VisualizandoDados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TrabalhandoComDatas.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgQcVQQRsI7r"
      },
      "source": [
        "# importando a biblioteca pandas\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3u5UQzFRV-1"
      },
      "source": [
        "# Carregando arquivos\n",
        "path = \"/content/drive/MyDrive/Colab Notebooks/DIO_AnaliseDadosPythonPandas/datasets\"\n",
        "df1 = pd.read_excel(path+\"/\"+\"Aracaju.xlsx\")\n",
        "df2 = pd.read_excel(path+\"/\"+\"Fortaleza.xlsx\")\n",
        "df3 = pd.read_excel(path+\"/\"+\"Natal.xlsx\")\n",
        "df4 = pd.read_excel(path+\"/\"+\"Recife.xlsx\")\n",
        "df5 = pd.read_excel(path+\"/\"+\"Salvador.xlsx\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9ye6C40RvUE"
      },
      "source": [
        "# Unindo todos os arquivos\n",
        "df = pd.concat([df1,df2,df3,df4,df5])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OFBkojDJaV9c"
      },
      "source": [
        "Caso sua coluna de data não esteja no formato datetime, é necessário transformar seu tipo para que seja mais facilmente manipulável com o pandas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSKyGH4kZ764"
      },
      "source": [
        "# Transformando uma coluna em um tipo de data\n",
        "df[\"Data\"] = pd.to_datetime(df[\"Data\"])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mC5jOs9PW4lJ"
      },
      "source": [
        "df[\"Receita\"] = df[\"Vendas\"] * df[\"Qtde\"]"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Hq-X3piXpz3"
      },
      "source": [
        "df[\"Receita/Vendas\"] = df[\"Receita\"] / df[\"Vendas\"]"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gLyx0XkakQO",
        "outputId": "9121f479-04b1-40fd-a193-4d9d7f46aa6c"
      },
      "source": [
        "# Utilizando a data, obtendo somente o ano e realizando um agrupamento\n",
        "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data\n",
              "2018    118176.53\n",
              "2019    228246.45\n",
              "Name: Receita, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXJwGS39dbUG"
      },
      "source": [
        "# Criando uma nova coluna com o Ano\n",
        "df[\"Ano_Venda\"] = df[\"Data\"].dt.year\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "RK70NmZidljF",
        "outputId": "7f8abe39-7c98-474e-b765-2d81814677b9"
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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",
              "      <th>Receita/Vendas</th>\n",
              "      <th>Ano_Venda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-09-20</td>\n",
              "      <td>676.00</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>2704.00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>37.68</td>\n",
              "      <td>1522</td>\n",
              "      <td>10</td>\n",
              "      <td>376.80</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>32.47</td>\n",
              "      <td>1036</td>\n",
              "      <td>2</td>\n",
              "      <td>64.94</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>182.75</td>\n",
              "      <td>983</td>\n",
              "      <td>8</td>\n",
              "      <td>1462.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>212.03</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "      <td>636.09</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>14.67</td>\n",
              "      <td>980</td>\n",
              "      <td>7</td>\n",
              "      <td>102.69</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>Salvador</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>2.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>38.63</td>\n",
              "      <td>980</td>\n",
              "      <td>2</td>\n",
              "      <td>77.26</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>38.70</td>\n",
              "      <td>982</td>\n",
              "      <td>4</td>\n",
              "      <td>154.80</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>35.30</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "      <td>35.30</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas  ...  Receita  Receita/Vendas  Ano_Venda\n",
              "48       Natal 2018-09-20  676.00  ...  2704.00             4.0       2018\n",
              "16     Aracaju 2018-01-01   37.68  ...   376.80            10.0       2018\n",
              "16    Salvador 2019-01-01   32.47  ...    64.94             2.0       2019\n",
              "141     Recife 2019-03-02  182.75  ...  1462.00             8.0       2019\n",
              "138      Natal 2019-01-02  212.03  ...   636.09             3.0       2019\n",
              "133     Recife 2019-03-02   14.67  ...   102.69             7.0       2019\n",
              "153   Salvador 2019-01-02   17.41  ...    34.82             2.0       2019\n",
