Case_Market_Share_Cargas.ipynb

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  "metadata": {
    "colab": {
      "name": "Case Market Share Cargas",
      "provenance": [],
      "collapsed_sections": [],
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      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Eng-Dan/air-freight-market-share/blob/master/Case_Market_Share_Cargas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introdução"
      ],
      "metadata": {
        "id": "wBtKkC9-DVbb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este notebook aborda uma análise de Market Share de transporte de carga aéreo entre três empresas brasileiras com voos doméstico.\n",
        "\n",
        "O período considerado é de janeiro a outubro de 2021.\n",
        "\n",
        "O estudo está dividido em três partes:\n",
        "- Análise da qualidade dos dados disponíveis.\n",
        "- Market Share de cada empresa.\n",
        "- Estratégia de cada empresa."
      ],
      "metadata": {
        "id": "z-giMID3Dcxu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Link de fontes de dados públicos"
      ],
      "metadata": {
        "id": "l-jywGfG8KxF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os dados utilizados nesse estudo são derivados dos dados públicos disponibilizados pela ANAC.\n",
        "\n",
        "[DADOS ESTATÍSTICOS DO TRANSPORTE AÉREO](https://www.anac.gov.br/acesso-a-informacao/dados-abertos/areas-de-atuacao/voos-e-operacoes-aereas/dados-estatisticos-do-transporte-aereo)\n",
        "\n",
        "* [Metadados do conjunto de dados: Dados Estatísticos do Transporte Aéreo](https://www.anac.gov.br/acesso-a-informacao/dados-abertos/areas-de-atuacao/voos-e-operacoes-aereas/dados-estatisticos-do-transporte-aereo/48-dados-estatisticos-do-transporte-aereo)\n",
        "\n",
        "* [Voos e operações aéreas - Dados Estatísticos do Transporte Aéreo - Formato CSV](https://sistemas.anac.gov.br/dadosabertos/Voos%20e%20opera%C3%A7%C3%B5es%20a%C3%A9reas/Dados%20Estat%C3%ADsticos%20do%20Transporte%20A%C3%A9reo/)"
      ],
      "metadata": {
        "id": "3h7YZqTODo0h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Configurações e bibliotecas utilizadas"
      ],
      "metadata": {
        "id": "fGp-MXxWDLH8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "invFBl6z2oLp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0SlJP7LCB7F",
        "outputId": "38d241d5-9836-4688-f143-04a2c9ed42f8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dicionário de dados das principais variáveis\n",
        "\n",
        "\n",
        "* `passageiros_pagos`: Refere-se aos passageiros que ocupam assentos comercializados ao público e que geram receita, com a compra de assentos, para a empresa de transporte aéreo. Incluem-se nesta definição as pessoas que viajam em virtude de ofertas promocionais, as que se valem dos programas de fidelização de clientes, as que se valem dos descontos concedidos pelas empresas, as que viajam com tarifas preferenciais, as pessoas que compram passagem no balcão ou através do site de empresa de transporte aéreo e as pessoas que compram passagem em agências de viagem.\n",
        "\n",
        "* `carga_paga_kg`: Refere-se à quantidade total, expressa em quilogramas, de todos os bens que tenham sido transportados na aeronave, exceto correio e bagagem, e tenham gerado receitas direta ou indireta para a empresa aérea.\n",
        "\n",
        "* `decolagens`: Refere-se ao número de decolagens que ocorreram entre os aeródromos de origem e destino da etapa."
      ],
      "metadata": {
        "id": "o4OrnIUY6KPN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset e qualidade dos dados"
      ],
      "metadata": {
        "id": "kail_ZZmDyr0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Caminho para o arquico \".csv\" que contém os dados para análise."
      ],
      "metadata": {
        "id": "1FqFl94LQrPN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "hau6HOpo2PG3"
      },
      "outputs": [],
      "source": [
        "arquivo_csv = '/content/drive/MyDrive/Colab Notebooks/Datasets/case_market_share.csv'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Carregamento dos dados do arquivo \".csv\" em um dataframe do Pandas. Dataframe nomeado como `df`."
      ],
      "metadata": {
        "id": "Rop09CBMI3Xf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(arquivo_csv, sep=';', encoding='windows-1252')"
      ],
      "metadata": {
        "id": "wV93VkFR20tY"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primeiramente vamos visualizar o conteúdo dos dados com base nos 10 primeiras registros."
      ],
      "metadata": {
        "id": "V1GtjnckA4FQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "8ceIxBUFAydj",
        "outputId": "fd6233ed-25d4-41c9-c6cd-0f8670ea242f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  empresa   ano  mês origem aeroporto_origem destion aeroporto_destino  \\\n",
              "0       A  2021   10    BEL            BELÉM     BEL             BELÉM   \n",
              "1       A  2021   10    BEL            BELÉM     BEL             BELÉM   \n",
              "2       A  2021   10    BEL            BELÉM     MAB            MARABÁ   \n",
              "3       A  2021   10    BEL            BELÉM     MEU          ALMEIRIM   \n",
              "4       A  2021   10    BEL            BELÉM     BVS            BREVES   \n",
              "5       A  2021   10    BEL            BELÉM     GGF          ALMEIRIM   \n",
              "6       A  2021   10    BPG  BARRA DO GARÇAS     CGB     VÁRZEA GRANDE   \n",
              "7       A  2021   10    BPG  BARRA DO GARÇAS     GYN           GOIÂNIA   \n",
              "8       A  2021   10    BPG  BARRA DO GARÇAS     QDV           JUNDIAÍ   \n",
              "9       A  2021   10    CNF          CONFINS     CNF           CONFINS   \n",
              "\n",
              "    tipo_voo  passageiros_pagos  carga_paga_kg  decolagens  \n",
              "0  DOMÉSTICA                6.0           27.0         1.0  \n",
              "1  DOMÉSTICA                0.0            0.0         NaN  \n",
              "2  DOMÉSTICA                0.0            0.0         1.0  \n",
              "3  DOMÉSTICA              128.0         2237.0        23.0  \n",
              "4  DOMÉSTICA              171.0         2218.0        33.0  \n",
              "5  DOMÉSTICA                0.0            0.0         NaN  \n",
              "6  DOMÉSTICA              119.0         1573.0        21.0  \n",
              "7  DOMÉSTICA               16.0          115.0         3.0  \n",
              "8  DOMÉSTICA                0.0            0.0         1.0  \n",
              "9  DOMÉSTICA                0.0            0.0         1.0  "
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>empresa</th>\n",
              "      <th>ano</th>\n",
              "      <th>mês</th>\n",
              "      <th>origem</th>\n",
              "      <th>aeroporto_origem</th>\n",
              "      <th>destion</th>\n",
              "      <th>aeroporto_destino</th>\n",
              "      <th>tipo_voo</th>\n",
              "      <th>passageiros_pagos</th>\n",
              "      <th>carga_paga_kg</th>\n",
              "      <th>decolagens</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>6.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>MAB</td>\n",
              "      <td>MARABÁ</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>MEU</td>\n",
              "      <td>ALMEIRIM</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>128.0</td>\n",
              "      <td>2237.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>BVS</td>\n",
              "      <td>BREVES</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>171.0</td>\n",
              "      <td>2218.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BEL</td>\n",
              "      <td>BELÉM</td>\n",
              "      <td>GGF</td>\n",
              "      <td>ALMEIRIM</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BPG</td>\n",
              "      <td>BARRA DO GARÇAS</td>\n",
              "      <td>CGB</td>\n",
              "      <td>VÁRZEA GRANDE</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>119.0</td>\n",
              "      <td>1573.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BPG</td>\n",
              "      <td>BARRA DO GARÇAS</td>\n",
              "      <td>GYN</td>\n",
              "      <td>GOIÂNIA</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>16.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BPG</td>\n",
              "      <td>BARRA DO GARÇAS</td>\n",
              "      <td>QDV</td>\n",
              "      <td>JUNDIAÍ</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>CNF</td>\n",
              "      <td>CONFINS</td>\n",
              "      <td>CNF</td>\n",
              "      <td>CONFINS</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos observar alguns pontos a serem trabalhados no dataset:\n",
        "\n",
        "1. Para efeitos desse estudo, serão desconsideradas as linhas em que o número de passageiros pagos e carga paga for igual a 0, pois essa condição pode ser devido a *non revenue flights*.\n",
        "\n",
        "2. Existem registros em que o aeroporto de destino é o mesmo de origem:\n",
        "\n",
        "    Nesse caso, esse registros serão desconsiderados. \n",
        "\n",
        "2. Dados contêm valores ausentes:\n",
        "    \n",
        "    Podemos ver que na culuna `decolagens` há valores do tipo \"NaN\" (**N**ot **a** **N**umber) ou nulos (*null*). Dessa forma, segue necessário uma melhor avaliação da qualidade dos registros de cada linha em relação à valores ausentes em todas as colunas.\n",
        "\n"
      ],
      "metadata": {
        "id": "RpcIanXPGNcn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tratamento de registros inválidos e valores ausentes"
      ],
      "metadata": {
        "id": "lnRK5IMdJ6Ra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Método para remover registros em que `passageiros_pagos` e `carga_paga` são iguais a 0:"
      ],
      "metadata": {
        "id": "OmLyrwyBtt-3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[(df.passageiros_pagos > 0) | (df.carga_paga_kg > 0)]"
      ],
      "metadata": {
        "id": "X69az95VsOui"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Método para remover registros em que `origem` e `destino` são iguais:"
      ],
      "metadata": {
        "id": "OZo-wLebuAR8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(df[df.origem == df.destion].index, inplace=True)"
      ],
      "metadata": {
        "id": "Soc1d7xYJ_uB"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Método para verificar quantidade de valores ausentes em cada coluna:"
      ],
      "metadata": {
        "id": "NYnGQbiALUhH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Qx9MqxjTuGL",
        "outputId": "1c5b636c-7b5a-472a-f869-663ce42f7489"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10710 entries, 3 to 13267\n",
            "Data columns (total 11 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   empresa            10710 non-null  object \n",
            " 1   ano                10710 non-null  int64  \n",
            " 2   mês                10710 non-null  int64  \n",
            " 3   origem             10710 non-null  object \n",
            " 4   aeroporto_origem   10710 non-null  object \n",
            " 5   destion            10710 non-null  object \n",
            " 6   aeroporto_destino  10710 non-null  object \n",
            " 7   tipo_voo           10710 non-null  object \n",
            " 8   passageiros_pagos  10710 non-null  float64\n",
            " 9   carga_paga_kg      10710 non-null  float64\n",
            " 10  decolagens         10467 non-null  float64\n",
            "dtypes: float64(3), int64(2), object(6)\n",
            "memory usage: 1004.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos confirmar que o dataset agora tem um total de 10710 registros, contudo a coluna `decolagens`é a única que possui valores ausentes.\n",
        "\n",
        "* O que há nesses registros em que existem valores ausentes?\n",
        "\n",
        "Vamos filtrar e visualizar essas linhas para ter uma ideia melhor do que pode ser."
      ],
      "metadata": {
        "id": "RhsSQGBiEtYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.decolagens.isnull()]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "Bmts26sXOjKK",
        "outputId": "3f824624-59c7-4b46-83ea-5f78bc716196"
      },
      "execution_count": 9,
      "outputs": [
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              "      empresa   ano  mês origem aeroporto_origem destion aeroporto_destino  \\\n",
              "92          A  2021    1    BEL            BELÉM     GYN           GOIÂNIA   \n",
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              "172         A  2021    1    CNF          CONFINS     PMW            PALMAS   \n",
              "177         A  2021    1    CNF          CONFINS     SDU    RIO DE JANEIRO   \n",
              "...       ...   ...  ...    ...              ...     ...               ...   \n",
              "13043       C  2021   10    FOR        FORTALEZA     MCP            MACAPÁ   \n",
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              "13151       C  2021   10    MCP           MACAPÁ     FOR         FORTALEZA   \n",
              "13168       C  2021   10    PVH      PORTO VELHO     FOR         FORTALEZA   \n",
              "13231       C  2021   10    CGH        SÃO PAULO     UNA               UNA   \n",
              "\n",
              "        tipo_voo  passageiros_pagos  carga_paga_kg  decolagens  \n",
              "92     DOMÉSTICA               20.0            0.0         NaN  \n",
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              "172    DOMÉSTICA              287.0            0.0         NaN  \n",
              "177    DOMÉSTICA              225.0            0.0         NaN  \n",
              "...          ...                ...            ...         ...  \n",
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              "13151  DOMÉSTICA               14.0            0.0         NaN  \n",
              "13168  DOMÉSTICA               27.0            0.0         NaN  \n",
              "13231  DOMÉSTICA              121.0            0.0         NaN  \n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A principio, não foi possível estabelecer uma correlação de causa para valores ausentes entre as colunas `passageiros_pagos`, `carga_paga_kg` e `decolagens`."
      ],
      "metadata": {
        "id": "uERclhxcbD1b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Método para descartar os registros que contém valores ausentes:"
      ],
      "metadata": {
        "id": "YBq4LNqBcDrg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "Oy1COtVCdu9I"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "id": "IOWtWQ34r7kD",
        "outputId": "93a1412b-9d57-4cdb-a965-32abfdbb6b7f"
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      "execution_count": 11,
      "outputs": [
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            "text/plain": [
              "   empresa   ano  mês origem aeroporto_origem destion aeroporto_destino  \\\n",
              "3        A  2021   10    BEL            BELÉM     MEU          ALMEIRIM   \n",
              "4        A  2021   10    BEL            BELÉM     BVS            BREVES   \n",
              "6        A  2021   10    BPG  BARRA DO GARÇAS     CGB     VÁRZEA GRANDE   \n",
              "7        A  2021   10    BPG  BARRA DO GARÇAS     GYN           GOIÂNIA   \n",
              "11       A  2021   10    CNF          CONFINS     QDV           JUNDIAÍ   \n",
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              "     tipo_voo  passageiros_pagos  carga_paga_kg  decolagens  \n",
              "3   DOMÉSTICA              128.0         2237.0        23.0  \n",
              "4   DOMÉSTICA              171.0         2218.0        33.0  \n",
              "6   DOMÉSTICA              119.0         1573.0        21.0  \n",
              "7   DOMÉSTICA               16.0          115.0         3.0  \n",
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              "      <td>BARRA DO GARÇAS</td>\n",
              "      <td>CGB</td>\n",
              "      <td>VÁRZEA GRANDE</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>119.0</td>\n",
              "      <td>1573.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>BPG</td>\n",
              "      <td>BARRA DO GARÇAS</td>\n",
              "      <td>GYN</td>\n",
              "      <td>GOIÂNIA</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>16.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>A</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>CNF</td>\n",
              "      <td>CONFINS</td>\n",
              "      <td>QDV</td>\n",
              "      <td>JUNDIAÍ</td>\n",
              "      <td>DOMÉSTICA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1376.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72f52e4c-f3cb-40b4-8d26-eb9d417a383f')\"\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Adição de colunas auxiliares "
      ],
      "metadata": {
        "id": "CDR2e3ATHcYI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Antes de prosseguir com as análises, precisamos de atributos que nos permita analisar as rotas voadas e valores médios na operação. As seguintes colunas serão adicionadas:\n",
        "\n",
        "Coluna `rota`:\n",
        "Exemplos:\n",
        "* Voo de GRU a MAO: rota = GRUMAO --> representa 1 rota\n",
        "* Voo de MAO a GRU: rota = MAOGRU --> representa 1 rota\n",
        "\n",
        "Coluna `carga_por_decolagem`:\n",
        "$$\n",
        "carga\\_por\\_decolagem = \\frac{carga\\_paga\\_kg}{decolagens}\n",
        "$$\n",
        "\n",
        "Coluna `pax_por_decolagem`:\n",
        "$$\n",
        "pax\\_por\\_decolagem = \\frac{passageiros\\_pagos}{decolagens}\n",
        "$$\n",
        "\n"
      ],
      "metadata": {
        "id": "SPVl3mZtXSkw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Adiciona a coluna \"rota\"\n",
        "df['rota'] = df.origem + df.destion;"
      ],
      "metadata": {
        "id": "SGWkRhpl8BqS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3194eba-521b-49cf-86f3-2a92ccc3f442"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adiciona a coluna \"carga_por_decolagem\"\n",
        "df['carga_por_decolagem'] = df.carga_paga_kg / df.decolagens;"
      ],
      "metadata": {
        "id": "H0MbFwp_bzyw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fabc4967-d44b-470d-eaea-06c34af4591f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adiciona a coluna \"pax_por_decolagem\"\n",
        "df['pax_por_decolagem'] = df.passageiros_pagos / df.decolagens;"
      ],
      "metadata": {
        "id": "ONpc1yFHc5cD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de1e0792-63f5-41a6-8676-f64c614e6dfe"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análise - Parte 1"
      ],
      "metadata": {
        "id": "1vq575R1_SKh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questões direcionadoras da análise"
      ],
      "metadata": {
        "id": "wH1L7I1ZJIHN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Qual é o market share de carga paga acumulado período em cada empresa?"
      ],
      "metadata": {
        "id": "HO6SyqZe_F1G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Market Share"
      ],
      "metadata": {
        "id": "mXeYf8K8m5CK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A tabela a seguir, resume os dados de market share acumulados no periodo."
