excel_init.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "excel_init.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNPokw59ZptsrYMJhknY/9v",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luisriego/Analise-de-dados-com-Python-e-Pandas/blob/main/excel_init.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOpphbUnfqgC"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQMkBkFhhJAW"
      },
      "source": [
        "# Leitura de arquivos excel\n",
        "df1 = pd.read_excel(\"Aracaju.xlsx\")\n",
        "df2 = pd.read_excel(\"Fortaleza.xlsx\")\n",
        "df3 = pd.read_excel(\"Natal.xlsx\")\n",
        "df4 = pd.read_excel(\"Recife.xlsx\")\n",
        "df5 = pd.read_excel(\"Salvador.xlsx\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wcw6tTGh6j0"
      },
      "source": [
        "# Concatenar os arquivos excel\n",
        "df = pd.concat([df1,df2,df3,df4,df5])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "3nNtLFoSiML7",
        "outputId": "9a68c378-f793-48fb-f493-6aefd4d4d3c8"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "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",
              "    }\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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Cidade       Data  Vendas  LojaID  Qtde\n",
              "0  Aracaju 2018-01-01  142.00    1520     1\n",
              "1  Aracaju 2018-01-01   14.21    1522     6\n",
              "2  Aracaju 2018-01-01   71.55    1520     1\n",
              "3  Aracaju 2018-01-01    3.01    1521     7\n",
              "4  Aracaju 2018-01-01   24.51    1522     8"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "n0yUExpqjGXD",
        "outputId": "5e58e02f-df2d-4c5c-b2f0-0672716bb7c2"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 5,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>41.84</td>\n",
              "      <td>1034</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>126.29</td>\n",
              "      <td>1035</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>38.06</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>139.64</td>\n",
              "      <td>1035</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>161.41</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Cidade       Data  Vendas  LojaID  Qtde\n",
              "235  Salvador 2019-01-03   41.84    1034     1\n",
              "236  Salvador 2019-01-03  126.29    1035     3\n",
              "237  Salvador 2019-01-03   38.06    1036     3\n",
              "238  Salvador 2019-01-03  139.64    1035     1\n",
              "239  Salvador 2019-01-03  161.41    1037     3"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "1_d0KOPUjgiT",
        "outputId": "ed1d212a-f485-44d4-c7c3-da5609f68135"
      },
      "source": [
        "df.sample(7)"
      ],
      "execution_count": 6,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>41.55</td>\n",
              "      <td>981</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>235.06</td>\n",
              "      <td>983</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>135.19</td>\n",
              "      <td>1034</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>4.57</td>\n",
              "      <td>1035</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>80.46</td>\n",
              "      <td>1520</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>606.00</td>\n",
              "      <td>853</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>773.00</td>\n",
              "      <td>852</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas  LojaID  Qtde\n",
              "121  Fortaleza 2019-03-02   41.55     981     3\n",
              "122  Fortaleza 2019-03-02  235.06     983     3\n",
              "87       Natal 2019-01-02  135.19    1034     2\n",
              "92    Salvador 2019-01-01    4.57    1035     1\n",
              "47     Aracaju 2018-01-01   80.46    1520     2\n",
              "0        Natal 2018-08-27  606.00     853     2\n",
              "73       Natal 2018-01-05  773.00     852     2"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68BX_qwsjpLA",
        "outputId": "9bc8b7b7-d249-43f8-f606-02f8c91fba3f"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade            object\n",
              "Data      datetime64[ns]\n",
              "Vendas           float64\n",
              "LojaID             int64\n",
              "Qtde               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eC6sfBLIjumh"
      },
      "source": [
        "# Alterar tipo de dado\n",
        "df[\"LojaID\"] = df[\"LojaID\"].astype(object)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZQDrAZikQim",
        "outputId": "d658d6dd-7073-4f68-8f3b-b15a3c2da52a"
      },
      "source": [
        "# Obter os tipos das colunas\n",
        "df.dtypes"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade            object\n",
              "Data      datetime64[ns]\n",
              "Vendas           float64\n",
              "LojaID            object\n",
              "Qtde               int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQtpj1xRkVGF",
        "outputId": "adf4e528-260c-4611-9a21-322b444469d7"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade    0\n",
              "Data      0\n",
              "Vendas    0\n",
              "LojaID    0\n",
              "Qtde      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyajb1vVkqto"
      },
      "source": [
        "# Criar a coluna Receita\n",
        "df[\"Receita\"] = df[\"Vendas\"].mul(df[\"Qtde\"])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "xe4P6_khpyIc",