              "113     Recife 2019-03-02   38.63  ...    77.26             2.0       2019\n",
              "128  Fortaleza 2019-03-02   38.70  ...   154.80             4.0       2019\n",
              "174   Salvador 2019-01-02   35.30  ...    35.30             1.0       2019\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPdpxy0gdwZd"
      },
      "source": [
        "# Criando uma nova coluna com o Mês\n",
        "df[\"Mes_Venda\"] = df[\"Data\"].dt.month\n",
        "df[\"Dia_Venda\"] = df[\"Data\"].dt.day"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "mRescUiWd9d4",
        "outputId": "66f8ce6a-ab65-4612-819a-db817de1d61a"
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "      <th>Receita/Vendas</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>62</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>28.66</td>\n",
              "      <td>1520</td>\n",
              "      <td>3</td>\n",
              "      <td>85.98</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>4.54</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "      <td>13.62</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>36.42</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "      <td>36.42</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>34.50</td>\n",
              "      <td>1035</td>\n",
              "      <td>1</td>\n",
              "      <td>34.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>142.23</td>\n",
              "      <td>1035</td>\n",
              "      <td>2</td>\n",
              "      <td>284.46</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>17.66</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "      <td>52.98</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>33.97</td>\n",
              "      <td>1034</td>\n",
              "      <td>3</td>\n",
              "      <td>101.91</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>76.59</td>\n",
              "      <td>1523</td>\n",
              "      <td>4</td>\n",
              "      <td>306.36</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>8.64</td>\n",
              "      <td>1522</td>\n",
              "      <td>2</td>\n",
              "      <td>17.28</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>218.76</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "      <td>218.76</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Cidade       Data  Vendas  ...  Ano_Venda  Mes_Venda  Dia_Venda\n",
              "62    Aracaju 2018-01-01   28.66  ...       2018          1          1\n",
              "61   Salvador 2019-01-01    4.54  ...       2019          1          1\n",
              "167     Natal 2019-01-02   36.42  ...       2019          1          2\n",
              "147     Natal 2019-01-02   34.50  ...       2019          1          2\n",
              "221     Natal 2019-01-02  142.23  ...       2019          1          2\n",
              "163  Salvador 2019-01-02   17.66  ...       2019          1          2\n",
              "20   Salvador 2019-01-01   33.97  ...       2019          1          1\n",
              "66    Aracaju 2018-01-01   76.59  ...       2018          1          1\n",
              "53    Aracaju 2018-01-01    8.64  ...       2018          1          1\n",
              "77   Salvador 2019-01-01  218.76  ...       2019          1          1\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTWMBXEJfLVa",
        "outputId": "580d2764-1dfd-4952-d2b3-c01b3485d6b0"
      },
      "source": [
        "# Menor data\n",
        "df[\"Data\"].min()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-01-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYMMSEfefPPB"
      },
      "source": [
        "# Realizando calculos com datas\n",
        "# Aqui terá a diferença de dias entre a menor data e a data da venda.\n",
        "df[\"diferenca_dias\"] = df[\"Data\"] - df[\"Data\"].min()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "9Xx5nzz7fcFv",
        "outputId": "ebd2130d-f8f5-45f5-e1d9-539612fd1b28"
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</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>Receita/Vendas</th>\n",
              "      <th>Ano_Venda</th>\n",
              "      <th>Mes_Venda</th>\n",
              "      <th>Dia_Venda</th>\n",
              "      <th>diferenca_dias</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>13.27</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "      <td>13.27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>366 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>238.73</td>\n",
              "      <td>980</td>\n",
              "      <td>3</td>\n",
              "      <td>716.19</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>365 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>44.89</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "      <td>134.67</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>366 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>84.62</td>\n",