      ],
      "metadata": {
        "id": "YliyPUtpX5e2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "market_share = df[['empresa', 'passageiros_pagos', 'carga_paga_kg', 'decolagens']]\\\n",
        "                .groupby(by=['empresa'])\\\n",
        "                .sum()\\\n",
        "                .sort_values(by=['empresa'], ascending=True)\\\n",
        "\n",
        "market_share['percentual_pax'] = market_share.passageiros_pagos / market_share.passageiros_pagos.sum() * 100\n",
        "market_share['percentual_carga'] = market_share.carga_paga_kg / market_share.carga_paga_kg.sum() * 100\n",
        "market_share['percentual_decolagens'] = market_share.decolagens / market_share.decolagens.sum() * 100\n",
        "market_share"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "6hJQtLJcQoiL",
        "outputId": "f7088299-34aa-4cb7-b6dd-ad0bcd5be412"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         passageiros_pagos  carga_paga_kg  decolagens  percentual_pax  \\\n",
              "empresa                                                                 \n",
              "A               17957627.0    113763076.0    186827.0       38.105851   \n",
              "B               14297679.0     31035704.0    101555.0       30.339489   \n",
              "C               14870336.0    105562737.0    108658.0       31.554660   \n",
              "\n",
              "         percentual_carga  percentual_decolagens  \n",
              "empresa                                           \n",
              "A               45.439522              47.054957  \n",
              "B               12.396356              25.578027  \n",
              "C               42.164123              27.367016  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad6bc5bb-cbf5-460f-98a0-0abcd4622ac6\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>passageiros_pagos</th>\n",
              "      <th>carga_paga_kg</th>\n",
              "      <th>decolagens</th>\n",
              "      <th>percentual_pax</th>\n",
              "      <th>percentual_carga</th>\n",
              "      <th>percentual_decolagens</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>empresa</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>17957627.0</td>\n",
              "      <td>113763076.0</td>\n",
              "      <td>186827.0</td>\n",
              "      <td>38.105851</td>\n",
              "      <td>45.439522</td>\n",
              "      <td>47.054957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>14297679.0</td>\n",
              "      <td>31035704.0</td>\n",
              "      <td>101555.0</td>\n",
              "      <td>30.339489</td>\n",
              "      <td>12.396356</td>\n",
              "      <td>25.578027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>14870336.0</td>\n",
              "      <td>105562737.0</td>\n",
              "      <td>108658.0</td>\n",
              "      <td>31.554660</td>\n",
              "      <td>42.164123</td>\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad6bc5bb-cbf5-460f-98a0-0abcd4622ac6');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Market share Cargas\n",
        "labels = 'Empresa A', 'Empresa B', 'Empresa C'\n",
        "sizes = [round(market_share.percentual_carga[0], 0),\n",
        "         round(market_share.percentual_carga[1], 0),\n",
        "         round(market_share.percentual_carga[2], 0)]\n",
        "explode = (0, 0.1, 0)\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')\n",
        "plt.title('Market Share: Cargas')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "lDPHtvoZfYFC",
        "outputId": "a93dd2b8-0fbc-432d-9d83-4a136536144d"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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1DD7rX+SvcLqIXKahmwK+qmoPcLlv2pnDXN6CIU7Xo9QxKAC+6HQRuUxDNzX8QGlh5cl69ZnKRjf4F/m1SyxFNHSTzFdVLcDl3uGVXndR2Tin61GqH8YDerFEimjoJt9EYHLh5FM1cFU2u9rpAnKVhm7yVQOxvIoJesmvymaX+xf5dTa8FNDQTSJ7kvKz80ZOwVVQrGeAVTYrBi5zuohcpKGbXJOBooLKk6ucLkSpJLjK6QJykYZucs0G4tq1oHLEMXcxiEhcRNZ1uy1IVXHJJiKXisgaEdkgIrUi8rNUHEeHhSSJdi2oHNTVxfDYMbym3RiT0kuJRcRtjIkneZ8nAXcAc40x74iIG/i3ZB6ji7Z0k0e7FlQuSkoXg4jUi8iP7NbvGhGZLSLPishWEbne3uY8EVkuIktEZJOI3CUiLvu5VhH5mYi8CZwhIp8Xkdfs/f1ORNz27V4ReVtEAiJyi/3aL4rI6yLypog8KiK+Hkr8D+AHxph3AIwxcWPMncl474fT0E2eU4C4t3yshq7KJRf7F/mPZSLowsO6F7oPPXvXbgW/AtwLXAmcDtzebZvTgJuBE4ApwKfsx4uAGmPMTOAg1pC2s+z9xYFrgFnAWGPMScYYP3CP/drHjDFz7NduBP6lh7pPAt44hvfZb9q9kAR218JZku9rcRUOGe10PUolUTlWmNX2cfujdS8str8GgGJjTAgIiUiniJTZz71mjNkGICL3A2cDj2AF66P2NhdgNXJetxcGKAT2AU8Ck0Xk18AS4Dl7+5NE5PtAGVaXybN9fC8poS3d5BgNFBWMO3GE6PIQKvecn6T9dNpfE93+3XW/qwFoDntN1/2Obv24Aiwyxsyyb9ONMbcZY5qAmcDLwPXAH+3t7wVuslu/t2PNL3G49VhBnnIauskxAcBbMXGC04UolQIfTeOxThORSXZf7tXAih62eRG4UkRGAIhIuYhMFJEKwGWMeRT4NtZoIoASoEFEvFjdED35X+BbIjLN3qerq6852bR7ITn8QIendOREpwtRKgXO9C/yS2B+4PBWaE8KRWRdt/vPGGOOZdjY61ijCKYCLwGPH76BMWaDiHwbeM4O5yhwI9AO3NN18g34L/vrrUANsN/+WtLDPt8Ska8C99sn2gzw1DHU3WcaugNkT3BzIm5PyF1UNtbpepRKgaHA8cCG3jY0xriP8Hhlt3/fi/WR/wPP2T1zQWPMhybbMcYUH3b/QeDBHg41+/AH7FEIvY5EMMY8RYqCtrukdy9k8+BoABH5u4isPoaXVADF+WOOKxeX25uqupRy2JlOF5ArUtHSzcrB0fZ+y7A601tFZHLXWdReTATIGzFZuxZULjud909MpYQx5mWsk2A5LW0n0rJgcDRYYwKfBB4APtPHt3YcEPWUjtCuBfUhJmHY8j9b2PHzHQDsvGsnmxdspu6/63jvT+9hYkfuJo23x3nnlnfY/ZfdACSiCep/Wk/df9dx8MWDh7bbdc8u2uvbU/tGrHGzKglSEbrZOjga4LPA/fbts318v36gxVU4ZHgft1eDyMHnDpI/Jv/Q/bIzyqj6URVTvz8VEzE0Lm884mv3PbaPoulFh+63vt2Kb5qPqd+bSvOqZgDa323HJAyFlYWpexOW6ak+wGCRitBt7zZ+bpbd4d2l++DoGmNMyBizH/jQ4Gi7+6BrcDQceXD0Ovv+ZGAb9uBoEbkECNrbnyQir4hIACucPzQhjYiMBKqAFcaYzUDUvh77iHxV1T5gOOJqd+X5yvv27VGDRbQxSujNEEM/MvTQYyUzSxARRITCyYXEGmM9vra9vp1YS4ziE98/fyRuIRFJYOLm0OjVfY/tY+SnRqb0fdjK/Yv82rBIgnSP083kwdGfxjpLu11E6oFKem/tVgAJ77DxQ8Xl6vGsrRq8Gu5rYNTVo6z/rYcxMUPzqmaK/cUffi5haLi/gVGfGfWBx4tPLCZ6IMq2721j2EXDCNYGKZhYgHdo2s7fHpeuA+WyTLw4wqnB0Z8FLjHGVNpDWE6h937dCkC85WN0VjH1AcF1QTxDPEf82L/7z7spmlb0ge6DLo1LGymZWYK3/INhKm5h/PXjmfrdqZTOKeXgcwepuKSChvsbePeOdwnWBj+0ryTT0E2CVIxeyLrB0SJSiTUK4dBQMWPMdhFpEZFqY0zNEWodDeAuKh96hOeTwiTiNCy6BU/JMEZc+R0OLPk5HTvfxpVvnQ+suOwW8kZO/tDrdvxkHt7h1qAKz5DhjLjifwDY/+T/Et2/g8Ipcxh67nwAmlc9QF7FRHzTzkjlWxk0wnVhgrVBQm+GMFFDvCPOzt/tZPyXxrPv7/uIhWJMuLbnCxjDW8KEN4dpfLGRRGcCEzO48l2M+vT7Ld+DSw9SdmYZ7VvbcRe6GfXlUWz/8XaGnDwklW9LQzcJkh662Tg42hhTD3xo9IEx5kP7OswEoN3lKy3rZbsBCa1ZjHfYeEwkfOixoeddR9FxZx/lVSCePMZc9+sPPBbZtx2XJ58xX7iDvQ98m0RnG4loJ5Hdmyg7s68DNlRvRl01ilFXWSHZurGVg88cZPyXxtO4rJHQ2yEm/cckxNXzNB3jrx9/6N9NrzTRXt/+gcCNt8UJvRmi8uuVhNaFDnVfmEhfLhgbED2ZlgSZ2L2QTUYDHa6C4pS1dGPBA7Rve53imRcnZX/i8pCIdWJMApOIgbhoeeWvlJ59pF4XlUy7F+0m1hJj2/e2seXWLex7Yh8A7dvb2XX3rj7tY98T+xh++XDEJRSfVEzb5ja2fHsLZWem9G8/wKRUH2AwyKjLgLNpcLR9+e8IYL8r31eaquM0vfh7ys77wgdauQDNr/yFllUPUDBxJkPPvRbxfPhkiolFaFj0VRA3padfiW/aGXgrxuMuLKXh3q9QfOJHiTU1YIwhf9TUVL2FQa/4+GKKj7c+qJ10d88DYgonFTJ20oeHeg89ZyhDz/ng3/TRn3t/9lBXnotJ30xbFg5L14FyWUaFbpbxAXlAXNzelAySDG95DVdRGfmjptLx7luHHi87dz7uoqEQj3Hw2V/TUvMIZWd9eKDF2BvuxlNSQbR5D3vv/xbe4ZV4h46m/ML3VyHZ98jtlH/sJlpWPUhk33YKKmdRMuuSVLwdlf1Seu5isNDuhf4rxRrqhrg8eak4QOeuDbTX1fDenV9g/+Kf0LHjLQ48+VM8xeXWWE+Pl2L/hUQaNvf4ek+JNajCWzaKggl+Inu3fuD5cN1q8kZNxUQ7iDY3MPwTCwhvWkki2pGKt6OyX55/UQ9j3NQx0ZZu/71/mZHbnX+U7fpt6LnXMvTcawHoePctgq89TsXHv0GstRFPcTnGGMKbV+Ot+PC0D/GOVlyefMTjJR5uoXPXBoZUX3HoeROPEVzzBCOu/A6xpt28fzYmAfEY6NQ9qmflQKvTRWQzDd3+ywcQb4FHxJXWTwwHnvwpiXALYMgbMZnyj90IQGdDHa3rnmbYpf9O9MBOGp+9A0TAGIZUX0VexftDlEJrl1B80gW4vAV4h0/CxDrZ/acbKZxyKq4CbcyoIyoH3nW6iGwmxqR8mElO8lVVzwT+3V0yrHHYxTd+w+l6Bht34d92+CoDOrNb+l0QmB9Y6nQR2Uz7dPsvH8CVX5ySrgWlMpSOYBggDd3+ywdcrrzClJxEUypD6f/3AdLQ7b8CwEheobZ01WCS9MUDBhsN3f4rBuJd1y6r9DJGEk7XMEhp6A6Qhm7/+YC4iUWiThcyGLVvlbdN3EScrmMQ0tAdIA3dATKxSM+zUKuUMSZh2res3NC+vf1Is7+p1NHQHSAN3f7rAFwm2qkt3TSLt+7al+g42NG4rHFVIpbQy+fSS0N3gDR0+68DcGvopl+8ZetGoDQeine0b21/1el6BhkN3QHS0O2/DsCViLZr6KZZorPxDuzJVxqXNa5ORBMpXwpXHaKhO0Aauv0XBTDRDu3TTSNjzLvtWx5+HNgJlCXCiUi4LrzS6boGkSMvX6z6REO3/6KAMdHOmF5KnT4i8lSwNpgAHgLKAJqWN72WiCR0Epb0aHC6gGynodt/73crJGKdR9lOJddT9te3gW1AeaIjEW3b1NbTAqYquQyw1+kisp2Gbv/FsJeHT0Q6mh2uZVAwxrRjLVZKsDZogIeBIQBNy5vWJDoTKV8Od5A7EJgf0O60AdLQ7b9DQ5USnWEN3fR4sX7h3EPf9+Z5zAQ2ARUmauKtG1qXO1faoLDH6QJygYZu/zXT1dLtbNXQTQMRWQIwb7p33P1X+PJdInf+7mQ2Yl2SLU0rmmrj7fEmZ6vMaRq6SaCh23/NgBsgEW7R0E2PJfOme4cA3xlayBeAss+M50tAABhOnETr+tZlzpaY0/QkWhJo6PZTuK6mA2gDvPG2Zg3dFDPGvF2/cO5OoAoYUpovXwAQkUserGYH1lwY0ryy+a14W/yAk7XmsL6tEa+OSkN3YPYCBbHQfg3dFBORJ+1/zgYaq4a5qrqeu3SU3AisBUZgMKFA6GUHShwM1jtdQC7Q0B2YPUBBrKlBQzf1lsyb7vUAp0wf5opX+Fyl3Z47b8lZ7Mea49jVsrplfaw1pkObku8tpwvIBRq6A7MbKEi0BztMLKqXoqaIMaYJWA1UAt7zJ7knH77NORVys73NCIDQutBL6awx1xljIsA7TteRCzR0B+YA9trl8XCLnmRIERF5un7h3DhwAmBOHOGu6mGzM5adSxv2MkrBNcFNsWBM+yCTRETeCcwP6DwjSaChOzCNQAIgFtz3nsO15LIl86Z7BTijJI/WsSUyqaeNTi6TW1zwCjAKILg2qK3d5NGuhSTR0B2YBuxhY9HG9zR0U8AYEweewVqFduTFUzwj3S5xH2Hz2TXnYwAP4A6tC22NNkd3pKvWHKehmyQaugMQrqtpBfYBvsjuTRq6qfFq/cK5jcA0QGaP7rFr4ZDpJfI1r/AyXa3d17W1myQaukmioTtwG4Eh8bam9kRnWKe9S7Kuq9CA04DWyUNdRw1d4KTaC61RDICndX3rjmhjdFtKi8xxxppGb43TdeQKDd2BewfIA4i3NmprN/mWzJvuLQBOOm2sO68oT4b09oIJPvlGgYvnsVu7zTXNS1NdZC4TkdrA/MBBp+vIFRq6A/ce9hwM0eY9GrpJZIx5r37h3AAwBZCzJ7in9vGlVYGLGIb1c/GGN4V3RfZHNqes0Nz3gtMF5BIN3YHbgzWCwR3dv11DN4lEpGvu3BlA4vgK17S+vnZkgXyzzNuttftq81KdbL7fnne6gFyioTtA4bqaGNZk2sWduzY2JKKduoJB8nQNFaseVSzh4UUy9hheO3HdhYzDmvc4r31b+97IvsiG1JSZu4wxHYBOEJ9EGrrJ8TZQAhBrbtjkcC05wf5lfxEYAwy5eIpnvEtEjmUf5Xny9ZH5PENXa3dl80tGm7vHakVgfkCXuU8iDd3k2IB9ZVrn7k16qWRyLK1fOLcdOA5gxsheRy30ZMzaC5gOdAL5He92HIg0RALJLDLXiYj25yaZhm5y1GOtJJHXsX3tdhOPRRyuJ+t1Gyp2ulsITSx19fUk2geUeOVrk4p4GhgJ0LSi6WWTMIlk1TkIaH9ukmnoJkG4riaONdnKMBOPxmPBfXVO15QDlsyb7i0Bppw/yT0k3yMF/dzP8NUfZRYQBgo7d3c2de7uXJe8MnOXMWZrYH5grdN15BoN3eRZC3gBInu2ahfDABhjNtQvnLsDa8JyqR539KvQelPolq/OLOVZ7BnImpY3LTMJE09CqTlNRP7qdA25SEM3ebZgnSl3t29bU2cSCf2l7qfDJizvqCp393mo2BEMfeEcTgeCgC+yLxLs2NnxxgD3ORho6KaAhm6ShOtqOoF1wLBER6gz3npwu9M1ZbFDE5ZXlbtiQwtl+EB3mO+Wm88exnPAcICm5U2vmLjRqQqPwCTMmsD8wBan68hFGrrJVQMUAnS8t77W4VqykjGmGVgFTADyLpj84QnL+6nkiTP5KNAEFEcPRlvbd7S/nqR95xxxySKna8hVGrrJtQn76rTwppXvJKIdIacLyjYi8ow9YfmJACcOH1h/bndel9zw8dG8AFQANC1rWmFiRkeaHMYYEwMedLqOXKWhm0T2VI81wAgS8URk7w0lpCoAABh2SURBVDY983vsDg0VK84jNHZIzxOW95Pvz3O4FGs6ziGxllh7eHt4dRL3nxsMLwTmB/Y7XUau0tBNvqXYs46F31nxhjEJHRPaR8aYBPD0vOneYcCYiyZ7Rnhc4knmMdwiX/znCbwMlAM0LWtalYgm9IqrbsQlv3O6hlymoZt8W7FWlBgSa9kTijXv1cuC+251/cK5B7EmLOeUMcnrWugm/1ez+CTWoqJl8dZ4Z/vW9lUpOE5WMnGzE1jsdB25TEM3ycJ1NQbrI/JQgI76Wj1Z00eHTVjeNnlo32cVOxYukWu/OpWVQBlA47LG1YlIIpyKY2WhnwfmB/TTWQpp6KZGLRAFvO3b1mxPdLTpBNB9s2TedG8+4D91jMtT3IcJy/vJc9sJXA3sAIYm2hPRcF140M+kZRKmTdzyR6fryHUauikQrqsJAy9jXwHVsWtDjaMFZQFjzK76hXPfxJ6w/JwJnn7NtdBXLpFrbjuBNUApQOOyxtcTnYlBPdrExM1dgfmBQf09SAcN3dRZjn1ZcFvghbWJSHuLw/VktG5dC34gcfzw1HQtdOO6ZSr/hHUl4TATMbG2d9peSfExM5ZJmIjL6/qJ03UMBhq6qbMLa9HK4SYejXfUr1vmdEEZrmvC8tNHFEl4RJGMS/UBReTKX8zkLWAIQNOKprXxjnhzqo+biUzU/CUwP7DP6ToGAw3dFLFPqD0KFAPS+vaL67Rvt2f2hOUvAKOB0ouneMYd64Tl/STXTeRfgfXAcBM18bYNbcvTcNyMYhIm6sp33e50HYOFhm5qbcWaj2EEJmHat695yemCMtTL9QvnhrEmLJeZ/ZuwvF9E5OP3nkodUARI04qmdfH2eGO6jp8JEh2JuwLzAzudrmOw0NBNIbu1+xjWfAzStmHZ+nh7cI/DZWWcbv251W4hWFnWvwnL++uTY7iBrj+OCUxroPXldB7fSYloIuj2uf/b6ToGEw3dFAvX1ezAujR4pH1/qbMVZaQl86Z7i4Gq8yrdJfkeKUznwUXkosfPYBf2H8fmV5vfjrfFB0X/ZqIjcbuOWEgvDd30eALIB1ztda/WxVub9KOczRjzTv3CuduxJixnoBOW99cFI+Rm4HVgJAYTejP0shN1pFO8I77DU+L5pdN1DDYaumkQrqvZDSzDOlFE69svPK2r0lpE5Cn7nycDkWnDBjxheX+d8/w5NAEFgKvltZaNsVCswaFa0sLEzM2B+QGdbD/NNHTTZwnW99vTuWtjQ2Tv1tecLihDPDVvutcNnDp5qETLC2WEU4VUl8tXgJXYXUHB2mDOnviMt8VXb7xx45O9b6mSTUM3TcJ1NfuBJ4GxAKE3Fi8d7PPtGmNasEJuApB/4WRPMqdx7I/TVp1nreoMuEJrQ3XRlmjOdQWZuIkiXOd0HYOVhm56PQMcAEoTHa2R8KZV/3C6IIc9W79wbgx7wvKTRjjTn9vdSaXyNZd1CfdogOCa3GvtRg9Gf7rhhg26eKpDNHTTKFxX0wHcizWXq4Q3rXgn2rhrvbNVOafbULHTi7y0jhsiyVqaZyBmvXEBHqzfDXdroHV7tCla73BNSRNtiW4ObwvrEDEHaeim3wasNcDGAARff/wfiVikzdmS0q/bhOXlwNgLJ3sqkj1heX9NKZave4UXsVu7La+15MQwv0QsEY0eiF65886dehLXQRq6aWZfMHE/EAGK4q2N4fCmlUt6eVkuer1+4dz9pHbC8v46/q2L6JpW0tO2sW1n5GAk61fGjeyNLNz63a0Bp+sY7DR0HRCuqwkCf8I6Sy7hd17ZGNlfP6jWU+s2VGwO0Da1POWzih2TsYXyjSI3z9HV2n01u1u70ebo23sf3vsdp+tQGrpOqgVexR7N0LLqgX/Ewy27nS0prZbMm+7NA2bMHu1yF+dJqdMFHWZK4CJGAXHAG94Sbojsi2TlyadEJNEe2R+5Ilgb1G6FDKCh6xC7m+FvQAsw1MQi8ZbVjzxkYpF2h0tLOWNMQ/3CubXAZMD1kYmpnbC8vyry5RsVeTwLjAJoXtX8UrZd1GISxrRvb7952/e3bXa6FmXR0HWQ3c1wB1AC5MeadrW0rl/6aLb9Yh+rbqMWZgCJ4ysyq2uhm/FrL2AS1tJL+e317fsieyNZNdokvCV8/4GnD9ztdB3qfRq6DgvX1WwHFmF1M7jat7y2tfO99S87W1XKdU1YXl3hk7aRxTLe6YKOpCxPvj62gGewr1JrXtH8crb8UezY1bHuwD8O/It2K2QWDd3MsBxrQP44gOBrjy2Pteyrc7SiFDHGdGJNWD4KGPqx9E1Y3l+j1lzACUAHUNDxXsfBzt2dbzpdVG+izdG9Latb5gZrgx1O16I+SEM3A9j9u/cBO7EXs2xedf9jic62XJxMe1n9wrmtWBOWM3NUzxOWf+GJdkb8b4iTftt66LFvPtfBcXe0MuPOVj75YJjmjp4bcD29FuA/n+9gxp2t/PPj73eb//WtCL9Y3XnUgos8cstxJTyN3dpteqVpmUmYjF2mPNGRaA+tC121b/G+wXRiNmto6GYI+2q13wICFCXCLR3NKx/4c67Nz9B9wnKXEDrShOXXzvLyzOd9H3jsoike3v5yEW/dUMy0chc/eqXnsOzptS0dhrV74rx1QzF5bgjsjdMeNdyzLsqNc/J6K7ti+bmcCrQBhZE9kebOXZ0ZOcTPxE08FAj9x657dg3aRTYznYZuBgnX1ezFCt4R2CfWgqsf+XOOjWhYMm+6twiYdu5Ed3GBR3w9bfSRiR7KCz/Y63DxFA8el/XY6ePcvBfqubHZ02tdAtE4GGMIRw1eN/x0VYSbT8vD6+69d6PALV+ZM/T9vt3G5Y3LTdzEen1hGpmESQTfCP6qeWXzb5yuRR2Zhm6GCdfVBIC7sU6seSP7th0IvrH4ryYeizhc2oAZYzbXL5y7FXvC8tMHMGH53euiXDq171cNl+QLl1V5OPl3bYwudlGaL9TsivOJ47x93UXp02dzFtYQv6Lo/mioY2fHmn6UnhImYUzLay0PNa9qXqAnzjKbhm4GCtfVLAMeAMYD7s73NuwOvfnM/SYRz6iW1bE6bMLy6LRh/Rsq9oPlnXhccI2/z4EJwH+clc+664v52ccKuPWlTr770Xz+uDbCpx8O8/3lR+/XBchzyU3nD+d5YDhA47LGFSZuov15D8lkjCH4RvDJltUtXwrWBrP+j3Ou09DNXE9jLfMzEXB1bF9b37b+pYeNSWTsCZw+WNI1YfmkMokM87lGHusO7l0X4am6GH/7VCH9HfRQ2xDHGJg+zMXDG6I8dJWPrU0J6g72uohC8SOncwFwECiJNcXa2uvba/pVRBKF1oWea17ZPD9YGww6XYvqnYZuhrJHNDwOPI8VvBLevGpzeNOqv2fLONHujDEh4BWs1nvBhZM9lce6j2e2xPjJygiLP1OIz9v/UWa3vtTJ987PJ5qAuP0nzAWE+9Bm9bjkS1eMZSkwDKDx5caVJmZ6byanSCgQWt60rOmaYG2w2aka1LHR0M1g4bqaBNZQspVYwUvb+qWBtvUvPZSFXQ3P1i+cGwVOAKS3Ccs/+2iYM/7UxqaDCcb9X4g/rY1w0z/aCUUMF/0lzKy7Wrn+Kev84u5Qgsv+Fj7qa7v8/Z0op45xMabERVmBMGuUG/+drXTEDTNHufvyPgr/eApzgb3AkHgo3hHeFn71mL8bA2SMIRQIrWh8sfHTwdrggXQfX/WfZGGjadDxVVV7gRuBWcAOwBRMnDm+ZNZlnxOPt8DZ6vrsuvqFc++dN9373UIPQ/76qcIve91ybJ2ymSPytTfNJ/9Yz9XAdneRO3/MtWO+4vK60rJ0vEmYePPq5qXB14LXBWuDu9JxTJU82tLNAuG6mihwJ1ADTAJcHTve3Nmy+qG7E5GOjO/Hs7tDnp433TsUGH/RFE9FFgcuQN5PZ3Al1sUsZfG2eGd4S3hlOg6ciCUiB184+GTwteAXNXCzk4ZulgjX1XQCv8daZ60S8Eb2bt3f/Mqf/xTvaN3vaHG9W1O/cO5euiYsH51RE5b3i0vkn745jdVAGUDTsqbXEpFESlcAiXfEwweeOnBf24a264O1wR2pPJZKHQ3dLBKuq4ljrTrRNZysINa8J9i09I93x1ob33W2uiPrdhXaHCCcaROW95Pnv4/js8B2oDzRkYi2bWpL2VVgsVCsed/j++5sr2//arA2uDdVx1Gpp6GbZcJ1NSZcV/MP4C6sSWOKEu3Bjqalf/hL5MC76xwu70ie6pqwfNYol6skX8qcLigZXCKf+cGJrAVraZ+m5U1rEp2JpHf3RPZFGvY+sndhZG/k1mBtsCXZ+1fppaGbpcJ1NauAn2J9vC0z0c5Y87J7n2jbtPLvJhFzfMB+F2PMXmAtVl+0+9yJnikOl5RMrpumcC2wCagwURNv3di6PFk7N8aY0FuhdQ0PNPxXrCX2s2BtMJcuBx+0NHSzWLiu5m3gh4DBXvan7e0X32xZ+cDvM6WfV0SW1C+cawA/YI4fnhNdC4eIyCd/ezIbsSail+YVzbXx9njTQPeb6Ey0HXj6wHONSxtvJcGfg7XBbBsiqI5AQzfL2ZOg3wasx2pNeiP7th1ofP7OP2RId0PXhOWnDyuUtlEZPGF5P8k14/ki8DYw3MRMonV967KB7DCyP/Jew30ND4U3h78erA0+pXMp5BYN3RwQrqtpAX6NdYJtLFBqIu1Rp7sbjDERrCvqRgLDLp7iGeMSybn/cyJy2d/msA3wAdK8qvmteDh+zBcs2N0JtQ1/a7gj1hL7RrA2mFVLA6m+yblfgMEqXFcTD9fVPA38wH7oUHdD84r7fxdrbXRiiNEr9QvnhoDpgJl1hAnLc8Hlo/ky1grPI0hgQm+FXj6W18dCsf37F+9/unFp43eBnwVrg7k4gb1CQzfnhOtq6oDv8H53Q150//aDjc/ecW9486tPpHNu3m6zilW7hNCkobkbuiJy/lNnsgcoBKRldcv6WGus16FdJm6ioTdDr+26Z9fD7dvbvxmsDf5d+29zm4ZuDrJXGf4V1hLvI7CGlklr4Pl1jS/87teRA++uS9Pl30vmTff6gOnnTDjyhOW54iPD5WasqwZHAYTWhV462vaR/ZHtDQ80PNX4UuN9JLg1WBvckI46lbM0dHNUuK4mEa6reQ74FvAOVqu3ON7W1N687N4nQm88eU8qRzgYY7bUL5xbB0wF5Izx7h6X5ckxZ738EYJAHuAKrgluigVjH1qnLBFJtDW90vRyw98anoruj34P+JV2JwweGro5LlxXsw/4JfALwIs9MXrHjnXvHnzmV3d17HjrRROPJX1qwmRNWJ5tZg+VrwIrsFu7wbXBpV3PmbiJhevCtbvu3bU4+Ebw98B/BWuDtTo6YXDp+3onKmvZc/Ou9VVVbwI+DlwCtBKPHQiu+fsK14aX1hTPuPiM/FFV1eL25CfpsEvmTfe6gFMnlkqkwucalaT9ZrpTX/sosdNewgO4Q+tCW0tmltTH2+LBxpcaN0UPRjcB9wRrg9ucLlQ5Q6d2HIR8VdWVwLVYXQ6NWOt+4fKVFtjhe7q4Pb0ukXskxphWERk24/FPjAb+519ne4fPm+79eBJKzxaBsifMTxNwDrAPF6NJEAEeA14K1gYz5opBlX7avTAIhetq6oHvYXU5tGPNWlaaCLd0BFc//NLB5377i45dG18ZwGKYz9UvnBvBnrDcP8I9KLoWuvGvu5B8QICRJFgCfDNYG3xOA1dp98IgZc9YVuurqn4LmAlchRW+jYlwczC4+uGlLl/Zq8UnnT8nb+SU2a68wtK+7rvbrGLVBR5C44bI5KS/gQxXWSRfH5ZnPn4wQmOwNnjQ6XpU5tDuBQWAr6rajRW+n8Y6CdSE3e2AiBROOW1KwYQZsz1lI6eLuI74CckYY0RkzIzHP9EB/N/l0zzefzsl7/NpeAuZ6Dpua7nX6SJUZtHQVR/QLXyvAMYAUWAfEANwl1QU+aafNSt/VNVsV76v/PDXG2PW7vjx5afMm+49Dbj+tvPyT5g92l2dxreQSbYD07mtRbsU1CHavaA+wO52WOurqq7F6m44Bzgba7hZczx0oCW05omVIVhZMOmUyoIJ/pneslHTxJPngw8MFTsV6MiRCcv7axLwL1hzHysFaEtX9YGvqtqHtSjmx4AJWK3e/YB1ok1E8sccd0rRiec3eUqG/e+Mxz+xEfjNjJGu2PfPL7jBqbozxC5gKre1dDhdiMoM2tJVvQrX1YSBVb6q6leBccBZwLlAPpDAmIOduzbu69y18ZZwXU3LvMeZBrjPq/RMdLDsTDEWuB5rpIhS2tJV/eOrqvZgfXyeCZwJ7A7X1fwUYN507xXApXfOLbhg7BBXpXNVZoy9wBRua0npwpUqO2hLV/VLuK4mBtQBdb6q6kexx3zbE5ZXDy2gdVSxTHCyxgwSB44D3nC6EOU8DV01YPZlxnH77ghg+MVTPEVuV+5NWH4sQp2m7Y2G+OLzKj3XcVtL0ue3UNlJQ1cl23TAnDzanbNz5/amsT2xd1l9PHBfILqnM87m/3s1Ell8m9NVqUyhoauSrVqgdVJZ7k5Y3pNYwkQ3HUisf2pzbOPKnfEI1uiOR4A3Fm+K6okTdYiGrkoae8Ly486e4DaFXilyup50aGw3+17dGVv74PronmZrUNhe4FGgdvGmqK4AoT5EQ1cl0xRAzhzvnuJ0IanUGTMdWxoT7yypi21Y8W68EzDAq8AyYOviTdGEsxWqTKahq5JpFhDLxQnLm9rN/rrG+ObV78W3vlwf74wl8GJdHv00VhdCyOESVZbQcboqKewJy38JtJ4zwT2sepx76rRhrqkjimRsNi67HkuY2K6gqV+/P7556fb49s0HE266LgaB1WirVvWThq5KinnTveOAHwMNQGvX48MKJf/cSvfEqeWu0WNLXKOHF8no4jwZ4lihRxDqNM372kzDe8FEQ11jomHp9tiB1gjFWHPiRrDG2K4FNi/eFG096s6UOgoNXZUU86Z784DLgDnAaPvhTiCENVH6of9oo4vFN3u0e1TVMNfo8UNk9Igi16iSfMpdIpLqOhPGmJYODuxrS+x5t8U01DUmGtbsju85EDYAJdhLqAO7sVq064F39aSYShYNXZV086Z7h2KdVPMD04CRWB/L3VitxhAQth8DwONCxpZI0dghrpLhPimu8ElxWYEUD8mX4iH5UlKUR3Gem6MuIWQMRBNE2qOE26Im3BoxbaFO2vaHE8E9rSa4syUR3NFiWmMJCrACNh/rj4FgzR+8BViH1ZrVicdVSmjoqpSbN91bgDUx+mhgMlYQj8UKO7AuIY5jBXL3W39al4J1gjgPK1TzsaalNPbNhTWsq86+NQANizdFdV4ElRYausoR86Z7vViXDFdgtTqH2v8eBpQDZVgf9RN065o4CrFvBmgDgsABYA/WKIMW4CCwZ/GmqE6zqByjoasylh3MxUBBHzaPYYVth44oUJlMQ1cppdIo68ZPKtUfIhIXkXXdbgucrqkvRORaEdlv17xeRB4REZ/Tdan+0yvS1GDRboyZlcoDiIjbGBPvfctj9qAx5ib7GPcBVwP3pOA4Kg20pasGNRGpF5Ef2S3JNSIyW0SeFZGtInK9vc15IrJcRJaIyCYRuUvsq+xEpFVEfiYibwJniMjnReQ1e3+/ExG3fbtXRN4WkYCI3GK/9osi8rqIvCkij/bWghURD1CENbxNZSkNXTVYFB7WvXB1t+fetVvBrwD3AlcCpwO3d9vmNOBm4ASsMcifsh8vAmqMMTOxRkdcDZxl7y8OXIM1J8VYY8xJxhg/77dSHzPGzLFfuxFr5eCeXC0i67AWuSwHnuz3d0E5TkNXDRbtxphZ3W4Pdntusf01gBWgIWPMfqBTRMrs514zxmyzuw/ux1qWHqxgfdT+9wXAKcDrdkhegDUueRswWUR+LSKXYA1nAzhJRF4RkQBWOJ94hNoftEN8lF3jN/v/bVBO09BVyrpcGawxwd2X1Unw/nmPw4f5dN3v6NaPK8CibsE+3RhzmzGmCWsBz5exVgb+o739vcBNduv3dnoZGmesoUZPAh85hvemMoyGrlJ9c5qITLL7cq8GVvSwzYvAlSIyAkBEykVkoohUAC5jzKPAt4HZ9vYlQIOIeLFaun1xNrB1IG9EOUtHL6jBotD+yN/lGWPMsQwbex24A5gKvAQ8fvgGxpgNIvJt4Dk7nKPAjVgT/twj709x+V/211uBGqylfWqwQrgnV4vI2ViNpPeAa4+hbpVh9OIIpXohIucB3zDGXO50LSr7afeCUkqlkbZ0lVIqjbSlq5RSaaShq5RSaaShq5RSaaShq5RSaaShq5RSaaShq5RSafT/AXRg2QgwDrXzAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Market share Passageiros\n",
        "labels = 'Empresa A', 'Empresa B', 'Empresa C'\n",
        "sizes = [round(market_share.percentual_pax[0], 0),\n",
        "         round(market_share.percentual_pax[1], 0),\n",
        "         round(market_share.percentual_pax[2], 0)]\n",
        "explode = (0, 0.1, 0)\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')\n",
        "plt.title('Market Share: Passageiros')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "ObQd6DpChokn",
        "outputId": "73f7bbf9-ab7c-4a4b-db08-31ec86174900"
      },
      "execution_count": 17,
      "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": [
        "# Market share Decolagens\n",
        "labels = 'Empresa A', 'Empresa B', 'Empresa C'\n",
        "sizes = [round(market_share.percentual_decolagens[0], 0),\n",
        "         round(market_share.percentual_decolagens[1], 0),\n",
        "         round(market_share.percentual_decolagens[2], 0)]\n",
        "explode = (0, 0.1, 0)\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')\n",
        "plt.title('Market Share: Decolagens')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "b49xNOl9h5sR",
        "outputId": "0434f70a-3abe-40ef-ec20-21d36bfd73ea"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Com base nos dados e gráficos acima, podemos verificar que a empresa **B** teve o menor market share nas operações de cargas no período de janeiro a outubro de 2021."
      ],
      "metadata": {
        "id": "xwCj9N2Ndr2Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análise - Parte 2"
      ],
      "metadata": {
        "id": "kwVW8gqioIV8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questões direcionadores da análise"
      ],
      "metadata": {
        "id": "pBadguYGI_05"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Como foi a evolução do total de carga transportada por mês por cada empresa?\n",
        "\n",
        "2. Quais foram as rotas operadas por cada empresa?\n",
        "\n",
        "2. Quais são as rotas com maior fluxo de carga paga?\n",
        "\n",
        "2. Qual é a média de carga paga transportada por voo? E como é a distribuição estatística da operação por mês?"
      ],
      "metadata": {
        "id": "tWXAntq5to7Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Série histórica"
      ],
      "metadata": {
        "id": "mgBs4hRr2os9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Query para definição de dataframes para análise da série histórica de cada empresa."
