DataAnalitics_I_Aula.ipynb

{
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  "metadata": {
    "colab": {
      "name": "DataAnalitics I.Aula",
      "provenance": [],
      "mount_file_id": "1NRmVjmmFZOrHYxZihdemKwrILGvH8Nga",
      "authorship_tag": "ABX9TyPbFEPEau+ZEswgjTX3sp8W",
      "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/1tjunior1/Aula-Sobre-Debug2/blob/master/DataAnalitics_I_Aula.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZeN9IN9xder",
        "outputId": "7d41cc32-b244-4923-9d8e-8d3d13cff323"
      },
      "source": [
        "print ('Hello World')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello World\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RW7HAK-QyzFB",
        "outputId": "cc66e564-8bfe-4f94-f943-4c81d1a53a2c"
      },
      "source": [
        "print ('I´m learning Python')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I´m learning Python\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "M2f_3SEJzEiM",
        "outputId": "21fe6c6b-bb6a-494e-8203-5e21b6adb9d3"
      },
      "source": [
        "'Learn Python'"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Learn Python'"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "86pfxiuwzRMs",
        "outputId": "cf0c4dd4-55c2-4177-96c1-1ec882f200fd"
      },
      "source": [
        "4 + 5"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnQ5VPGpzXyr",
        "outputId": "962e7343-5849-41c0-9f30-d350531ca898"
      },
      "source": [
        "20-15"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVMB0LUdzgMP"
      },
      "source": [
        "#This is a commenting in Python"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuPlDIjizmgw",
        "outputId": "fbc43437-4526-4e06-d601-a2a80d924a37"
      },
      "source": [
        "#Multiplication \n",
        "45 * 5"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "225"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17-32ghnz6p2",
        "outputId": "6487a166-6961-4f4e-da7c-153594e2b7a8"
      },
      "source": [
        "#Division \n",
        "25 / 5"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "worWc9cg0Fwk"
      },
      "source": [
        "a = 4\n",
        "b = 6"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5BspXxP0mxD",
        "outputId": "d528dfee-817b-436e-ce46-f096fe6a7c99"
      },
      "source": [
        "a + b"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQs-jiCn11Pt"
      },
      "source": [
        "#Creating a variable as a String\n",
        "Phrase = 'Python to data analytics'"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2_CZ1FLW2Q1j",
        "outputId": "8e40e2b8-c6c6-4376-a935-26dc94f5a4b2"
      },
      "source": [
        "Phrase[0] "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'P'"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "xVpZnIXA2bJq",
        "outputId": "8f685d6b-d5cc-4627-81e9-4526a90ec540"
      },
      "source": [
        "Phrase[0:10]"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Python to '"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tMgaIkTm20P5",
        "outputId": "7d2aa16a-ae7f-406c-cf93-596b89062024"
      },
      "source": [
        "Phrase[10:]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'data analytics'"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "pglZFfdO3C8s",
        "outputId": "90dd1ab3-8dea-4ce1-82c9-91abf3debf56"
      },
      "source": [
        "Phrase[:17]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Python to data an'"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        },
        "id": "dnIx-Fli3Ytu",
        "outputId": "a4520838-3dc0-49b5-d571-435dbe9062df"
      },
      "source": [
        "Phrase.count('data')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "RBaG-TUi48v0",
        "outputId": "a04f09bd-d97c-4851-8282-835086922d3d"
      },
      "source": [
        "Phrase.lower()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'python to data analytics'"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Zx6RJV_d5LwO",
        "outputId": "9d4f7852-e17b-487a-c7b9-c3ea80ef5dad"
      },
      "source": [
        "Phrase.upper()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'PYTHON TO DATA ANALYTICS'"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "LWI2f4Nx5dYj",
        "outputId": "5f516413-88a7-40aa-93d6-e794acc158dc"
      },
      "source": [
        "Phrase.replace('Python','Phrases')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Phrases to data analytics'"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwKf2YjVR9bm",
        "outputId": "26ede43f-fab5-4a5b-a208-4755c4e4f1f5"
      },
      "source": [
        "#Python Data Structure\n",
        "#Lists\n",
        "\n",
        "#Creating a list called Animals\n",
        "Animals = [1, 2, 3]\n",
        "Animals"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KATF_wsTjNF",
        "outputId": "da0badf2-a714-44bd-c8b6-c5ae2acbe281"
      },
      "source": [
        "Animals = ['Dog', 'Cat', 12345, 6.5 ]\n",
        "Animals"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Dog', 'Cat', 12345, 6.5]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "dyidm0xNT_lL",
        "outputId": "7231d134-1b24-482e-fe45-bfa73028cf0b"
      },
      "source": [
        "#Printintg the first element of the list\n",
        "Animals[0]"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Dog'"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42cTohyAUX9Z",
        "outputId": "15a322e6-2b93-4be1-f554-b106c4577de0"
      },
      "source": [
        "#Printing the fourth element of my list\n",
        "Animals[3]"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.5"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9nfQwJVU0TE"
      },
      "source": [
        "#Changing the first element in the list\n",
        "Animals[0] = 'Parrot'"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psAlOaEVU1_k",
        "outputId": "e0960ef0-b0a3-457e-d52c-d94aa37fd999"
      },
      "source": [
        "Animals"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Parrot', 'Cat', 12345, 6.5]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4naq7LX6WWzj"
      },
      "source": [
        "#Removing the element of the list \n",
        "Animals.remove('Cat')"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0ElNB2LWfse",
        "outputId": "dc2caae3-0f99-408b-97f0-c8a335350e36"
      },
      "source": [
        "len(Animals)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJwv0xk9W5m4",
        "outputId": "8b38e94a-ce82-4083-9921-a7ba529992a2"
      },
      "source": [
        "'Cat' in Animals"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kd8dWQW0XJ4K"
      },
      "source": [
        "List = [500, 30, 300, 80, 10]"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sM__K2HoXa0m",
        "outputId": "6d27f454-00a3-460c-da26-693a180cb6bb"
      },
      "source": [
        "max(List)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWcgYTZDXjIy",
        "outputId": "8b235181-3d14-46df-b57b-152a837971e8"
      },
      "source": [
        "min(List)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnyUPZgLXpu-"
      },
      "source": [
        "Animals.append('Lion')"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NgqaB8OuY_Cs",
        "outputId": "ef90ed83-a56f-4806-a236-e5b536bf4294"
      },
      "source": [
        "Animals"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Parrot', 12345, 6.5, 'Lion']"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WyEWJLLZKZk"
      },
      "source": [
        "Animals.extend(['Giraffe', 6])"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoVNfNLqaELL",
        "outputId": "eae3c05f-df6e-49b2-c528-f5f82da2c110"
      },
      "source": [
        "Animals"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Parrot', 12345, 6.5, 'Lion', 'Giraffe', 6]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XlcKGUPad60",
        "outputId": "ff6335e0-f4f5-42be-bba7-9a0ec8f2c9a3"
      },
      "source": [
        "Animals.count('Lion')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNgyc5pMa4xc"
      },
      "source": [
        "List.sort()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyy3s83_a-uJ",
        "outputId": "95d724ac-7b39-4b60-acc1-68dfb2ef0882"
      },