        "outputId": "f7c7fbeb-84e4-456f-83f8-27014c6138b9"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 12,
      "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",
              "    }\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",
              "    </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"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NtolxzzLqQph",
        "outputId": "219aba1b-9710-4a35-c7a1-5a5304109b85"
      },
      "source": [
        "# Obter a maior receita\n",
        "df[\"Receita\"].max()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3544.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qV10PwGaqveD",
        "outputId": "81b91269-4aad-4c8a-b11e-950fd2d3cdf9"
      },
      "source": [
        "# Obter a menor receita\n",
        "df[\"Receita\"].min()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.34"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "Exp6hs94q4a6",
        "outputId": "342d5469-702d-4917-da62-7a78662d09aa"
      },
      "source": [
        "# Obter as maiores receitas\n",
        "df.nlargest(3, \"Receita\")"
      ],
      "execution_count": 15,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-18</td>\n",
              "      <td>886.0</td>\n",
              "      <td>853</td>\n",
              "      <td>4</td>\n",
              "      <td>3544.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-01-21</td>\n",
              "      <td>859.0</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>3436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>859.0</td>\n",
              "      <td>854</td>\n",
              "      <td>4</td>\n",
              "      <td>3436.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cidade       Data  Vendas LojaID  Qtde  Receita\n",
              "7   Natal 2019-03-18   886.0    853     4   3544.0\n",
              "51  Natal 2018-01-21   859.0    852     4   3436.0\n",
              "55  Natal 2019-01-08   859.0    854     4   3436.0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "ueB3DYNsrC0N",
        "outputId": "e24bb9fc-873e-45bd-f322-3ab23db8786a"
      },
      "source": [
        "# Obter as menores receitas\n",
        "df.nsmallest(3, \"Receita\")"
      ],
      "execution_count": 16,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>3.34</td>\n",
              "      <td>1522</td>\n",
              "      <td>1</td>\n",
              "      <td>3.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>4.01</td>\n",
              "      <td>981</td>\n",
              "      <td>1</td>\n",
              "      <td>4.01</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Cidade       Data  Vendas LojaID  Qtde  Receita\n",
              "118  Aracaju 2018-01-01    3.34   1522     1     3.34\n",
              "65    Recife 2019-01-01    4.01    981     1     4.01\n",
              "92     Natal 2019-01-02    4.57   1035     1     4.57"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAls7VYUrNf7",
        "outputId": "7967c5ce-6a7d-4627-a877-690b62a8a958"
      },
      "source": [
        "# Obter a receita por cidade\n",
        "df.groupby(\"Cidade\")[\"Receita\"].sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade\n",
              "Aracaju       48748.25\n",
              "Fortaleza     37913.97\n",
              "Natal        167227.52\n",
              "Recife        51936.51\n",
              "Salvador      40596.73\n",
              "Name: Receita, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcSP9ObsrhcF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "3d8cd047-d1d8-4f53-94e3-0c8acf0364ee"
      },
      "source": [
        "# Obter os valores maiores da receita de maior a menor\n",
        "df.sort_values(\"Receita\",ascending=False).head(10)"
      ],
      "execution_count": 18,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-18</td>\n",
              "      <td>886.0</td>\n",
              "      <td>853</td>\n",
              "      <td>4</td>\n",
              "      <td>3544.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>859.0</td>\n",
              "      <td>854</td>\n",
              "      <td>4</td>\n",
              "      <td>3436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-01-21</td>\n",
              "      <td>859.0</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>3436.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-10-02</td>\n",
              "      <td>856.0</td>\n",
              "      <td>853</td>\n",
              "      <td>4</td>\n",
              "      <td>3424.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-05-20</td>\n",
              "      <td>835.0</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>3340.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-02-25</td>\n",
              "      <td>828.0</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>3312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-10-27</td>\n",
              "      <td>828.0</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>3312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>817.0</td>\n",
              "      <td>852</td>\n",
              "      <td>4</td>\n",
              "      <td>3268.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-02-10</td>\n",
              "      <td>793.0</td>\n",
              "      <td>854</td>\n",
              "      <td>4</td>\n",
              "      <td>3172.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-04-27</td>\n",
              "      <td>778.0</td>\n",
              "      <td>854</td>\n",
              "      <td>4</td>\n",