      ],
      "metadata": {
        "id": "p3JY9XyTjDW_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "serie_hist_a = df[df.empresa == 'A'].groupby(by='mês').sum().sort_values('mês').reset_index()\n",
        "serie_hist_b = df[df.empresa == 'B'].groupby(by='mês').sum().sort_values('mês').reset_index()\n",
        "serie_hist_c = df[df.empresa == 'C'].groupby(by='mês').sum().sort_values('mês').reset_index()"
      ],
      "metadata": {
        "id": "dec9PMq8U9QK"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gráficos das séries históricas de `carga_paga_kg`, `decolagens` e `passageiros_pagos`:"
      ],
      "metadata": {
        "id": "iaxX_Ce8jIQY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(serie_hist_a['mês'], serie_hist_a['carga_paga_kg'], marker='.', label='A')\n",
        "plt.plot(serie_hist_b['mês'], serie_hist_b['carga_paga_kg'], marker='.', label='B')\n",
        "plt.plot(serie_hist_c['mês'], serie_hist_c['carga_paga_kg'], marker='.', label='C')\n",
        "plt.xlabel('Mês')\n",
        "plt.ylabel('Carga paga [x10^7]')\n",
        "plt.title('Acumulado: Cargas')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "fIU0qV37tHlp",
        "outputId": "aed288f9-3650-424e-a954-1924ab47cbd6"
      },
      "execution_count": 20,
      "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": [
        "plt.plot(serie_hist_a['mês'], serie_hist_a['decolagens'], marker='.', label='A')\n",
        "plt.plot(serie_hist_b['mês'], serie_hist_b['decolagens'], marker='.', label='B')\n",
        "plt.plot(serie_hist_c['mês'], serie_hist_c['decolagens'], marker='.', label='C')\n",
        "plt.xlabel('Mês')\n",
        "plt.ylabel('Decolagens')\n",
        "plt.title('Acumulado: Decolagens')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "s1rjygQu3tii",
        "outputId": "e0ae8d12-edb7-429b-f386-47fbd3751501"
      },
      "execution_count": 21,
      "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": [
        "plt.plot(serie_hist_a['mês'], serie_hist_a['passageiros_pagos'], marker='.', label='A')\n",
        "plt.plot(serie_hist_b['mês'], serie_hist_b['passageiros_pagos'], marker='.', label='B')\n",
        "plt.plot(serie_hist_c['mês'], serie_hist_c['passageiros_pagos'], marker='.', label='C')\n",
        "plt.xlabel('Mês')\n",
        "plt.ylabel('PAX pagos [milhões]')\n",
        "plt.title('Acumulado: Passageiros')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "urEkSRF94Sqq",
        "outputId": "6a833edd-15b9-4ad1-d9b2-c4930b78fe18"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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UbDwAJgEXJxMTnwrG29OVqEvxRMXGcfpyAlGx8Zy6FM++fdFcuJZ40/kmgQql3P4zFGXc8Std4ibD4e7iOGepxaK4lpTC5bhkLsffvF1Kt++K9efpS/Ecu3CdtLDbgEql+OLhxuAZwtehn+GU4sS37b/l3mr3Ouwz2YJiYwhum/ds2Bu2jIcDyyHwCUfL02gczqFzRoqDtrW9aV6j3E0+ggaVbk+IBkZStNOX4q2G4r+fpy7Fs/14LEvCzpBquTl7QTkPlxsjiJsNhjuVy7jj5e6MiGTqq1BKcS0x5eYbeQY39oy2K/HJWLJIpuBkEkqXcKaUuzNe7s4ouGEETAL3NizNrvgJLNmzhLt87uLztp9TwaPCnXztBYJiYQhCI2PpN2kLiSmW/zL3VQ0Gr6rG9JA2BJpijsWieG1eGJ6uTnzzWCDlPHOWgsXN2UyN8p7UKJ+xYzQl1cK5q4lWIxFn/ZlA1KV4DkVfZd3BaBKSLTed4+FipqyHC1GX4rEo4wZco7wnqRZ144Z+q3FJj9kkeFlv5KXcnSldwgX/ch439t187OZ9JVzMN/lAQiNj6Td5i5Hjx+M0yy5+z/mEMwxrMozBjQfjZCoat9Ci8SmyYcvRiySmWFAYnv4bmfsCesHmHyAuBkqUdbRMjcZhzN4ayY4TlxjzaJMcG4Gc4GQ23ZgSgtv/xpRSxFxPumlEcSo2no2HL9x4crcow6AE+HnddNNOu4mXummfi5Gp00YO7aBqZXj3YQ9m75/CqYTdIN5M7TyVIN8gm/RfUCgWhqBFjXK4OptISLZgUdDIz8s40LA3/Psd7FsMQc84VKNG4yhOX4rn8xX7aVPbm15N/fL12iJCOU9Xynm60rjyf766m57EnUx8/WigQ5aybjq9ic92j8SiLJjExPv3vF/kjABA0XLvZ0JQtTLMGdSCp1tWQ4A1e88ZByo2gbI1IWKeQ/VpNI5CKcXbCyOwKPikV6MCszQ07W/2pU5181SExRYcv3yc19a/hkUZU1eCsC9mX77ryA+KxYgA/svcZxJhxubj9AmqTGCV0hDQGzZ8DdeiwTP/ikVrNAWBZeFn+HN/NG91r0+VsiUcLecm7JltMzs2RW1i9PrRKKVwNjljURacTc4E+9q+XEpBoFiMCNLzcqc6lPd05c0F4aSkWozcQ8oCexc5WppGk69cikvivcV7aFLZi2dbVXe0nAKBUopZe2fx/J/PU8GjAr8/8DtTO09lRNMRTOo0KcuawoUZuxkCEakiIn+LyF4R2SMiL2TQRkRkrIgcFpEwEbnLXnrSKOnmzDsPNGDP6SvM2hIJPvWhfH09PaQpdny8bB+xccl82rsx5gKSBdORJKUm8c6md/hi+xd0qNKB2V1nU7lkZQJ9AhnUaFCRNQJg3xFBCvCyUqoB0AIYLiINbmnTFaht3YYAE+yo5wbdG1WkbZ3yfL36IOeuJBjTQyc2w+Wo/Li8RuNwNh66wO+hp3iubY1MYwSKExfiLzBg1QAWHl7I802eZ0z7MZRwLlhTZfbEboZAKXVGKbXD+voqsA+4dUlCT2CmMtgClBaRivbSlIaI8MGDDUlKtfDB0r3G6iGAvQvtfWmNxuHEJ6XyxoJwqnt78L97C19eHFuz9+JeHl/6OAdjD/J1u68ZFjgMkxSvWfN8+bQi4g80BbbecsgPOJnu/SluNxaIyBARCRGRkPPnz9tEk7+3ByM61GJZ2Bn+ifGCCo319JCmWPDtnwc5ERPHp70bFcjc+PnJymMr6b+iPyYxMbPrTDr5d3K0JIdgd0MgIp7APOBFpdSVvPShlJqolApWSgWXL1/eZtqea1eDGt4evLMoguT6vSAqFGKP26x/jaagERF1mckbjtH37iq0qFHO0XIchkVZGLtjLK+sf4X65erzS/dfqFe2nqNlOQy7GgIRccYwAnOUUvMzaBIFVEn3vrJ1X77g6mTmw4cCiLwYx4wrVj91REYyNZrCT0qqhdfmhVHWw4XXu9Z3tByHcT35Oi/8/QKTwifRu3ZvpnSaQjn34msUwb6rhgSYAuxTSo3JpNli4Gnr6qEWwGWl1Bl7acqIVrW86RlYiS+2xJPg2xT2aEOgKZpM2XiMPaev8MGDDfFyd3a0HIdw8upJnlz+JBtObeD1u1/nvZbv4Wwunt9Feuw5ImgFPAV0FJFd1q2biAwVkaHWNsuBo8BhYBIwzI56MuXN7vVxdTbxe8LdcDYcLhxyhAyNxm4cv3CdMWsO0qmBL10CCn+2zLyw7cw2+i7rS3RcND/e/yP96vcrMJHUjsZukcVKqY1Alt+yUkoBw+2lIaf4lHTj1c51GbcomifdBImYD+1fc7QsjcYmKKV4c2E4LmYTH/QMKHY3P6UUvx34jc+2fYZ/KX++7/g9VUpVyf7EYkTxWiOVBU80r0aFytXZST1Sw/XqIU3R4Y/QU/x7+CKvd6tHBa/CWUErrySnJvPhlg/5eOvHtPZrzexus7URyABtCKyYTcLHvRqxMLkF5osH4NxeR0vSaO6Y81cT+WjZPu72L0vfZlUdLSdfiUmIYfCawfx+8HcGNRrEdx2+w9Ol8BSUz0+0IUhHgJ8XJe/qQ6oSzm2a42g5Gs0d8/6SPcQnpfJJ70aYilEaiQMxB+i7tC8RFyL4vM3nvHDXC5hNxTtmIiu0IbiFod1bEGJqRGr4PFJTLdmfoNEUUNbuPcfSsDOM7FiLWj7F50l4beRanlrxFCkqhRldZtCtRjdHSyrwaENwCyXdnHELfJhKljMsX73C0XI0mjxxNSGZtxdFUNe3JM+1q+loOfmCRVmYsHsCo9aNonbp2vza/Vcaejd0tKxCgTYEGdD4/qdIwcz5Lb8aSek0mkLGl6sOcPZKAp/1aYSLU9H/M49LjmP0P6MZv2s8D9Z8kKldplK+hO2yEBR1iv5vSB6QEmVJqtaezmziwyV7HC1Ho8kVoZExzNoSyTP3+NO0qmMKu+Qnp6+d5ukVT/PniT8ZHTyaj1p9hKvZdnWXiwPaEGRCiaaP4CcXOB2xnvUHbZPoTqOxN4kpqbw2L5xKXu6M7lTX0XLsTui5UPou68vpa6f54d4f6N+wf7GLk7AF2hBkRr1uKLMr/TxDeXtRBAnJqY5WpNFky4R1RzgcfY2PegXg4Vq0K9H+cfAPBq0eRCmXUszpPofWfq0dLanQog1BZrh5IbXv5wGnrZy8eI3x6444WpFGkyWHzl3lh78P0zOwEh3qFt3628mWZD7Z+gnvb36f5hWaM6f7HKp7FYNSmye3GfXVT26zeddF+5HhTmnYC5f9S3mxzgXGrXPiocBK1ChffJbhaQoPFovitXlheLo68U6PWwsBFh0uJVxi9D+j2Xp2K/0b9GdU0KjiER8QuQlm9AClwOwK/RdDlbtt1r0eEWRF3a7gXIJBZXbh6mzi7UURGOmRNJqCxeytkew4cYm3ezSgnOcdOkpPboP19nnyvBMOxx7mieVPsCN6Bx+1+ojRzUYXDyNw/QIseB4sqaAskJoExzfY9BLaEGSFiwfU6UyJQ0t57f6a/Hv4Iot3n3a0Ko3mJk5fiueLlQdoU9ubXk1vK/CXO05ug+nd4a8PYHqPAmMM1p1cx5MrniQ+JZ5pXabRs1ZPR0vKH86EwcQOcCUKzM4gZjC7gH8bm14m06khERmbg/OvKKXesqGegkfD3rBnAX19TjC3shcfLt1H+7o+xTafu6ZgoZTi7YURpFoUn/RqdOcrZo5tYJcThHiWIjghgcB9S206BZFblFJMiZjC2B1jqV+uPt91+I4KHsUkjfaeBbBwGLiVhoGrwZJijAT829j8/yQrH0FP4J1szn8dKNqGoPb94FIS8975fPzQx/T8YSNfrz7ABz0DHK1Mo2FZ+Bn+3B/NW93rU6VsiTvub5eKY2AFH5JEMOFF88gFlF8Xh9m5BE4mJ8xivvHTbDLf9t5JnG7an9WxW/u49fiB2ANMCZ/CwdiDdKvejffveR83p2KQPdVigb8/hg1fQZXm8OgsKOlrHLOTUc7KEHyjlJqR1ckiUvSjVZzdoV432LeYRt3H8HRLf2ZsPk6fuyrTpEppR6vTFGMuxSXx3uI9NK7sxTP3+Nukz5CojSSJgAgWYK85FY/ja0kpUY5UlWpsllRSVAqpltQb++yJkzjRt17f4mEEEq7A/CFwcAXc9TR0+wqc7B8cl6khUEp9m93JWbURkalADyBaKXXb47OIeAGzgapWHV8ppablRHS+E9AHwn6Do3/zUqeOLA8/w5sLw1k0vDXmYpTRUVOw+HjZPmLjkpk5oDlOZhu4+66cxvPsXihXGkFwNbsyru6TBK58B+o3gkemQwbOWaXUTUYi2ZJ843WqSiXFkpKhAblpvyXlpmOrI1ez8thKlPVfyLkQAn0C7/wzFmQuHIZfn4CYI4YBaDYI8ik4LtvloyLyBfAREA+sBBoDo5RSs7M5dTowDpiZyfHhwF6l1AMiUh44ICJzlFJJORWfb9ToYMzTRcyjVJ3OvN2jASN/2cmszcd5plUxWL+sKXD8e/gCv4eeYlj7mjSoVMomfaqQaaws4YaXc0n6NXiKlpVaGjdf5Qyr/g9W/h90/fy2m5OI4CROOOEENlrE4+3uzd8n/ibZkoyzyZlg32DbdFxQObQW/hgAZid4aiFUt60zODtyEkfQSSn1qoj0Ao4DvYH1GE/zmaKUWi8i/lk1AUpai9x7AjFASg705D9OLlC/B+xZBMkJ9GhckbkhJ/lq9UG6NqqIb6liMGTVFBjik1J5Y0E4/uVK8L97a9um05QktoXNJLSMG683HU6/+v3+O9ZymLFqZfM48PKDVi/Y5ppZEOgTyKROkwg5F0Kwb3DRHQ0oBZvGwtr3wKchPD4HylTLdxk5GU+mGYvuwO9Kqcs2uvY4oD5wGggHXlBKZVgAQESGiEiIiIScP++gvD8BfSDpKhxeg4jwYc8AklItfLhUVzLT5C/f/nmQyItxfNK7EW7OtnkEV3sX8YO7BR8XLx6u8/DtDe7/0FhBt+YdCPvdJtfMjkCfQAY1GlR0jUByPMwfbHyn9R+EgascYgQgZ4ZgqYjsB4KAP63TOLbIzdwZ2AVUAgKBcSKS4RhXKTVRKRWslAouXz5vqWV3Ru/k3X/fZVf0rryp9W8LJbwhwqhn7O/twfD2tVgadkYnpdPkGxFRl5m84RiPN6vCPTW9bdbv5u3fs9PNjSGBIzLO3GkyQa8fjaWLC5+Ho+tsdu1iyeVTMLULhP8B975j+F9cPBwmJ1tDoJR6HbgHCFZKJQNxGEtL75RngfnK4DBwDKhng35vY1f0LgasGsD8w/MZsGpA3oyB2Qka9ISDqyDpOgBD29egurcH7+ikdJp8ICXVwmvzwijr4cL/da1vs35V1A7GpZylopMnver0zryhkys8Nhu8a8OvT8LZcJtpKFZEboaJ7eHiEej7K7R5Od+cwpmRrSEQkRLAMGCCdVclwBaemxPAvdZr+AJ1gaM26Pc2Qs6FYLEYs07JlmS2n92et44CekNyHBxcCYCrk5kPewZw/GIcE3RSOo2dmbLxGHtOX+GDBxviVcJ2AY0bNn1BuJsrzwUOw8XsknVj99LQ7w9wKwVzHoFLJ22mo1gQOh1mPACupWDwn1C3i6MVATmbGpoGJGGMCgCiMFYRZYmI/AJsBuqKyCkRGSgiQ0VkqLXJh8A9IhIO/Am8ppS6kOtPkAOCfYNxMbsgGFY3z/mCqrYEzwoQMf/Grta1vXmwSSUmrDvC0fPXbCFXo7mNyIvXGbPmIJ0a+NIlwHaRter6RcZd2oWfyZ0H6z+es5O8/AxjkBQHs/tAXIzN9BRZUpNh2cuw5AWo0Q4G/wXlC069iJwYgppKqS+AZAClVByQ7ThGKdVXKVVRKeWslKqslJqilPpRKfWj9fhppVQnpVQjpVRADpaj5pm0FQgjAkfg5+nH4qOLSbYk574jkxka9oJDayDhP5/5Wz3q4+ps4p1Fe3RSOo3NUUrxxoJwXMwmPugZYNPCK39v/Ih9Ls4MbfAMzqZcjDJ8G0DfnyH2GPzaD5J1SddMuXYeZvaE7ZONFVdPzDVGVgWInBiCJBFxx1juiYjUBBLtqsoOBPoEMqTJEF6/+3Uir0Sy4NCCvHUU0I/7mhQAACAASURBVBtSE2H/8hu7fEq68Urnumw8fEEnpdPYnD9CT/Hv4Yu81rUeFbxst1TZkprM+FNrqaac6NF0SO478G8NvX6CE5tgwRAjO6bmZs7shkkdICoUek+G+z/IMCjP0eTEELyLEUhWRUTmYEzjvGpXVXakXeV23OVzF+N3jScuOS73HVRuBl5VYM/8m3b3a16NxpW9+GjZPi7H52G0odFkwPmriXy0bB/N/MvwxN1Vbdr32i1fc8AJhtZ8CCdTHkuTBPSGzp/A3kWw6g1jXbzGIGIeTOlspI4esBIaP+JoRZmSk1VDazCCyJ4BfsFYPbTOvrLsh4gwKmgUFxMuMnNvZkHPWXZgTA8d+eumuVGzSfj4oUZcvJbI16sP2FCxpjjz/pI9xCel8mnvxphsmM4k1ZLK+MNzqZ6i6Nri9TvrrOVwaDkCtv4Im763jcDCjCUV1r5vRApXbAJD1kGlpo5WlSU5TVDSCugAtAda2E1NPhHoE8i9Ve9lWsQ0LsZfzH0HAb2NlLD7lty0u1FlL55u6c+sLZGEnbpkI7Wa4sravedYGnaGkR1rUcvHtpXxVofP4AjJDKvYDrOzDZKa3Qg4ezvfAs4KJAmX4Ze+sHEMBD0D/ZeAZ8EvG5qpIRCRYdafnwEvAHut2wsi8kn+yLMfL9z1AompiUwMm5j7kysGQtkat00PAbzUqQ7enq68ucDIEa/R5IWrCcm8vSiCur4lea5dTZv2nWpJZXz4RGolJdOpTXaZ5nPIbQFn/9im38LEhUMw6V448id0HwMPfGekpykEZDUi6GT92Q24Xyk1VSk1FeiCkVW0UFPdqzq9avdi7sG5nLySy7XQIsbTz7H1xoqAdJRyc+adHg0Ij7rM7C2RNlSsKU58teoAZ68k8GmfRrg42baQ4PKD8zieep3hpQIwpeW5twXpA85+exLORtiu74LOoTWGEYiPgacXQ7OBjlaUK7L6DUsfv1423WsvO2nJd4Y1GYazyZnvd+ZhXjOgt+EE2rvwtkM9GlekTW1vvlp1gOgrelmdJneERsYwc0sk/Vv6c1dV25b8SLGkMGHHWOolJtHxntds2jfwX8CZa0mY83DRDzhTCjZ+awTXlalq+AP8WzlaVa7JyhCkeZA+BXaIyAwRmQGEAh/bXVk+UL5EeZ6s/yQrjq9gz8U9uTvZpwGUr2eUk7sFEeGDngEkplr4cNk+G6nVFAcSU1J5bV44lbzcGd3Z9gFHS44s4WTyZYaZymGq0szm/QO3B5zFx9rnOo4mKQ7mDYK17xoLSAashtK2XdmVX2RqCJRSG60/fwGaA/OsW0ul1G/5I8/+PBvwLKVdS/NN6De5CwZLmx6K3ARXbo8dqO7twbD2NVmy+zQbDumkdJqc8fbCCA5HX+OZVv54uuZxSWcmJFuS+Sn0WxokJtI+aIRN+74N3wZGSuXYY/DLE0Uv4OzSSZja2Vgieu+78PBUcLnzUqGOIqeTjybgAnAJqCMibe0nKX8p6VKS5xo/x9YzW9l8enPuTg7oDSjYc/v0EMDQdjWp7u3B2wt1UjpN9kzdeJS5IacA+Hr1AUIjbfskvejwIqISYxgep5CALJLL2YrqbQwH8o2AswyzzBc+IjcZSeNij8MTv0GblxyeNO5OyUnSuc+Bf4E3gVes22g768pXHq37KH6efnyz4xssGZdEyBjv2lChUYarhwDcnHVSOk32KKWY9u+xm6YRk1MsbDmah6XNmZCUmsRPu8bTOCGRNg37gXM+FVMK6JMu4Oz/Cn/AWchUI2mce2kY9CfU6exoRTYhJyOCh4C6SqnuSqkHrNuD9haWn7iYXRjZdCT7Y/az/Njy7E9IT8PecGo7xGa8Qih9UrpjF67bQK2mKJGUYuGNBeG8v2QvQVXL4OZkwizg7GSiRY1yNrvO/EPzORt/nuGXriD5vaKl5XBoMbxwB5ylJMHSUcZWo4NhBMrXcbQqm5ETQ3AUsF3O2wJK1+pdqV+2PuN2jiMpNRdlk9OG2Bk4jdN4q0d9XJ1MvL0wQiel09wg5noST07Zyi/bTjKsfU3mPteSOYNb8FKnuswZ1IKgarZZMZSYmsiksInclZRKy2r3QukqNuk3V3T6yHCornnbKMZSmDi4Cr4PMkYDrUcZ00EFLGncnZJVQNn3IjIWoxDNLhH5SUTGpm35JzF/MImJF4NeJOpaFHMPzM35iWX8wS/oRuWyjPAp6cYrXYykdEvCzty5WE2h58DZq/T8YSO7Tl7i28cCebVLPUwmIahaGYZ3qGUzIwDwx8E/iI4/z/CLF5Dmz9ms31xhMhkJ6qq1hgVDC37AWWoKHFwNMx+Cnx+FyyfA7Ax1uxXIpHF3SlYjghCMpaKLMWoHbLK+T9uKHPdUuocWFVvwU9hPXE26mvMTA/rA2TCj4lAmpCWle2dRBGPW2N4RqCk8/LnvHL3H/0tCsoW5z7XkoaZ+drtWfEo8k8Mnc3eqE3eXrG5E/joKJ1djJVG5WgUz4EwpI0voitfg67rw8yNwYjM3su5bLHB8g0Ml2ouslo/OyGrLT5H5yYtBL3Ip8RLTIqbl/KQGDxk/IzJ2GoORlO6p5tW4FJfM2D8P02/yFm0MihlKKX785wiDZoZQo7wni0e0IrCKfacY5h6Yy4X4Cww7FwV3D3b86hb30vDkH+DiWXACzmKOwT9fwLhgmNQRQqYZQWGP/wxPzgcnNxAzmF0ca0jtSKYLla2VwzKd0FZKNc6qYxGZipGKIlopFZBJm/bAtxg+iAtKqXY50GxXGpZrSNfqXZm1dxaP13scnxI5SBjl5WdUL4uYB+1eybRZ9LVEBONLTUi2sDLijE2nADQFl4TkVN6YH878nVF0b1yRrx5ugruLfacY4pLjmBoxlZZmL4K4BI1zWIHM3nhVNozB1K6GMRiwEtzz+e8gLsZY7Rc2F05uBcSor9DqBaj/4M0+gP6LjZGAfxuocnf+6swnsopYudN8QtOBcUCGuZ5FpDQwHuiilDohIgUmRd/IpiNZE7mGCbsn8G7Ld3N2UkAfWD4aoveBT8aFxVvUKIers4nEZAsKmLbpOJ6uzgxtXwNXp6I376gxiL6awHOzQtl54hIv3V+HkR1r2bTKWGb8sv8XYhJiGHbmAgQ+Da62zWB6R/g2NKaJZvc2As6eWmD/Ja3J8Ua98bC5cGi1kUG4fH247z1o9IhhoDKiyt1F1gCkIfZcxSIi/sDSjEYE1uymlZRSb+Wmz+DgYBUSEmIbgVnw2bbP+HX/r8zvOZ8aXjWyP+FatDGv2GY0dHwz02ahkbFsOXqRur4lWbT7NEt2n6aGtwcfPhRAq1remZ6nKZxERF1m8MwQLsUlM+bRJnRtVDFfrnst6Rpd5nehkcmTCXs3w4gQI+6loBExz8jb36AnPDzdcCrbEosFIjdC2G+wdzEkXoGSFaHRw9D4MfANcPx0WT4hIqFKqeAMj2VmCERko1KqtYhc5eYpIgGUUqpUDi7sT+aGIG1KqCFQEvhOKZXZ6GEIMASgatWqQZGR9s/qGZMQQ7f53WhRsQXfdvg2ZyfNeAAuR8HI0Bz/cq0/eJ63F0UQeTGOnoGVeLN7fXxK5lOwj8aurAg/w0tzd1OmhDMTnw4mwC//8jX+tPsnxu0ax6+XUmhYtp7xxF1Q2TQOVr8JzZ+HLp/a5sZ8bo9x8w//A65EgUtJaPAgNH7UmOIpgit/siMrQ5Dp1JBSqrX1Z0k76XICgoB7AXdgs4hsUUodzEDLRGAiGCMCO+m5ibJuZXmm4TP8sOsHdkXvItAnMPuTAvrAkheMFUQVm+ToOm3rlGfVi20Zv+4IP647wl/7o3m1Sz2euLsqZhtWpNLkH0opxv55mG/WHqRp1dL89FRQvhr3K0lXmLF3Bu1L16PhsdXQZUy+XTtP3DPCyNe15QfD33bPyLz1czkKIv4wpn7ORYDJCWrdB50+hDpdC3UuIHuTo3GYiJQRkcYiclfaZoNrnwJWKaWuK6UuAOuBnN0984mnGzxNObdyOU9IV/9B45cvi5iCjHBzNvPS/XVY+WIbGlf24u2FEfSesImIqMt5VK5xFPFJqYz4ZSffrD1I77v8+GVwi3wf4c3eO5urSVcZFnPJyIZZu1P2JzmatICz1W/lLuAs4TLsnG2Mxr9pCGveAWd36PYVvHzACP4K6KONQDZkm95QRD7EqFd8FEhLxKOAjnd47UXAOBFxAlwwMpx+c4d92pQSziUYFjiMD7d8yD+n/qF9lfbZnFDWCD/fswDuez/XQ9wa5T2ZPbA5i3ef5sOle3lw3Eaeuac6L3WqY/NMlBrbc+ZyPINnhrDn9BX+r2s9hrStkS9O4fRcTrzMrL2zuM/3bupv+cMoIVkYpkFMJnjoR6PQ04Kh4FEeamSyiDAlyagCFvYbHFgBKQlGxcD2rxtO33K2rehWHMjJ3eVRoKZSKhd5F0BEfsGocewtIqeAd7GmqlBK/aiU2iciK4EwDAMzWSlVwCJMoFftXszaO4tvQ7+ljV8bzNn9UQX0Nkr1RYVC5Qyn47JEROgZ6Ef7Oj58uXo/0zYdY3n4Gd59oAFdAirk+41FkzN2nohlyKxQ4pNSmfx0MPfWt2Hlr1wwY88MridfZ1g8xvr3pk86REeecHYzVhJN7WIEnD27AipY3YtKGTm9wn4z4nXiY6BEObjracPp6xdUbJy+9iDbVUMiMg94XikVnT+Ssia/Vg2lZ03kGl5a9xIf3PMBvWr3yrpxwmX4shY0G2Q4vu6QnSdieXNBBHvPXKFD3fJ80DOAKmX1MLcgsXBnFK/OC8O3lCtT+jejjq+93GpZE5sQS5d5XWhbsQVfbp5rTIn0HOcQLXfE5VMw+X5ITYR6PcCSaqz8iT1uGLd63Y2bf82ORtoHTY7IylmcEx/Bp8BOEVklIovTNttKLNjcV/U+Gns3ZtyucSSkZFNgw80Lat1v1CiwQf71plXLsHhEK97u0YBtx2K4b8w//PD3YZJSikhu90KMxaL4fOV+XvxtF02rlGbR8NYOMwIA0/ZMIz4lnudN5SE5zogkLox4VYb73oW4i7BjBuyabQScPTQBRh8yisDU6ayNgA3JiSGYAXwOfAZ8nW4rNogILwa9SHRcNHP2zcn+hIDecPU0nNxik+s7mU0MbF2dtS+3o2M9H75cdYBuYzfYNF+9JndcS0xhyKxQJqw7Qt+7qzBrYHPKerg4TM+F+Av8uv9XulXvSo3df0CVFjleuVYguRIFYr09iRnqPwCBT4BbtqvWNXkgJ4YgTik1Vin1t1Lqn7TN7soKGM0qNKNt5bZMCZ/C5cRsVvPU6QJO7rlePZQdFb3cmfBkENOeaUZCciqPT9zCy3N3c/Faok2vo8makzFxPDxhE38fiOa9BxrwSa9GuDjZOBAql0yLmEZiaiJDSzc2ykMW1tFAGv5twOxa5HP8FBRy8tu7QUQ+FZGWNl4+Wuh48a4XuZZ8jcnhk7Nu6OppDF33LjLS2dqYDvV8WDOqHcM71GTx7ig6fv0Pv247gcWiax3Ym23HYuj5w7+cvhTP9Geb8Uyr6g534EfHRfPbgd94oMYD+IcvBE9fYylzYabK3UaOn45vGj+LeIoHR5MTQ9AUaAF8wn/TQl/ZU1RBpXaZ2jxY80F+3vczZ65lU1cgoDdcP2+3tLXuLmZe6VyP5f9rQ90KJXl9fjgP/7iJfWeu2OV6Gvht+wn6Td5CaXdnFg5vRZva5R0tCYAp4VNIsaTwXNWucGgNBD0LTo6bprIZVe6GNi9rI5APZGsIlFIdMtjuNIag0DKi6QgAxu3KZjVG7U5Gqt1M6hnbitq+JfltSAu+eqQJxy/G0eP7jXyyfB/XE20/EimupKRa+GDJXl6bF06LGuVYMKwVNcoXjARuZ6+f5feDv/NQrYeosm+5ETMQ9IyjZWkKGVlVKMs2+2hO2hQ1KnhUoF/9fiw5soQDMQcyb+jsblQz2rfECICxIyLCw0GV+fOldjwaXJmJ649y/5h/WL3nrF2vWxy4HJ/MgBkhTP33GM/c48+0Z5rhVaLgrFaZHD4ZhWJI/Sdh5yxjSqhU/iS20xQdshoRfCkiTdP7BW7dMKaLih0DGw3E08WT73Z8l3XDgD4QHwtH1+WLrjIeLnzauzF/DG1JKXdnhswKZdCMEE7FxuXL9Ysaxy5cp9f4f9l0+AKf9m7Eew82xMnsWKdwek5fO828Q/PoU7sPlY5uNGJYHFWKUlOoySqy+ByQXbaqQzbUUmjwcvVicKPBjAkdw/az22lWoVnGDWt2NOIK9syHOvmX7yXYvyxLRrZm2r/H+GbNIe4fs54X76vNgNbVcS5AN7KCzMZDFxj+8w5MArMHNadFjXKOlnQbE8MmIgiDAgbCzD5QoRFUae5oWZpCSFalKttn4h9Iv/XJT7EFib71+uJbwjfrhHROLlDvAdi/DJKzCUSzMc5mE0Pa1mTty+1oXdubT1fsp8fYjYQcj8lXHYWRmZuP03/aNnxLubJ4ROsCaQROXjnJwsMLeaTOI1S4cBSi98DdQ3SaBU2e0I+HecTNyY0RTUcQfiGcNZFrMm8Y0NsohnF4bf6JS4dfaXcmPR3MxKeCuJqQzMM/bub1eWGsOxDND38f1nWT05GcauHNBeG8s2gP7euUZ97z9xTYdB4/hf2Ek8mJgY0GwraJ4FYaAh52tCxNIUWntLwDHqjxADP2zGDszrF0qNoBZ1MGTsTq7YzkWHvmQ33H+dY7NaxAq1rejP3zEJM3HOXX7ScRwNXZxJxBLYp97eR1B6J5e2EEJ2PjGdquJq90rltg60FEXolkydEl9KvfD5+UFGNBQsthOtWyJs/oEcEdYDaZGRU0isgrkcw/mMkyUbOTsZLjwApIup6/Am/Bw9WJ/+tWn6da+gNGLvGEZAurivnqoun/HuPZads5GRuPs1m4v4FvgTUCAD/u/hFXsysDAgZAyDRQFgge6GhZmkJMtoZARB4RkZLW12+JyPziGlmcEW382hDkG8SE3ROIS85kdU5AHyMJ2MFV+SsuEx5oUgk3ZxNpt7rp/x5n8oajpBazyOSL1xJ56bddvLdk741arBaLKtA5nI5eOsqyo8t4vN7jeDuXhNBpRhR72eqOlqYpxORkRPC2UuqqiLQG7gOmABPsK6vwICKMChrFxYSLzNg7I+NG1e4xwv7tHFyWU4KqlWHOoBaM7lyXn54Kok1tbz5ato/eEzax/2zRj0xWSjF3+0nuHfMPS8JO0+cuP9ycTJgFnJ1MBdI5nMaE3RNwd3Ln2YbPGsXYr58v/HmFNA4nJz6CVOvP7sBEpdQyEfkou5NEZCrQA4jOqHh9unbNgM3A40qpXNSoKzg0Kd+E+6vdz/SI6Txa51HKud9yIzGZjTJ8IdMg4UqByKAYVK3MDb9Apwa+LAk7w/uL99Bj7EaGta/J8I61cHUqBJWtcsnh6Ku8sSCCbcdiaOZfho97NaKOb0meaF6NLUcv0qJGuQLrLzkYe5BVx1cxqNEgyriVMZzEZWtCjWIb6K+xETkZEUSJyE/AY8ByEXHN4XnTgS5ZNRARM0aK69U56K9AM7LpSBJTE/kp7KeMGzTsbRTaOLAif4XlABHhwSaVWPNSOx5sUomxfx2m+9iNhEYWnaWmCcmpjFl9gK7fbeDA2at83qcRvw1peaN+QFC1MgzvUKvAGgEwfAMezh70b9gfTu+EU9uM0YBJu/o0d0ZOfoMeBVYBnZVSl4CywCvZnaSUWg9kdycZCcwDCkT1szuhuld1+tTuw+8HfufklZO3N6jcDEpVtnlqaltS1sOFMY8FMv3ZZsQnpfLwj5t5d1EE1wp53qJ/D1+g63cbGPvXYXo0rsSfL7fjsWZVMRVgh/Ct7I/Zz5rINTzV4Cm8XL1g2yRw9jBy9Gs0d0hOks7FAUeAziIyAvBRSt3xE7yI+AG9KEL+hqFNhuJsdub7nd/fftBkgoBecOQvI+1EAaZ9XR9Wj2pL/5b+zNwSSacx//D3/sJnq9Ocwf0mb8WiFLMHNuebxwLx9nR1tLRc88OuHyjpUpInGzwJ1y9C+B/Q5HEjcl2juUNysmroBWAO4GPdZovISBtc+1vgNaVUtjUXRWSIiISISMj58+dtcGn7UL5EeZ5q8BQrjq9gz4U9tzdo2BssybB4JJzclv8Cc4GHqxPvPdiQP4beg4erE89O384Lv+4sFEVwbnUGj+xYi1UvtqV1bW9HS8sTey7sYd3JdfRv0J9SLqVg50xjmlE7iTU2IifF68OAlkqp69b3HsBmpVTjbDsX8QeWZuQsFpFjcGMFozcQBwxRSi3Mqk9HFK/PDdeSrtFtfjfqlKnDpE6Tbi5acmIrTO0MKKOCWSEpuJGYksr4v48wft1hSro5806PBvQMrOTwgiwZcasz+JNejajtwDrCtmDY2mGEXQhjVZ9VeJjd4LsmUMYfnlnqaGmaQsSdFq8X/ls5hPX1Hd8BlFLVlVL+Sil/4A9gWHZGoDDg6eLJc02eY+vZrWw6venmg5Eb/3udmmi3ojW2xtXJzKj767Dsf22oWrYEL/62i2enbyfqUryjpd0gM2dwYTcCu8/vZkPUBp5t+Cwezh5wcCVcPmnkFdJobERODME0YKuIvCci7wFbMGIJskREfsFYFlpXRE6JyEARGSoiQ+9IcSHg0TqP4ufpxzeh32BJP/Pl3wacrPPTSkHVexwjMI/U8S3JvOfv4Z0eDdh6NIZOY/5hxqbjDi+RWRScwZnxw84fKOtWlr71+ho7tk2EUn5GrQuNxkbkxFk8BngWYwVQDPCsUurbHJzXVylVUSnlrJSqrJSaopT6USn1YwZtnymsMQQZ4Wx25n9N/8eB2AMsP7b8vwNV7ob+SwxfAQrOhjtMY14xm4QBrauzelRb7qpWhncX7+GRnzZzOPpqvmtJ7wxWhdwZnBGh50LZfGYzAwIGUMK5BJw/YNS2CB5gpC7RaGxETnwEZTPYfVUplWwfSVlT0H0EaViUhceXPs6VpCssfmgxLuZ0NWSVglm94FQIjNheaCtKKaWYvyOKD5ftJS4xlREdazG0XU1cnOy7rl0pxe8hp/hkhVGSc2i7mgzvUAs356IVADdw1UCOXj7K8t7LcXdyh+WvQOh0GLUXPAtGvWRN4eFOfQQ7gPPAQYxCNOeB4yKyQ0SCbCezaGESE6OCRhF1LYrfDvx280ER6P41pCbBqjccI9AGiAh9giqz9qV2dGroy5g1B3ng+43sOnnJbtc8HH2VxyZu4dV5YdT28WT5/9rwcqe6Rc4IbDuzjW1ntzGo0SDDCCRcgV0/G6NJbQQ0NiYnhmAN0E0p5a2UKgd0BZYCw4Dx9hRX2GlZqSUtK7ZkYthEribdMnVSria0HW3kH3JQrQJb4e3pyrgn7mLy08Fcjk+m9/h/+XDpXuKSbBeIVlSdwRmhlOKHXT/g4+7Dw3WsNQZ2/wpJ17STWGMXcmIIWiilbqTNtAaTtVRKbQGKxmSsHRkVNIpLiZeYFjHt9oOtXoBytWDZy5BccFbg5JX7Gviy+qW2PNG8KlM2HqPTN+vZcOjO4z6KsjM4Izaf2cyO6B0MbjwYV7OrMZW4bSL4BUFlPQjX2J6cGIIzIvKaiFSzbq8C56x5grINBivu1C9Xn27VuzFr7yyi426JznVyhe5jIPY4bPjaIfpsTSk3Zz56qBFzn2uJi9nEU1O28fLc3VyKS8p1X0XdGZwRaaOBCh4V6F27t7Hz6Dq4eEiPBjR2IyeG4AmgMrDQulW17jNj5CHSZMPIpiNJUSmM35XBTFqNdtD4Mdj4LZw/mP/i7MTd1cuy/IU2DO9Qk4W7orhvzD8sDTudeX3ndGQUGbyyEEcG54aNURsJOx/Gc42f+2+BwbZJUMIbGjzkWHGaIktOlo9eUEqNVEo1tW4jlFLnlVJJSqnD+SGysFO5ZGUer/s4Cw4v4Ojlo7c36PSxUWZw2UvGNEARwc3ZzCud67F4RCsqeLkx4uedDJ4ZytnLCZmek94ZXMenJCteKJrO4IxQSvHl9i8p5VIKfy9/Y2dsJBxcAUH9wdnNofo0RZec5BoqLyJfishyEfkrbcsPcUWJwY0H4+7kztgdY28/6Fke7nvfiDQO++3244WchpW8WDisFW90q8fGw+e5f8w/zNkaeVMgWkbO4F+HtKCWT9FzBmdEfEo8I/4cwbErx7iadJXn1zzPruhdEDLVaBA8wLECNUWanEwNzQH2A9WB94HjwHY7aiqSlHUry4CAAfx54k8+2PyB8Ueenrv6G6mqV70JcUWnDkAaTmYTQ9rWZNWLbQnw8+LNBRH0nbSFJbujePWP3XT4al2xcQbfyrHLx+i3vB/ro9YjCApFsiWZkNObYcdMqNcdvCo7WqamCJMTQ1BOKTUFSFZK/aOUGgDokkh5oIl3EwB+P/g7g1cPvtkYmEzQ4xsjRfXa9xwjMB+oVs6Dnwc35/M+jQiPuszIX3YxN+QUZy8n8G6PBkXeGXwrK4+t5PGlj3Mh7gKvBr+Kq9kVs5hxNjkTfO0qxMdoJ7HG7uQkTj0tgviMiHQHTmMUp9HkkvCL4Tee+BJTEwk5F0KgT+B/DSo0ghbPw+ZxENgPqjZ3nFg7IiI81qwqkRfjGL/uCAAmgbjk1GzOLDokpSbx5fYv+fXArwSWD+TLdl9SwaMCjco3IuRcCMG+wQQu+B+Ur2fkqNJo7EhORgQfiYgX8DIwGpgMjLKrqiJKsG/wTakmAssH3t6o/f8ZlcyWjoJUh2TxyDfure+Lm3PhKBpvS05dPcXTK57m1wO/0r9Bf6Z2mUoFjwoABPoEMqjRIAITk+HMbqPmQAFM960pWuRk1dBSpdRlpVSEUqqDUipIKbU4P8QVNQJ9ApncaTK9avVCoQi/kEHSOVdP6PYFRO+BLUWmeFuGBFUrw5xBLXipU13mDGpRoOsF24q/T/zNo0sf5cSVE3zX4TtGNxuNs8n59obbD7CBRQAAIABJREFUfgLXUtD48fwXqSl2ZDs1JCI1gO+AlhgBZJuBUUqpDNZBarIj0CeQQJ9AYhJimBg2kQdrPkg591uehOt1N9IMr/sUGvaC0lUcIzYfCKpWplgYgGRLMmN3jGX6nuk0KNeAr9p9RZWSmfy/Xj0HexZCs0HGg4FGY2dyMjX0MzAXqABUAn4HfrGnqOLAy8Evk5CSkHGQGUDXz42fK17LP1Eau3D2+lkGrhrI9D3TeazuY8zsOjNzIwCwY4ZR0rTZoPwTqSnW5MQQlFBKzVJKpVi32YCObLlDqntV57F6j/HHoT84GJtBRHHpqtD+dTiwDPYvy3+BGpuwKWoTjy55lP0x+/m8zee81eItI39QZqQmG7EDNe8F71r5J1RTrMmJIVghIq+LiH+6XEPLRaRsJrUKABCRqSISLSIRmRzvJyJhIhIuIptEpEleP0Rh5fkmz+Pp7MlX27/KOPVCi2Hg0xCWvwqJ1/JfoCbPpP5/e3ceZ3P9PXD8dWZh7DEijGXsDBlMoo2IRPblS1mL4qtUX22ypOUb37RIX/G1FImRpBKFQpHdaOxL1gzZZYhhlvP743P7hWbGjJl778zc83w8PMz93Pf9fM5cZs59fz6f9zlJiYyLHke/7/sRnCeYWQ/OokX5NHQV2zkfzv1mt4waj0pLIugMPA4sA34A+gNdgCggtQ4xU4HmqTy/H2ioqjWB14CJaYglRymUuxD9a/Vn9W+rWXE4mf7F/oHO2oLYGPhxlOcDNDfk5MWTPP7940zYNIHWFVozs+VMyhcqn7YXr3gbgm6CoELuDdKYK6TlrqHQVP6k+L9bVZfjtLZM6flVqnrG9XANTmE7n/OPqv+gXMFyjF4/mvikZG4XLXO7s+p49QdwNNnJlclCNhzdQOevOxN9PJpX73iV1+963Wkscz2XzsOXTzjtS+POOh3sDq1zf8DGkLYZgSc8Cnyb0pMi8piIbBCRDSdOZLy+fVYS6BfIoIhBHIg9wOxds5MfdN8IyHOTs7YgySp/Z0VJmsSULVPos7gP+QLzMaPFDNpVanf9FyYmwIaP4P06ED3dtVGd7nUHkpklGuMGXk8EInIvTiJI8fYYVZ2oqhGqGnHzzTmvTV/DkIbUL1Gf8ZvGc/bS2b8PyFvEqVAasw5+/tjzAZpU/R73O08ufZIxG8dwX9n7iGwZSZUiVVJ/kSrs+hbG3wHzn4bC5eDBMRCQB8Qf/HPZimLjMV5NBCJyK85K5TaqesqbsXiTiPDcbc9x7vI5JmyakPygWl2g7F3w3ctwPmfNirKzzSc203l+Z1YdWcVLt7/E6HtGkz/Xde79j4mCqQ9CZBdISoB/fAKPLIKI3tBzHjQe4vxdup5nvgnj81JMBCLSIYXtuURkWEYPLCJlgLlAd1XNOR1ZblDlwpVpX6k9s3bO4sDZA38fIOJcOL78Bywe6vH4zNVUlRk7ZtBzYU/8xI/pD0yna9WuSGrlIE7vg896w+TGcGIntHgLBqyFaq3+KiNRuh7cPciSgPGo1GYEj7l6EIT+uUFEHgA2A9ctCiMikTirkKuISIyIPCoi/USkn2vIcNd+PhCRaBFJ7Q4knzAgfAC5A3LzdlQKbStvrgx3PQ2bZ8H+5Z4Nzvy/c5fPMejHQYxaN4q7St7Fpw9+So2iNVJ+wYXT8O2L8N96sHsh3PM8PBXt1BHyT6a8hDEeJqm1DhSRrsDrOKuLawDFgAGqGp3ii9wsIiJCN2zIuTljypYpjNk4hsnNJnN7iWSqj8ZfhA/qg18g9F/p9D02HrPz9E4G/TCIw+cP81Sdp+gV1ivlWUD8RVg7AVa8C5fPQe1u0OglKFjCs0EbA4hIlKpGJPfc9a4RzMYpJ/EMcBvQ25tJwBd0q96NUvlL8eb6N0lMSqYsc2AeaPG208x8ZTLdzoxbqCqf7/6chxc8TFxCHB/e/yG9a/ROPgkkJUJ0JLwf4fSWKNsA+q+C1u9bEjBZUmrXCO4CNuKcvikNPAF8LSKvioh9DHWT3P65eabuM+w+s5sv9nyR/KBK9znF6JaPhlN7PRugD7oQf4EhPw1hxOoR1C1el89af0ad4nWSH7xnCfyvIXzZz2lB2vNreOhTKFbNs0Ebkw6pzQjGAH1Vtb+qnlHVL4HaQG5gk0ei81HNyjajTrE6vP/z+5y/nEJpiftHOqeFvnk2RzW8z2r2/r6XhxY8xPx98/ln+D8Zf994igQlU1nl6BZnEdgn7eFSLHSYAn2WQug9ng/amHRKLRHUU9Wrljaq6gVVfQGn7IRxkz9vJz0dd5rJWyYnP6hgCWg8DPYuhW1zPRugj5i/bz5dF3TlzKUzTGw2kf61+uPv53/1oLMx8EU/mHA3HN4I978BT6yHmh2d9qPGZAMp9iNQ1auWsIpzMrQx8BDwIFDcvaH5thpFa9CqfCumb59OpyqdKJW/1N8H3fYoRM+AhYOh4n1WnyaTXEq8xKh1o5izew51itVhdMPRFMtb7OpBF3+Hn979q3nQnQPhrmcgT87vrWBynut+ZBGR+iIyFjgIfAUsB6q6OzADA+sMxE/8eDfq3eQH+PlDqzHwxwlY+rpng8uhDsUeots33Zizew6P1HiEKfdPuToJJFxy6j6NDYeV70GN9vBkFDR91ZKAybZSnBGIyBtAJ+BXnDuHXgE2qOo0D8Xm827Jdwu9a/Rm/KbxPFztYWoXq/33QSVrw219Yd1EqNUVSqVwEdOkKvp4NLN2zmLpr0sJ9A/kv43/S8PSDf8aoOqcgvv+Ffj9IJRv5PzyL+Fz1dNNDpTajKAPcAwYD0x3lYCwq5Ie1iusF8XyFuPNdW+SpCkUnGs8BPIXd2rWJHfLqUlV9PFoei3sxYL9C4hLjGPEHSOuTgIHfoJJjWHOI5C7AHSbCz2+siRgcozUEkEJnMVkrYC9IjIdyCMi1+1zbDJP3sC8PF3nabae2sqCfSl0KgsqBM1Hwm+bYH0KF5dNiqZum0qiOgnUT/w4GHvQeeL4TpjZBaa2hPPHoO14eHw5VGzixWiNyXwpJgJVTVTVharaE6gAfAmsBA6LyExPBWigZfmWhAWH8d7G97iYcDH5QWHtnAvGS16D2COeDTAbO3L+CCsPr8QPP/zFn0C/QCIKlId5A2F8Azi40ikD/mQUhD/kXJcxJodJbUFZkIg8LSL/BXoCX6lqR6ASsNBTARrnU+rztz3PsQvHmLptavKDRKDFaKfp+cLBHo0vu0rSJIavGo6f+PFWjX48UbAGk4KqEj7jYYieCfUeh4HRzt1AgWloLmNMNpXaaZ5pQDywAmgBVAeeVtVYwIrie1id4nVoVrYZH239iPYV21M8XzJ37xYpD/c869xB9Mt3UKmp5wPNRmbvms3a39bycpUeNF0wzEmi4CwCa/We834a4wNSu0ZQXVW7qer/gI6ALZH0smfqPkNCUgJjf06lxtAdA6FoZVgwyCl6ZpJ1KPYQ70S9w50l76TDoR1/JQHxc+4IsiRgfEhqieD/G+iqaoIHYjHXEVIghO7VuzNv7zy2ndyW/KCA3NDyHecWx+VveTbAbCJJkxi6cigBEsCIWk8ivywGxNUZLLd1BjM+J7VEUEtEYl1/zgG3/vm1iMR6KkBztb41+1IkqAhvrn+TFEuIh94NtR5yFjyd2OXZALOBT7Z/wsbjG3mh3vPcsuR1pz9w+0nWGcz4rNTuGvJX1YKuPwVUNeCKrwt6Mkjzl/y58vNE7SfYeHwj3x38LuWBzV6DXPlg/r+sKN0V9p/dz9ifx9IopBGtY8/Drm+gyXC4tZN1BjM+y21VsUTkQxE5LiJbU3heRGSsiOwRkc0iYkti06h9xfZUKlyJd6Le4VLipeQH5SvqrHw9+BNsivRsgFlUYlIiQ1cOJSggiJfD+iCLBkOZO6B+f2+HZoxXubM84lSgeSrPP4BzK2ol4DGcFcwmDfz9/Hku4jkOnz/MjB0zUh5YuzuUvt3pcXzhtOcCzKKmbpvK5hObeem2wRRdNMxZhd32A1sbYHye2xKBqi4HUvvt0wb4WB1rgJtExNo3pVGDkg1oGNKQiZsncuriqeQH+fk5De8v/g7fv+zZALOYX878wrjocTQt25QHTsTA/h/h/n9DkdDrv9iYHM6b5SJKAYeueBzj2vbbtQNF5DGcWQNlypT5247i4+OJiYkhLi7OPZFmkqCgIEJCQggMzJyG5YMiBtH+q/aMix7H8AbDkx9UPAwaDIBVYyH8YShTP1OOnZ3EJ8Uz5KchFMhVgKGVuyEftnBWYdft5e3QjMkSskXdIFWdCEwEp3n9tc/HxMRQoEABypUrl3IjcS9TVU6dOkVMTAyhoZnzKTS0UChdqnZh5s6ZdKnahcqFKyc/sNGLsO0LmP+MUyvHP3MSUXYxZcsUdpzewbsN36bINy9AQC6nf3AW/b9ijKd5s4XSYZxeyH8KcW1Lt7i4OIKDg7NsEgCn61hwcHCmz1r61epH/sD8jF4/OuXbSXPlc8pPHN8Oaz7I1ONndTtP7+R/m/5Hi9AW3HdoK8SsgxZvQ8GS3g7NmCzDm4lgHtDDdfdQfeCsqv7ttFBaZeUk8Cd3xFgodyH+Gf5P1vy2huUxy1MeWOUBqPog/DAKfv810+PIiuITnVNCNwXdxEuh7WHZG1CttdNG0hjz/9x5+2gksBqoIiIxIvKoiPQTkX6uId8A+4A9wCTgn+6KJafrXKUz5QqW460NbxGfFJ/ywOajAIFvnvOJtQXjN41n95ndjKg3hELzB0Gem5yL59ngQ4MxnuTOu4a6qmoJVQ1U1RBVnaKqE1R1gut5VdUBqlpBVWuq6gZ3xeIpX375JSLCzp07PXrcQL9Ano14lgOxB5i9a3bKA28qDfcOht0LYWcKvQ1yiK0nt/Lh1g9pU6ENDfeuhmNboNVYZ32FMeYq3jw15FVRB88wbtkeog6eybR9RkZGctdddxEZ6fkFXPeE3EP9EvX5IPoDzl46m/LA2/tD8Zrw9VOwbCQcWue5ID3kUuIlhvw0hKJ5ivJ8qWbw0ztOyY2qLbwdmjFZUra4ayg9Xvl6G9uPpF4K6VxcPDuPniNJwU+g6i0FKBCU8p001UsW5OVWYanu8/z58/z0008sW7aMVq1a8corr9xQ/DdKRHjutufo9HUnJmyawAv1Xkh+oH+A0+N4/kD48T9OPaIcVl9n3M/j2Hd2HxMavUfBef+CAiXhgVHeDsuYLMsnZwSxcQkkuU6RJ6nzOKO++uormjdvTuXKlQkODiYqKirD+0yvyoUr075Se2btnMX+s/tTHnjxJCCAQkIcHFjhqRDdLvp4NFO3TaVj5Y7cuX0xnPoF2vzXaedpjElWjpsRXO+TOzinhR6evIb4hCQCA/x4r0tt6pYtnKHjRkZG8tRTTwHQpUsXIiMjqVu3bob2eSMGhA/g2/3f8s6Gd3i/yfvJDyp3NwQEQcJFQOH8cY/G6C4XEy4ydOVQSuYvybNFG8CiTs7sp8K93g7NmCwtxyWCtKhbtjAz+tRnzb5T1C8fnOEkcPr0aZYuXcqWLVsQERITExERRo8e7fHbWovmKUrfmn0Zs3EMa35bQ/0SyawkLl3POR2070en1MLaCRBcEer19Wisme29je9xMPYgUxqNJd/cgU5zmaaePUVnTHbkk6eGwEkGA+6tmOEkADBnzhy6d+/OwYMHOXDgAIcOHSI0NJQVK7xzyqVb9W6Uyl+K0etHk5iUmPyg0vWg4XPQ/Quo0gK+eRY2Zt8OpOuPrmfGjhk8VPUh6m2aC7Ex0HaCs5jOGJMqn00EmSkyMpJ27dpdta1Dhw5euXsIILd/bv5V91/sPrObL/Z8kfpg/0DoNNWpvTNvIGya5ZEYM9OF+AsMWzmMMgXK8FTBMCeh3TEQytzu7dCMyRZ88tRQZlu2bNnftg0cONALkfyladmm1ClWh/d/fp/m5ZqTP1f+lAcH5IZ/fAIz/wFf9neSQ40Ongs2g97e8DZHzh9h2r3vk3d2XyhWHe59ydthGZNt2IwghxIRnr/teU7HnWbSlknXf0FgHugaCaXrw+d9YcfX7g8yE6w6sorZu2fTo3oPaq+bBhdOQbsJTnIzxqSJJYIcLKxoGK0rtGb69unEnIu5/gty5YOHZ0OpuvBZb9i10P1BZsC5y+cYvnI4oYVCeSJ3adj6OTR8EUrU8nZoxmQrlghyuIG1BxLgF8C7Ue+m7QW5C0C3OXBLDZjdHfYscW+AGfDm+jc5cfEE/679L4K+fQFK1oG7nvF2WMZkO5YIcrji+YrTO6w3iw8uZuOxjWl7UVAh6DYXbq4Csx6C/VlvwdnymOV8uedLHgnrTc2V4yH+IrT7n7Ny2hiTLpYIfEDPsJ4Uy1uMN9e/SZImpe1FeYtA9y+hcKhzEfnXNe4NMh3OXjrLiFUjqFS4Ev21kFNEr8nLcHMKjXmMMamyROAD8gbm5ek6T7Pt1DYW7EtH1dF8RaHHV1CwBHzSEWKyRoHYketGcibuDP++9QlyLR4GZe+C2/td/4XGmGRZIsgk/v7+hIeHU6tWLerUqcOqVau8HdJVWpZvSVhwGGM2juFC/IW0v7BAcej5tZMUpreHI9HuCzINlhxcwoJ9C3isZl+q/fAOoNB2HPjZf2VjbpTv/vQcWgcr3s60Msx58uQhOjqaTZs2MXLkSAYPHpwp+80sfuLH87c9z/ELx5m2bVr6XlywpJMMggrB9LZwdKt7gryO03GneXXNq1QrUo0+ceoUy7v/DShczivxGJNTuPXKmog0B94D/IHJqjrqmufLANOAm1xjXlTVbzJ00G9fhKNbUh9zKRaObQVNAvGD4jUgd8GUx99SM11ljGNjYylcOOOlKzJbneJ1aFa2GR9t+4j2ldpTPF/xtL/4ptJOfaKpLeHjNtBrARSr6r5gk/HvNf8m9nIsk24bRuDMh6FSM6jTw6MxGJMTubNVpT8wDngAqA50FZHq1wwbCsxW1dpAF8AzndXjzjpJAJy/41Jp5JJGFy9eJDw8nKpVq9KnTx+GDRuW4X26wzN1nyEhKYGxP49N/4uLhDozAz9/+Lg1nNqb+QGmYOH+hSw+uJgBt/aj8pKRTvXUVmOt7aQxmcCdM4J6wB5V3QcgIrOANsD2K8Yo8OdH8ULAkQwfNS2f3A+tg2mtIfEy+OeCDpMz3Jjlz1NDAKtXr6ZHjx5s3brV49VHryekQAg9qvdgytYp5A3IS8vyLQkvFp72HQRXgB6umcG0VtD7G7efmjl58SSvr32dmkVr0uv3s3B4A3SY4lzENsZkmDuvEZQCDl3xOMa17UojgG4iEoPTzP7J5HYkIo+JyAYR2XDixImMR/ZnGebGQ9zSnatBgwacPHmSTInVDW4v4RRjm7VrFr0X9mb5oeXp20Gxqs7dRPEXYGor+P3Q9V9zg1SVV1a/QlxCHK9X6UHAj29CWDuo2dFtxzTG13j7YnFXYKqqhgAtgOki8reYVHWiqkaoasTNN9+cOUcuXQ/uHuSWFo07d+4kMTGR4ODgTN93Zth2aht+rn/6BE3gyWVP8uyPz7L2t7Woatp2cksNZ51B3FlnZhCb8clccr7e9zU/HPqBJ2v1p/x3r0GewtDibbccyxhf5c5TQ4eB0lc8DnFtu9KjQHMAVV0tIkFAUSDbtcz68xoBOJ9ip02bhr+/v5ejSl5E8Qhy+eciPikef/Hn3tL3svrIahYdWETZgmXpWKkjbSq2oXDQdS54lwyH7nPh47bOqbZeC5zbTTPJsT+OMWrtKGoXq023o4ecC/xdZ0G+rJlgjcmu3JkI1gOVRCQUJwF0AR66ZsyvQBNgqohUA4KArHk+5ToSE1NoAJMFhRcLZ1KzSWw4toGI4hGEFwsnLiGO7w5+x2e7P+PtqLcZ+/NY7itzH52qdCKieETK1zpCIuDhz+CTDq67ieY7aw4ySFV5efXLJGgCr5fvjP+s7hDeDao8kOF9G2Ou5rZEoKoJIvIEsAjn1tAPVXWbiLwKbFDVecAgYJKIPINz4biXpvnchMmI8GLhV10kDgoIolWFVrSq0Io9Z/Yw55c5zNs7j28PfEu5guXoWLkjrSu0Tn6WULYBPDQLZnRyZgc95zklKjJg7i9zWXl4JYPrPkuZxS9DwVLQfGSG9mmMSZ5kt9+7ERERumHD1aUOduzYQbVq1bwUUfpkp1gvJlxk8YHFzNk9h+gT0QT6BdK0bFM6Ve5E3eJ1/z5L2LMEIrtA8TDnYnJQoRs67pHzR2g/rz1hwWFMSiiC3/qJzp1K5RtmwndljG8SkShVjUjuOW9fLDZZWJ6APLSp2IbpLabzeevP6Vi5IytiVtB7UW/afNWGj7d9zNlLV6zBqNgEOk93Vh5/0hEunUv3MZM0ieErh6OqvFq6pZME6j1uScAYN7JEYNKkcuHKvHT7SyzpvITX7nyNArkKMHrDaBrPbszgFYPZeGyjc8dRlebQ6SM4HOVULb38R7qO8+muT1l7dC3Phg+g1KLhEFwR7hvhlu/JGOOw4u0mXfIE5KFtxba0rdiWXad3MWf3HObvm8/8ffOpUKgCHSt3pFWFVhTqMAk+7wORXeGhT51WmNdxKPYQ70a9y50l76Tj7jUQexgeWQy58nrgOzPGd9mMwNywKkWqMKT+EJZ0WsKrd7xK3sC8/Gf9f2jyWROG/L6Rn+97Cd2/HD7tBgmXUt1XkiYxdOVQAiSAEcUbIptmwJ1PQ+nbPPTdGOO7LBFkoqNHj9KlSxcqVKhA3bp1adGiBbt37/Z2WG6XNzAv7Sq1Y2bLmXzW6jPaVmzLkl+X0GPPx7SvWpsZx9cQO7s7JFxOcR+fbP+Ejcc38kKtAdyyaLhTCLDRix78LozxXT6bCKKPRzN5y2Sij2dOfX1VpV27djRq1Ii9e/cSFRXFyJEjOXbsWKbsP7uoWqQqQ+sPZWmnpYxoMIKg/LcwKrgITS5tY8ispkQfjfrb6uX9Z/cz9uexNAy5h9ZbF8HFM9BuAgTk9tJ3YYxvyXHXCP6z7j/sPL0z1THnL59n15ldKIogVClchfy58qc4vmqRqrxQ74VU97ls2TICAwPp1++vTlm1atVKX/A5SN7AvHSo3IEOlTuw/dR25qx4hQVntjJvUS8q3VSJTlU68WD5B9l9ZjcvLn8Rf/Hn5cIRyIqnofEwp/S3McYjfHJGcC7+HIrzqVRRzsWn/zbHa23dupW6detmeD85UfXg6gxv+ylLy/fg5ZOnCDz3G2+sfYNGnzai98LeHL1wlPjEyxxe9hqE3OZcGzDGeEyOmxFc75M7OKeF+i7uS3xSPIF+gYy6e1T6SjGbG5Kv4Qt0VKXjDyPZVqsDrwVeYNupbQAkaQIbApTwthPAP8f9tzQmS/PJn7jkau1kVFhYGHPmzMmE6HK4hi9AwiXCfnqHF2t3po9/bhISLxOYlEjErT2haEVvR2iMz/HJU0PgJIM+Nftk2kygcePGXLp0iYkTJ/7/ts2bN7NixYpM2X+OIQJNhkODJwj/eTaTKcETv8cyKb4Q4Q2Hezs6Y3ySzyaCzCYifPHFF3z//fdUqFCBsLAwBg8ezC233OLt0LIeEWj2OlRrQ/ie5fQ5c4bw43udzmPGGI/zyVND7lKyZElmz57t7TCyBxEocSvsmAcoJMbDgRVuaRRkjEmdzQiM94Te4zShF3+nd3S5u70dkTE+yWYExnv+7B19YIWTBGw2YIxXuHVGICLNRWSXiOwRkWTrBYhIZxHZLiLbRGTmjR4rO/RVyA4xepwbe0cbY9LGbTMCEfEHxgFNgRhgvYjMU9XtV4ypBAwG7lTVMyJS7EaOFRQUxKlTpwgODk65paKXqSqnTp0iKCjI26EYY8xV3HlqqB6wR1X3AYjILKANsP2KMX2Bcap6BkBVb6hpfUhICDExMZw4kbXbHQcFBRESEuLtMIwx5iruTASlgENXPI4Bbr9mTGUAEVmJ09d4hKouvHZHIvIY8BhAmTJl/nagwMBAQkNDMydqY4zxMd6+aygAqAQ0ArriNLK/6dpBqjpRVSNUNeLmm2/2cIjGGJOzuTMRHAZKX/E4xLXtSjHAPFWNV9X9wG6cxGCMMcZD3JkI1gOVRCRURHIBXYB514z5Emc2gIgUxTlVtM+NMRljjLmG264RqGqCiDwBLMI5//+hqm4TkVeBDao6z/VcMxHZDiQCz6nqqdT2GxUVdVJEDrorbg8pCpz0dhBZiL0fV7P34y/2XlwtI+9H2ZSeELu33fNEZIOqRng7jqzC3o+r2fvxF3svruau98PbF4uNMcZ4mSUCY4zxcZYIvGPi9Yf4FHs/rmbvx1/svbiaW94Pu0ZgjDE+zmYExhjj4ywRGGOMj7NE4EEiUlpEll1Rdvspb8fkbSLiLyI/i8h8b8fibSJyk4jMEZGdIrJDRBp4OyZvEpFnXD8nW0UkUkR8qnSviHwoIsdFZOsV24qIyHci8ovr78KZcSxLBJ6VAAxS1epAfWCAiFT3ckze9hSww9tBZBHvAQtVtSpQCx9+X0SkFDAQiFDVGjiLUrt4NyqPmwo0v2bbi8ASVa0ELHE9zjBLBB6kqr+p6kbX1+dwftBLeTcq7xGREKAlMNnbsXibiBQC7gGmAKjqZVX93btReV0AkEdEAoC8wBEvx+NRqrocOH3N5jbANNfX04C2mXEsSwReIiLlgNrAWu9G4lVjgOeBJG8HkgWEAieAj1ynyiaLSD5vB+UtqnoYeAv4FfgNOKuqi70bVZZQXFV/c319FCieGTu1ROAFIpIf+Bx4WlVjvR2PN4jIg8BxVY3ydixZRABQBxivqrWBP8ikaX925Dr33QYnQZYE8olIN+9GlbWoc+9/ptz/b4nAw0QkECcJzFDVud6Ox4vuBFqLyAFgFtBYRD7xbkheFQPEqOqfM8Q5OInBV90H7FfVE6oaD8wF7vByTFnBMREpAeD6+4a6Ol7LEoEHidNQeQqwQ1Xf8XY83qSqg1U1RFXL4VzhZt5kAAACF0lEQVQEXKqqPvuJT1WPAodEpIprUxOubuvqa34F6otIXtfPTRN8+OL5FeYBPV1f9wS+yoydWiLwrDuB7jiffqNdf1p4OyiTZTwJzBCRzUA48IaX4/Ea18xoDrAR2ILzu8qnyk2ISCSwGqgiIjEi8igwCmgqIr/gzJpGZcqxrMSEMcb4NpsRGGOMj7NEYIwxPs4SgTHG+DhLBMYY4+MsERiTyURkgIhU8HYcxqSVJQJj0khE9MpFbyISICInrqycKiIDcOrDDPPlEhEmewnwdgDGZCN/ADVEJI+qXgSaAoevHKCq41xfRno6OGNulM0IjEmfb3AqpgJ05Ypf+CKSz1VDfp2rcFwb1/Yw17ZoEdksIpW8ELcxKbJEYEz6zAK6uJqk3MrV1WOH4JTKqAfcC4x2nR7qB7ynquFABE5dIWOyDDs1ZEw6qOpmVwnxrjizgys1wymk96zrcRBQBqdMwBBX/4W5qvqLh8I1Jk0sERiTfvNwauU3AoKv2C5AB1Xddc34HSKyFueU0jci8riqLvVIpMakgZ0aMib9PgReUdUt12xfBDzpqpaJiNR2/V0e2KeqY3GqRd7qyWCNuR5LBMakk6rGuH6pX+s1IBDYLCLbXI8BOgNbRSQaqAF87JlIjUkbqz5qjDE+zmYExhjj4ywRGGOMj7NEYIwxPs4SgTHG+DhLBMYY4+MsERhjjI+zRGCMMT7u/wAS8PX8+GcnbwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pontos importante que podem ser observados:\n",
        "\n",
        "* Embora a frequência e operação de passageiros pagos foi decrescente de janeiro a abril, a operação de carga se manteve crescente.\n",
        "\n",
        "* Em abril de 2021, todas as empresas tiveram um recuo na sua operação doméstica de cargas como na de passageiros. Isso pode ser explicado pelo aumento de casos de Covid-19 com pico em meados de março de 2021.\n",
        "\n",
        "* Após abril, a operação de passageiros e carga ganha força novamente e se manteve em tendencia crescente.\n",
        "\n",
        "* Com relação a empresa **B**, podemos ver que sua operação de cargas foi muito menor em comparação com as demais empresas."
      ],
      "metadata": {
        "id": "G9wYOaH1JfKw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Rotas voadas"
      ],
      "metadata": {
        "id": "M9bLQXcQA5sn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Query para identicar as rotas operadas por cada uma das três empresas."
      ],
      "metadata": {
        "id": "T0NfT7Z0TTSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Rotas únicas\n",
        "rotas_a = df[df.empresa == 'A'].rota.unique()\n",
        "rotas_b = df[df.empresa == 'B'].rota.unique()\n",
        "rotas_c = df[df.empresa == 'C'].rota.unique()\n",
        "\n",
        "# Número de rotas únicas\n",
        "num_rotas_a = rotas_a.size\n",
        "num_rotas_b = rotas_b.size\n",
        "num_rotas_c = rotas_c.size"
      ],
      "metadata": {
        "id": "GWq1a-QxMoU2"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quantidade de rotas:"
      ],
      "metadata": {
        "id": "AKDHX57zTe1n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Empresa A: ' + str(num_rotas_a) + ' rotas')\n",
        "print('Empresa B: ' + str(num_rotas_b) + ' rotas')\n",
        "print('Empresa C: ' + str(num_rotas_c) + ' rotas')"
      ],
      "metadata": {
        "id": "_euBe00JTlJv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9870530-8e4a-4f00-9b51-35eb6eae1339"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empresa A: 783 rotas\n",
            "Empresa B: 381 rotas\n",
            "Empresa C: 297 rotas\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando as rotas voadas, podemos começar a inferir que a baixa participação no mercado de cargas da empresa **B** pode ser devido ao número de rotas operadas quando comparado com as demais empresas."
      ],
      "metadata": {
        "id": "gZLNopFOL_vy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fluxo ou taxa percentual de carga paga por rota"
      ],
      "metadata": {
        "id": "gqG5_Kc_8oou"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisar o fluxo de carga transportada por rota permite identificar a estratégia das empresas com relação aos aeroportos em que os voos transportam mais cargas.\n",
        "\n",
        "O fluxo ou taxa percentual de carga paga em uma rota será calculado da seguinte forma:\n",
        "\n",
        "$$\n",
        "fluxo\\_carga\\_percentual_{rota} = \\frac{\\sum carga\\_paga_{rota}}{\\sum carga\\_paga_{todas\\ rotas}}\n",
        "$$\n",
        "\n",
        "\n",
        "A query a seguir consolida os dados para avaliação do fluxo de carga paga:"
      ],
      "metadata": {
        "id": "x3ruJO8Nfx5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Queries\n",
        "fluxo_carga_a = df[df.empresa == 'A'].groupby(by='rota').sum().sort_values('carga_paga_kg', ascending=False).reset_index()\n",
        "fluxo_carga_b = df[df.empresa == 'B'].groupby(by='rota').sum().sort_values('carga_paga_kg', ascending=False).reset_index()\n",
        "fluxo_carga_c = df[df.empresa == 'C'].groupby(by='rota').sum().sort_values('carga_paga_kg', ascending=False).reset_index()\n",
        "\n",
        "# Adição das colunas de fluxo_carga_percentual\n",
        "fluxo_carga_a['fluxo_carga_percentual'] = round(fluxo_carga_a.carga_paga_kg / fluxo_carga_a.carga_paga_kg.sum() * 100, 1)\n",
        "fluxo_carga_b['fluxo_carga_percentual'] = round(fluxo_carga_b.carga_paga_kg / fluxo_carga_b.carga_paga_kg.sum() * 100, 1)\n",
        "fluxo_carga_c['fluxo_carga_percentual'] = round(fluxo_carga_c.carga_paga_kg / fluxo_carga_c.carga_paga_kg.sum() * 100, 1)"
      ],
      "metadata": {
        "id": "iU56Ldaw71_w"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando a tabela preliminar da query para a empresa A:\n",
        "\n"
      ],
      "metadata": {
        "id": "yOMzmIYRT1_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fluxo_carga_a.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "NeUiSwhL523H",
        "outputId": "092ff66a-3c1b-4de6-ab19-473912e264c3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     rota    ano  mês  passageiros_pagos  carga_paga_kg  decolagens  \\\n",
              "0  MAOVCP  40420  110           166285.0     16211778.0      1279.0   \n",
              "1  VCPMAO  40420  110           150758.0     14434231.0      1106.0   \n",
              "2  VCPREC  36378   99           236349.0      8345222.0      1275.0   \n",
              "3  VCPBEL  38399  101            96093.0      3058698.0       730.0   \n",
              "4  RECVCP  40420  110           246021.0      2641721.0      1239.0   \n",
              "\n",
              "   carga_por_decolagem  pax_por_decolagem  fluxo_carga_percentual  \n",
              "0        206325.627653        1898.035132                    14.3  \n",
              "1        197379.650775        1764.216835                    12.7  \n",
              "2         96076.698416        2516.242075                     7.3  \n",
              "3         98120.962472        1448.588364                     2.7  \n",
              "4         28179.802923        2946.798843                     2.3  "
            ],
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              "\n",
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              "      <td>14.3</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VCPMAO</td>\n",
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              "      <td>110</td>\n",
              "      <td>150758.0</td>\n",
              "      <td>14434231.0</td>\n",
              "      <td>1106.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>VCPREC</td>\n",
              "      <td>36378</td>\n",
              "      <td>99</td>\n",
              "      <td>236349.0</td>\n",
              "      <td>8345222.0</td>\n",
              "      <td>1275.0</td>\n",
              "      <td>96076.698416</td>\n",
              "      <td>2516.242075</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VCPBEL</td>\n",
              "      <td>38399</td>\n",
              "      <td>101</td>\n",
              "      <td>96093.0</td>\n",
              "      <td>3058698.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>98120.962472</td>\n",
              "      <td>1448.588364</td>\n",
              "      <td>2.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RECVCP</td>\n",
              "      <td>40420</td>\n",
              "      <td>110</td>\n",
              "      <td>246021.0</td>\n",
              "      <td>2641721.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>28179.802923</td>\n",
              "      <td>2946.798843</td>\n",
              "      <td>2.3</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para considerar apenas as rotas em que houve carga paga transportada:"
      ],
      "metadata": {
        "id": "sAL1uMMfgLXP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fluxo_carga_a = fluxo_carga_a[fluxo_carga_a.carga_paga_kg > 0]"
      ],
      "metadata": {
        "id": "VY2GAf4OCwz0"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fluxo_carga_b = fluxo_carga_b[fluxo_carga_b.carga_paga_kg > 0]"
      ],
      "metadata": {
        "id": "k2avvQ0GVfSV"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fluxo_carga_c = fluxo_carga_c[fluxo_carga_c.carga_paga_kg > 0]"
      ],
      "metadata": {
        "id": "1qxDEBjRVnG4"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora podemos avaliar as 10 rotas com maior fluxo de carga paga na operação de cada empresa e ter uma ideia de como foi a estratégia adotada."