      "source": [
        "List"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[10, 30, 80, 300, 500]"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uvXe03wbZ1X"
      },
      "source": [
        "#Tuples - Parenthesis as a Syntax\n",
        "tp = ('Banana', 'Maçã', 10, 50)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "0Hz-5gSNcPmc",
        "outputId": "80dad858-3e9f-473c-abd5-b1e5e2a5013d"
      },
      "source": [
        "#Returning the first element\n",
        "tp [0]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Banana'"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwas3kImeYmX",
        "outputId": "fe462064-aec9-4a2c-9683-2191d5051cfa"
      },
      "source": [
        "tp.count('Banana')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iim7OdPYenCJ",
        "outputId": "9cc4fce7-7dac-40c3-e18d-c0409d7fd8a0"
      },
      "source": [
        "tp[0:2]"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Banana', 'Maçã')"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKrHyDPsf5ju"
      },
      "source": [
        "#Dictionaries use of {}\n",
        "dc = {'Apple':20, 'Banana':10, 'Orange':15, 'Grape':5}"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8e7bKYKie7I",
        "outputId": "ed1c5d5d-0632-450a-d31f-f0cafa11207a"
      },
      "source": [
        "dc"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Apple': 20, 'Banana': 10, 'Grape': 5, 'Orange': 15}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeHDNJCri5Fq",
        "outputId": "26ccb370-86c2-41a2-a762-ecfef9c804ef"
      },
      "source": [
        "dc['Apple']"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "orbmDgkAjOjE",
        "outputId": "5ef0b423-4731-4219-e094-7de5b1a1c474"
      },
      "source": [
        "dc ['Apple'] = 25\n",
        "dc"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Apple': 25, 'Banana': 10, 'Grape': 5, 'Orange': 15}"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7wN97nyj4mI",
        "outputId": "81091466-9ab8-4dbd-abb0-877bd863b83e"
      },
      "source": [
        "dc.keys()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['Apple', 'Banana', 'Orange', 'Grape'])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nw1VcAyNkF6M",
        "outputId": "8f5d9147-f6d7-456f-8c4d-ab33291824a5"
      },
      "source": [
        "dc.values()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([25, 10, 15, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZ0H_puCkQkA",
        "outputId": "8ebd314b-6c6a-4999-cc4f-2560cb849563"
      },
      "source": [
        "dc.setdefault('Lemon', 22)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvKzGHJRlZn0",
        "outputId": "629cfbda-3e72-4f33-ce53-ba9683294576"
      },
      "source": [
        "dc"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Apple': 25, 'Banana': 10, 'Grape': 5, 'Lemon': 22, 'Orange': 15}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kGljTMi6TZ9"
      },
      "source": [
        "#Intro to Pandas tutorial "
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qk9h__yB6pEy"
      },
      "source": [
        "#Importing the Pandas tutorial \n",
        "import pandas as pd\n"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6_z-i4oD00l"
      },
      "source": [
        "df = pd.read_excel('/Aracaju.xlsx')"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "g2mYJkNtEu_a",
        "outputId": "bdda7f56-2770-400d-94fe-f68593e60bf1"
      },
      "source": [
        "#Visualizing only five lines\n",
        "df.head()"
      ],
      "execution_count": null,
      "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>Nome</th>\n",
              "      <th>idade</th>\n",
              "      <th>cep</th>\n",
              "      <th>pais</th>\n",
              "      <th>estado</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jackson</td>\n",
              "      <td>42</td>\n",
              "      <td>6765000</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>São Paulo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jack</td>\n",
              "      <td>41</td>\n",
              "      <td>6867500</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>São Paulo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Michael</td>\n",
              "      <td>40</td>\n",
              "      <td>13456</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chales</td>\n",
              "      <td>39</td>\n",
              "      <td>13546</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>Seatle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Menson</td>\n",
              "      <td>38</td>\n",
              "      <td>13456</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Nome   idade     cep            pais      estado \n",
              "0  Jackson     42  6765000         Brasil   São Paulo \n",
              "1     Jack     41  6867500         Brasil   São Paulo \n",
              "2  Michael     40    13456  Estados Unidos  California\n",
              "3  Chales      39    13546  Estados Unidos      Seatle\n",
              "4   Menson     38    13456  Estados Unidos  California"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ou1stelGFliE"
      },
      "source": [
        "df = df.rename(columns = {\"Nome\" : \"Name\", \"idade\" : \"age\", \"cep\" : \"zip code\", \"pais\" : \"country\", \"estado\" : \"state\"})"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "vO4EyCGhIwPt",
        "outputId": "fadf6fc7-1b77-4cad-eca1-dccc985ea89c"
      },
      "source": [
        "df.head(7)"
      ],
      "execution_count": null,
      "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>Nome</th>\n",
              "      <th>age</th>\n",
              "      <th>cep</th>\n",
              "      <th>pais</th>\n",
              "      <th>estado</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jackson</td>\n",
              "      <td>42</td>\n",
              "      <td>6765000</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>São Paulo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jack</td>\n",
              "      <td>41</td>\n",
              "      <td>6867500</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>São Paulo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Michael</td>\n",
              "      <td>40</td>\n",
              "      <td>13456</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chales</td>\n",
              "      <td>39</td>\n",
              "      <td>13546</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>Seatle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Menson</td>\n",
              "      <td>38</td>\n",
              "      <td>13456</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Marilyn</td>\n",
              "      <td>37</td>\n",
              "      <td>13546</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>Seatle</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Nome   age     cep            pais      estado \n",
              "0   Jackson   42  6765000         Brasil   São Paulo \n",
              "1      Jack   41  6867500         Brasil   São Paulo \n",
              "2   Michael   40    13456  Estados Unidos  California\n",
              "3   Chales    39    13546  Estados Unidos      Seatle\n",
              "4    Menson   38    13456  Estados Unidos  California\n",
              "5  Marilyn    37    13546  Estados Unidos      Seatle"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXjvZh-xJPen",
        "outputId": "973f9cd6-43a0-46f0-e3e4-995a73976b02"
      },
      "source": [
        "#Total of lines and columns\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9Dyy9feJhCB",
        "outputId": "664db28c-a7e8-411b-d7e8-e8031c1676a1"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Nome ', 'age', 'cep ', 'pais ', 'estado '], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M4FUM6t0J7Ps",
        "outputId": "a468a885-c30e-4f28-e5bd-3033ded5a0bf"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Nome       object\n",
              "age         int64\n",
              "cep         int64\n",
              "pais       object\n",
              "estado     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "BFmDYgvjKEk3",
        "outputId": "795997c4-c6f2-4742-9499-25ac493f1127"
      },
      "source": [