              "      <td>3112.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cidade       Data  Vendas LojaID  Qtde  Receita\n",
              "7   Natal 2019-03-18   886.0    853     4   3544.0\n",
              "55  Natal 2019-01-08   859.0    854     4   3436.0\n",
              "51  Natal 2018-01-21   859.0    852     4   3436.0\n",
              "30  Natal 2018-10-02   856.0    853     4   3424.0\n",
              "41  Natal 2018-05-20   835.0    852     4   3340.0\n",
              "38  Natal 2018-02-25   828.0    852     4   3312.0\n",
              "10  Natal 2018-10-27   828.0    852     4   3312.0\n",
              "69  Natal 2019-03-24   817.0    852     4   3268.0\n",
              "62  Natal 2018-02-10   793.0    854     4   3172.0\n",
              "52  Natal 2018-04-27   778.0    854     4   3112.0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eL5rIL_QeJHu"
      },
      "source": [
        "# Converter datetime a int64 para testes\n",
        "df[\"Data\"] = df[\"Data\"].astype(\"int64\")"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AvC_IzuRfQAA",
        "outputId": "14bad152-6136-4b94-fb53-f1113dcfbd3b"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade      object\n",
              "Data         int64\n",
              "Vendas     float64\n",
              "LojaID      object\n",
              "Qtde         int64\n",
              "Receita    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aX06ybCffZJH"
      },
      "source": [
        "# Converter data em formato int64 a datetime\n",
        "df[\"Data\"] = pd.to_datetime(df[\"Data\"])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0trJl1T3fwlv",
        "outputId": "44939b0d-b720-44d3-d383-bc61a7a48fa9"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade             object\n",
              "Data       datetime64[ns]\n",
              "Vendas            float64\n",
              "LojaID             object\n",
              "Qtde                int64\n",
              "Receita           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQQYO5tIfzYf",
        "outputId": "a2ad7670-9ac2-477c-ac88-de6ba874e594"
      },
      "source": [
        "# Obter a receita por ano\n",
        "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum()"
      ],
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcMxtUspixCa"
      },
      "source": [
        "# Criar a coluna ano\n",
        "df[\"Ano_Venda\"] = df[\"Data\"].dt.year"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "KqXB8g6yjbUH",
        "outputId": "6777c373-feec-4d67-fc0e-0a5e043b29ba"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": 25,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>11.54</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "      <td>11.54</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Recife</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>35.34</td>\n",
              "      <td>982</td>\n",
              "      <td>7</td>\n",
              "      <td>247.38</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>200.85</td>\n",
              "      <td>1522</td>\n",
              "      <td>6</td>\n",
              "      <td>1205.10</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>21.36</td>\n",
              "      <td>1035</td>\n",
              "      <td>2</td>\n",
              "      <td>42.72</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas LojaID  Qtde  Receita  Ano_Venda\n",
              "165      Natal 2019-01-02   11.54   1037     1    11.54       2019\n",
              "111     Recife 2019-03-02    8.00    981     3    24.00       2019\n",
              "129  Fortaleza 2019-03-02   35.34    982     7   247.38       2019\n",
              "91     Aracaju 2018-01-01  200.85   1522     6  1205.10       2018\n",
              "71    Salvador 2019-01-01   21.36   1035     2    42.72       2019"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGQ-2cA9jkJ3"
      },
      "source": [
        "# Criar coluna mes e dia\n",
        "df[\"Mes_Venda\"], df[\"Dia_Venda\"] = (df[\"Data\"].dt.month, df[\"Data\"].dt.day)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "tgAmWcZdkRrx",
        "outputId": "e789ba5e-1255-4600-e611-b4f61de9212b"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": 27,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>174.66</td>\n",
              "      <td>1004</td>\n",
              "      <td>1</td>\n",
              "      <td>174.66</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>165.34</td>\n",
              "      <td>1034</td>\n",
              "      <td>3</td>\n",
              "      <td>496.02</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>185.93</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "      <td>557.79</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>19.42</td>\n",
              "      <td>1035</td>\n",
              "      <td>3</td>\n",
              "      <td>58.26</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>165.36</td>\n",
              "      <td>982</td>\n",
              "      <td>7</td>\n",
              "      <td>1157.52</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",
              "105  Fortaleza 2019-01-01  174.66  ...      2019          1          1\n",
              "38    Salvador 2019-01-01  165.34  ...      2019          1          1\n",
              "110   Salvador 2019-03-02  185.93  ...      2019          3          2\n",
              "176      Natal 2019-01-02   19.42  ...      2019          1          2\n",
              "96      Recife 2019-01-01  165.36  ...      2019          1          1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hk2jDcmDkT-t",
