Trabalhando_planilhas_excel.ipynb

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        "<a href=\"https://colab.research.google.com/github/elpperger/analise_dados_iniciante/blob/main/Trabalhando_planilhas_excel.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "metadata": {
        "id": "ZLoiqq4RM8Ev"
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      "source": [
        "#Importanto a biblioteca Pandas\n",
        "\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Leitura dos arquivos no Drive\n",
        "#Importante: quando se trabalha com arquivos no Drive, os dados ficam salvos no Colab.\n",
        "#Importante: quando faz upload de arquivos da máquina, para trabalhar, os dados não ficam salvos.\n",
        "\n",
        "df1 = pd.read_excel(\"Aracaju.xlsx\")\n",
        "df2 = pd.read_excel(\"Fortaleza.xlsx\")\n",
        "df3 = pd.read_excel(\"Natal.xlsx\")\n",
        "df4 = pd.read_excel(\"Recife.xlsx\")\n",
        "df5 = pd.read_excel(\"Salvador.xlsx\")"
      ],
      "metadata": {
        "id": "eYFnNg2jNZ1h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Para ver a estrutura do arquivo\n",
        "\n",
        "df5.head()"
      ],
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        "id": "nRTtUDERPXyg",
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              "     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"
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "#Como os arquivos possuem a mesma estrutura, vamos juntar os dados com o método \"concat\" = concatenar\n",
        "#Nesse caso, estamos colocando um arquivo embaixo do outro na sequencia\n",
        "\n",
        "df = pd.concat([df1, df2, df3, df4, df5])"
      ],
      "metadata": {
        "id": "9HKZ3b5sPiKl"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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        "id": "Ilce_aNpQVoq",
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "    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"
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              "       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",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pegar uma amostra aleatória dos dados, neste caso, 5 amostras\n",
        "#sample = amostra\n",
        "\n",
        "df.sample(5)"
      ],
      "metadata": {
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          "height": 206
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        "id": "lJHGrPz1QfLZ",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "        Cidade       Data  Vendas  LojaID  Qtde\n",
              "134  Fortaleza 2019-03-02  195.18     980     5\n",
              "42   Fortaleza 2019-01-01   38.71    1004     5\n",
              "0    Fortaleza 2019-01-01   45.27    1002     2\n",
              "118      Natal 2019-01-02   92.21    1035     2\n",
              "79     Aracaju 2018-01-01    5.59    1521     4"
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      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXb5DszpQ1xa",
        "outputId": "f6f549f3-e4c5-420d-a4ad-25ca32eda674"
      },
      "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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como mudar o tipo do dado de uma coluna, nesse caso vamos alterar o \"LojaID\"\n",
        "#Para isso vamos usar o método \"astype\"\n",
        "#Nesse caso, mudamos o tipo do dado porque não iremos fazer cálculos com esse dado\n",
        "#Não é obrigado a fazer isso\n",
        "\n",
        "df[\"LojaID\"] = df[\"LojaID\"].astype(\"object\")"
      ],
      "metadata": {
        "id": "Fx2DUOByQ9rJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ngIf-NlxRrVg",
        "outputId": "6537b039-bd2d-4d86-ab12-81325c6f9616"
      },
      "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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como verificar se há valores nulos nos dados\n",
        "#Usar o método \"isnull\"\n",
        "\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rP5IvVSOSH4W",
        "outputId": "8991c703-25ba-4c3a-ec5f-8933b04333e8"
      },
      "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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Nesse caso, não tivemos valores nulos, mas se tivesse o comando para alterar os valores nulos:\n",
        "#Exemplo com a coluna vendas\n",
        "# o comando \"fillna\" indica o preenchimento dos dados nulos, que nesse caso é a média (mean)\n",
        "# O comando \"inplace=True\", significa que eu estou passando o valor para ficar na memória\n",
        "\n",
        "## df[\"Vendas\"].fillna(df[\"Vendas\"].mean(), inplace=True)  ##\n",
        "\n",
        "#Se eu quiser substituir todos os valores nulos por zero (0), segue o comando:\n",
        "\n",
        "## df[\"Vendas\"].fillna(0, inplace=True) ##\n",
        "\n",
        "#Caso eu queira apagar as linhas com os valores nulos, segue o comando?\n",
        "\n",
        "## df.dropna(inplace=True) ##\n",
        "\n",
        "#Se eu quiser apagar os valores nulos apenas da coluna \"Vendas\", segue o comando:\n",
        "#Usar o método \"subset\"\n",
        "\n",
        "## df.dropna(subset=[\"Vendas\"], inplace=True) ##\n",
        "\n",
        "#Para remover valores nulos em todas as colunas, segue o comando:\n",
        "\n",
        "## df.dropna(how=\"all\", inplace=True) ##"
      ],
      "metadata": {
        "id": "WHOXsVoKSi0s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Como criar colunas nas tabelas\n",
        "#Neste caso criamos uma coluna chamada \"Receitas\"\n",
        "\n",
        "df[\"Receita\"] = df[\"Vendas\"].mul(df[\"Qtde\"])"
      ],
      "metadata": {
        "id": "VTEeaFUA_rmj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
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        "id": "KzJpVtxoA_bo",
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      "execution_count": null,
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            "text/plain": [
              "    Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita\n",
              "0  Aracaju 2018-01-01  142.00   1520     1    142.00   142.00\n",
              "1  Aracaju 2018-01-01   14.21   1522     6     85.26    85.26\n",
              "2  Aracaju 2018-01-01   71.55   1520     1     71.55    71.55\n",
              "3  Aracaju 2018-01-01    3.01   1521     7     21.07    21.07\n",
              "4  Aracaju 2018-01-01   24.51   1522     8    196.08   196.08"
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    {
      "cell_type": "code",
      "source": [
        "#Cálculo para calcular a Receita por Vendas, para mostrr a quantidade de produto vendido\n",
        "\n",
        "df[\"Receita/Vendas\"] = df[\"Receita\"] / df[\"Vendas\"]"
      ],
      "metadata": {
        "id": "xfjrqIfhBG2U"
      },
      "execution_count": null,
      "outputs": []
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    {
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      "source": [
        "df.head()"
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        "outputId": "3ad13bd9-d51f-45c6-9353-7a3843c06644"
      },
      "execution_count": null,
      "outputs": [
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              "    Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  Receita/Vendas\n",
              "0  Aracaju 2018-01-01  142.00   1520     1    142.00   142.00             1.0\n",
              "1  Aracaju 2018-01-01   14.21   1522     6     85.26    85.26             6.0\n",
              "2  Aracaju 2018-01-01   71.55   1520     1     71.55    71.55             1.0\n",
