",
       "Name: nome, dtype: object"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['nome'].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Usando o operador de atribuição, podemos facilmente alterar a escala da pontuação dos estudantes, neste exemplo vamos dividir ela por 100:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"pontuação\"] /= 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### O método **head** nos permite controlar quantas linhas desejamos ver no topo do DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.70\n",
       "1    0.80\n",
       "2    0.87\n",
       "Name: pontuação, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### O método **tail** nos permite controlar quantas linhas desejamos ver na cauda do DataFrame: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    1.0\n",
       "7    NaN\n",
       "Name: pontuação, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### O método **skew** retorna a inclinação imparcial do eixo normalizada em **N-1**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.2482135835044105"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['pontuação'].skew() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### O método **corr** computa a correlação de colunas emparelhadas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>idade</th>\n",
       "      <th>pontuação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>idade</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.124085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontuação</th>\n",
       "      <td>0.124085</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              idade  pontuação\n",
       "idade      1.000000   0.124085\n",
       "pontuação  0.124085   1.000000"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### O método **cov** computa a covariância entre as Series do DataFrame, o DataFrame retornado é a matriz de covariância das colunas do DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>idade</th>\n",
       "      <th>pontuação</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>idade</th>\n",
       "      <td>21.642857</td>\n",
       "      <td>0.06000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontuação</th>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.01169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               idade  pontuação\n",
       "idade      21.642857    0.06000\n",
       "pontuação   0.060000    0.01169"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pontuação Representada em um Gráfico de Barras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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": [
    "df['pontuação'].plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Idade Representada em um Gráfico de Linha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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": [
    "df['idade'].plot.line();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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