        "outputId": "ead4f561-446e-4ff2-859a-4a83594bc807"
      },
      "source": [
        "# Obter a data mais antiga\n",
        "df[\"Data\"].min()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-01-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nY1F5MUVmds6"
      },
      "source": [
        "# Calcular a diferencia de dias\n",
        "df[\"Diferenca_dias\"] = df[\"Data\"] - df[\"Data\"].min()"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "vaDbyA0Qm-t6",
        "outputId": "a1ae4860-b866-4deb-caed-4ba876c353a0"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": 30,
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>31.20</td>\n",
              "      <td>1036</td>\n",
              "      <td>1</td>\n",
              "      <td>31.20</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>365 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>39.28</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "      <td>117.84</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>366 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>173.59</td>\n",
              "      <td>1034</td>\n",
              "      <td>2</td>\n",
              "      <td>347.18</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>366 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>9.26</td>\n",
              "      <td>1035</td>\n",
              "      <td>3</td>\n",
              "      <td>27.78</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>366 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>44.28</td>\n",
              "      <td>1034</td>\n",
              "      <td>1</td>\n",
              "      <td>44.28</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",
              "88   Salvador 2019-01-01   31.20  ...         1          1        365 days\n",
              "178     Natal 2019-01-02   39.28  ...         1          2        366 days\n",
              "191     Natal 2019-01-02  173.59  ...         1          2        366 days\n",
              "170  Salvador 2019-01-02    9.26  ...         1          2        366 days\n",
              "42   Salvador 2019-01-01   44.28  ...         1          1        365 days\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRbDalShnF2c"
      },
      "source": [
        "#Obter tambem o trimestre da venda\n",
        "df[\"Trimestre_venda\"] = df[\"Data\"].dt.quarter"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8Gn6v6vKnk3M",
        "outputId": "89b80ecb-a483-480f-8ee0-930cc9ef7962"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": 32,
      "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",
              "    }\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>78</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>37.66</td>\n",
              "      <td>1005</td>\n",
              "      <td>2</td>\n",
              "      <td>75.32</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>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>14</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>148.21</td>\n",
              "      <td>1521</td>\n",
              "      <td>3</td>\n",
              "      <td>444.63</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>142.46</td>\n",
              "      <td>1520</td>\n",
              "      <td>1</td>\n",
              "      <td>142.46</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>151.04</td>\n",
              "      <td>1522</td>\n",
              "      <td>6</td>\n",
              "      <td>906.24</td>\n",
              "      <td>2018</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0 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",
              "78   Fortaleza 2019-01-01   37.66  ...         1        365 days                1\n",
              "153      Natal 2019-01-02   17.41  ...         2        366 days                1\n",
              "14     Aracaju 2018-01-01  148.21  ...         1          0 days                1\n",
              "38     Aracaju 2018-01-01  142.46  ...         1          0 days                1\n",
              "24     Aracaju 2018-01-01  151.04  ...         1          0 days                1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y5R057Ennt0k"
      },
      "source": [
        "# Filtrado de vendas por ano e mes\n",
        "vendas_marco_19 = df.loc[(df[\"Data\"].dt.year == 2019) & (df[\"Data\"].dt.month == 3)]"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "Md_JJYhJoq3l",
        "outputId": "40da4783-946c-4a5a-d428-dac4e33e74d6"
      },
      "source": [
        "vendas_marco_19.sample(20)"
      ],
      "execution_count": 34,
      "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",
              "    }\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>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>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>119</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>136.37</td>\n",
              "      <td>1034</td>\n",
              "      <td>2</td>\n",
              "      <td>272.74</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>42</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>775.00</td>\n",
              "      <td>854</td>\n",
              "      <td>2</td>\n",
              "      <td>1550.00</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>124</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>44.82</td>\n",
              "      <td>1034</td>\n",
              "      <td>3</td>\n",