              "3  Aracaju 2018-01-01    3.01   1521     7     21.07    21.07             7.0\n",
              "4  Aracaju 2018-01-01   24.51   1522     8    196.08   196.08             8.0"
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    },
    {
      "cell_type": "code",
      "source": [
        "#Ver a maior receita\n",
        "\n",
        "df[\"Receita\"].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfGU3rWwCDlb",
        "outputId": "b768db44-ae24-446b-bd58-218d2b646994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3544.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ver menor receita\n",
        "\n",
        "df[\"Receita\"].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64Z4qpYxCZCf",
        "outputId": "52f4de9d-2402-425d-ce70-a0db4fc50943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.34"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificar a informação da linha inteira dos maiores resultados\n",
        "\n",
        "df.nlargest(3, \"Receitas\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "GY_sC8m_CxS3",
        "outputId": "804ec219-775b-406b-e43f-a67db3f80fc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  Receita/Vendas\n",
              "7   Natal 2019-03-18   886.0    853     4    3544.0   3544.0             4.0\n",
              "51  Natal 2018-01-21   859.0    852     4    3436.0   3436.0             4.0\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificar a informação da linha inteira dos menores resultados\n",
        "\n",
        "df.nsmallest(3, \"Receita\")"
      ],
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        "colab": {
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        "id": "qY8eWwdQDBUZ",
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      "execution_count": null,
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            "text/plain": [
              "      Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "118  Aracaju 2018-01-01    3.34   1522     1      3.34     3.34   \n",
              "65    Recife 2019-01-01    4.01    981     1      4.01     4.01   \n",
              "92     Natal 2019-01-02    4.57   1035     1      4.57     4.57   \n",
              "\n",
              "     Receita/Vendas  \n",
              "118             1.0  \n",
              "65              1.0  \n",
              "92              1.0  "
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fazer agrupamento das receitas, neste caso das cidades\n",
        "\n",
        "df.groupby(\"Cidade\")[\"Receita\"].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rV2ZGKMmDTIS",
        "outputId": "7c419e38-032d-4bb4-9bb1-eb7f344bef99"
      },
      "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: Receita, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ordenar do manior para o menor\n",
        "#Usa-se o comando \"sort_values\"\n",
        "#O comando \"ascending\" informa como deve ser ordenado\n",
        "\n",
        "df.sort_values(\"Receita\", ascending=False).head(10)"
      ],
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        "colab": {
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          "height": 363
        },
        "id": "WIf9my6LD5CW",
        "outputId": "e06771c3-3167-46c5-8c5c-5b28dfd3f8f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  Receita/Vendas\n",
              "7   Natal 2019-03-18   886.0    853     4    3544.0   3544.0             4.0\n",
              "55  Natal 2019-01-08   859.0    854     4    3436.0   3436.0             4.0\n",
              "51  Natal 2018-01-21   859.0    852     4    3436.0   3436.0             4.0\n",
              "30  Natal 2018-10-02   856.0    853     4    3424.0   3424.0             4.0\n",
              "41  Natal 2018-05-20   835.0    852     4    3340.0   3340.0             4.0\n",
              "38  Natal 2018-02-25   828.0    852     4    3312.0   3312.0             4.0\n",
              "10  Natal 2018-10-27   828.0    852     4    3312.0   3312.0             4.0\n",
              "69  Natal 2019-03-24   817.0    852     4    3268.0   3268.0             4.0\n",
              "62  Natal 2018-02-10   793.0    854     4    3172.0   3172.0             4.0\n",
              "52  Natal 2018-04-27   778.0    854     4    3112.0   3112.0             4.0"
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "############################## Aula \"Trabalhando com datas\" ################################################"
      ],
      "metadata": {
        "id": "ElQWPMuhEadF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Transformando a coluna de data em tipo inteiro\n",
        "\n",
        "df[\"Data\"] = df[\"Data\"].astype(\"int64\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQW_z77AFNy_",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: casting datetime64[ns] values to int64 with .astype(...) is deprecated and will raise in a future version. Use .view(...) instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bRuIMcNGTjG",
        "outputId": "65e0e628-7a55-45fc-861c-88af034eb308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cidade             object\n",
              "Data                int64\n",
              "Vendas            float64\n",
              "LojaID             object\n",
              "Qtde                int64\n",
              "Receitas          float64\n",
              "Receita           float64\n",
              "Receita/Vendas    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Caso haja uma planilha com algum dado que não seja \"data\", o comando para transformar em data segue a seguir:\n",
        "#Para isso use o comando \"pd.to_datetime\". neste caso a coluna \"Data\"\n",
        "\n",
        "df[\"Data\"] = pd.to_datetime(df[\"Data\"])"
      ],
      "metadata": {
        "id": "PemWqCXKGVzF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaex-KE8G_kA",
        "outputId": "911c54fe-67c5-4f9e-ca3f-0a782a0ab845"
      },
      "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",
              "Receitas                 float64\n",
              "Receita                  float64\n",
              "Receita/Vendas           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Agrupamento de receita para verificar o comportamento das vendas por ano\n",
        "#Neste caso utilizamos o \"dt.year\" (dt = datetime e year = os anos)\n",
        "\n",
        "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYd3CJOLHIeX",
        "outputId": "e4766370-38d9-4768-a49e-528a2d6d3f3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data\n",
              "2018    118176.53\n",
              "2019    228246.45\n",
              "Name: Receita, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Criar a coluna de \"Ano\" no banco de dados\n",
        "\n",
        "df[\"Ano_Venda\"] = df[\"Data\"].dt.year"
      ],
      "metadata": {
        "id": "9ipmdoSqIak-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "df.sample(5)"
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        "outputId": "1bf5b94c-7316-4827-b56c-db9a5909efe4"
      },
      "execution_count": null,
      "outputs": [
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              "        Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "54   Fortaleza 2019-01-01   16.73   1002     5     83.65    83.65   \n",
              "72       Natal 2018-05-02  612.00    854     4   2448.00  2448.00   \n",
              "235   Salvador 2019-01-03   41.84   1034     1     41.84    41.84   \n",
              "18      Recife 2019-01-01   49.40    980     5    247.00   247.00   \n",