        "df.tail(2)"
      ],
      "execution_count": null,
      "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>Nome</th>\n",
              "      <th>age</th>\n",
              "      <th>cep</th>\n",
              "      <th>pais</th>\n",
              "      <th>estado</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Menson</td>\n",
              "      <td>38</td>\n",
              "      <td>13456</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>California</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Marilyn</td>\n",
              "      <td>37</td>\n",
              "      <td>13546</td>\n",
              "      <td>Estados Unidos</td>\n",
              "      <td>Seatle</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Nome   age   cep            pais      estado \n",
              "4    Menson   38  13456  Estados Unidos  California\n",
              "5  Marilyn    37  13546  Estados Unidos      Seatle"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ZEW8apM9KmZ-",
        "outputId": "1f8e13dc-1d84-4509-f8db-18fee0641977"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>age</th>\n",
              "      <th>cep</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.500000</td>\n",
              "      <td>2.281084e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.870829</td>\n",
              "      <td>3.513074e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>1.345600e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>1.347850e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.500000</td>\n",
              "      <td>1.354600e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>40.750000</td>\n",
              "      <td>5.077136e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42.000000</td>\n",
              "      <td>6.867500e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             age          cep \n",
              "count   6.000000  6.000000e+00\n",
              "mean   39.500000  2.281084e+06\n",
              "std     1.870829  3.513074e+06\n",
              "min    37.000000  1.345600e+04\n",
              "25%    38.250000  1.347850e+04\n",
              "50%    39.500000  1.354600e+04\n",
              "75%    40.750000  5.077136e+06\n",
              "max    42.000000  6.867500e+06"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIMH-HoqLz78",
        "outputId": "ceafacf3-989d-495a-8a12-6836f7e81c5e"
      },
      "source": [
        "df['pais '].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Brasil ', 'Estados Unidos'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "56O8n4EbL0UM",
        "outputId": "b0ee70d6-1ade-4cb4-ecf6-deb0dbe2c948"
      },
      "source": [
        "Brasil = df.loc[df['pais '] == 'Brasil']\n",
        "Brasil.head()"
      ],
      "execution_count": null,
      "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>Nome</th>\n",
              "      <th>age</th>\n",
              "      <th>cep</th>\n",
              "      <th>pais</th>\n",
              "      <th>estado</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Nome , age, cep , pais , estado ]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbgNBfyqO_le"
      },
      "source": [
        "#Trabalhando com Planilhas Excel \n",
        "#Importação \n",
        "import pandas as pd\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sgGBHTOx13j"
      },
      "source": [
        "#files analysis\n",
        "df1 = pd.read_excel(\"/content/drive/MyDrive/Aracaju.xlsx\")\n",
        "df2 = pd.read_excel(\"/content/drive/MyDrive/Fortaleza.xlsx\")\n",
        "df3 = pd.read_excel(\"/content/drive/MyDrive/Natal.xlsx\")\n",
        "df4 = pd.read_excel(\"/content/drive/MyDrive/Recife.xlsx\")\n",
        "df5 = pd.read_excel(\"/content/drive/MyDrive/Salvador.xlsx\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YL4BX7Dp1ss0",
        "outputId": "c16e5aa8-e4fc-47ef-e01c-8f88a8e2e9ab"
      },
      "source": [
        "df1.head()"
      ],
      "execution_count": null,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "YS121SqnuaL8",
        "outputId": "2b1d65ba-2b61-4c86-9ed1-856f88bc6d16"
      },
      "source": [
        "df2.head()"
      ],
      "execution_count": null,
      "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>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>45.27</td>\n",
              "      <td>1002</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>115.61</td>\n",
              "      <td>1002</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-02-10</td>\n",
              "      <td>35.33</td>\n",
              "      <td>1004</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-02-10</td>\n",
              "      <td>20.95</td>\n",
              "      <td>1004</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>14.25</td>\n",
              "      <td>1004</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Cidade       Data  Vendas  LojaID  Qtde\n",
              "0  Fortaleza 2019-01-01   45.27    1002     2\n",
              "1  Fortaleza 2019-01-01  115.61    1002     3\n",
              "2  Fortaleza 2019-02-10   35.33    1004     3\n",
              "3  Fortaleza 2019-02-10   20.95    1004     6\n",
              "4  Fortaleza 2019-01-01   14.25    1004     6"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "GPXFEfR4vioP",
        "outputId": "d83dc578-fd45-42f3-9cee-f0596a25551b"
      },
      "source": [
        "df3.head()"
      ],
      "execution_count": null,
      "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>Natal</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>606.0</td>\n",
              "      <td>853</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-11-25</td>\n",
              "      <td>563.0</td>\n",
              "      <td>853</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-11</td>\n",
              "      <td>308.0</td>\n",
              "      <td>852</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>271.0</td>\n",
              "      <td>854</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-03-09</td>\n",
              "      <td>699.0</td>\n",
              "      <td>853</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Cidade       Data  Vendas  LojaID  Qtde\n",
              "0  Natal 2018-08-27   606.0     853     2\n",
              "1  Natal 2018-11-25   563.0     853     2\n",
              "2  Natal 2019-03-11   308.0     852     3\n",
              "3  Natal 2019-01-07   271.0     854     3\n",
              "4  Natal 2019-03-09   699.0     853     3"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "NIx-Nt_QvnN0",
        "outputId": "e0c74d24-5991-4c24-c14c-76194d0f87f4"
      },
      "source": [
        "df4.head()"
      ],
      "execution_count": null,
      "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>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>162.61</td>\n",
              "      <td>981</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>11.44</td>\n",
              "      <td>983</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-02-10</td>\n",
              "      <td>12.94</td>\n",
              "      <td>983</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-02-10</td>\n",
              "      <td>160.54</td>\n",
              "      <td>981</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-02-14</td>\n",
              "      <td>19.89</td>\n",
              "      <td>983</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cidade       Data  Vendas  LojaID  Qtde\n",
              "0  Recife 2019-01-01  162.61     981     1\n",
              "1  Recife 2019-01-01   11.44     983     1\n",
              "2  Recife 2019-02-10   12.94     983     5\n",
              "3  Recife 2019-02-10  160.54     981     3\n",
              "4  Recife 2019-02-14   19.89     983     7"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "P8314Zckvq4l",
        "outputId": "eabaf0b3-f011-4c92-ee58-ee0c8bda9fe9"
      },
      "source": [
        "df5.head()"
      ],
      "execution_count": null,
      "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>Salvador</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>31.06</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>19.02</td>\n",