              "      <td>134.46</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>132</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>19.61</td>\n",
              "      <td>1036</td>\n",
              "      <td>2</td>\n",
              "      <td>39.22</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>130</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>12.36</td>\n",
              "      <td>983</td>\n",
              "      <td>4</td>\n",
              "      <td>49.44</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>113</th>\n",
              "      <td>Fortaleza</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>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>114</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>16.82</td>\n",
              "      <td>983</td>\n",
              "      <td>4</td>\n",
              "      <td>67.28</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>125</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>37.60</td>\n",
              "      <td>983</td>\n",
              "      <td>4</td>\n",
              "      <td>150.40</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>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>116</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>79.10</td>\n",
              "      <td>982</td>\n",
              "      <td>2</td>\n",
              "      <td>158.20</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>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>141.16</td>\n",
              "      <td>980</td>\n",
              "      <td>7</td>\n",
              "      <td>988.12</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>74</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-10</td>\n",
              "      <td>303.00</td>\n",
              "      <td>854</td>\n",
              "      <td>3</td>\n",
              "      <td>909.00</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>433 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-31</td>\n",
              "      <td>282.00</td>\n",
              "      <td>854</td>\n",
              "      <td>2</td>\n",
              "      <td>564.00</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>31</td>\n",
              "      <td>454 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-24</td>\n",
              "      <td>559.00</td>\n",
              "      <td>853</td>\n",
              "      <td>2</td>\n",
              "      <td>1118.00</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>447 days</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>9.95</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "      <td>29.85</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>122</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>235.06</td>\n",
              "      <td>983</td>\n",
              "      <td>3</td>\n",
              "      <td>705.18</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>121</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>100.70</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "      <td>302.10</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>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>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>71</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-20</td>\n",
              "      <td>840.00</td>\n",
              "      <td>854</td>\n",
              "      <td>3</td>\n",
              "      <td>2520.00</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>443 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",
              "113     Recife 2019-03-02   38.63  ...         2        425 days                1\n",
              "119   Salvador 2019-03-02  136.37  ...         2        425 days                1\n",
              "42       Natal 2019-03-02  775.00  ...         2        425 days                1\n",
              "124   Salvador 2019-03-02   44.82  ...         2        425 days                1\n",
              "132   Salvador 2019-03-02   19.61  ...         2        425 days                1\n",
              "130     Recife 2019-03-02   12.36  ...         2        425 days                1\n",
              "113  Fortaleza 2019-03-02   38.63  ...         2        425 days                1\n",
              "114  Fortaleza 2019-03-02   16.82  ...         2        425 days                1\n",
              "125  Fortaleza 2019-03-02   37.60  ...         2        425 days                1\n",
              "108  Fortaleza 2019-03-02  152.89  ...         2        425 days                1\n",
              "116  Fortaleza 2019-03-02   79.10  ...         2        425 days                1\n",
              "139     Recife 2019-03-02  141.16  ...         2        425 days                1\n",
              "74       Natal 2019-03-10  303.00  ...        10        433 days                1\n",
              "19       Natal 2019-03-31  282.00  ...        31        454 days                1\n",
              "66       Natal 2019-03-24  559.00  ...        24        447 days                1\n",
              "135   Salvador 2019-03-02    9.95  ...         2        425 days                1\n",
              "122  Fortaleza 2019-03-02  235.06  ...         2        425 days                1\n",
              "121   Salvador 2019-03-02  100.70  ...         2        425 days                1\n",
              "5        Natal 2019-03-30  674.00  ...        30        453 days                1\n",
              "71       Natal 2019-03-20  840.00  ...        20        443 days                1\n",
              "\n",
              "[20 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEtmCm6LbyeG",