              "111   Salvador 2019-03-02  147.35   1037     2    294.70   294.70   \n",
              "\n",
              "     Receita/Vendas  Ano_Venda  \n",
              "54              5.0       2019  \n",
              "72              4.0       2018  \n",
              "235             1.0       2019  \n",
              "18              5.0       2019  \n",
              "111             2.0       2019  "
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como extrair o mês e o dia da Data e criar novas colunas com o dia e mês\n",
        "\n",
        "df[\"mes_venda\"], df[\"dia_venda\"] = (df[\"Data\"].dt.month, df[\"Data\"].dt.day)"
      ],
      "metadata": {
        "id": "JJbbfpHYIsSQ"
      },
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              "        Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "96    Salvador 2019-01-01   42.94   1034     3    128.82   128.82   \n",
              "133      Natal 2019-01-02  153.71   1035     1    153.71   153.71   \n",
              "225      Natal 2019-01-02   36.23   1035     1     36.23    36.23   \n",
              "108   Salvador 2019-03-02   11.72   1037     1     11.72    11.72   \n",
              "138  Fortaleza 2019-03-02  150.38    983     6    902.28   902.28   \n",
              "\n",
              "     Receita/Vendas  Ano_Venda  mes_venda  dia_venda  \n",
              "96              3.0       2019          1          1  \n",
              "133             1.0       2019          1          2  \n",
              "225             1.0       2019          1          2  \n",
              "108             1.0       2019          3          2  \n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3bbc56df-b3c6-4e3a-bbc4-c03428bad6ad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ver a data mais antiga de venda\n",
        "\n",
        "df[\"Data\"].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AjskQ8ndJVpt",
        "outputId": "72c168c2-f0ae-4201-9887-0bd0bf7e2b3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2018-01-01 00:00:00')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como calcular a diferença de dias entre uma venda e outra\n",
        "\n",
        "df[\"diferenca_dias\"] = df[\"Data\"] - df[\"Data\"].min()"
      ],
      "metadata": {
        "id": "qSqiwl78J4o7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(5)"
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          "height": 206
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        "outputId": "8b83167a-8b7f-45b4-d317-30bfc7d01a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "116     Recife 2019-03-02   79.10    982     2    158.20   158.20   \n",
              "31      Recife 2019-01-01  176.23    981     7   1233.61  1233.61   \n",
              "60   Fortaleza 2019-01-01  182.14   1002     4    728.56   728.56   \n",
              "144   Salvador 2019-01-02   23.38   1034     1     23.38    23.38   \n",
              "159   Salvador 2019-01-02  172.52   1035     1    172.52   172.52   \n",
              "\n",
              "     Receita/Vendas  Ano_Venda  mes_venda  dia_venda diferenca_dias  \n",
              "116             2.0       2019          3          2       425 days  \n",
              "31              7.0       2019          1          1       365 days  \n",
              "60              4.0       2019          1          1       365 days  \n",
              "144             1.0       2019          1          2       366 days  \n",
              "159             1.0       2019          1          2       366 days  "
            ],
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              "      <th>116</th>\n",
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              "      <td>365 days</td>\n",
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              "      <th>144</th>\n",
              "      <td>Salvador</td>\n",
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              "      <td>23.38</td>\n",
              "      <td>1034</td>\n",
              "      <td>1</td>\n",
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              "      <td>366 days</td>\n",
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              "      <td>172.52</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 43
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como calcular o trimestre de vendas\n",
        "\n",
        "df[\"trimestre_venda\"] = df[\"Data\"].dt.quarter"
      ],
      "metadata": {
        "id": "dhnQE-osKh5d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "aKUwHiLFK4LC",
        "outputId": "e14e4fce-de04-4b77-929a-58bc7ef186b4"
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      "execution_count": null,
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            "text/plain": [
              "        Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "30      Recife 2019-01-01   12.35    980     5     61.75    61.75   \n",
              "106   Salvador 2019-01-01  149.19   1035     2    298.38   298.38   \n",
              "66   Fortaleza 2019-01-01   35.11   1005     6    210.66   210.66   \n",
              "127   Salvador 2019-03-02  240.61   1035     3    721.83   721.83   \n",
              "62     Aracaju 2018-01-01   28.66   1520     3     85.98    85.98   \n",
              "\n",
              "     Receita/Vendas  Ano_Venda  mes_venda  dia_venda diferenca_dias  \\\n",
              "30              5.0       2019          1          1       365 days   \n",
              "106             2.0       2019          1          1       365 days   \n",
              "66              6.0       2019          1          1       365 days   \n",
              "127             3.0       2019          3          2       425 days   \n",
              "62              3.0       2018          1          1         0 days   \n",
              "\n",
              "     trimestre_venda  \n",
              "30                 1  \n",
              "106                1  \n",
              "66                 1  \n",
              "127                1  \n",
              "62                 1  "
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como filtrar as vendas do ano de 2019 e do mês de março\n",
        "\n",
        "vendas_marco_2019 = df.loc[(df[\"Data\"].dt.year == 2019) & (df[\"Data\"].dt.month == 3)]"
      ],
      "metadata": {
        "id": "ddN6O_TsK6Yz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vendas_marco_2019"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "AKY90pn0Lvkv",
        "outputId": "6cbc644b-a561-4827-a107-41346c049d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "108  Fortaleza 2019-03-02  152.89    981     4    611.56   611.56   \n",
              "109  Fortaleza 2019-03-02   18.90    982     6    113.40   113.40   \n",
              "110  Fortaleza 2019-03-02   51.98    983     6    311.88   311.88   \n",
              "111  Fortaleza 2019-03-02    8.00    981     3     24.00    24.00   \n",
              "112  Fortaleza 2019-03-02  133.59    982     1    133.59   133.59   \n",
              "..         ...        ...     ...    ...   ...       ...      ...   \n",
              "137   Salvador 2019-03-02   51.66   1036     3    154.98   154.98   \n",
              "138   Salvador 2019-03-02  212.03   1037     3    636.09   636.09   \n",
              "139   Salvador 2019-03-02  169.01   1036     1    169.01   169.01   \n",
              "140   Salvador 2019-03-02   20.79   1036     2     41.58    41.58   \n",
              "141   Salvador 2019-03-02    9.08   1036     1      9.08     9.08   \n",
