5_trabalhando_com_datas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ccabd4ce",
   "metadata": {},
   "source": [
    "# Python para análise de dados (Pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "952da555",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para instalar:\n",
    "#!pip3 install pandas\n",
    "#Importação da biblioteca\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4c555553",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install openpyxl\n",
    "#Leitura dos arquivos excel\n",
    "df1 = pd.read_excel(\"datasets\\\\Aracaju.xlsx\")\n",
    "df2 = pd.read_excel(\"datasets\\\\Fortaleza.xlsx\")\n",
    "df3 = pd.read_excel(\"datasets\\\\Natal.xlsx\")\n",
    "df4 = pd.read_excel(\"datasets\\\\Recife.xlsx\")\n",
    "df5 = pd.read_excel(\"datasets\\\\Salvador.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e24ea8e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Juntando diferentes dataframes com estrutura igual (colunas e tipos de dados) em um único dataframe:\n",
    "df = pd.concat([df1, df2, df3, df4, df5])\n",
    "df[\"Receita\"] = df[\"Vendas\"].mul(df[\"Qtde\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "974eeb68",
   "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>Cidade</th>\n",
       "      <th>Data</th>\n",
       "      <th>Vendas</th>\n",
       "      <th>LojaID</th>\n",
       "      <th>Qtde</th>\n",
       "      <th>Receita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>142.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>14.21</td>\n",
       "      <td>1522</td>\n",
       "      <td>6</td>\n",
       "      <td>85.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>71.55</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>71.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1521</td>\n",
       "      <td>7</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>24.51</td>\n",
       "      <td>1522</td>\n",
       "      <td>8</td>\n",
       "      <td>196.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cidade       Data  Vendas  LojaID  Qtde  Receita\n",
       "0  Aracaju 2018-01-01  142.00    1520     1   142.00\n",
       "1  Aracaju 2018-01-01   14.21    1522     6    85.26\n",
       "2  Aracaju 2018-01-01   71.55    1520     1    71.55\n",
       "3  Aracaju 2018-01-01    3.01    1521     7    21.07\n",
       "4  Aracaju 2018-01-01   24.51    1522     8   196.08"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2471ea",
   "metadata": {},
   "source": [
    "# Trabalhando com datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4db946dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Temporariamente transformando data em inteiro\n",
    "df[\"Data\"] = df[\"Data\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "85f229ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cidade      object\n",
       "Data         int64\n",
       "Vendas     float64\n",
       "LojaID       int64\n",
       "Qtde         int64\n",
       "Receita    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checando os tipos de dados\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e0afeacc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cidade</th>\n",
       "      <th>Data</th>\n",
       "      <th>Vendas</th>\n",
       "      <th>LojaID</th>\n",
       "      <th>Qtde</th>\n",
       "      <th>Receita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>1514764800000000000</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>142.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>1514764800000000000</td>\n",
       "      <td>14.21</td>\n",
       "      <td>1522</td>\n",
       "      <td>6</td>\n",
       "      <td>85.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>1514764800000000000</td>\n",
       "      <td>71.55</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>71.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>1514764800000000000</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1521</td>\n",
       "      <td>7</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>1514764800000000000</td>\n",
       "      <td>24.51</td>\n",
       "      <td>1522</td>\n",
       "      <td>8</td>\n",
       "      <td>196.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cidade                 Data  Vendas  LojaID  Qtde  Receita\n",
       "0  Aracaju  1514764800000000000  142.00    1520     1   142.00\n",
       "1  Aracaju  1514764800000000000   14.21    1522     6    85.26\n",
       "2  Aracaju  1514764800000000000   71.55    1520     1    71.55\n",
       "3  Aracaju  1514764800000000000    3.01    1521     7    21.07\n",
       "4  Aracaju  1514764800000000000   24.51    1522     8   196.08"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "34bf880e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformando campo para o tipo data\n",
    "df[\"Data\"] = pd.to_datetime(df[\"Data\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fa540789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cidade             object\n",
       "Data       datetime64[ns]\n",
       "Vendas            float64\n",
       "LojaID              int64\n",
       "Qtde                int64\n",
       "Receita           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ca518a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data\n",
       "2018    118176.53\n",
       "2019    228246.45\n",
       "Name: Receita, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Somando receita por ano\n",
    "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7bdf944c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extraindo ano\n",
    "df[\"Ano_Venda\"] = df[\"Data\"].dt.year\n",
    "#Extraindo mês e dia\n",