              "      <td>1034</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-02-10</td>\n",
              "      <td>173.59</td>\n",
              "      <td>1035</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-02-10</td>\n",
              "      <td>176.34</td>\n",
              "      <td>1037</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-02-14</td>\n",
              "      <td>19.47</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",
              "0  Salvador 2018-01-01   31.06    1037     3\n",
              "1  Salvador 2018-01-01   19.02    1034     3\n",
              "2  Salvador 2019-02-10  173.59    1035     3\n",
              "3  Salvador 2019-02-10  176.34    1037     3\n",
              "4  Salvador 2019-02-14   19.47    1037     3"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3l48qlIvwEl"
      },
      "source": [
        "#Concatenating files to work in only one plan\n",
        "df = pd.concat([df1, df2,df3, df4, df5]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2T0y6F35wy0A",
        "outputId": "ac6d175b-b4a6-44f6-d93a-1f20c9b5b1b1"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    <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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
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        "id": "cxpdCpMFw35b",
        "outputId": "cd8a1d7e-d948-43d7-ed96-5ce36e8c59ec"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "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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              "  <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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
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        "id": "BaFQ4b3vxJLn",
        "outputId": "50b0f7cb-cd83-4f99-de3f-fd403dfb70fa"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": null,
      "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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              "      <th></th>\n",
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              "      <th>Vendas</th>\n",
              "      <th>LojaID</th>\n",
              "      <th>Qtde</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>42.11</td>\n",
              "      <td>1004</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>13.00</td>\n",
              "      <td>983</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>9.58</td>\n",
              "      <td>1037</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>37.88</td>\n",
              "      <td>1034</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas  LojaID  Qtde\n",
              "53     Aracaju 2018-01-01    8.64    1522     2\n",
              "86   Fortaleza 2019-01-01   42.11    1004     3\n",
              "24      Recife 2019-01-01   13.00     983     2\n",
              "154   Salvador 2019-01-02    9.58    1037     1\n",
              "49    Salvador 2019-01-01   37.88    1034     2"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4-iQ6u8uxaA1",
        "outputId": "1419a9e5-3658-44f5-c01f-ac1018476335"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkLWk7kfxumT"
      },
      "source": [
        "df['LojaID'] = df['LojaID'].astype('object')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0xfbZKOzySed",
        "outputId": "99f619ea-311e-4912-8fc8-313dc2895913"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "V3KZvCyNyfNR",
        "outputId": "cc0afea0-f9ef-4c3a-ad16-ddfa4792a077"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\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",
              "    </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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0MsYjGMymRo",
        "outputId": "1cdc1895-22d5-4359-e916-5278f393613e"
      },
      "source": [
        "#Tratando valores faltantes\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf0MweeVzJBi"
      },
      "source": [
        "df['Vendas'].fillna(df['Vendas'].mean(), inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9-8ksi31BFK",
        "outputId": "8084b04c-b14d-4d8f-83c9-4f885cf22611"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "WF5esKkI1WaC",
        "outputId": "5446316c-8e8e-4910-db8a-6a2a17453bc0"
      },
      "source": [
        "df.sample(15)"
      ],
      "execution_count": null,
      "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>LojaID</th>\n",
              "      <th>Qtde</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>198.24</td>\n",
              "      <td>1521</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>21.90</td>\n",
              "      <td>1004</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>17.49</td>\n",
              "      <td>983</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>69.98</td>\n",
              "      <td>1034</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>21.00</td>\n",
              "      <td>1523</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>8.60</td>\n",
              "      <td>983</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>3.10</td>\n",
              "      <td>1003</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>47.98</td>\n",
              "      <td>983</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>59.78</td>\n",
              "      <td>1036</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>39.40</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Recife</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>142.21</td>\n",
              "      <td>983</td>\n",
              "      <td>8</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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>237.81</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>150.17</td>\n",
              "      <td>1036</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas LojaID  Qtde\n",
              "94     Aracaju 2018-01-01  198.24   1521     9\n",
              "107  Fortaleza 2019-01-01   21.90   1004     6\n",
              "132     Recife 2019-03-02   17.49    983     5\n",
              "209      Natal 2019-01-02   69.98   1034     2\n",
              "64     Aracaju 2018-01-01   21.00   1523     5\n",
              "47      Recife 2019-01-01    8.60    983     3\n",
              "34   Fortaleza 2019-01-01    3.10   1003     5\n",
              "124     Recife 2019-03-02   47.98    983     7\n",
              "130   Salvador 2019-03-02   59.78   1036     1\n",
              "208   Salvador 2019-01-02   39.40   1036     3\n",
              "44      Recife 2019-01-01  142.21    983     8\n",
              "137     Recife 2019-03-02   51.99    983     3\n",
              "20    Salvador 2019-01-01   33.97   1034     3\n",
              "155      Natal 2019-01-02  237.81   1036     3\n",
              "157      Natal 2019-01-02  150.17   1036     2"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VA6swgzm1hsW",
        "outputId": "dc357f69-f452-4ae6-f1c2-d1a698be8dac"
      },
      "source": [
        "df['Vendas'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "122.61180089485438"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSenSHwQ2Kkk"
      },
      "source": [
        "#Replacing null values to zero\n",
        "df['Vendas'].fillna(0, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YS0rQSgG3MsV"
      },
      "source": [
        "df.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HAURzH83x_J"
      },
      "source": [
        "#Criando novas colunas \n",
        "df['Receitas'] = df['Vendas'].mul(df['Qtde'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "OMaQgTfH5CMa",
        "outputId": "228b7a52-60e6-418b-bf1d-152de89097b3"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Data</th>\n",
              "      <th>Vendas</th>\n",
              "      <th>LojaID</th>\n",
              "      <th>Qtde</th>\n",
              "      <th>Receitas</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  Receitas\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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBtixE4k5SxF"
      },
      "source": [
        "df['Receitas/Vendas'] = df['Receitas'] / df['Vendas']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "sVjueTwi54Eb",
        "outputId": "70168f3c-8ffc-481b-f249-8b2947d534dc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Data</th>\n",