              "\n",
              "     Receita/Vendas  Ano_Venda  mes_venda  dia_venda diferenca_dias  \\\n",
              "108             4.0       2019          3          2       425 days   \n",
              "109             6.0       2019          3          2       425 days   \n",
              "110             6.0       2019          3          2       425 days   \n",
              "111             3.0       2019          3          2       425 days   \n",
              "112             1.0       2019          3          2       425 days   \n",
              "..              ...        ...        ...        ...            ...   \n",
              "137             3.0       2019          3          2       425 days   \n",
              "138             3.0       2019          3          2       425 days   \n",
              "139             1.0       2019          3          2       425 days   \n",
              "140             2.0       2019          3          2       425 days   \n",
              "141             1.0       2019          3          2       425 days   \n",
              "\n",
              "     trimestre_venda  \n",
              "108                1  \n",
              "109                1  \n",
              "110                1  \n",
              "111                1  \n",
              "112                1  \n",
              "..               ...  \n",
              "137                1  \n",
              "138                1  \n",
              "139                1  \n",
              "140                1  \n",
              "141                1  \n",
              "\n",
              "[121 rows x 13 columns]"
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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",
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              "      <th>diferenca_dias</th>\n",
              "      <th>trimestre_venda</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>152.89</td>\n",
              "      <td>981</td>\n",
              "      <td>4</td>\n",
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              "      <td>425 days</td>\n",
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              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>18.90</td>\n",
              "      <td>982</td>\n",
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              "      <td>113.40</td>\n",
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              "      <td>2</td>\n",
              "      <td>425 days</td>\n",
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              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>51.98</td>\n",
              "      <td>983</td>\n",
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              "      <td>311.88</td>\n",
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              "      <td>6.0</td>\n",
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              "      <th>111</th>\n",
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              "      <th>112</th>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>2019-03-02</td>\n",
              "      <td>133.59</td>\n",
              "      <td>982</td>\n",
              "      <td>1</td>\n",
              "      <td>133.59</td>\n",
              "      <td>133.59</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
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              "      <td>425 days</td>\n",
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              "      <td>212.03</td>\n",
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              "      <td>636.09</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>140</th>\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##############  Visualização dos dados - geração de gráficos   #################"
      ],
      "metadata": {
        "id": "nQkuglLgMFza"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Vendo o método \"value_counts\". Faz uma contagem de quantas linhas aparece a loja pelo seu \"ID\", isso significa que cada vez que a \n",
        "#loja aparece é porque realizou uma venda\n",
        "\n",
        "df[\"LojaID\"].value_counts(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zIRTLlmVM5Nu",
        "outputId": "e3379715-9499-4531-caf2-4f32924cdc55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1036    117\n",
              "1035    112\n",
              "1037    101\n",
              "1034     67\n",
              "983      65\n",
              "982      44\n",
              "1522     41\n",
              "1520     39\n",
              "980      36\n",
              "981      31\n",
              "1005     30\n",
              "1002     30\n",
              "852      29\n",
              "1523     29\n",
              "1004     28\n",
              "854      28\n",
              "853      26\n",
              "1521     21\n",
              "1003     20\n",
              "Name: LojaID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Usando a biblioteca \"MatplotLib\" que já faz parte do Pandas\n",
        "\n",
        "df[\"LojaID\"].value_counts(ascending=False).plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
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        "id": "xgATlUr8Ng-z",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f118b3279d0>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gerando gráfico de barras horizontais\n",
        "\n",
        "df[\"LojaID\"].value_counts(ascending=True).plot.barh();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nXk8H6NOOGfA",
        "outputId": "8d68ca7a-ec34-4e0a-b210-2fdf4773b981"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gerando gráfico de pizza para data (ano)\n",
        "\n",
        "df.groupby(df[\"Data\"].dt.year)[\"Receita\"].sum().plot.pie()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "mAMtkbMtOj_6",
        "outputId": "78840757-e9db-4ea2-c2c5-0fdf856bc6d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f118a8b5310>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total de vendas por cidade\n",
        "\n",
        "df[\"Cidade\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-RUHiMePKxN",
        "outputId": "00dcc66a-38eb-422a-f90d-927ef2ed11bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Natal        240\n",
              "Salvador     240\n",
              "Fortaleza    142\n",
              "Recife       142\n",
              "Aracaju      130\n",
              "Name: Cidade, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como colocar um título no gráfico e nomear os eixos\n",
        "#É preciso importar a biblioteca \"pyplot\"\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "df[\"Cidade\"].value_counts().plot.bar(title=\"Total de vendas por cidade\")\n",
        "plt.xlabel(\"Cidade\")\n",
        "plt.ylabel(\"Total de vendas\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "BC5qWeh9PfBb",
        "outputId": "ce344d7e-be7f-496e-906d-5991f93a73a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como alterar a cor do gráfico\n",
        "\n",
        "df[\"Cidade\"].value_counts().plot.bar(title=\"Total de vendas por cidade\", color=\"red\")\n",
        "plt.xlabel(\"Cidade\")\n",
        "plt.ylabel(\"Total de vendas\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "K_f5q-UcQRT4",
        "outputId": "4ae19040-3570-4430-f55d-89178d41ef6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como alterar o estilo do gráfico\n",
        "#Neste caso iremos utilizar o estilo \"ggplot\"\n",