              "      <td>1034</td>\n",
              "      <td>2</td>\n",
              "      <td>169.24</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>366 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>241.45</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "      <td>241.45</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>365 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>51.99</td>\n",
              "      <td>983</td>\n",
              "      <td>3</td>\n",
              "      <td>155.97</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>425 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>63.48</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "      <td>190.44</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>425 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>150.38</td>\n",
              "      <td>983</td>\n",
              "      <td>6</td>\n",
              "      <td>902.28</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>425 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>42.94</td>\n",
              "      <td>1034</td>\n",
              "      <td>3</td>\n",
              "      <td>128.82</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>365 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>33.97</td>\n",
              "      <td>1034</td>\n",
              "      <td>3</td>\n",
              "      <td>101.91</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>365 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Cidade       Data  Vendas  ...  Mes_Venda  Dia_Venda  diferenca_dias\n",
              "122     Natal 2019-01-02   13.27  ...          1          2        366 days\n",
              "78     Recife 2019-01-01  238.73  ...          1          1        365 days\n",
              "195  Salvador 2019-01-02   44.89  ...          1          2        366 days\n",
              "145  Salvador 2019-01-02   84.62  ...          1          2        366 days\n",
              "35   Salvador 2019-01-01  241.45  ...          1          1        365 days\n",
              "137    Recife 2019-03-02   51.99  ...          3          2        425 days\n",
              "131  Salvador 2019-03-02   63.48  ...          3          2        425 days\n",
              "138    Recife 2019-03-02  150.38  ...          3          2        425 days\n",
              "96   Salvador 2019-01-01   42.94  ...          1          1        365 days\n",
              "20   Salvador 2019-01-01   33.97  ...          1          1        365 days\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADIXFgpKffwM"
      },
      "source": [
        "# Criando a coluna de trimestre\n",
        "df[\"trimestre_venda\"] = df[\"Data\"].dt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_ZM12N9gCav"
      },
      "source": [
        "# Coluna com o trimestre\n",
        "df[\"trimestre_venda\"] = df[\"Data\"].dt.quarter"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "SF_4SxJPgHVW",
        "outputId": "94b00cc0-4297-4060-e5b8-91c24f431004"
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>Receita/Vendas</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>37.49</td>\n",
              "      <td>1002</td>\n",
              "      <td>1</td>\n",
              "      <td>37.49</td>\n",
              "      <td>1.0</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>5</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-30</td>\n",
              "      <td>674.00</td>\n",
              "      <td>854</td>\n",
              "      <td>2</td>\n",
              "      <td>1348.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>453 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>169.09</td>\n",
              "      <td>1036</td>\n",
              "      <td>2</td>\n",
              "      <td>338.18</td>\n",
              "      <td>2.0</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>63</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-02-12</td>\n",
              "      <td>552.00</td>\n",
              "      <td>852</td>\n",
              "      <td>2</td>\n",
              "      <td>1104.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>42 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>37.75</td>\n",
              "      <td>1035</td>\n",
              "      <td>2</td>\n",
              "      <td>75.50</td>\n",
              "      <td>2.0</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>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>1.0</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>77</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>426.00</td>\n",
              "      <td>853</td>\n",
              "      <td>3</td>\n",
              "      <td>1278.00</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>455 days</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>68.60</td>\n",
              "      <td>1004</td>\n",
              "      <td>6</td>\n",
              "      <td>411.60</td>\n",