        "outputId": "85b72040-f29a-4f91-a6f8-4d11a283211b"
      },
      "source": [
        "df[\"LojaID\"].value_counts(ascending=False)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "1002     30\n",
              "1005     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"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "qcKvYKHRcPoQ",
        "outputId": "a72f87f5-afdf-4dba-c878-6c7131a986b0"
      },
      "source": [
        "df[\"LojaID\"].value_counts(ascending=False).plot.bar()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3363159a50>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "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": 282
        },
        "id": "f5IDo4facqPq",
        "outputId": "8786e1fe-b6d2-4ab3-95f2-139f1cffdce2"
      },
      "source": [
        "# Gráfica de barras horizontais de menor a maior\n",
        "df[\"LojaID\"].value_counts().plot.barh()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3362b6aa10>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "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": "0fxzr_zZc42z",
        "outputId": "65c9cfa4-cc44-4e02-8722-bde6fce7a65e"
      },
      "source": [
        "# Gráfica de barras horizontais de maior a menor\n",
        "df[\"LojaID\"].value_counts(ascending=True).plot.barh();"
      ],
      "execution_count": 42,
      "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": "OpL6Dftic8_U",
        "outputId": "602f85ae-829d-4331-cdf0-7e1fa8c6a569"
      },
      "source": [
        "# Obter gráfica de Pizza para a receita por ano\n",
        "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum().plot.pie();"
      ],
      "execution_count": 44,
      "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": "_4rtVFdbd19E",
        "outputId": "db55a79e-902d-41f5-bf50-fd861026ae79"
      },
      "source": [
        "# Total vendas por cidade\n",
        "df[\"Cidade\"].value_counts()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Natal        240\n",
              "Salvador     240\n",
              "Recife       142\n",
              "Fortaleza    142\n",
              "Aracaju      130\n",
              "Name: Cidade, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "peUl2KJseryv",
        "outputId": "39c4b74f-134a-4d86-b569-5c2fb3715e36"
      },
      "source": [
        "# Adicionar um título e alterar 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": 48,
      "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": 331
        },
        "id": "xsHfEOChfYGq",
        "outputId": "d8eed6ca-3c22-4ebd-ad91-cb2c026bb4f0"
      },
      "source": [
        "# Alterar a cor da barra\n",
        "df[\"Cidade\"].value_counts().plot.bar(title=\"Total vendas por cidade\", color=\"red\")\n",
        "plt.xlabel(\"Cidade\")\n",
        "plt.ylabel(\"Total_Vendas\");"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anAIPZdDg252"
      },
      "source": [
        "# Alterar o estilo\n",
        "plt.style.use(\"ggplot\")"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "o8BuPHXNhMXs",
        "outputId": "e325fa2f-2686-4e41-86e9-5365150791e6"
      },
      "source": [
        "# Alterar a cor da barra\n",
        "df.groupby(df[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title=\"Total produtos vendidos por mes\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.ylabel(\"Total Produtos vendidos\")\n",
        "plt.legend();"
      ],
      "execution_count": 56,
      "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": "3KEcdOoKhmsB",
        "outputId": "4036398c-2581-47d9-beb8-eab84fb4b09b"
      },
      "source": [
        "df.groupby(df[\"Mes_Venda\"])[\"Qtde\"].sum()"
      ],
      "execution_count": 57,
      "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": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3nXY39rjgdS"
      },
      "source": [
        "# Selecionar apenas as vendas de 2019\n",
        "df_2019 = df[df[\"Ano_Venda\"] == 2019]"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "Yzzjga0nkNun",
        "outputId": "fd3b13d1-9519-45f4-bc21-922dd7eeda78"
      },
      "source": [
        "# Total produtos vendidos por mês\n",
        "df_2019.groupby(df_2019[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title=\"Total produtos vendidos por mes em 2019\", marker =\"v\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.ylabel(\"Total Produtos vendidos\")\n",
        "plt.legend();"
      ],
      "execution_count": 60,
      "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": "C_PxXNDWk03E",
        "outputId": "db7dc1f6-58d9-40f6-f858-264efb4a7fc4"
      },
      "source": [
        "plt.hist(df[\"Qtde\"], color=\"orangered\");"
      ],
      "execution_count": 61,
      "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": "QWB1wdSFmESS",
        "outputId": "d65140de-ff89-4923-a463-55f637074214"
      },
      "source": [
        "# Concentração de vendas\n",
        "plt.scatter(x=df_2019[\"Dia_Venda\"] , y=df_2019[\"Receita\"]);"
      ],
      "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": 299
        },
        "id": "MRzNfJ5lmtOF",
        "outputId": "f026185c-0898-463d-b81a-6160e8d46a3c"
      },
      "source": [
        "# Salvar total produtos vendidos por mês\n",
        "df_2019.groupby(df_2019[\"Mes_Venda\"])[\"Qtde\"].sum().plot(title=\"Total produtos vendidos por mes em 2019\", marker =\"v\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.ylabel(\"Total Produtos vendidos\")\n",
        "plt.legend()\n",
        "plt.savefig(\"grafico_qtde_X_mes.png\");"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}