      ],
      "metadata": {
        "id": "xFeeDvmUYdQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa A\n",
        "fluxo_carga_a[['rota', 'passageiros_pagos', 'carga_paga_kg', 'decolagens', 'fluxo_carga_percentual']].head(10)                                                                                                         \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "qyssSUVlIiAZ",
        "outputId": "bcc4aabf-40d8-49a5-bd03-6536d9e8bdd6"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     rota  passageiros_pagos  carga_paga_kg  decolagens  \\\n",
              "0  MAOVCP           166285.0     16211778.0      1279.0   \n",
              "1  VCPMAO           150758.0     14434231.0      1106.0   \n",
              "2  VCPREC           236349.0      8345222.0      1275.0   \n",
              "3  VCPBEL            96093.0      3058698.0       730.0   \n",
              "4  RECVCP           246021.0      2641721.0      1239.0   \n",
              "5  POAVCP           262697.0      2530391.0      2198.0   \n",
              "6  VIXVCP           122115.0      2242683.0      1475.0   \n",
              "7  BELMAO            54233.0      2142069.0       507.0   \n",
              "8  VCPVIX           118512.0      2114304.0      1482.0   \n",
              "9  GRUREC           205388.0      2042882.0      1403.0   \n",
              "\n",
              "   fluxo_carga_percentual  \n",
              "0                    14.3  \n",
              "1                    12.7  \n",
              "2                     7.3  \n",
              "3                     2.7  \n",
              "4                     2.3  \n",
              "5                     2.2  \n",
              "6                     2.0  \n",
              "7                     1.9  \n",
              "8                     1.9  \n",
              "9                     1.8  "
            ],
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              "      <td>14.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VCPMAO</td>\n",
              "      <td>150758.0</td>\n",
              "      <td>14434231.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>12.7</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VCPREC</td>\n",
              "      <td>236349.0</td>\n",
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              "      <td>1275.0</td>\n",
              "      <td>7.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VCPBEL</td>\n",
              "      <td>96093.0</td>\n",
              "      <td>3058698.0</td>\n",
              "      <td>730.0</td>\n",
              "      <td>2.7</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RECVCP</td>\n",
              "      <td>246021.0</td>\n",
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              "      <td>1239.0</td>\n",
              "      <td>2.3</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>POAVCP</td>\n",
              "      <td>262697.0</td>\n",
              "      <td>2530391.0</td>\n",
              "      <td>2198.0</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>VIXVCP</td>\n",
              "      <td>122115.0</td>\n",
              "      <td>2242683.0</td>\n",
              "      <td>1475.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BELMAO</td>\n",
              "      <td>54233.0</td>\n",
              "      <td>2142069.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>VCPVIX</td>\n",
              "      <td>118512.0</td>\n",
              "      <td>2114304.0</td>\n",
              "      <td>1482.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GRUREC</td>\n",
              "      <td>205388.0</td>\n",
              "      <td>2042882.0</td>\n",
              "      <td>1403.0</td>\n",
              "      <td>1.8</td>\n",
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              "          document.querySelector('#df-659f05c0-2f4e-4c1f-9161-4c1d4c70d081 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-659f05c0-2f4e-4c1f-9161-4c1d4c70d081');\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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa B\n",
        "fluxo_carga_b[['rota', 'passageiros_pagos', 'carga_paga_kg', 'decolagens', 'fluxo_carga_percentual']].head(10)"
      ],
      "metadata": {
        "id": "bHYo5DdbDVEU",
        "outputId": "d92bf3c5-c467-4f7f-d0fa-dc35922fa605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     rota  passageiros_pagos  carga_paga_kg  decolagens  \\\n",
              "0  GRUBSB           163972.0      1082828.0      1189.0   \n",
              "1  CGHBSB           139755.0       928132.0      1088.0   \n",
              "2  GRUREC           166898.0       760252.0      1030.0   \n",
              "3  GRUFOR           153410.0       729613.0       977.0   \n",
              "4  GRUSSA           160543.0       715733.0      1076.0   \n",
              "5  BSBGRU           167116.0       510837.0      1183.0   \n",
              "6  BSBMAO            50952.0       500154.0       352.0   \n",
              "7  FORGRU           158904.0       469914.0       974.0   \n",
              "8  RECGRU           174106.0       450201.0      1033.0   \n",
              "9  FORMAO            52483.0       429344.0       348.0   \n",
              "\n",
              "   fluxo_carga_percentual  \n",
              "0                     3.5  \n",
              "1                     3.0  \n",
              "2                     2.4  \n",
              "3                     2.4  \n",
              "4                     2.3  \n",
              "5                     1.6  \n",
              "6                     1.6  \n",
              "7                     1.5  \n",
              "8                     1.5  \n",
              "9                     1.4  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a73d858-f44b-486c-9354-d80fff2e77d2\">\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>rota</th>\n",
              "      <th>passageiros_pagos</th>\n",
              "      <th>carga_paga_kg</th>\n",
              "      <th>decolagens</th>\n",
              "      <th>fluxo_carga_percentual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GRUBSB</td>\n",
              "      <td>163972.0</td>\n",
              "      <td>1082828.0</td>\n",
              "      <td>1189.0</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CGHBSB</td>\n",
              "      <td>139755.0</td>\n",
              "      <td>928132.0</td>\n",
              "      <td>1088.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GRUREC</td>\n",
              "      <td>166898.0</td>\n",
              "      <td>760252.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GRUFOR</td>\n",
              "      <td>153410.0</td>\n",
              "      <td>729613.0</td>\n",
              "      <td>977.0</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GRUSSA</td>\n",
              "      <td>160543.0</td>\n",
              "      <td>715733.0</td>\n",
              "      <td>1076.0</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>BSBGRU</td>\n",
              "      <td>167116.0</td>\n",
              "      <td>510837.0</td>\n",
              "      <td>1183.0</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BSBMAO</td>\n",
              "      <td>50952.0</td>\n",
              "      <td>500154.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>FORGRU</td>\n",
              "      <td>158904.0</td>\n",
              "      <td>469914.0</td>\n",
              "      <td>974.0</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RECGRU</td>\n",
              "      <td>174106.0</td>\n",
              "      <td>450201.0</td>\n",
              "      <td>1033.0</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>FORMAO</td>\n",
              "      <td>52483.0</td>\n",
              "      <td>429344.0</td>\n",
              "      <td>348.0</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a73d858-f44b-486c-9354-d80fff2e77d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6a73d858-f44b-486c-9354-d80fff2e77d2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6a73d858-f44b-486c-9354-d80fff2e77d2');\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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa C\n",
        "fluxo_carga_c[['rota', 'passageiros_pagos', 'carga_paga_kg', 'decolagens', 'fluxo_carga_percentual']].head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "tqgK0a4hqFPi",
        "outputId": "a3c5225b-f05c-4594-dfed-7264de82ae1e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     rota  passageiros_pagos  carga_paga_kg  decolagens  \\\n",
              "0  GRUMAO           133602.0     17256094.0      1168.0   \n",
              "1  MAOGRU           140302.0     16280701.0      1130.0   \n",
              "2  FORGRU           225953.0      3614836.0      1392.0   \n",
              "3  RECGRU           211268.0      2937169.0      1239.0   \n",
              "4  GRUSSA           223973.0      2477669.0      1464.0   \n",
              "5  GRUREC           206398.0      2451814.0      1236.0   \n",
              "6  GRUFOR           222341.0      2366190.0      1393.0   \n",
              "7  VCPMAO                0.0      1943914.0        48.0   \n",
              "8  GRUBSB           195689.0      1895946.0      1415.0   \n",
              "9  SSAGRU           229701.0      1658147.0      1444.0   \n",
              "\n",
              "   fluxo_carga_percentual  \n",
              "0                    16.3  \n",
              "1                    15.4  \n",
              "2                     3.4  \n",
              "3                     2.8  \n",
              "4                     2.3  \n",
              "5                     2.3  \n",
              "6                     2.2  \n",
              "7                     1.8  \n",
              "8                     1.8  \n",
              "9                     1.6  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0455a624-000d-41a6-a9e3-17884d9fe083\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rota</th>\n",
              "      <th>passageiros_pagos</th>\n",
              "      <th>carga_paga_kg</th>\n",
              "      <th>decolagens</th>\n",
              "      <th>fluxo_carga_percentual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GRUMAO</td>\n",
              "      <td>133602.0</td>\n",
              "      <td>17256094.0</td>\n",
              "      <td>1168.0</td>\n",
              "      <td>16.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MAOGRU</td>\n",
              "      <td>140302.0</td>\n",
              "      <td>16280701.0</td>\n",
              "      <td>1130.0</td>\n",
              "      <td>15.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FORGRU</td>\n",
              "      <td>225953.0</td>\n",
              "      <td>3614836.0</td>\n",
              "      <td>1392.0</td>\n",
              "      <td>3.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RECGRU</td>\n",
              "      <td>211268.0</td>\n",
              "      <td>2937169.0</td>\n",
              "      <td>1239.0</td>\n",
              "      <td>2.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>GRUSSA</td>\n",
              "      <td>223973.0</td>\n",
              "      <td>2477669.0</td>\n",
              "      <td>1464.0</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GRUREC</td>\n",
              "      <td>206398.0</td>\n",
              "      <td>2451814.0</td>\n",
              "      <td>1236.0</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>GRUFOR</td>\n",
              "      <td>222341.0</td>\n",
              "      <td>2366190.0</td>\n",
              "      <td>1393.0</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>VCPMAO</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1943914.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GRUBSB</td>\n",
              "      <td>195689.0</td>\n",
              "      <td>1895946.0</td>\n",
              "      <td>1415.0</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SSAGRU</td>\n",
              "      <td>229701.0</td>\n",
              "      <td>1658147.0</td>\n",
              "      <td>1444.0</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0455a624-000d-41a6-a9e3-17884d9fe083');\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": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos observar que na operação da **empresa A**, as rotas **MAO-VCP** e **VCP-MAO** representam 27% do seu fluxo total de carga paga sendo as demais rotas menos do que 8%.\n",
        "\n",
        "A **empresa C** também opera de forma similar, com as rotas **GRU-MAO** e **MAO-GRU** representando 31,7% do seu fluxo total de carga paga e as demais rotas representando menos do que 4%.\n",
        "\n",
        "Com base nesse dado, podemos verificar que essa duas empresas tem uma estratégia de cargas entre os maiores aeroportos hub do Brasil para entrada e saída de cargas, que são GRU, VCP e MAO.\n",
        "\n",
        "Já a **empresa B** apresenta um fluxo mais homogêneo, com valor máximo de 3,5% na rota GRU-BSB."
      ],
      "metadata": {
        "id": "eINnYcpMgm2h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribuição estatística mensal"
      ],
      "metadata": {
        "id": "wvbOPaa_Og2V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Essa parte da análise permite entender como está o aproveitamento de transporte de carga por voo. Para isso, vamos definir os dados para análise da distribuição estatística de carga paga transportada por voo em cada mês pelas seguintes queries:"
      ],
      "metadata": {
        "id": "Y_HCjnS59600"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa A\n",
        "carga_por_decolagem_a = []\n",
        "for i in range(1, 11):\n",
        "    series = df[(df['mês'] == i) & (df.carga_paga_kg > 0) & (df['empresa'] == 'A')].carga_por_decolagem\n",
        "    carga_por_decolagem_a.append(series)\n",
        "\n",
        "# Empresa B\n",
        "carga_por_decolagem_b = []\n",
        "for i in range(1, 11):\n",
        "    series = df[(df['mês'] == i) & (df.carga_paga_kg > 0) & (df['empresa'] == 'B')].carga_por_decolagem\n",
        "    carga_por_decolagem_b.append(series)\n",
        "\n",
        "# Empresa C\n",
        "carga_por_decolagem_c = []\n",
        "for i in range(1, 11):\n",
        "    series = df[(df['mês'] == i) & (df.carga_paga_kg > 0) & (df['empresa'] == 'C')].carga_por_decolagem\n",
        "    carga_por_decolagem_c.append(series)"
      ],
      "metadata": {
        "id": "ZoD2JZ6ykxnk"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consolidando os valores de médias:"
      ],
      "metadata": {
        "id": "9DBkF72ZKiqg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "carga_media_a = []\n",
        "carga_media_b = []\n",
        "carga_media_c = []\n",
        "\n",
        "for mes in range(10):\n",
        "    carga_media_a.append(round(carga_por_decolagem_a[mes].mean(), 1))\n",
        "\n",
        "for mes in range(10):\n",
        "    carga_media_b.append(round(carga_por_decolagem_b[mes].mean(), 1))\n",
        "\n",
        "for mes in range(10):\n",
        "    carga_media_c.append(round(carga_por_decolagem_c[mes].mean(), 1))"
      ],
      "metadata": {
        "id": "djz8dBHgXbHK"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "medias_carga_df = pd.DataFrame({'mes': range(1, 11),\n",
        "                                'kg_por_voo_a': pd.Series(carga_media_a),\n",
        "                                'kg_por_voo_b': pd.Series(carga_media_b),\n",
        "                                'kg_por_voo_c': pd.Series(carga_media_c),})"
      ],
      "metadata": {
        "id": "-dcANbV_lpk4"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora temos o resultado que buscamos. Médias mensais de carga transportada por voo de cada empresa."
      ],
      "metadata": {
        "id": "r4Q7xV5VKU_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medias_carga_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "kOdP8WZMmpOI",
        "outputId": "c0ff92cb-e9cd-4ee7-f2f4-dd1a94dcaa16"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mes  kg_por_voo_a  kg_por_voo_b  kg_por_voo_c\n",
              "0    1         518.7         200.9        1732.2\n",
              "1    2         498.6         323.2        1544.7\n",
              "2    3         606.5         447.4        2158.8\n",
              "3    4         733.0         474.4        2651.3\n",
              "4    5         830.6         386.3        1854.3\n",
              "5    6         535.6         343.7        2304.9\n",
              "6    7         474.0         262.7        1673.1\n",
              "7    8         525.3         310.5        1500.5\n",
              "8    9         432.5         310.8         980.2\n",
              "9   10         518.9         323.5        1651.7"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>mes</th>\n",
              "      <th>kg_por_voo_a</th>\n",
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              "      <th>kg_por_voo_c</th>\n",
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              "      <td>200.9</td>\n",
              "      <td>1732.2</td>\n",
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              "      <th>1</th>\n",
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              "      <td>498.6</td>\n",
              "      <td>323.2</td>\n",
              "      <td>1544.7</td>\n",
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              "      <th>2</th>\n",
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              "      <td>2158.8</td>\n",
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              "      <th>3</th>\n",
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              "      <td>474.4</td>\n",
              "      <td>2651.3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>830.6</td>\n",
              "      <td>386.3</td>\n",
              "      <td>1854.3</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>535.6</td>\n",
              "      <td>343.7</td>\n",
              "      <td>2304.9</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>474.0</td>\n",
              "      <td>262.7</td>\n",
              "      <td>1673.1</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>525.3</td>\n",
              "      <td>310.5</td>\n",
              "      <td>1500.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>432.5</td>\n",
              "      <td>310.8</td>\n",
              "      <td>980.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>518.9</td>\n",
              "      <td>323.5</td>\n",
              "      <td>1651.7</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "Verificação dos dados de estatística descritiva sobre as médias mensais de carga transportada por voo."
      ],
      "metadata": {
        "id": "jIiC_A9wKeWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medias_carga_df[['kg_por_voo_a', 'kg_por_voo_b', 'kg_por_voo_c']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yiO-fGflpFIG",
        "outputId": "1c59c3aa-3c1f-4f7e-b97f-d971ddff0046"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       kg_por_voo_a  kg_por_voo_b  kg_por_voo_c\n",
              "count     10.000000     10.000000      10.00000\n",
              "mean     567.370000    338.340000    1805.17000\n",
              "std      123.553408     81.192107     469.12425\n",
              "min      432.500000    200.900000     980.20000\n",
              "25%      503.625000    310.575000    1571.45000\n",
              "50%      522.100000    323.350000    1702.65000\n",
              "75%      588.775000    375.650000    2082.67500\n",
              "max      830.600000    474.400000    2651.30000"
            ],
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              "      <th></th>\n",
              "      <th>kg_por_voo_a</th>\n",
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              "      <th>kg_por_voo_c</th>\n",
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              "      <td>10.000000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>338.340000</td>\n",
              "      <td>1805.17000</td>\n",
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              "      <th>std</th>\n",
              "      <td>123.553408</td>\n",
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              "      <th>min</th>\n",
              "      <td>432.500000</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-745021af-e987-4928-9553-e4f370230f9a');\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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualização dos histogramas:"
      ],
      "metadata": {
        "id": "fvk9KN_q-GXw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa A\n",
        "fig, ax = plt.subplots(2, 5, figsize=(20, 8))\n",
        "plt.subplots_adjust(hspace=0.2)\n",
        "fig.suptitle('Distribuição de carga paga por decolagens - Empresa A')\n",
        "x_axis = ['jan', 'fev', 'mar', 'abr', 'mai', 'jun', 'jul', 'ago', 'set', 'out']\n",
        "ax = ax.ravel()\n",
        "for i, axi in enumerate(ax):\n",
        "    axi.hist(carga_por_decolagem_a[i])\n",
        "    axi.set_xlabel(x_axis[i])\n",
        "\n",
        "ax[0].set_ylabel('frequência')\n",
        "ax[5].set_ylabel('frequência')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "f139i1xynwCN",
        "outputId": "afaa2baf-e04c-4476-c0f1-6a918f829152"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa B\n",
        "fig, ax = plt.subplots(2, 5, figsize=(20, 8))\n",
        "plt.subplots_adjust(hspace=0.2)\n",
        "fig.suptitle('Distribuição de carga paga por decolagens - Empresa B')\n",
        "# x_axis = ['jan', 'fev', 'mar', 'abr', 'mai', 'jun', 'jul', 'ago', 'set', 'out']\n",
        "ax = ax.ravel()\n",
        "for i, axi in enumerate(ax):\n",
        "    axi.hist(carga_por_decolagem_b[i])\n",
        "    axi.set_xlabel(x_axis[i])\n",
        "\n",
        "ax[0].set_ylabel('frequência')\n",
        "ax[5].set_ylabel('frequência')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "9kfHkTfx8Ru-",
        "outputId": "ddb3d376-86ec-4ff2-ad8c-ca33f374dc44"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Empresa C\n",
        "fig, ax = plt.subplots(2, 5, figsize=(20, 8))\n",
        "plt.subplots_adjust(hspace=0.2)\n",
        "fig.suptitle('Distribuição de carga paga por decolagens - Empresa C')\n",
        "# x_axis = ['jan', 'fev', 'mar', 'abr', 'mai', 'jun', 'jul', 'ago', 'set', 'out']\n",
        "ax = ax.ravel()\n",
        "for i, axi in enumerate(ax):\n",
        "    axi.hist(carga_por_decolagem_c[i])\n",
        "    axi.set_xlabel(x_axis[i])\n",
        "\n",
        "ax[0].set_ylabel('frequência')\n",
        "ax[5].set_ylabel('frequência')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "bN3B3_eu8u7M",
        "outputId": "8edd78ab-106d-4275-ae8b-c9624d1190f1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Insights\n",
        "\n",
        "Com base nos dados, podemos identificar as seguintes variáveis estratégicas:\n",
        "- Aeroportos principais para cargas (GRU, MAO, VCP).\n",
        "- Frequência de voos entre os aeroportos (GRU, MAO e VCP).\n",
        "- Taxa de carga transportada por voo."
      ],
      "metadata": {
        "id": "zEI6aErfsosw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plano de ação para a empresa B\n",
        "Aumentar disponibilidade de voos para Manaus (MAO)\n",
        "\n",
        "**Rotas recomendadas:**\n",
        "- GRU-MAO\n",
        "- MAO-GRU\n",
        "- BSB-MAO\n",
        "- MAO-BSB\n",
        "\n",
        "\n",
        "**Complementar:**\n",
        "\n",
        "Estabelecer parceria com empresas de logística para aumentar a taxa de carga por voo."
      ],
      "metadata": {
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}