              "      <th>Vendas</th>\n",
              "      <th>LojaID</th>\n",
              "      <th>Qtde</th>\n",
              "      <th>Receitas</th>\n",
              "      <th>Receitas/Vendas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1520</td>\n",
              "      <td>1</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>14.21</td>\n",
              "      <td>1522</td>\n",
              "      <td>6</td>\n",
              "      <td>85.26</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>71.55</td>\n",
              "      <td>1520</td>\n",
              "      <td>1</td>\n",
              "      <td>71.55</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>3.01</td>\n",
              "      <td>1521</td>\n",
              "      <td>7</td>\n",
              "      <td>21.07</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>24.51</td>\n",
              "      <td>1522</td>\n",
              "      <td>8</td>\n",
              "      <td>196.08</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Cidade       Data  Vendas  LojaID  Qtde  Receitas  Receitas/Vendas\n",
              "0  Aracaju 2018-01-01  142.00    1520     1    142.00              1.0\n",
              "1  Aracaju 2018-01-01   14.21    1522     6     85.26              6.0\n",
              "2  Aracaju 2018-01-01   71.55    1520     1     71.55              1.0\n",
              "3  Aracaju 2018-01-01    3.01    1521     7     21.07              7.0\n",
              "4  Aracaju 2018-01-01   24.51    1522     8    196.08              8.0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOqeglKL644X",
        "outputId": "3466182b-bba3-4463-8c2b-536d17539a0d"
      },
      "source": [
        "df['Receitas'].max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3544.0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9-22OVy7FiP",
        "outputId": "5142194d-6c98-4e0a-fc94-bef5f8f25584"
      },
      "source": [
        "df['Receitas'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.34"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "TgVeOkk_7UA5",
        "outputId": "d4d49333-1e2d-4325-a8aa-15c493fcc9d8"
      },
      "source": [
        "df.nlargest(3, 'Receitas')"
      ],
      "execution_count": null,
      "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>Receitas</th>\n",
              "      <th>Receitas/Vendas</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cidade       Data  Vendas  LojaID  Qtde  Receitas  Receitas/Vendas\n",
              "7   Natal 2019-03-18   886.0     853     4    3544.0              4.0\n",
              "51  Natal 2018-01-21   859.0     852     4    3436.0              4.0\n",
              "55  Natal 2019-01-08   859.0     854     4    3436.0              4.0"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "S9F8IMhl7x1f",
        "outputId": "5b94833d-994d-431a-ee0b-1032c550b4ab"
      },
      "source": [
        "df.nsmallest(3, 'Receitas')"
      ],
      "execution_count": null,
      "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 tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cidade</th>\n",
              "      <th>Data</th>\n",
              "      <th>Vendas</th>\n",
              "      <th>LojaID</th>\n",
              "      <th>Qtde</th>\n",
              "      <th>Receitas</th>\n",
              "      <th>Receitas/Vendas</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",
              "      <td>1.0</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",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>4.57</td>\n",
              "      <td>1035</td>\n",
              "      <td>1</td>\n",
              "      <td>4.57</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Cidade       Data  Vendas  LojaID  Qtde  Receitas  Receitas/Vendas\n",
              "118  Aracaju 2018-01-01    3.34    1522     1      3.34              1.0\n",
              "65    Recife 2019-01-01    4.01     981     1      4.01              1.0\n",
              "92     Natal 2019-01-02    4.57    1035     1      4.57              1.0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1O3Qgnc8KJG",
        "outputId": "2f6541b1-12d3-4750-faa4-1f7630e55373"
      },
      "source": [
        "#Agrupamento por Cidades\n",
        "df.groupby('Cidade')['Receitas'].sum()"
      ],
      "execution_count": null,
      "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: Receitas, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "oIUTgUqV8tn9",
        "outputId": "bb6c2eda-ded7-4cc3-e348-3fae8e68b46b"
      },
      "source": [
        "#Ordenando o conjunto de Dados\n",
        "df.sort_values('Receitas', ascending=False).head(10)"
      ],
      "execution_count": null,
      "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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              "</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>Receitas</th>\n",
              "      <th>Receitas/Vendas</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</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",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cidade       Data  Vendas  LojaID  Qtde  Receitas  Receitas/Vendas\n",
              "7   Natal 2019-03-18   886.0     853     4    3544.0              4.0\n",
              "55  Natal 2019-01-08   859.0     854     4    3436.0              4.0\n",
              "51  Natal 2018-01-21   859.0     852     4    3436.0              4.0\n",
              "30  Natal 2018-10-02   856.0     853     4    3424.0              4.0\n",
              "41  Natal 2018-05-20   835.0     852     4    3340.0              4.0\n",
              "38  Natal 2018-02-25   828.0     852     4    3312.0              4.0\n",
              "10  Natal 2018-10-27   828.0     852     4    3312.0              4.0\n",
              "69  Natal 2019-03-24   817.0     852     4    3268.0              4.0\n",
              "62  Natal 2018-02-10   793.0     854     4    3172.0              4.0\n",
              "52  Natal 2018-04-27   778.0     854     4    3112.0              4.0"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXUHAS7w-SGn"
      },
      "source": [
        "#Trabalhando com datas\n",
        "df['Data'] = df['Data'].astype('int64')\n"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQL6mugU-zMR",
        "outputId": "4d7cdfe3-ac0c-4184-8f44-5adaface57c8"
      },
      "source": [
        "#Verificadndo o tipo de dados em cada coluna \n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade              object\n",
              "Data                 int64\n",
              "Vendas             float64\n",
              "LojaID               int64\n",
              "Qtde                 int64\n",
              "Receitas           float64\n",
              "Receitas/Vendas    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xscySJtm_Tqm"
      },
      "source": [
        "df['Data'] = pd.to_datetime(df['Data'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpC_25La_2jo",
        "outputId": "d8d22f88-6e19-4a35-b0a2-d298aeda3c1f"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade                     object\n",
              "Data               datetime64[ns]\n",
              "Vendas                    float64\n",
              "LojaID                      int64\n",
              "Qtde                        int64\n",
              "Receitas                  float64\n",
              "Receitas/Vendas           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqcPMloRFhUP"
      },
      "source": [
        "#Creatinf a new column with the year\n",
        "df['Ano_Venda'] = df['Data'].dt.year"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "vVD7K7VdGNUw",
        "outputId": "3f48824f-21a2-42b0-f1fd-212fdad83227"
      },
      "source": [
        "df.sample(5)"
      ],
      "execution_count": null,
      "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>Receitas</th>\n",
              "      <th>Receitas/Vendas</th>\n",
              "      <th>Ano_Venda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>Salvador</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>111.20</td>\n",
              "      <td>1036</td>\n",
              "      <td>3</td>\n",