    "df[\"Mes_Venda\"], df[\"Dia_Venda\"] = (df[\"Data\"].dt.month, df[\"Data\"].dt.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c313542a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cidade</th>\n",
       "      <th>Data</th>\n",
       "      <th>Vendas</th>\n",
       "      <th>LojaID</th>\n",
       "      <th>Qtde</th>\n",
       "      <th>Receita</th>\n",
       "      <th>Ano_Venda</th>\n",
       "      <th>Mes_Venda</th>\n",
       "      <th>Dia_Venda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>142.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>14.21</td>\n",
       "      <td>1522</td>\n",
       "      <td>6</td>\n",
       "      <td>85.26</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>71.55</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>71.55</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>3.01</td>\n",
       "      <td>1521</td>\n",
       "      <td>7</td>\n",
       "      <td>21.07</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>24.51</td>\n",
       "      <td>1522</td>\n",
       "      <td>8</td>\n",
       "      <td>196.08</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cidade       Data  Vendas  LojaID  Qtde  Receita  Ano_Venda  Mes_Venda  \\\n",
       "0  Aracaju 2018-01-01  142.00    1520     1   142.00       2018          1   \n",
       "1  Aracaju 2018-01-01   14.21    1522     6    85.26       2018          1   \n",
       "2  Aracaju 2018-01-01   71.55    1520     1    71.55       2018          1   \n",
       "3  Aracaju 2018-01-01    3.01    1521     7    21.07       2018          1   \n",
       "4  Aracaju 2018-01-01   24.51    1522     8   196.08       2018          1   \n",
       "\n",
       "   Dia_Venda  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2c15b82a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-01-01 00:00:00')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data mais antiga\n",
    "df[\"Data\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e609911c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cidade</th>\n",
       "      <th>Data</th>\n",
       "      <th>Vendas</th>\n",
       "      <th>LojaID</th>\n",
       "      <th>Qtde</th>\n",
       "      <th>Receita</th>\n",
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       "      <th>Dia_Venda</th>\n",
       "      <th>Diferenca_Dias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>14.25</td>\n",
       "      <td>1004</td>\n",
       "      <td>6</td>\n",
       "      <td>85.50</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>365 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Salvador</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>36.89</td>\n",
       "      <td>1037</td>\n",
       "      <td>2</td>\n",
       "      <td>73.78</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>365 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>369.00</td>\n",
       "      <td>853</td>\n",
       "      <td>2</td>\n",
       "      <td>738.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>14 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>38.90</td>\n",
       "      <td>1004</td>\n",
       "      <td>4</td>\n",
       "      <td>155.60</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>365 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Salvador</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>36.23</td>\n",
       "      <td>1035</td>\n",
       "      <td>1</td>\n",
       "      <td>36.23</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>366 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>6.70</td>\n",
       "      <td>1035</td>\n",
       "      <td>1</td>\n",
       "      <td>6.70</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>366 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>118.17</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>118.17</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>365 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Salvador</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>156.47</td>\n",
       "      <td>1037</td>\n",
       "      <td>3</td>\n",
       "      <td>469.41</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>365 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>4.57</td>\n",
       "      <td>1035</td>\n",
       "      <td>1</td>\n",
       "      <td>4.57</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>366 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>71.55</td>\n",
       "      <td>1520</td>\n",
       "      <td>1</td>\n",
       "      <td>71.55</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cidade       Data  Vendas  LojaID  Qtde  Receita  Ano_Venda  \\\n",
       "4    Fortaleza 2019-01-01   14.25    1004     6    85.50       2019   \n",
       "67    Salvador 2019-01-01   36.89    1037     2    73.78       2019   \n",
       "59       Natal 2018-01-15  369.00     853     2   738.00       2018   \n",
       "58   Fortaleza 2019-01-01   38.90    1004     4   155.60       2019   \n",
       "225   Salvador 2019-01-02   36.23    1035     1    36.23       2019   \n",
       "172      Natal 2019-01-02    6.70    1035     1     6.70       2019   \n",
       "63   Fortaleza 2019-01-01  118.17    1004     1   118.17       2019   \n",
       "86    Salvador 2019-01-01  156.47    1037     3   469.41       2019   \n",
       "92       Natal 2019-01-02    4.57    1035     1     4.57       2019   \n",
       "2      Aracaju 2018-01-01   71.55    1520     1    71.55       2018   \n",
       "\n",
       "     Mes_Venda  Dia_Venda Diferenca_Dias  \n",
       "4            1          1       365 days  \n",
       "67           1          1       365 days  \n",
       "59           1         15        14 days  \n",
       "58           1          1       365 days  \n",
       "225          1          2       366 days  \n",
       "172          1          2       366 days  \n",