              "      <td>6.0</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>109</th>\n",
              "      <td>Recife</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>6.0</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>127</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>39.56</td>\n",
              "      <td>982</td>\n",
              "      <td>8</td>\n",
              "      <td>316.48</td>\n",
              "      <td>8.0</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  ...  Dia_Venda  diferenca_dias  trimestre_venda\n",
              "91   Fortaleza 2019-01-01   37.49  ...          1        365 days                1\n",
              "5        Natal 2019-03-30  674.00  ...         30        453 days                1\n",
              "186   Salvador 2019-01-02  169.09  ...          2        366 days                1\n",
              "63       Natal 2018-02-12  552.00  ...         12         42 days                1\n",
              "203      Natal 2019-01-02   37.75  ...          2        366 days                1\n",
              "112  Fortaleza 2019-03-02  133.59  ...          2        425 days                1\n",
              "77       Natal 2019-04-01  426.00  ...          1        455 days                2\n",
              "88   Fortaleza 2019-01-01   68.60  ...          1        365 days                1\n",
              "109     Recife 2019-03-02   18.90  ...          2        425 days                1\n",
              "127     Recife 2019-03-02   39.56  ...          2        425 days                1\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLGZzcNUgJct"
      },
      "source": [
        "# Filtrando as vendas de Março de 2019 \n",
        "filtro1 = df[\"Data\"].dt.year == 2019\n",
        "filtro2 = df[\"Data\"].dt.month == 3\n",
        "vendas_03_2019 = df.loc[(filtro1 & filtro2)]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "eaStLK4Bgg1R",
        "outputId": "e9581da4-c383-44be-b2b0-4e04b2490fb3"
      },
      "source": [
        "vendas_03_2019"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>Receita/Vendas</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>4.0</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>6.0</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>6.0</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>3.0</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>1.0</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>51.66</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "      <td>154.98</td>\n",
              "      <td>3.0</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>138</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>212.03</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "      <td>636.09</td>\n",
              "      <td>3.0</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>139</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>169.01</td>\n",
              "      <td>1036</td>\n",
              "      <td>1</td>\n",
              "      <td>169.01</td>\n",
              "      <td>1.0</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>140</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>20.79</td>\n",
              "      <td>1036</td>\n",
              "      <td>2</td>\n",
              "      <td>41.58</td>\n",
              "      <td>2.0</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>141</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>9.08</td>\n",
              "      <td>1036</td>\n",
              "      <td>1</td>\n",
              "      <td>9.08</td>\n",
              "      <td>1.0</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",
              "<p>121 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas  ...  Dia_Venda  diferenca_dias  trimestre_venda\n",
              "108  Fortaleza 2019-03-02  152.89  ...          2        425 days                1\n",
              "109  Fortaleza 2019-03-02   18.90  ...          2        425 days                1\n",
              "110  Fortaleza 2019-03-02   51.98  ...          2        425 days                1\n",
              "111  Fortaleza 2019-03-02    8.00  ...          2        425 days                1\n",
              "112  Fortaleza 2019-03-02  133.59  ...          2        425 days                1\n",
              "..         ...        ...     ...  ...        ...             ...              ...\n",
              "137   Salvador 2019-03-02   51.66  ...          2        425 days                1\n",
              "138   Salvador 2019-03-02  212.03  ...          2        425 days                1\n",
              "139   Salvador 2019-03-02  169.01  ...          2        425 days                1\n",
              "140   Salvador 2019-03-02   20.79  ...          2        425 days                1\n",
              "141   Salvador 2019-03-02    9.08  ...          2        425 days                1\n",
              "\n",