        "#No site do matplotlib há mais opções, link: https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\n",
        "\n",
        "plt.style.use(\"ggplot\")"
      ],
      "metadata": {
        "id": "06Es8-kCRaI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(df[\"mes_venda\"])[\"Qtde\"].sum().plot(title=\"Total de vendas por mês\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.xlabel(\"Total de produtos vendidos\");\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "bRstnk3gRMKb",
        "outputId": "bd758ec2-8c84-44d9-d9ef-62e7f993d7df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f118a32b490>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Quantidade de produtos vendidos por mês\n",
        "\n",
        "df.groupby(df[\"mes_venda\"])[\"Qtde\"].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aavIhDqSdi9",
        "outputId": "a1c45317-f4e5-48e5-8c03-9d68af875734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mes_venda\n",
              "1     2208\n",
              "2      144\n",
              "3      467\n",
              "4       23\n",
              "5       17\n",
              "6       13\n",
              "7       13\n",
              "8        2\n",
              "9       10\n",
              "10      14\n",
              "11       2\n",
              "12       3\n",
              "Name: Qtde, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Selecionar apenas as vendas de 2019\n",
        "\n",
        "df_2019 = df[df[\"Ano_Venda\"] == 2019]"
      ],
      "metadata": {
        "id": "FR88xgXaTlUG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualizando as vendas somente de 2019\n",
        "\n",
        "df_2019"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "u-nrB1CoT--m",
        "outputId": "a07aa338-d57b-4f08-fb2f-1ecadbda87ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Cidade       Data  Vendas LojaID  Qtde  Receitas  Receita  \\\n",
              "7     Aracaju 2019-01-01  172.22   1522     6   1033.32  1033.32   \n",
              "8     Aracaju 2019-01-01   12.16   1523     1     12.16    12.16   \n",
              "9     Aracaju 2019-01-01  149.36   1523     3    448.08   448.08   \n",
              "10    Aracaju 2019-01-01   42.34   1522     6    254.04   254.04   \n",
              "11    Aracaju 2019-01-01  161.53   1520     4    646.12   646.12   \n",
              "..        ...        ...     ...    ...   ...       ...      ...   \n",
              "235  Salvador 2019-01-03   41.84   1034     1     41.84    41.84   \n",
              "236  Salvador 2019-01-03  126.29   1035     3    378.87   378.87   \n",
              "237  Salvador 2019-01-03   38.06   1036     3    114.18   114.18   \n",
              "238  Salvador 2019-01-03  139.64   1035     1    139.64   139.64   \n",
              "239  Salvador 2019-01-03  161.41   1037     3    484.23   484.23   \n",
              "\n",
              "     Receita/Vendas  Ano_Venda  mes_venda  dia_venda diferenca_dias  \\\n",
              "7               6.0       2019          1          1       365 days   \n",
              "8               1.0       2019          1          1       365 days   \n",
              "9               3.0       2019          1          1       365 days   \n",
              "10              6.0       2019          1          1       365 days   \n",
              "11              4.0       2019          1          1       365 days   \n",
              "..              ...        ...        ...        ...            ...   \n",
              "235             1.0       2019          1          3       367 days   \n",
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              "237             3.0       2019          1          3       367 days   \n",
              "238             1.0       2019          1          3       367 days   \n",
              "239             3.0       2019          1          3       367 days   \n",
              "\n",
              "     trimestre_venda  \n",
              "7                  1  \n",
              "8                  1  \n",
              "9                  1  \n",
              "10                 1  \n",
              "11                 1  \n",
              "..               ...  \n",
              "235                1  \n",
              "236                1  \n",
              "237                1  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando o gráfico das vendas de produtos por mês, dentro do ano de 2019\n",
        "#Neste gráfico acrescentamos um marcador que foi o \"v\", mas temos o \"#\", \"o\"\n",
        "\n",
        "df_2019.groupby(df_2019[\"mes_venda\"])[\"Qtde\"].sum().plot(title=\"Total de produtos vendidos por mês no ano de 2019\", marker = \"v\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.ylabel(\"Total de produtos vendidos\");\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "pVeYI6PsURrf",
        "outputId": "8eb041ed-944e-42bd-c3e4-0bec5c4bf81e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f118a1fc990>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando um histograma com os mesmos dados do gráfico anterior\n",
        "#Neste caso alteramos a cor para azul, mas há uma enorme variedade, segue link: https://matplotlib.org/2.0.2/examples/color/named_colors.html\n",
        "\n",
        "plt.hist(df[\"Qtde\"], color = \"blue\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "vRVJBvvnVPlo",
        "outputId": "c210c0fe-8a43-4d41-b7e8-d0136e155142"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gerando um gráfico de scatter plot\n",
        "\n",
        "plt.scatter(x=df_2019[\"dia_venda\"], y = df_2019[\"Receita\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Hb_sD7L5WYlv",
        "outputId": "b0d6cbce-01df-408b-a930-6fecd2043c8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Como gerar uma imagem PNG do gráfico\n",
        "#comando \"plt.savefig\". A imagem ficará salva na mesma pasta dos arquivos onde estão as tabelas.\n",
        "\n",
        "df_2019.groupby(df_2019[\"mes_venda\"])[\"Qtde\"].sum().plot(title=\"Total de produtos vendidos por mês no ano de 2019\", marker = \"v\")\n",
        "plt.xlabel(\"Mês\")\n",
        "plt.ylabel(\"Total de produtos vendidos\");\n",
        "plt.legend()\n",
        "plt.savefig(\"grafico_qtde_prod_vend_mes.png\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "33tbVlaBXhAL",
        "outputId": "87119c57-5007-4869-bc71-2282353e152d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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3t0dycrLasYuOjlZbp/CXwOvrODs7w9jYuNgYWrZsqXotlUrh5eWFpUuX4vLly6hbty7+85//lNguqVSKUaNGYceOHTh+/DiysrLw4Ycfqt738PBAXFwc6tWrV+Q4l3XSv87V1RWXLl1Sa9M/j01xn0lNTVW78kpNTcWtW7dUrwvPx38el7Nnz6odF2NjY/Tp0wfffPMNrl27htzcXISEhJS6/9LOE7H71aQWLVrAxMQE9+7dK/LdODs7q36ZC4KA9u3bY+HChTh79iy6du2KwMDACu+7uHPR1NQUTZo0KfYz58+fh1wux1dffYUOHTrAxcWlXHWBLVq0QEREhFryunLlCrKysko89q6urgCg9vOZn5+PyMhIte2KOZ7FuXv3Lrp06QJXV1cEBQWpXU0CQNu2bWFsbIwTJ06olikUCoSGhpb7romZmRkcHByQn5+PAwcOYMCAAWVeEelVH9E/O+aPHz9epGP+m2++IblcTtevX6eUlBR6/vw5XblyhQRBoGXLltG9e/coODiYmjVrRgDo1KlTRCSujygoKIikUin5+/tTQkICbdmyhWrXrq0WY3x8PFlYWNDIkSPp4sWLdO/ePQoLCyM/Pz+6e/duidv+50CEXbt2kbm5Oa1evVptnWXLlql9TswxWb16NZmbm9P27dspISGB/u///o9sbGzU+ohu3rxJEomEFi5cSHfu3KF9+/ZR48aN1e7jX7p0iQBQUFAQJScnU3Z2NhERzZkzp9TBCiEhIbR69WqKioqihw8fUlBQEJmbm1NAQECJx6OwbQCodevWNGjQILX3kpKSqG7dutSrVy9VJ/O5c+do4cKFdOHCBSL630in1/31119q3/ujR4/I1NSUxo8fT/Hx8RQaGkru7u5lDlZwd3en9u3b08WLFykmJoZ69epVZLDC0KFDVYMGbty4UWTQQEBAAG3cuJFiY2PpwYMHtHnzZpJIJKrjVhwx50lZ+xU7QpTofyPVXldcP+yUKVPI09NT9Xrp0qVkaWlJ69ato5s3b9L169dp9+7dqtF5Fy5coKVLl1JERAQ9fPiQQkNDqW7duqV2vhcXyz/7LY8ePUoSiYRWrlxJt27dor1795Y5WOHw4cMkCAIFBATQ3bt3adu2beTo6Kh27os5l5KSklSDFa5duyZ6sEK/fv2oadOmFBYWRnFxcTRmzJgigxXKOp7FiYuLo7p161Lv3r3p0aNHqkFGiYmJ9OrVK9V6n332Gcnlcjp8+DBdv36dxo4dSzY2Nmr9OmlpaRQTE0MxMTEEgJYvX04xMTFqgxxOnjxJR44cobt379Lp06fJy8uL6tSpo+qHL41eJSIi9aHKcrmcpk6dqjZUOS0tjd59912ysrJSG769bt06qlevHpmYmJCnpycdP378jRMRkbLj08HBgUxMTKhHjx60devWIjFevXqV+vXrRzY2NmRiYkJNmjShSZMmUVpaWonbLRyW6+vrS5aWliSTyWjevHlFhm//MxGJOSYFBQW0YMECsrOzIzMzMxo8eHCR4dtEyhE1jRs3JhMTE+rTpw/t3r27SIfyZ599RrVq1Xqj4dtnzpyhbt26kVwuVw09XblyZanHuVDr1q0JAIWEhBR578GDBzRy5EjVMOUGDRrQqFGjVCPAxPzyICIKDQ2lli1bkpGREbVo0YJ+//33Modv379/n3r27EnGxsbk6OhI/v7+RYZvZ2VllTqM+uDBg9SpUyeysbEhU1NTatGiRZnJWcx5UtZ+qyIRESmH7Lu7u5OxsTHZ2NhQ+/btVSPrrl+/Tu+++y7Vrl1b9d3Nnj271AEFYhIRkXL4dvPmzcnQ0JAcHBxo4cKFZQ5zXrx4Mdnb25OZmRm9++679J///OeNExGR+vBta2trUcO3U1NTaejQoWRmZkZyuZzmz59f7PDt0o5ncYorZyn87/Wf6fz8fJozZw7Vrl2bjI2NqXPnzhQZGam2rcLBGv/8r/B3ABHRgQMHyNnZmYyMjEgmk9GIESPKLFspJBDxvBu6oFGjRpg4cSIWL16s7VCYDuPzhFVHetVHxBhjrPrhRMQYY0yr+NYcY4wxraqSSU83bNiA6OhoWFtb47vvvlMtP378OE6cOAGJRIK3334bo0ePBqCc6TUsLAwSiQTjxo1D69atAQCxsbEIDAxU1ZAMGDCgKsJnjDGmQVWSiLy9vdGnTx+sX79etez69euIiorCt99+C0NDQ2RlZQFQFl+Fh4dj9erVyMjIwLJly/D9998DUE4rv3jxYtjZ2WHBggXw8PBAvXr1qqIJjDHGNKRKEpGrq6tq+o9CJ0+eRP/+/VVVvtbW1gCUxaedO3eGoaEh7O3tUadOHdy5cweAchqR2rVrA1BOiREZGSkqET1+/Ljcscvl8jKno9EH1aUdALdFV1WXtlSXdgAVa0vhFEZVQWvPI0pMTMTNmzexZ88eGBoaYsyYMXB2dkZ6ejqaNm2qWk8mkyE9PR0AYGdnp1puZ2dX5txtjDHGdJ/WEpFCoUBOTg6WL1+Ou3fvYs2aNVi3bl2lbDs0NBShoaEAgFWrVkEul5d7WwYGBhX6vK6oLu0AuC26qrq0pbq0A9CftmgtEclkMrRv3x6CIMDZ2RkSiQTZ2dmQyWRIS0tTrZeenq6a1O/15WlpaSVO9ufj46P2vPeKXGZXl8v06tIOgNuiq6pLW6pLOwC+NVemdu3aIS4uDi1btsTjx4/x6tUrWFpawsPDA2vXrsX777+PjIwMJCYmwtnZGUSExMREJCcnQyaTITw8HH5+ftoKnzFWjRERnj9/DoVCoZplWx89efIEL168KPF9IoJEIoGJiYlW21klicjf3x/x8fHIzs7G1KlTMWzYMHTv3h0bNmzArFmzYGBggE8++QSCIKB+/fro1KkTPv/8c0gkEkyYMEE1c+v48eOxfPlyKBQKdOvWDfXr16+K8BljNczz589haGio9pwqfWRgYFDq7NwA8OrVKzx//hympqZVFFVRNaKg9U1HzRUs/Qz4637RN+o3hvSL7yspqqrFtxt0E7dF98jlcjx8+FBjz/6qSgYGBnj16lWZ6z179qxIe6vy1hxP8VMcp+aA9B9/CUkNgCbNtRMPY6xK6fPtuPLQdns5ERVD8v5wQPKPL0YigeT9D4v/AGOMsXLjRFQMwUYGeLz2dEKpAeDZA4J16U9YZYyxyvT48WOMGzcOnp6e6NSpExYtWoQXL17g+vXr+P3330v8XIcOHVT1l/pAv3viNEgyaCwUF88CigJAEPhqiDFWLE31KRMRJk2ahI8++giBgYEoKCjA3LlzsXz5crRo0QJXr15Fjx49KhC57uBEVALBRgZ07gGcPwmYmgFWNtoOiTGmi5yaA4//AgpeGxRQCX3K58+fh7GxMYYPH67cpFSKf//73+jQoQMOHjwIiUSCS5cu4dNPP0WXLl3wySefICkpCW3btsXrY9AOHjyILVu2ID8/H23atMHKlSvLHElX1TgRlULSfySEezdQ8PgvUNQFCO3eKftDjLFqRbFnE6i4K55Cr14q75yofagA+PMeCr5dWOxHhPqNIflwUqn7TUhIgJubm9oyS0tL1KtXD0OHDsWDBw+wfPlyAMC//vUvtG/fHjNnzkRoaCh2796t2sahQ4cQEhICQ0NDLFiwAEFBQRg6dGgZra5anIhKIdjIYLf2P0j+bDQoaBuodQcIf0/SyhhjAAADQ8DKFsh6rU/Gyla5vIpEREQgICAAgHJmGRsb5R2cc+fO4dq1a3jvvfcAKOujdHHKH05EZRCkUkiGjofCfwno1FEIvfgZSIzVJGVduQAAZaZDsXAy8DIfMDSC5F9rKjy4qWnTpjh69KjasuzsbKSkpMDIyEjUNogIQ4cOxYIFCyoUi6bxqDkRhBZtgBZtQEf3gp5lazscxpiOUfUpC0KljbDt0qUL8vLysH//fgBAQUEBli5dCl9fX8jlcuTk5KjW7dixI4KDgwEAYWFhyMzMVG3jyJEjqkLjjIwMPHr0qMKxVTZORCJJhowD8vJAR/ZpOxTGmA6SvD8ccHattBG2giAgICAAR48ehaenJ1q2bAlBEPDZZ5+hc+fOuH37Nnr27IlffvkFM2fOxMWLF9GtWzccP34cjo6OAIBmzZph7ty5GDFiBHx8fDBixAg8efKkUuKrTDzFTxlen7ZEsX0dKDwMkqXrINhX3fQXlaG6TL8CcFt0VXVpi1wux59//gkzMzNth6ImMjISn3zyCTZv3lxkEENJxE7xk5ubW6S9PMWPjhL6jQSkUiiCtms7FMZYDdOuXTtcunRJdBLSJ5yI3oBgI4PQexBwORx054a2w2GMsWqBE9EbEnoPBKxlUOzfghpwV5OxGqmm/Wxru72ciN6QYGwCof9I4N4t4PIFbYfDGNMAiUQiqm+lOnj16pXqmW/awnVE5SB49gD9fhiKg9sgceciV8aqGxMTEzx//hwvXrzQ+iMSKsLY2Fj0E1q1iRNROQgSLnJlrDoTBEGrTyytLPoykrFKEtGGDRsQHR0Na2trfPfdd2rvHT58GDt27EBAQACsrKxARAgMDERMTAyMjY0xbdo0ODk5AQBOnz6NoKAgAMCgQYPg7e1dFeEXS63I1bMHBHNLrcXCGGP6rEpuDHp7e2PhwqKT/6WmpuLq1atqcx/FxMQgKSkJa9euxeTJk1XzJ+Xk5ODAgQNYsWIFVqxYgQMHDqhVFmsDF7kyxljFVUkicnV1hYWFRZHl27Ztw6hRo9TuwUZFRcHLywuCIMDFxQXPnj1DRkYGYmNj0apVK1hYWMDCwgKtWrVCbGxsVYRfIqFeI2V/0amjoORErcbCGGP6Smt9RJGRkZDJZGjUqJHa8vT0dLUrJDs7O6SnpyM9PR12dnaq5TKZrMQnEIaGhiI0NBQAsGrVqgrNNmtgYFDq5wvGfYrUyHMwPLoXNnO+Kvd+NK2sdugTbotuqi5tqS7tAPSnLVpJRC9evEBwcDAWL16ske37+PjAx8dH9boinXVld/YJEHoNxIvDu5EScQ6C81vl3pcm6UunpRjcFt1UXdpSXdoBVKwtOjfFz/nz51Uztj5+/BhLlizBl19+if/+97/l2umTJ0+QnJyMOXPm4JNPPkFaWhrmzZuHzMxMyGQytQOXlpYGmUwGmUyGtLQ01fL09HTIZLJy7b+ycZErY4yVn6hEtHfvXlUfz/bt29GkSRO89dZbqoEEb6pBgwYICAjA+vXrsX79etjZ2eHrr7+GjY0NPDw8cPbsWRAREhISYGZmBltbW7Ru3RpXrlxBTk4OcnJycOXKFbRu3bpc+69sXOTKGGPlJ+rW3NOnT2FjY4P8/HzcunULs2bNglQqxYQJE0TtxN/fH/Hx8cjOzsbUqVMxbNgwdO/evdh127Rpg+joaPj5+cHIyAjTpk0DAFhYWGDw4MGqBzwNGTKk2AEQ2qIqcg3azkWujDH2BkQlIisrKyQlJeHPP/9EkyZNYGhoWGq17j/NmDGj1PfXr1+v+rcgCJg4cWKx63Xv3r3EBKZtgkQKyZBxUHz/b9DpYxB69td2SIwxphdEJaLBgwdj3rx5kEgkmDlzJgDg2rVraNiwoUaD0zdCy7cB1zagI3tBnbtzkStjjIkgKhF5e3ujU6dOAJRzFwHK56mXdaVTE0mGjoNi6QzQkX0Qhou7dckYYzWZ6OHbL1++xOXLl1Wj1dq2batTfTS6Qq3Itdt7EOzrajskxhjTaaJGzSUkJGD69On47bff8PDhQ4SGhmL69OlISEjQdHx6SeivfJIr8ZNcGWOsTKKuiLZu3YqJEyfC09NTtSw8PByBgYFYuXKlxoLTV4KNHYTeA0GH94Du3oTQpLm2Q2KMMZ0l6oooMTFR1UdUqGPHjkhKStJIUNWB0GsgYG3LRa6MMVYGUYmoTp06CA8PV1v2xx9/oHbt2hoJqjoQTEwh9B8F3L3JRa6MMVYKUbfmfH19sWrVKhw/fhxyuRwpKSlITEzE/PnzNR2fXuMiV8YYK5uoRNSsWTP88MMPiI6ORkZGBtq2bYu3336bR82VgYtcGWOsbKKHb1tYWMDLy0uTsVRLXOTKGGOlKzERffHFF2oPrCvJl19+WakBVUeSob7KItej+yAM4yJXxhh7XYmJ6PU53Z48edwD4NwAACAASURBVIJTp06ha9euqFWrFlJTU3HmzBl069atSoLUd0K9xhA69wCFHQV16wuhVh1th8QYYzqjxETk7e2t+veiRYuwaNEi1K9fX7XsnXfewY8//ohhw4ZpNMDqQhgwChR5DnRwG4Sp87QdDmOM6QxRw7cfPXpUZKi2vb19uR+MVxOpilwvXwDdvantcBhjTGeISkSurq7YsGEDEhMTkZ+fj8ePH+PHH39E8+Y8Y8Cb4CJXxhgrSlQi+uSTTwAAn3/+OcaMGYPZs2eDiFQPrWPiqBW5RoeX/QHGGKsBRA3ftrCwwIwZM6BQKPD06VNYWVlBIhGVw9g/qIpcD26DxL09BAMucmWM1WwlJqLk5GTY29sDUI6ae11KSorq32Km+dmwYQOio6NhbW2N7777DgCwY8cOXL58GQYGBqhduzamTZsGc3NzAEBwcDDCwsIgkUgwbtw4tG7dGgAQGxuLwMBAKBQK9OjRAwMGDHjD5mqfWpHrKS5yZYyxEhPR7NmzsX278jEGfn5+JW5g7969Ze7E29sbffr0UXskeKtWrTBy5EhIpVLs3LkTwcHBGD16NB49eoTw8HCsXr0aGRkZWLZsGb7//nsAwObNm7F48WLY2dlhwYIF8PDwQL169UQ3VldwkStjjP1PiYmoMAkB4pJNaVxdXZGcnKy2zN3dXfVvFxcXREREAAAiIyPRuXNnGBoawt7eHnXq1MGdO3cAKCdfLbwC69y5MyIjI/UyEQFc5MoYY4VET/GjSWFhYejcuTMAID09HU2bNlW9J5PJkJ6eDgCws7NTLbezs8Pt27eL3V5oaChCQ0MBAKtWrYJcLi93bAYGBhX6fInkcmR174vnp47CZtBoGNRxrPx9vEZj7dACbotuqi5tqS7tAPSnLVqf4icoKAhSqRRdunSp0HZe5+PjAx8fH9Xr1NTUcm9LLpdX6POlod6DgXO/IX3z95BMmauRfRTSZDuqGrdFN1WXtlSXdgAVa4uDg0MlR1MyrU7xc/r0aVy+fFkt6clkMqSlpanWSU9Ph0wmAwC15Wlpaarl+kqwtYPQayDoyB6QTz9+kitjrEYqcQy2t7e36r+rV69i0aJFGDFiBHx8fPDhhx9i0aJFuHLlSrl3HBsbi19++QXz5s2DsbGxarmHhwfCw8Px8uVLJCcnIzExEc7OzmjSpAkSExORnJyMV69eITw8HB4eHuXev64QenORK2OsZhPVR1TRKX78/f0RHx+P7OxsTJ06FcOGDUNwcDBevXqFZcuWAQCaNm2KyZMno379+ujUqRM+//xzSCQSTJgwQVWzNH78eCxfvhwKhQLdunVTm/tOXxUWudL2dcoi17ae2g6JMcaqlEAi/gz/+uuvYWxsjOHDh8POzg6pqanYv38/8vLy9OIprY8fPy73Z6vifjEpCqBYOgPIfwHJ0vUaKXLl+966iduie6pLOwD96SMq1xQ/s2bN4il+KpGyyNUXSEkCnT6m7XAYY6xK8RQ/uqLF24Bra9DhvaBOPSCY82PYGWM1g+hskpubi3v37uHRo0eIj4/H9evXcf36dU3GVqMIggDJkHFA3jPQsX3aDocxxqqMqCui06dPY/PmzTAxMYGRkZFquSAIWLduncaCq2mE+oVPcj0C8n6Pn+TKGKsRRCWi3bt34/PPP0ebNm00HU+NJ/T/+0muQdshaLjIlTHGdIGoW3MKhUJtbjimOaoi16jz/CRXxliNICoR9e/fHwcPHoRCodB0PAx/F7la2XCRK2OsRhB1a+7o0aPIzMzEoUOHYGGhPprrxx9/1EhgNZmqyHXHeiD6D6BtZ22HxBhjGiMqEU2fPl3TcbB/EN7xAYUdgeLgVkjc2/GTXBlj1ZaoROTq6qrpONg/FBa5Kr7/EnT6GAQffpIrY6x6EtVH9PLlS+zevRuffvopxo4dCwC4cuUKfv31V40GV+O9XuT6LEfb0TDGmEaISkTbtm3DX3/9BT8/P9XjGurXr4+TJ09qNLiajotcGWM1gahEdOnSJfj5+cHFxUXtuUGFT05lmqMscu2uLHJNSdJ2OIwxVulEJSIDA4MiQ7efPn0KS0tLjQTF1An9RwMSKSh4h7ZDYYyxSicqEXXs2BHr1q1DcnIyACAjIwObN29G5848rLgqqIpcI89xkStjrNoRlYhGjhwJe3t7zJo1C7m5ufDz84OtrS2GDh2q6fjY37jIlTFWXYkavm1gYABfX1/4+vqqbskV9hWxqsFFroyx6kpUIvrmm2/QpUsXeHh4wMrK6o13smHDBkRHR8Pa2hrfffcdACAnJwdr1qxBSkoKatWqhZkzZ8LCwgJEhMDAQMTExMDY2BjTpk2Dk5MTAOUs4EFBQQCAQYMGwdvb+41j0WeCpw/o98Nc5MoYq1ZE3ZpzdXXFoUOHMHHiRKxbtw6xsbFvNO+ct7c3Fi5cqLYsJCQEbm5uWLt2Ldzc3BASEgIAiImJQVJSEtauXYvJkycjICAAgDJxHThwACtWrMCKFStw4MAB5OTUrNoaQSpVDudOSQKdPq7tcBhjrFKISkTvv/8+Vq5ciVWrVqF27drYtm0bpkyZgi1btojaiaura5E56iIjI9G1a1cAQNeuXREZGQkAiIqKgpeXFwRBgIuLC549e4aMjAzExsaiVatWsLCwgIWFBVq1aoXY2Ng3aWv10PLvItcjXOTKGKseRN2aK1S3bl0MHToU7dq1w86dO3HixAmMHz++XDvOysqCra0tAMDGxgZZWVkAgPT0dMjlctV6dnZ2SE9PR3p6Ouzs7FTLS6tjCg0NRWhoKABg1apVatt7UwYGBhX6vCa8nDgT6bN8YXLqMCx9xc0DqIvtKC9ui26qLm2pLu0A9KctohNRUlISLly4gAsXLuDp06fo2LEjhgwZUilBCIJQqYMffHx84OPjo3qdmppa7m3J5fIKfV4jLG0hdO6O3KP78bxDN1FPctXJdpQTt0U3VZe2VJd2ABVri4ODQyVHUzJRiWjBggV4/Pgx2rVrhzFjxqBVq1aQSqUV2rG1tTUyMjJga2uLjIwM1SAImUymduDS0tIgk8kgk8kQHx+vWp6enl6jJ2MV+o9W1hUF74AweY62w2GMsXIT1Uf0wQcfYNOmTfj000/Rpk2bCichAPDw8MCZM2cAAGfOnEG7du1Uy8+ePQsiQkJCAszMzGBra4vWrVvjypUryMnJQU5ODq5cuYLWrVtXOA59xUWujLHqQtQVUUVnUPD390d8fDyys7MxdepUDBs2DAMGDMCaNWsQFhamGr4NAG3atEF0dDT8/PxgZGSEadOmAQAsLCwwePBgLFiwAAAwZMiQIgMgahqh90DQ2RNQHAiEZO4qru1ijOklgWpAmf7jx4/L/Vldv1+sOHsCtGM9JFPnQyilyFXX2/EmuC26qbq0pbq0A9CfPiJRt+aY7hI8fQCHBlAc3Ap69VLb4TDG2BvjRKTnuMiVMabvRCWiR48eITMzEwDw/Plz7Nu3D/v378eLFy80GhwTqeXbwFvuXOTKGNNLohLR999/j9zcXADA9u3bcePGDdy+fRsbN27UaHBMHNWTXHNzQMf2azscxhh7I6ISUXJyMhwcHEBEuHTpEmbOnInPP/8cV65c0XR8TCShgROETt1BYYf5Sa6MMb0iKhEZGRkhLy8Pd+7cgVwuh5WVFQwNDfHyJXeO6xJhwGhAIuEnuTLG9IqoOiJPT08sXboUeXl56NOnDwDg/v37sLe312hw7M2oilyP7AX1+ABCk+baDokxxsokKhH5+vriypUrkEqlaNmyJQBlv8TYsWM1Ghx7c1zkyhjTN6KHb7u7u6NOnTpISEhAamoqmjRpokpKTHcIJmYQ+o8E7twAYv7QdjiMMVYmUVdEGRkZ8Pf3x+3bt2FhYYHs7Gy4uLjgs88+g0wm03SM7A0Jnj1BoYehOLgNklb8JFfGmG4TdUW0adMmNGzYEFu2bMHGjRsRGBiIRo0aYdOmTZqOj5WDIJVCMnQ8kJzIRa6MMZ0nKhHdunULH330EUxMTAAAJiYmGD16NBISEjQaHKsALnJljOkJUYnI3Nwcjx49Ulv2+PFjmJmZaSQoVnFc5MoY0xei+oj69euHZcuWoXv37qhVqxZSUlJw+vRpDB8+XNPxsQp4vci1YNAoQGqk7ZAYY6wIUVdEPj4+mDlzJrKzs3H58mVkZ2fDz89P7XHcTDcJ/UcBEgmyd/6k7VAYY6xYoq6I/vjjD3Tq1KnIcO2IiAh07NhRI4GxyiHI5BB6DsCLo/sg6dIbglMzbYfEGGNqRCWin376CZ06dSqy/Oeff65wIjpy5AjCwsIgCALq16+PadOmITMzE/7+/sjOzoaTkxOmT58OAwMDvHz5EuvWrcO9e/dgaWmJGTNm8OwOIgh9BkG4EArF/i1c5MoY0zml3pp78uQJnjx5AoVCgeTkZNXrJ0+e4OrVqzAyqlifQ3p6Oo4fP45Vq1bhu+++g0KhQHh4OHbu3Im+ffvihx9+gLm5OcLCwgAAYWFhMDc3xw8//IC+ffti165dFdp/TSGYmMF8xEQucmWM6aRSr4j8/PxU/54+fbraezY2Nhg6dGiFA1AoFMjPz4dUKkV+fj5sbGwQFxeHzz77DADg7e2N/fv3o1evXoiKilLts2PHjtiyZQuIiP/CF8G0x/vIDtnNRa6MMZ1TaiLau3cvAGDJkiX48ssvK33nMpkMH3zwAT7++GMYGRnB3d0dTk5OMDMzg1QqVa2Tnp4OQHkFZWdnBwCQSqUwMzNDdnY2rKysKj226kaQGkAydBwUa5eCzvwKoccH2g6JMcYAiOwj0kQSAoCcnBxERkZi/fr1MDMzw+rVqxEbG1vh7YaGhiI0NBQAsGrVKsjl8nJvy8DAoEKf1xUGBgaQe/dG5uljeHlkL2TvD4HE3FLbYZVLdflOAG6LLqou7QD0py2iEtEXX3xR4u2viiSpa9euwd7eXnVF06FDB9y6dQu5ubkoKCiAVCpFenq6aj47mUyGtLQ02NnZoaCgALm5ubC0LPrL1MfHR21oeWpqarljlMvlFfq8rpDL5UhLSwP1Hw36aiZSt/8EydBx2g6rXKrLdwJwW3RRdWkHULG2ODg4VHI0JROViLp37672OjMzE6dOnUKXLl0qtHO5XI7bt2/jxYsXMDIywrVr19CkSRO0aNECERER8PT0xOnTp+Hh4QEAaNu2LU6fPg0XFxdERESgRYsW3D/0htSe5Or9LoRadbQdEmOshhOViLy9vYss69ixIzZs2IAhQ4aUe+dNmzZFx44dMW/ePEilUjRq1Ag+Pj54++234e/vjz179qBx48aqRNi9e3esW7cO06dPh4WFBWbMmFHufddkQv9RoKhzoOAdECbP0XY4jLEaTlQiKo5MJsPDhw8rHMCwYcMwbNgwtWW1a9fGypUri6xrZGSEzz//vML7rOkKi1zp6D6QTz8ucmWMaZWoRFRYx1MoPz8fFy9ehIuLi0aCYpon9BmkfJLr/kBI5q7kW5yMMa0RlYjOnTun9trY2BjNmjVD3759NRIU0zzlk1xHgXZuAGIigLeLzpzBGGNVQVQiWrJkiabjYFogvNMT9PthKA5uhaSVBxe5Msa0osRE9OTJE1EbqF27dqUFw6qW8kmuXOTKGNOuEhPR69P7lKZw9gWmp1q2VT7J9fAeUKduEMwstB0RY6yGKTERvZ5gTp06hWvXrmHo0KGqB+MdOHAAbm5uVRIk05zCJ7kqvpoJOrYfwhD9LHJljOkvUQ/G27t3L6ZOnYq6devCwMAAdevWxeTJk7Fnzx5Nx8eqgNDACULHbqDfD4NSxd2SZYyxyiIqERERkpOT1ZalpKRAoVBoJChW9YQBowGJBBS8Q9uhMMZqGFGj5vr27YulS5fC29tbNXfRmTNnePh2NVKkyLUx14gxxqqGqCuifv36Ydq0acjKykJUVBQyMzPx8ccfo3///pqOj1Uhoc8gwNIain3K5zwxxlhVED3FT+vWrdG6dWtNxsK0jItcGWPaICoRvXr1CkFBQTh79iwyMjJga2sLLy8vDBo0CAYG5Z6ujukgLnJljFU1UVlk586duHv3LiZNmqQavn3w4EHk5ubC19dXwyGyqiRIpZAM8YXih2WgMycg9Hhf2yExxqo5UX1EERERmDt3Ltzd3eHg4AB3d3fMnj0bf/zxh6bjY9rg5qEscj2yG5Sbo+1oGGPVnOjh26zmKCxyxbMc0LH92g6HMVbNiUpEnTp1wtdff43Y2Fg8evQIsbGx+Pbbb9GpE3dmV1dc5MoYqyqi+ohGjx6NgwcPYvPmzcjIyIBMJkPnzp0xePBgTcfHtEgYMBoUdV75JNdJs7UdDmOsmiozESkUCvz000+YMmUKhg8fXukBPHv2DD/99BP++usvCIKAjz/+GA4ODlizZg1SUlJQq1YtzJw5ExYWFiAiBAYGIiYmBsbGxpg2bRqcnJwqPSampCpyPcZFrowxzSnz1pxEIsHVq1c19gTPwMBAtG7dGv7+/vj222/h6OiIkJAQuLm5Ye3atXBzc0NISAgAICYmBklJSVi7di0mT56MgIAAjcTE/kd49+8i1/1c5MoY0wxRfUR9+/bFvn378OrVq0rdeW5uLm7cuIHu3bsDAAwMDGBubo7IyEh07doVANC1a1dERkYCAKKiouDl5QVBEODi4oJnz54hIyOjUmNi6gqLXHE7XlnkyhhjlUxUH9Gvv/6KzMxMHD16FFZWVmrv/fjjj+XeeXJyMqysrLBhwwY8fPgQTk5O8PX1RVZWFmxtbQEANjY2yMrKAgCkp6dDLperPm9nZ4f09HTVukwzuMiVMaZJohLR9OnTNbLzgoIC3L9/H+PHj0fTpk0RGBioug1XSBCEN74tGBoaitDQUADAqlWr1JLXmzIwMKjQ53VFRdvxYrwfMpfPgfnl8zDrO7QSI3tz1eU7Abgtuqi6tAPQn7aISkSurq4a2bmdnR3s7OzQtGlTAEDHjh0REhICa2tr1VRCGRkZqqswmUyG1NRU1efT0tIgk8mKbNfHxwc+Pj6q169/5k0Vzjau7yraDmroAjRvhew9AXjm1k6rT3KtLt8JwG3RRdWlHUDF2uLg4FDJ0ZRMVB/Rq1evsHfvXvj5+WHMmDHw8/PDnj17kJ+fX6Gd29jYwM7ODo8fPwYAXLt2DfXq1YOHhwfOnDkDADhz5gzatWsHAPDw8MDZs2dBREhISICZmRnflqsigiBAMrSwyPWAtsNhjFUjoq6INm3ahMePH2PcuHGqueaCg4ORnp6OadOmVSiA8ePHY+3atXj16hXs7e0xbdo0EBHWrFmDsLAw1fBtAGjTpg2io6Ph5+cHIyOjCu+bvRmhQZO/i1wPgbzfhSCvre2QGGPVgKhEFBkZiR9++AHm5uYAgHr16qFp06aV0nfUqFEjrFq1qsjyL774osgyQRAwceLECu+TlR8XuTLGKpuoW3M2NjZ48eKF2rL8/Hy+LVYDqYpcL50F3U/QdjiMsWpA1BWRl5cXVqxYgT59+sDOzg5paWk4ceIEvLy8cP36ddV6LVu21FigTHcI7w4CnTsBxf4tkMxZqbFiZ8ZYzSAqEf32228AgODg4CLLC98TBAHr1q2r5PCYLhJMzCD0Gwna9SMQexFo01HbITHG9JioRLR+/XpNx8H0jNClFyjsCBQHtkLi5gGBn9TLGCsnUX1EjP1T4ZNckfwYdOZXbYfDGNNjnIhY+bl5AM1b8ZNcGWMVwomIlRsXuTLGKgMnIlYhyiJXb36SK2Os3EQnouzsbJw9exa//PILAOVM2GlpaRoLjOkPYcBoQBBAwTu1HQpjTA+JSkTx8fGYMWMGzp07h4MHDwIAkpKSsGnTJo0Gx/SDIKv1d5HrGdD929oOhzGmZ0SNud26dStmzJgBNzc3jBs3DgDg7OyMu3fvajQ4pj/+V+S6mYtca5CCpZ8Bf90HAKjdmK3fGNIvvtdKTEz/iLoiSklJgZubm9oyAwMDFBQUaCQopn8Ki1xxO15Z5MpqBqfmgPQff89KDYAmzbUTD9NLohJRvXr1EBsbq7bs2rVraNCggUaCYvpJ6NILqFMPigNbQZX8WHmmm4Q+gwGQ+kKJBJL3P9RKPEw/ibo1N2bMGHz99ddo06YN8vPzsXHjRly+fBlz5szRdHxMjyiLXMdBsW4Z6OyvELq/r+2QmIZQeirozK+gcyeAf94ZaeUBwZonRGbiiUpELi4u+Pbbb3Hu3DmYmJhALpdjxYoVsLOz03R8TN+08gCauYEO7wZ17AbBzFzbEbFKQkRAQhwUYUeA2AjlhZB7OwjtvEBbvwde/v2gzMvhUGzxhzBoDAQb/h3ByiZ6gjCZTIb+/ftrMhZWDQiCAMmw8VB89Tno2H4IQ3y1HRKrIHrxHBRxGnTqKPDfh4C5JYSeAyC89nDEgoTrwNlfAU8fCJZWoN9+AV2+AOHdIRB6DYBgZKzlVjBdVmIi+uGHH0SNfPr0008rNSCm/9SKXPlJrnqLkh+DTh0HXQgF8p4BDZwg+PpBaNelSGKRvD8c0pREFAwYDcHaFtSlt7Kv8JddoHMnIQweq/wcj6ZkxSgxEdWpU0f17+zsbJw5cwZt27aFXC5HamoqLl++jK5du1ZKEAqFAvPnz4dMJsP8+fORnJwMf39/ZGdnw8nJCdOnT4eBgQFevnyJdevW4d69e7C0tMSMGTNgb29fKTGwyqV8kusFUPBOCJNmaTscJhIpFEBcDBSnjgLXLwMSCYS2nhC69QWaNC8xkQg2MsiWb0Bqaqryda06kH48H3TrOhR7N4E2/R8o7AgkwydBaNy0KpvE9ECJiWjo0KGqfy9fvhzz58/HW2+9pVp28+ZNVXFrRR07dgyOjo7Iy8sDAOzcuRN9+/aFp6cnNm7ciLCwMPTq1QthYWEwNzfHDz/8gAsXLmDXrl2YOXNmpcTAKpeyyLU/6Nh+kE8//uWj4yg3B3Thd+Xtt5QkwNoWwvsfQvDqDcFGVu7tCs1aQrJ4NSg8DBS8A4oVsyB06gZh4EcQbLn/iCmJGr6dkJCApk3Vf5E4OzsjIaHij4pOS0tDdHQ0evToAUDZIRoXF4eOHZUPW/P29kZkZCQAICoqCt7e3gCAjh074vr168oOVKaThD6DAUtrKA5s4e9JR9GjB1Ds2ADFnHGgfZuVCWjyHEhWBUDSb0SFklAhQSKF5J2ekCz/CcK7g0GR56BYPBWKI3tAL15UQiuYvhM1WKFx48bYvXs3hg8fDiMjI+Tn52Pfvn1o1KhRhQPYunUrRo8erboays7OhpmZGaRSKQDlIIn09HQAyvntCkfqSaVSmJmZITs7G1ZWVhWOg1U+wfS1J7leuQi05ie56gIqKABiLypvv926BhgaQejQFUK39yA0aKKx/QomZhAGjVX2Hx3cCvrlP8r+o0FjIbT34v6jGkxUIpo2bRrWrl2LsWPHwsLCAjk5OWjSpAn8/PwqtPPLly/D2toaTk5OiIuLq9C2XhcaGorQ0FAAwKpVqyCXy8u9LQMDgwp9Xldoqx00cATSTh8DgnfCzrtPpTzJtbp8J0DVtkWRmY7c3w4h70QIFGnJkNjXhdlHn8C0x/uQWFlXePui2yKXA4v/D/lxMcje8j1eBXwHg3MnYDl+BgxdXCscR0Xx+VX1RP1WsLe3x1dffYXU1FRkZGTA1ta2Uhp369YtREVFISYmBvn5+cjLy8PWrVuRm5uLgoICSKVSpKenQyZT3h6QyWRIS0uDnZ0dCgoKkJubC0tLyyLb9fHxgY+Pj+p1YQdqeRQOztB32mwHDfwIinXLkBK0E5JKKHKtLt8JUDVtofsJoLCjoKhzwKtXgGtrSD6cBLTyQJ5Eirz8l0AlxPDGbaldHzTvawh/nMLL4B1InzcRQsduEAZpt/+Izy8lBweHSo6mZG/056lcLq/U7Dpy5EiMHDkSABAXF4fDhw/Dz88Pq1evRkREBDw9PXH69Gl4eHgAANq2bYvTp0/DxcUFERERaNGiBV/O6wMucq1y9PIlKOq8cvDB/QTAxBSCVx8I3u9BqFtP2+GpCBIpBE8fUNvOoOMHQSdDQNHhEPoMhtBrIARjrj+qCXTywXijRo3CkSNHMH36dOTk5KB79+4AgO7duyMnJwfTp0/HkSNHMGrUKC1HysRQPsl1PJCTDTrOT3LVJEpPgSJ4JxTzxoO2rAHyciGMnALJN4GQjJisU0nodYKJGSQDx0CydD0ENw/Qof9A8a+Pobh4hge61AAC1YBv+fHjx+X+bHW5TNeFdig2rwFFnYfkqx8h2JW//ksX2lJZKqMtyql3rkMRdlRt6h1Jt77AW+5VdtegMr8XSoiDYm8A8OddwKkZJMMnQnBqVinbLgufX0o6e2uOsYoQBo4GXb4ACtrBRa6VoNipd3oNhNC1j97PZiG4tIBk0XegP06BgrdDsXIOhI7eyvojme53vrM3U2IievLkSUlvqaldW79PeFZ1uMi1crzJ1Dv6TJBIIHj2ALXtBDoeBDoZrOw/6j0YQu9B3H9UjZSYiMQOzd67d2+lBcOqP6HPYNC5k1Ac2ALJ7BU82EQk1dQ7YUeUU+9IpaKm3qkOBBMz5dV0l56gg9uUg17O/6YcXdfeC4JEJ7u62RsoMRFxgmGaoCxyHQHa9RMXuYpQ7NQ7H4yo8NQ7+kiQ14YwZS6o+/tQ7A0AbV799/x1EyHwE2H1GvcRsSonvNML9PsRKA5sg6SlR6UUuVY39OgB6NQxUMQpIP8F4PwWhIFjILTpCMHAUNvhaZXQ1BWShf8HijgFCtoBxaq5ypkhBn0EQVZL2+GxchD1G6CgoAAnTpxAfHw8srOz1d778ssvNRIYq74EAwNIhvhCse4r0LkTyttL7H9T74QdARKuV9nUO/pIkEggdO4Beruw/igYFPOHsu+o9yAIxibaDpG9AVE3V7dt24bQ0FC4urri3r176NChA7KystCiRQtNEfAVpAAAFKRJREFUx8eqq1btlEWuh3aDcp9pOxqtoqeZUBzdB8WCSVD8tApIS4YwxBeSb7ZAMnY6J6FSCCamkAwcrSwJcO8AOrwHisUfQxFxStmvxvSCqCuiixcvYvny5ZDL5di3bx/ee+89uLu7Y+PGjZqOj1VThUWuiq9mgo4fgDB4rLZDqnJ0PwFZuzZAcT70f1PvjJwCtPKAIJFqOzy9ItjZQ5g8B9S9LxR7AkCb14DCjnL/kZ4QlYjy8/NVs14bGRnhxYsXcHR0xIMHDzQZG6vmhIZNIHTsBgo9pHySawWKXPWFauqdsCPAg9t4YWqmk1Pv6CvBuZj+o/ZdIQzm/iNdJioROTo64u7du3B2doaTkxP2798PU1NT1WSkjJWXMODvItfgHRAmVt8iV0pPAZ35FXTuJJCdBdSpB2HkFMj7DkF6bp62w6tW1PqPfv17/rrYPyD0GgShD/cf6SJRicjX1xeSv8fqjx07FgEBAcjLy8PkyZM1Ghyr/gS7WhB8+oGOH1AWuTaqPkWuJU690/19oHkr5e1JM3OAE5FGCCamyj90uvRS1h8d2QM6//fzjzp05fojHSJqrrnMzEzY2NiIXq5reK453W4H5eVCsWgKULc+JLOXl1mcqcttAUqYeqdLr2Kn3tH1trwJXW8L3YmHYu9m4MFtoFFTZf+R81tF1tP1drwJfZlrTtSfBJ999lmxy2fOnFmpwbCaqbDIFQnXlUWueoqSH0OxN0D52O2dG5SzH/j6KUe/DR6r9/O/6TvB2RWSBd9CGD8TyEyD4ut5UGz6P1BairZDq/FE3Zor7qIpNzdXdbuOsYrS1yJX5dQ70crbbzVs6h19JEgkEDp1A7XpCDoRBDoRDIqJgNB7oLL+yMRU2yHWSKX+tH/88ccAlKPmCv9dKCcnB56enpqLjNUo+lbkylPv6DfBxBRC/1Ggd3qBgraBjuxVzl838CPQ+0O0HV6NU2oimj59OogIK1euxPTp09Xes7GxqdJ7iKwGeL3ItYO3Tj7JVTn1zlFQxGmeeqcaEOxqQZg0G9Str3L+ukB/pJ87ARrsW2z/EdMMUYMVXrx4AWM9nnKdByvoTzvo4R0ovvocwruDIRlUfJFrVbdFOfVOhPL2WyVPvaMv34sY+t4WUihAl85ACNkJRVqK8rEag8fqdX2bvgxWEHUjXiqVYt++fTh79iwyMjJga2sLLy8vDBo0CAYVuJefmpqK9evXIzMzE4IgwMfHB++99x5ycnKwZs0apKSkoFatWpg5cyYsLCxARAgMDERMTAyMjY0xbdo0ODk5lXv/TPcIDZ2VRa6/HQJ11W6RKz3NBJ07CTrzK5CRCtjZQxjiC8HTB4KFldbiYpohSCQQOnaDnc/7SNm1SdmHFHsRQq8BEPoM5v4jDRKVRXbu3Im7d+9i0qRJqFWrFlJSUnDw4EHk5ubC19e33DuXSqUYM2YMnJyckJeXh/nz56NVq1Y4ffo03NzcMGDAAISEhCAkJASjR49GTEwMkpKSsHbtWty+fRsBAQFYsWJFuffPdJO2i1zpfgIo7Cgo6tzfU++0gWTUVMCtLU+9UwMIJqaQ9B8JeqcnKGg76Og+0PlQCIPGQOjYjeuPNEDUEY2IiMDcuXPh7u4OBwcHuLu7Y/bs2fjjjz8qtHNbW1vVFY2pqSkcHR2Rnp6OyMhIdO3aFQDQtWtXREZGAgCioqLg5eUFQRDg4uKCZ8+eISMjo0IxMN2jKnK9eAb04HaV7JNe5kMRHoaC5bOgWDEbFBsBwasPJEs3QDrzSwju7TkJ1TCCXS1IJs2CZP43gEwOCvxeeW7cidd2aNVOuYdvV7bk5GTcv38fzs7OyMrKgq2tLQDloIj/b+/+o6oq1wSOf/c5BIIQeUC0SNcYabe0phRDRKDiRGpMsQohypRbzYwLZ3kd71ioLXU1sbIQ9TpiNGqaTb/ldmfZ6nKNCrliWfijBkv8uTJn8HLlqHDBX5z9zh9Hz/VcEE4I7L1Pz2et1oq9N57nWS/4uN9n7/c9ffo0AC6Xi+jov+5XHxUVhcvl8l4rAoc2KQu17RP0D9b79ZJrd11p6R0t8V60fmG98pnCWrS4X2AreAX11VZU2Ub0lwsCon9kJp0Wom3btjFhwgQSExN5+eWXycrK8ja/ysrKSExM7JEgzp49S3FxMXl5eYSF+f7ya5r2k/8SqqiooKKiAoAlS5b4FK+fKigo6Kq+3yysmEdr7jM0/2cxEUf20e/uZO/xq81FKcWFvbtp/XgT53b8EVCExCcR+uAUgm8f06fv/lhxXK4kUHK5Yh4ZU1DODFp+9xYtH76F2rOD/g/nEvbIk9hCzfmPFquMSaeFaM2aNUyYMIGpU6dSVlbGunXrvA8rJCUl8eijj151AG1tbRQXF5OcnExCQgIAkZGR3s85efIk117raQw7HA6fJ0AaGxs7XHjV6XTidDq9X1/NkzxWfxLoEivmoe5KgsHvcXr9SpqHDve+5NrdXNTZM57pvsuX3knPREudSFv0IJoBGht7NokuWHFcriRQcukyD2cmttFJqN9upGXTG7R8stnzCH+i+fpHAfHU3KUpuaCgIHJycsjJyenRD1dKUVpaSmxsLBkZGd7j8fHxbN26lczMTLZu3crYsWO9x8vLy0lKSuLAgQOEhYXJtFwA66mXXNWf/g9V+TGq+lM40wJDb0LLm+WZXgm27msJwjiaYyDaM7/+6/tHG36D+vzi/kfDbzM6PMvptBDpuk5tbW2nf8CoUaO6/eF1dXVUVVUxdOhQ5s6dC0Bubi6ZmZksX76czz77zPv4NsBdd93Frl27mDVrFsHBweTn53f7s4VFdPMlV1l6R/SFv/aPqlBlb6C/UoAWP8HTP5K1Bf3W6QutOTk5DBw48IoPK2iaxqpVq3otuJ4iL7RaO4+/fcm1s1w6XHonZaJpl96x8rj8rUDJpdtTv+fOetau+0MZ6Moz7TvpUUMfegmIqbl+/fpZotCIwOZ5yfWeiy+5ToYOmq+y9I4wmhbSD+2hXM/7Rx9uRH38Aaq64mL/6D7T9Y/MxBpLHIufPfXDIWi7gF7wNH+6/ERUjOe/Hl56R4ju0hzRaE/Puax/tBL12cX+0YiRRodnSn49rCCE4UaMguP/C0r3Pd7YAICW9Uu0CU60/hEGBCdEe9pNt/j2j4rmefqUj05HGzjY6PBMpdNCtHHjxr6KQ4hO2TJy0LdXwIXLCpGmoeX9Cm1cqqx6IExJ0zS0hFTUneNQWz5ElZehvvkK7f6H0SZnyUvTF8mkpbAE7ToHjHfCpYJjt0PqRGzj75MiJExPCwnB9g+PYfv3V9HiJ6B+vwl9wQz0bZ94nvD8mZNCJCzDlpHjKUAANju2jMeMDUiIn0hzRGN7+l+xzV8KAwej3vgP9MI5qP2dvyYT6KQQCcvw3BWlgaZBUhpapLzMLKxJGzYC23Mve1aX/0sTetF83KVLUH8+bnRohpCn5oSl2DJysP+5HrfcDQmL8+kfffIh6vdl6Jf6R5OmoJl0/breIHdEwlK06xw4ClfL3ZAIGFpICLaMx7C9WIo2NtlTkJ6fgf7HLSjdbXR4fUIKkRBCmIA2IArbU5f1jzauQi/8Naou8PtHUoiEEMJEvP2jf/w3T/9o6XzcrwZ2/0h6REIIYTKapqHdnYK6MwG15Xeex72//QrN+TDa5MDrH8kdkRBCmJQWHIItI+di/ygFVV6GvuCfA65/JIVICCFMztM/mo1tfjHEXO/pH704B1X3P0aH1iOkEAkhhEVow4Z7+kf/NBda/oK+dAHuV19CNdQbHdpVkR6REEJYiKZpnse8//5u1Cf/fbF/9DVa2kNoD2Zbsn8khUgIISxICw5BezAblZSG+vC/UH/4LWr7pxB0DZz0bIbns2XKkGHYF/7GkFi7YslCtGfPHtavX4+u66SlpZGZmWl0SEIIYQjtuii0X/4Kde9k9PfWwsHv219kD4K4X/R9cH6yXI9I13XWrVvH/PnzWb58OdXV1Rw7dszosIQQwlDa3w3H9uwSmJrf/qTNZupFgi1XiA4ePMjgwYMZNGgQQUFBjB8/nq+//trosIQQwnCapmFPnQjJ6aBd/OvdHmT6RYItNzXncrmIioryfh0VFcWBAwd8rqmoqKCiogKAJUuWEB0d3e3PCwoKuqrvN4tAyQMkF7MKlFwCIQ933r9wYkclnD8PdjvR0/KxD4jq8vuMYrlC5A+n04nT6fR+feLEiW7/WdHR0Vf1/WYRKHmA5GJWgZJLoORBYhpUlcP4+zjpVvATc7rhhht6KbD2LFeIHA4HjY2N3q8bGxtxOBwGRiSEEOZjpS1TLNcjiouLo76+noaGBtra2ti+fTvx8fFGhyWEEKZipS1TLHdHZLfbeeqppygsLETXde69916GDBlidFhCCCG6yXKFCGD06NGMHj3a6DCEEEL0AMtNzQkhhAgsUoiEEEIYSgqREEIIQ2lKKWV0EEIIIX6+5I6oCwUFBUaH0CMCJQ+QXMwqUHIJlDzAOrlIIRJCCGEoKURCCCEMZV+8ePFio4Mwu5tuusnoEHpEoOQBkotZBUougZIHWCMXeVhBCCGEoWRqTgghhKEsucRPT1u9ejW7du0iMjKS4uLidueVUqxfv57du3cTEhJCfn6+aW93u8pl7969vPLKK8TExACQkJBAVlZWX4fZpRMnTlBSUsKpU6fQNA2n08nkyZN9rrHKuPiTixXG5fz58yxatIi2tjbcbjfjxo0jOzvb55oLFy6watUqDh8+TEREBLNnz/bmZCb+5FJZWcmbb77pXd1/4sSJpKWlGRGuX3Rdp6CgAIfD0e5pOdOPixJq79696tChQ2rOnDkdnt+5c6cqLCxUuq6ruro6NW/evD6O0H9d5VJbW6teeumlPo7qp3O5XOrQoUNKKaVaW1vVrFmz1I8//uhzjVXGxZ9crDAuuq6rM2fOKKWUunDhgpo3b56qq6vzuaa8vFy99tprSimltm3bppYtW9bncfrDn1w+//xztXbtWiPC65bNmzerFStWdPhzZPZxkak54LbbbiM8PPyK52tqakhJSUHTNEaMGEFLSwsnT57swwj911UuVjFgwADv3U1oaCixsbG4XC6fa6wyLv7kYgWaptGvXz8A3G43brcbTdN8rqmpqeGee+4BYNy4cdTW1qJM2Ib2JxcraWxsZNeuXVe8YzP7uMjUnB9cLpfP1sFRUVG4XC4GDDD/Ph8d2b9/P3PnzmXAgAE8+eSTpt9Go6GhgSNHjnDzzTf7HLfiuFwpF7DGuOi6znPPPcfx48d54IEHGD58uM95l8tFVJRnS2q73U5YWBjNzc1ce+21RoTbqa5yAdixYwfff/89119/PdOnTzftFuIbNmxg6tSpnDlzpsPzZh8XuSP6mRk2bBirV6+mqKiIiRMnUlRUZHRInTp79izFxcXk5eURFhZmdDhXpbNcrDIuNpuNoqIiSktLOXToEEePHjU6pG7rKpcxY8ZQUlLC0qVLueOOOygpKTEo0s7t3LmTyMhIU/ZH/SWFyA8Oh8NnD3srb08eFhbmnZIYPXo0brebpqYmg6PqWFtbG8XFxSQnJ5OQkNDuvJXGpatcrDQuAP3792fkyJHs2bPH57jD4aCxsRHwTHm1trYSERFhRIh+u1IuERERXHPNNQCkpaVx+PBhI8LrUl1dHTU1NcycOZMVK1ZQW1vLypUrfa4x+7hIIfJDfHw8VVVVKKXYv38/YWFhpp7+6cypU6e8c8MHDx5E13VT/UBeopSitLSU2NhYMjIyOrzGKuPiTy5WGJempiZaWloAz1Nn3377LbGxsT7XjBkzhsrKSgC+/PJLRo4cacreiz+5XN5vrKmp4cYbb+zTGP31+OOPU1paSklJCbNnz2bUqFHMmjXL5xqzj4u80AqsWLGC7777jubmZiIjI8nOzqatrQ2A9PR0lFKsW7eOb775huDgYPLz84mLizM46o51lUt5eTlbtmzBbrcTHBzMtGnTuOWWWwyOur19+/axcOFChg4d6v2Fyc3N9d4BWWlc/MnFCuPyww8/UFJSgq7rKKVITEwkKyuL9957j7i4OOLj4zl//jyrVq3iyJEjhIeHM3v2bAYNGmR06O34k8vbb79NTU0Ndrud8PBwnnnmmXbFymz27t3L5s2bKSgosNS4SCESQghhKJmaE0IIYSgpREIIIQwlhUgIIYShpBAJIYQwlBQiIYQQhpIlfoQwiK7rFBYW4na7ef755wkKkl9H8fMkd0RC9LCZM2eSm5vbbmWEZ599luzsbBoaGgAoKysjNTWV9PR03n//fSNCFcIU5J9gQvSCmJgYqqurmTRpEgBHjx7l3LlzPtdMmTLF+//jx4/v0/iEMBMpREL0gpSUFKqqqryFqLKyktTUVN59913As1HZO++8wxdffEFbWxtjx44lLy+P4OBgmpqaWL16Nfv27UPTNIYMGcLixYux2WQCQwQm+ckWohcMHz6c1tZWjh07hq7rbN++neTkZO/5t956i/r6eoqKili5ciUul4tNmzYB8NFHH+FwOFi7di1r1qwhNzfXVOuCCdHTpBAJ0Usu3RVdWlDz8pXBP/30U6ZPn054eDihoaE88sgjVFdXA579Yk6dOsWJEycICgri1ltvlUIkAppMzQnRS1JSUli0aBENDQ2kpqZ6jzc1NXHu3DkKCgq8x5RS6LoOwEMPPcQHH3zAiy++CIDT6SQzM7NvgxeiD0khEqKXDBw4kJiYGHbv3s2MGTO8xyMiIggODmbZsmUd7p8UGhrKtGnTmDZtGkePHuWFF14gLi6O22+/vS/DF6LPyNScEL1oxowZLFy40LvpHYCmaaSlpbFhwwZOnz4NeLZyvrQx286dOzl+/DhKKcLCwrDZbDI1JwKa3BEJ0YsGDx7c4fEnnniCTZs2sWDBApqbm3E4HNx///3ceeed1NfX8/rrr9PU1ET//v1JT09n1KhRfRy5EH1H9iMSQghhKJmaE0IIYSgpREIIIQwlhUgIIYShpBAJIYQwlBQiIYQQhpJCJIQQwlBSiIQQQhhKCpEQQghDSSESQghhqP8HcsP1QyMavcMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}