       "63           1          1       365 days  \n",
       "86           1          1       365 days  \n",
       "92           1          2       366 days  \n",
       "2            1          1         0 days  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculo de diferença de dias\n",
    "df[\"Diferenca_Dias\"] = df[\"Data\"] - df[\"Data\"].min()\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0c4a0c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criando coluna trimestre\n",
    "df[\"Trimestre_Venda\"] = df[\"Data\"].dt.quarter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "129053aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>Cidade</th>\n",
       "      <th>Data</th>\n",
       "      <th>Vendas</th>\n",
       "      <th>LojaID</th>\n",
       "      <th>Qtde</th>\n",
       "      <th>Receita</th>\n",
       "      <th>Ano_Venda</th>\n",
       "      <th>Mes_Venda</th>\n",
       "      <th>Dia_Venda</th>\n",
       "      <th>Diferenca_Dias</th>\n",
       "      <th>Trimestre_Venda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Aracaju</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>161.53</td>\n",
       "      <td>1520</td>\n",
       "      <td>4</td>\n",
       "      <td>646.12</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>365 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>364.00</td>\n",
       "      <td>853</td>\n",
       "      <td>2</td>\n",
       "      <td>728.00</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>415 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2018-07-02</td>\n",
       "      <td>322.00</td>\n",
       "      <td>852</td>\n",
       "      <td>4</td>\n",
       "      <td>1288.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>182 days</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>17.41</td>\n",
       "      <td>1035</td>\n",
       "      <td>2</td>\n",
       "      <td>34.82</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>366 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Natal</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>521.00</td>\n",
       "      <td>854</td>\n",
       "      <td>3</td>\n",
       "      <td>1563.00</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>71 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cidade       Data  Vendas  LojaID  Qtde  Receita  Ano_Venda  Mes_Venda  \\\n",
       "11   Aracaju 2019-01-01  161.53    1520     4   646.12       2019          1   \n",
       "76     Natal 2019-02-20  364.00     853     2   728.00       2019          2   \n",
       "6      Natal 2018-07-02  322.00     852     4  1288.00       2018          7   \n",
       "153    Natal 2019-01-02   17.41    1035     2    34.82       2019          1   \n",
       "47     Natal 2018-03-13  521.00     854     3  1563.00       2018          3   \n",
       "\n",
       "     Dia_Venda Diferenca_Dias  Trimestre_Venda  \n",
       "11           1       365 days                1  \n",
       "76          20       415 days                1  \n",
       "6            2       182 days                3  \n",
       "153          2       366 days                1  \n",
       "47          13        71 days                1  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "41de24bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filtrando as vendas de 2019 do mês de março\n",
    "vendas_marco_19 = df.loc[(df[\"Data\"].dt.year == 2019) & (df[\"Data\"].dt.month == 3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4b6b7e8c",
   "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>Cidade</th>\n",
       "      <th>Data</th>\n",
       "      <th>Vendas</th>\n",
       "      <th>LojaID</th>\n",
       "      <th>Qtde</th>\n",
       "      <th>Receita</th>\n",
       "      <th>Ano_Venda</th>\n",
       "      <th>Mes_Venda</th>\n",
       "      <th>Dia_Venda</th>\n",
       "      <th>Diferenca_Dias</th>\n",
       "      <th>Trimestre_Venda</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>152.89</td>\n",
       "      <td>981</td>\n",
       "      <td>4</td>\n",
       "      <td>611.56</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>425 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>18.90</td>\n",
       "      <td>982</td>\n",
       "      <td>6</td>\n",
       "      <td>113.40</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>425 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>51.98</td>\n",
       "      <td>983</td>\n",
       "      <td>6</td>\n",
       "      <td>311.88</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>425 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>8.00</td>\n",
       "      <td>981</td>\n",
       "      <td>3</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>425 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Fortaleza</td>\n",
       "      <td>2019-03-02</td>\n",
       "      <td>133.59</td>\n",
       "      <td>982</td>\n",
       "      <td>1</td>\n",
       "      <td>133.59</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>425 days</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cidade       Data  Vendas  LojaID  Qtde  Receita  Ano_Venda  \\\n",
       "108  Fortaleza 2019-03-02  152.89     981     4   611.56       2019   \n",
       "109  Fortaleza 2019-03-02   18.90     982     6   113.40       2019   \n",
       "110  Fortaleza 2019-03-02   51.98     983     6   311.88       2019   \n",
       "111  Fortaleza 2019-03-02    8.00     981     3    24.00       2019   \n",
       "112  Fortaleza 2019-03-02  133.59     982     1   133.59       2019   \n",
       "\n",
       "     Mes_Venda  Dia_Venda Diferenca_Dias  Trimestre_Venda  \n",
       "108          3          2       425 days                1  \n",
       "109          3          2       425 days                1  \n",
       "110          3          2       425 days                1  \n",
       "111          3          2       425 days                1  \n",
       "112          3          2       425 days                1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendas_marco_19.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e1ff7e3",
   "metadata": {},
   "source": [
    "# Visualização de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "222cfa6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1036    117\n",
       "1035    112\n",
       "1037    101\n",
       "1034     67\n",
       "983      65\n",
       "982      44\n",
       "1522     41\n",
       "1520     39\n",
       "980      36\n",
       "981      31\n",
       "1005     30\n",
       "1002     30\n",
       "852      29\n",
       "1523     29\n",
       "1004     28\n",
       "854      28\n",
       "853      26\n",
       "1521     21\n",
       "1003     20\n",
       "Name: LojaID, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"LojaID\"].value_counts(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "6898dbcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (3.6.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (1.0.5)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (21.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (4.37.4)\n",
      "Requirement already satisfied: numpy>=1.19 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (1.23.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from matplotlib) (9.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\eugen\\appdata\\local\\programs\\python\\python310\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3833d169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Gráfico de barras\n",
    "df[\"LojaID\"].value_counts(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5dd8155a",
   "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[\"LojaID\"].value_counts(ascending=False).plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "bf961bdf",
   "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[\"LojaID\"].value_counts(ascending=True).plot.barh();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "79c0e127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico de pizza\n",
    "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum().plot.pie();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "44ace475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Natal        240\n",
       "Salvador     240\n",
       "Fortaleza    142\n",
       "Recife       142\n",
       "Aracaju      130\n",
       "Name: Cidade, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total de vendas por cidade\n",
    "df[\"Cidade\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "34eaa240",
   "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": [
    "#Adicionando título e alterando nome dos eixos\n",
    "import matplotlib.pyplot as plt\n",
    "df[\"Cidade\"].value_counts().plot.bar(title=\"Total de vendas por cidade\")\n",
    "plt.xlabel(\"Cidade\")\n",
    "plt.ylabel(\"Total de vendas\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "e412ecb0",
   "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": [
    "#Alterando cor\n",
    "df[\"Cidade\"].value_counts().plot.bar(title=\"Total de vendas por cidade\", color=\"red\")\n",
    "plt.xlabel(\"Cidade\")\n",
    "plt.ylabel(\"Total de vendas\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "adfa92d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Alterando estilo\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "84b86e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(df[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title = \"Total de produtos vendidos por mês\")\n",
    "plt.xlabel(\"Mês\")\n",
    "plt.ylabel(\"Total de Produtos Vendidos\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "989f5575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mes_Venda\n",
       "1     2208\n",
       "2      144\n",
       "3      467\n",
       "4       23\n",
       "5       17\n",
       "6       13\n",
       "7       13\n",
       "8        2\n",
       "9       10\n",
       "10      14\n",
       "11       2\n",
       "12       3\n",
       "Name: Qtde, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df[\"Mes_Venda\"])[\"Qtde\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d8c58641",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecionando apenas as vendas de 2019\n",
    "df_2019 = df[df[\"Ano_Venda\"] == 2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ea5f0d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total de produtos veniddos por mês\n",
    "df_2019.groupby(df_2019[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title = \"Total de Vendas por Mês de 2019\", marker = \"v\")\n",
    "plt.xlabel(\"Mês\")\n",
    "plt.ylabel(\"Total de Produtos Vendidos\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "5e71bfdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histograma\n",
    "plt.hist(df[\"Qtde\"], color=\"orangered\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "30f3b844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df_2019[\"Dia_Venda\"], y=df_2019[\"Receita\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "97e455cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Salvando PNG\n",
    "df_2019.groupby(df_2019[\"Mes_Venda\"])[\"Qtde\"].sum().plot(marker=\"v\")\n",
    "plt.title(\"Quantidade de produtos vendidos por mês\")\n",
    "plt.xlabel(\"Mês\")\n",
    "plt.ylabel(\"Total de produtos vendidos\")\n",
    "plt.legend()\n",
    "plt.savefig(\"Grafico qtde x mes.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5279a892",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}