              "[121 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLp8af2PhE5V"
      },
      "source": [
        "# Visualizando dados com a Biblioteca MathLib\n",
        "  A biblioteca Mathlib é totalmente integrada com o Pandas e assim não são necessários muitos ajustes para exibir um gráfico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "tMWj5Zk8iFrg",
        "outputId": "efbb6e5d-0de5-4e32-85bd-0c101577faae"
      },
      "source": [
        "# Grafico de barras\n",
        "df[\"Cidade\"].value_counts(ascending=False).plot.bar();"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "zTMdO7VLiYFc",
        "outputId": "5c88bd26-eed7-40f1-cb6f-badba1f38abd"
      },
      "source": [
        "# Gráfico de barras horizontais\n",
        "df[\"Cidade\"].value_counts(ascending=True).plot.barh();"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "U4wkmPkfific",
        "outputId": "b2b1a376-146b-4cf9-8065-e0b0bc8ef8a2"
      },
      "source": [
        "# Grafico de Pizza\n",
        "df[\"Cidade\"].value_counts(ascending=False).plot.pie();"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "7R1ZWnmIisc6",
        "outputId": "7d7d77ab-3d69-455d-9096-503faf925937"
      },
      "source": [
        "# Grafico de Pizza\n",
        "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum().plot.pie();"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "OyZI2JYcjBdS",
        "outputId": "60b2783d-2a3a-4c08-ad22-f04c104b5403"
      },
      "source": [
        "# Adicionando um título e alterando o nome dos eixos\n",
        "import matplotlib.pyplot as plt\n",
        "df[\"Cidade\"].value_counts().plot.bar(title=\"Total Vendas Por Cidade\")\n",
        "plt.xlabel(\"Cidade\")\n",
        "plt.ylabel(\"Total Vendas\");"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQVRyWLFjh9e"
      },
      "source": [
        "# Adicionando um título e alterando o nome dos eixos\n",
        "import matplotlib.pyplot as plt\n",
        "df[\"Cidade\"].value_counts().plot.bar(title=\"Total Vendas Por Cidade\")\n",
        "plt.xlabel(\"Cidade\")\n",
        "plt.ylabel(\"Total Vendas\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "tfNKrbNnjASN",
        "outputId": "f9d2c7d9-7591-43d2-9ef2-2c9e4d141c0f"
      },
      "source": [
        "# Alterando as cores\n",
        "df[\"Cidade\"].value_counts().plot.bar(title=\"Total Vendas Por Cidade\", color=\"red\")\n",
        "plt.xlabel(\"Mes\")\n",
        "plt.ylabel(\"Cidade\")"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cidade')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "iE3wnArSlz4A",
        "outputId": "034f1986-00c5-4af0-d5d9-d99f0fe93198"
      },
      "source": [
        "# Alterando estilos matplotlib.org/3.2.1/\n",
        "plt.style.use(\"ggplot\")\n",
        "df.groupby(df[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title=\"Total de Produtos Vendidos por Mes\")\n",
        "plt.xlabel(\"Mes\")\n",
        "plt.ylabel(\"Total Produtos Vendidos\")\n",
        "plt.legend();"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A_4KMz0dminx",
        "outputId": "84263ea3-af3f-4b54-e168-6e9cfdc8f0e1"
      },
      "source": [
        "df.groupby(df[\"Mes_Venda\"])[\"Qtde\"].sum()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVGqrI7Emr6I"
      },
      "source": [
        "filtro1 = df[\"Ano_Venda\"] == 2019\n",
        "df_2019 = df[filtro1]"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "bnxdJMqFmylA",
        "outputId": "a6518a5e-941c-4d4e-dd94-a6e6c55dfcc6"
      },
      "source": [
        "df_2019.groupby(df_2019[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title=\"Total de Vendas em 2019\" ,marker = 'o') # *, o. v \n",
        "plt.xlabel = \"Mes\"\n",
        "plt.ylabel = \"Total de Produtos Vendidos\"\n",
        "plt.legend();"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "HK_hiLZYnY8K",
        "outputId": "41547037-1d0c-4749-c193-3a250536c4ee"
      },
      "source": [
        "# Grafico em Histograma\n",
        "plt.hist(df[\"Qtde\"], color=\"peru\");"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "LoLWX4trnxi1",
        "outputId": "8f4cd649-bc48-46fe-e686-4cc4aa3f95ff"
      },
      "source": [
        "# Gráfico de dispersão\n",
        "plt.scatter(x=df_2019[\"Dia_Venda\"], y = df_2019[\"Receita\"])\n",
        "plt.title(\"Total de Vendas Por Dia\");"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "gHMRJhykoGhr",
        "outputId": "a958c2e0-8633-4232-adaf-8a631f458ea5"
      },
      "source": [
        "# Salvando o gráfico gerado em um arquivo\n",
        "df_2019.groupby(df_2019[\"Mes_Venda\"])[\"Qtde\"].sum().plot(marker = \"v\",xlabel=\"Mes\", ylabel=\"Total Produtos Vendidos\")\n",
        "plt.title(\"Quantidade de Produtos Vendidos por Mes\")\n",
        "#plt.ylabel(\"Total Produtos Vendidos\")\n",
        "#plt.xlabel(\"Mes da Venda\")\n",
        "plt.legend();\n",
        "plt.savefig(\"Grfico_Qtde_Por_Mes.png\")"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}