              "      <td>333.60</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Natal</td>\n",
              "      <td>2018-01-11</td>\n",
              "      <td>759.00</td>\n",
              "      <td>854</td>\n",
              "      <td>2</td>\n",
              "      <td>1518.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>12.23</td>\n",
              "      <td>981</td>\n",
              "      <td>3</td>\n",
              "      <td>36.69</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2019-01-01</td>\n",
              "      <td>9.78</td>\n",
              "      <td>1520</td>\n",
              "      <td>8</td>\n",
              "      <td>78.24</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>43.96</td>\n",
              "      <td>1520</td>\n",
              "      <td>9</td>\n",
              "      <td>395.64</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Cidade       Data  Vendas  ...  Receitas  Receitas/Vendas  Ano_Venda\n",
              "78    Salvador 2019-01-01  111.20  ...    333.60              3.0       2019\n",
              "33       Natal 2018-01-11  759.00  ...   1518.00              2.0       2018\n",
              "115  Fortaleza 2019-03-02   12.23  ...     36.69              3.0       2019\n",
              "12     Aracaju 2019-01-01    9.78  ...     78.24              8.0       2019\n",
              "19     Aracaju 2018-01-01   43.96  ...    395.64              9.0       2018\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaBO5dylHzow",
        "outputId": "5d97dd05-8dad-411a-cd95-7d08db3dba11"
      },
      "source": [
        "df['Data'].min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-01-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3Stn0s9IN3G"
      },
      "source": [
        "df['diferenca_dias'] = df['Data'] - df['Data'].min()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "xKmd3al6IoUZ",
        "outputId": "8b414914-b118-4c1b-f573-2b27d596ea95"
      },
      "source": [
        "df.sample()"
      ],
      "execution_count": 76,
      "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>68</th>\n",
              "      <td>Aracaju</td>\n",
              "      <td>1514764800000000000</td>\n",
              "      <td>179.74</td>\n",
              "      <td>1520</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Cidade                 Data  Vendas  LojaID  Qtde\n",
              "68  Aracaju  1514764800000000000  179.74    1520     2"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XUEy49ZQLUG",
        "outputId": "d24527d7-2521-4ec4-a3e8-b7d1f3698d76"
      },
      "source": [
        "#Visualização de dados\n",
        "df['LojaID'].value_counts(ascending = False)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1522    41\n",
              "1520    39\n",
              "1523    29\n",
              "1521    21\n",
              "Name: LojaID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "yS5LSWFmUS2R",
        "outputId": "44ac1122-0f45-49b2-f097-bb24731c9e50"
      },
      "source": [
        "#Bar Graph\n",
        "df['LojaID'].value_counts(ascending = False).plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe72f4adf90>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "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": 180
        },
        "id": "Fs0ZJR0mUUZ4",
        "outputId": "fbf39d03-579f-4715-dacc-deb4d08c03d9"
      },
      "source": [
        "#Bar horizontal\n",
        "df['LojaID'].value_counts(ascending = false).plot.barh()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-79-73b90f13dcdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Bar horizontal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LojaID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbarh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'false' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "HHdXiMPEjetV",
        "outputId": "f0750f5d-6dcc-4c75-8ee6-c314268c43a5"
      },
      "source": [
        "df.groupby(df['Data'].dt.year)['Receita'].sum().plot.pie()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-80-7feff323018d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Receita'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5135\u001b[0m             \u001b[0;32mor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5136\u001b[0m         ):\n\u001b[0;32m-> 5137\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5138\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5139\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    185\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/accessors.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data)\u001b[0m\n\u001b[1;32m    478\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mPeriodProperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .dt accessor with datetimelike values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: Can only use .dt accessor with datetimelike values"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCvGf55klrCZ"
      },
      "source": [
        "#Revisão \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn')"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "cFsAJMXVqxoq",
        "outputId": "438eb32c-1771-47d7-946b-6a85653feca1"
      },
      "source": [
        "#Upload file\n",
        "from google.colab import files\n",
        "arq = files.upload()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0d563dd9-7cde-4169-bfab-8f7b1154a316\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0d563dd9-7cde-4169-bfab-8f7b1154a316\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving AdventureWorks.xlsx to AdventureWorks.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZjcOIJ4sr2G4"
      },
      "source": [
        "#Creating the Dataframe\n",
        "df = pd.read_excel('/AdventureWorks.xlsx')"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "fdrVnm8JsX3u",
        "outputId": "601d9173-61c0-4297-93b3-941304facd93"
      },
      "source": [
        "#Visualizing\n",
        "df.head()"
      ],
      "execution_count": 84,
      "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>Data Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2008-05-12</td>\n",
              "      <td>2008-05-17</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19051</td>\n",
              "      <td>200805123CS567</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2008-05-14</td>\n",
              "      <td>2008-05-20</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19052</td>\n",
              "      <td>200805143CS576</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2008-05-21</td>\n",
              "      <td>2008-05-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19052</td>\n",
              "      <td>200805213CS576</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2008-06-20</td>\n",
              "      <td>2008-06-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19053</td>\n",
              "      <td>200806203CS586</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ...            Marca   Classe  Cor\n",
              "0 2008-05-09 2008-05-29      199  ...  Adventure Works  Regular  Red\n",
              "1 2008-05-12 2008-05-17      306  ...  Adventure Works  Regular  Red\n",
              "2 2008-05-14 2008-05-20      306  ...  Adventure Works  Regular  Red\n",
              "3 2008-05-21 2008-05-27      306  ...  Adventure Works  Regular  Red\n",
              "4 2008-06-20 2008-06-27      306  ...  Adventure Works  Regular  Red\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7ky9br8svE7",
        "outputId": "f5e7b7a4-67cf-49e0-fdee-f8ffe02b5aec"
      },
      "source": [
        "#lines x columns\n",
        "df.shape"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(904, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNlacEJHtAdi",
        "outputId": "a838be06-3b82-4ed1-e2da-b3939b7213fa"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data Venda        datetime64[ns]\n",
              "Data Envio        datetime64[ns]\n",
              "ID Loja                    int64\n",
              "ID Produto                 int64\n",
              "ID Cliente                 int64\n",
              "No. Venda                 object\n",
              "Custo Unitário           float64\n",
              "Preço Unitário           float64\n",
              "Quantidade                 int64\n",
              "Valor Desconto           float64\n",
              "Valor Venda              float64\n",
              "Produto                   object\n",
              "Fabricante                object\n",
              "Marca                     object\n",
              "Classe                    object\n",
              "Cor                       object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_cmj6xsZtS4F",
        "outputId": "ae79b7be-2267-4c11-b751-3dceb49dd492"
      },
      "source": [
        "df['Valor Venda'].sum()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5984606.1426"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zC_9GuVGttiD"
      },
      "source": [
        "#Total cost\n",
        "df['custo'] = df['Custo Unitário'].mul(df['Quantidade']) #creating column cost "
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "DcwInwYGuqGE",
        "outputId": "08b8233d-de13-4490-ae92-ec68336f0a1a"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 90,
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "      <th>custo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ...   Classe  Cor    custo\n",
              "0 2008-05-09 2008-05-29      199  ...  Regular  Red  2091.48\n",
              "\n",
              "[1 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9sSTHb3Mu3L2",
        "outputId": "42e326c8-b5fe-4c74-9dd4-af5529b9beb9"
      },
      "source": [
        "#Total cost\n",
        "round (df['custo'].sum(), 2)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2486783.05"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-T2cwoQvbTi"
      },
      "source": [
        "#profit = revenues - cost\n",
        "df['lucro'] = df['Valor Venda'] - df['custo']"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "ni1JPGu9v70k",
        "outputId": "0a136ebf-496f-498e-8985-fe10669e0460"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 93,
      "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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "      <th>custo</th>\n",
              "      <th>lucro</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ID Produto  ...   Classe  Cor    custo    lucro\n",
              "0 2008-05-09 2008-05-29      199         384  ...  Regular  Red  2091.48  2456.52\n",
              "\n",
              "[1 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKbK6BdYwHQS",
        "outputId": "64716f7f-4b60-498b-db6e-17bb31176c93"
      },
      "source": [
        "#profit\n",
        "round(df['lucro'].sum(), 2)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3497823.09"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSMefU9bw0Ww"
      },
      "source": [
        "df['tempo envio'] = df['Data Envio'] - df['Data Venda']"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "id": "Kw_4NEm_xIUn",
        "outputId": "42aa34e3-1ac9-428e-ace2-95e791e2cc65"
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 96,
      "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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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
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              "      <th>Custo Unitário</th>\n",
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              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
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              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "      <th>custo</th>\n",
              "      <th>lucro</th>\n",
              "      <th>tempo envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2008-05-09</td>\n",
              "      <td>2008-05-29</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18839</td>\n",
              "      <td>200805093CS607</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>20 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Venda Data Envio  ID Loja  ID Produto  ...  Cor    custo    lucro  tempo envio\n",
              "0 2008-05-09 2008-05-29      199         384  ...  Red  2091.48  2456.52      20 days\n",
              "\n",
              "[1 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "rjcTt7oC0L19",
        "outputId": "c8971f29-bcf9-4707-8c69-1cb3c81c207f"
      },
      "source": [
        "df['tempo_envio'] = df(['Data Envio'] - df['Data Venda']).dt.days"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-99-b855c696c0a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tempo_envio'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data Envio'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data Venda'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 343\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    345\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshould_extension_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTimedelta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0;31m# Timedelta is included because numexpr will fail on it, see GH#31457\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m         \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/ops/roperator.py\u001b[0m in \u001b[0;36mrsub\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrsub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/arrays/datetimelike.py\u001b[0m in \u001b[0;36m__rsub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;31m# We get here with e.g. datetime objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iadd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'DatetimeArray' and 'list'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5Z72_zB1dIs",
        "outputId": "38b41652-c2c7-4581-b054-c8688b720b45"
      },
      "source": [
        "df['tempo envio'].dtype"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('<m8[ns]')"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "caKoL6Yf15dJ",
        "outputId": "b1bfc27c-479d-45f0-a2bb-999551a8473e"
      },
      "source": [
        "df.groupby('Marca')['tempo_envio'].mean()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-104-63e9bb97c07f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Marca'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tempo_envio'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1608\u001b[0m                 \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m             )\n\u001b[0;32m-> 1610\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Column not found: {key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Column not found: tempo_envio'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qm71JWfa2foh",
        "outputId": "400dd631-d0a4-45bc-b279-533ab33e9a07"
      },
      "source": [
        "#Missing Values\n",
        "df.isnull().sum()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data Venda        0\n",
              "Data Envio        0\n",
              "ID Loja           0\n",
              "ID Produto        0\n",
              "ID Cliente        0\n",
              "No. Venda         0\n",
              "Custo Unitário    0\n",
              "Preço Unitário    0\n",
              "Quantidade        0\n",
              "Valor Desconto    0\n",
              "Valor Venda       0\n",
              "Produto           0\n",
              "Fabricante        0\n",
              "Marca             0\n",
              "Classe            0\n",
              "Cor               0\n",
              "custo             0\n",
              "lucro             0\n",
              "tempo envio       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "hCOGecln20rb",
        "outputId": "24e87ff9-cc67-4e56-9e80-d1f3bfbdc17f"
      },
      "source": [
        "#Profit for year and trademark\n",
        "df.groupby{(df['Data Venda'].dt.year, 'Marca']}['lucro'].sum()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-112-829b9e48d2da>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    df.groupby{(df['Data Venda'].dt.year, 'Marca']}['lucro'].sum()\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "RWB2bjPO3644",
        "outputId": "7ba216ff-2b46-4643-fb29-b3754e15d88c"
      },
      "source": [
        "lucro_ano = df.groupby{[df['Data Venda'].dt.year, 'Marca']}['Lucro'].sum().reset_Index()\n",
        "lucro_ano"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-114-f51693e3c989>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    lucro_ano = df.groupby{[df['Data Venda'].dt.year, 'Marca']}['Lucro'].sum().reset_Index()\u001b[0m\n\u001b[0m                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gxJ_Gpzx5nSf",
        "outputId": "b3eac8fb-4cb5-402e-d597-37f72093d8c0"
      },
      "source": [
        "#Quantidade de produtos vendido\n",
        "df.groupby('Produto')['Quantidade'].sum().sort_values(ascending=False)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Produto\n",
              "Headphone Adapter for Contoso Phone E130 Silver    25232\n",
              "Headphone Adapter for Contoso Phone E130 White     25008\n",
              "Adventure Works Laptop15.4W M1548 Black             1089\n",
              "Fabrikam Trendsetter 2/3'' 17mm X100 Grey           1087\n",
              "Adventure Works Laptop15.4W M1548 Red               1047\n",
              "Fabrikam Trendsetter 2/3'' 17mm X100 Black           926\n",
              "Fabrikam Trendsetter 1/3'' 8.5mm X200 Black          884\n",
              "Fabrikam Trendsetter 1/3'' 8.5mm X200 Grey           845\n",
              "Fabrikam Trendsetter 1/3'' 8.5mm X200 White          789\n",
              "Name: Quantidade, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "-7aYYO-C6faK",
        "outputId": "9aaa8fd5-18f5-41f4-e09e-b9ef6ca90f31"
      },
      "source": [
        "df.groupby('Produto')['Quantidade'].sum().sort_values(ascending=True).plot.barh(title ='Total Produtos Vendidos')\n",
        "plt.xlabel('Total')\n",
        "plt.ylabel('Produto')"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Produto')"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "7mE6VgGM8Crx",
        "outputId": "9dfaa034-cb70-43db-af6f-6cb7c234a3af"
      },
      "source": [
        "df.groupby(df['Data Venda'].dt.year)['Lucro'].sum().plot.bar(title ='Lucro x Ano')\n",
        "plt.xlabel('Ano')\n",
        "plt.ylabel('Receita')"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-123-f0b062febdeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data Venda'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Lucro'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;34m'Lucro x Ano'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Ano'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Receita'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1608\u001b[0m                 \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m             )\n\u001b[0;32m-> 1610\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Column not found: {key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Column not found: Lucro'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "3kPPc4pU9qJM",
        "outputId": "9f652abd-0708-45d3-db60-f25ddf1abf08"
      },
      "source": [
        "df.groupby(df['Data Venda'].dt.year)['Lucro'].sum()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-124-cc410ece0c69>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Data Venda'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Lucro'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1608\u001b[0m                 \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m             )\n\u001b[0;32m-> 1610\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Column not found: {key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Column not found: Lucro'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHVsyFTu-TTq"
      },
      "source": [
        "df_2009 = df[df['Data Venda'].dt.year == 2009]"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "BOeOntdx_JFv",
        "outputId": "afcc4fe1-f6e5-4358-a170-a3fe27a456cc"
      },
      "source": [
        "df_2009.head()"
      ],
      "execution_count": 128,
      "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>Data Venda</th>\n",
              "      <th>Data Envio</th>\n",
              "      <th>ID Loja</th>\n",
              "      <th>ID Produto</th>\n",
              "      <th>ID Cliente</th>\n",
              "      <th>No. Venda</th>\n",
              "      <th>Custo Unitário</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Valor Desconto</th>\n",
              "      <th>Valor Venda</th>\n",
              "      <th>Produto</th>\n",
              "      <th>Fabricante</th>\n",
              "      <th>Marca</th>\n",
              "      <th>Classe</th>\n",
              "      <th>Cor</th>\n",
              "      <th>custo</th>\n",
              "      <th>lucro</th>\n",
              "      <th>tempo envio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2009-05-02</td>\n",
              "      <td>2009-05-14</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18938</td>\n",
              "      <td>200905023CS847</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>12 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2009-05-16</td>\n",
              "      <td>2009-05-27</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19067</td>\n",
              "      <td>200905163CS746</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>11 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2009-05-31</td>\n",
              "      <td>2009-06-12</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19068</td>\n",
              "      <td>200905313CS748</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>12 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2009-06-28</td>\n",
              "      <td>2009-07-11</td>\n",
              "      <td>199</td>\n",
              "      <td>384</td>\n",
              "      <td>18959</td>\n",
              "      <td>200906283CS870</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>13 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2009-07-08</td>\n",
              "      <td>2009-07-12</td>\n",
              "      <td>306</td>\n",
              "      <td>384</td>\n",
              "      <td>19070</td>\n",
              "      <td>200907083CS757</td>\n",
              "      <td>348.58</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4548.0</td>\n",
              "      <td>Adventure Works Laptop15.4W M1548 Red</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Adventure Works</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Red</td>\n",
              "      <td>2091.48</td>\n",
              "      <td>2456.52</td>\n",
              "      <td>4 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Data Venda Data Envio  ID Loja  ...    custo    lucro tempo envio\n",
              "11 2009-05-02 2009-05-14      199  ...  2091.48  2456.52     12 days\n",
              "12 2009-05-16 2009-05-27      306  ...  2091.48  2456.52     11 days\n",
              "13 2009-05-31 2009-06-12      306  ...  2091.48  2456.52     12 days\n",
              "14 2009-06-28 2009-07-11      199  ...  2091.48  2456.52     13 days\n",
              "15 2009-07-08 2009-07-12      306  ...  2091.48  2456.52      4 days\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "wAtUYJ3f_tDT",
        "outputId": "559bb64e-376b-450f-e4b4-50bf819f5ab4"
      },
      "source": [
        "df_2009.groupby(df_2009[\"Data Venda\"].dt.month)[\"Lucro\"].sum().plot(title=\"Lucro x Mês\")\n",
        "plt.xlabel(Mês)\n",
        "plt.ylabel(\"Lucro\");"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-130-415dcf41f3c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_2009\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_2009\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Data Venda\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Lucro\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Lucro x Mês\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMês\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Lucro\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1608\u001b[0m                 \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m             )\n\u001b[0;32m-> 1610\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 228\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Column not found: {key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    229\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Column not found: Lucro'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cD1Bsbl6Aqzd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}