Profissao_Analista_de_dados_M27_Exercicio.ipynb

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        "<img src=\"https://raw.githubusercontent.com/andre-marcos-perez/ebac-course-utils/main/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
        "\n",
        "---\n",
        "\n",
        "# **Módulo** | Análise de Dados: Visualização Interativa de Dados\n",
        "Caderno de **Exercícios**<br>\n",
        "Professor [André Perez](https://www.linkedin.com/in/andremarcosperez/)\n",
        "<br>\n",
        "Elaborado por [Douglas Rizzi](https://www.linkedin.com/in/douglas-rizzi/)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9jDtUbDOE1-"
      },
      "source": [
        "# **Tópicos**\n",
        "\n",
        "<ol type=\"1\">\n",
        "  <li>Google Data Studio;</li>\n",
        "  <li>Dados;</li>\n",
        "  <li>Relatório.</li>\n",
        "</ol>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmoHgt-lwkpD"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# **Exercícios**"
      ]
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      "source": [
        "## 1\\. Contexto"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Você é o analista de dados de um grande aeroporto. O time de operações precisa acompanhar o fluxo mensal de passageiros para tomar decisões sobre manutenção, obras, etc. Você propõe a construção de um *dashboard* de dados para auxiliar o time na tomada de decisão."
      ],
      "metadata": {
        "id": "kbJ_AVnqhD6F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2\\. Preparação"
      ],
      "metadata": {
        "id": "Os54EG52g8BN"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCQi-s0Hpd5V"
      },
      "source": [
        "Carregue as bibliotecas Python de manipulação de dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lblH8kIXzPZZ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1eLSuCbi40u"
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      "source": [
        "### **2.1. Extração**"
      ]
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      "source": [
        "Carregue os dados de **voos** (*flights*) e responda as perguntas abaixo:"
      ]
    },
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      "source": [
        "flights = sns.load_dataset('flights')"
      ],
      "execution_count": 2,
      "outputs": []
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        "flights.head()"
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              "   year month  passengers\n",
              "0  1949   Jan         112\n",
              "1  1949   Feb         118\n",
              "2  1949   Mar         132\n",
              "3  1949   Apr         129\n",
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              "variable_name": "flights",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Nov\",\n          \"Oct\",\n          \"Jan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119,\n        \"min\": 104,\n        \"max\": 622,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293,\n          340,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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              "     year month  passengers\n",
              "139  1960   Aug         606\n",
              "140  1960   Sep         508\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-7e6f759d-37b2-4ac7-87de-ffb802f1de1b 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-7e6f759d-37b2-4ac7-87de-ffb802f1de1b');\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",
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              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
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              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
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              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0f8a9601-6d80-4e7f-8613-f1cb0ea93a28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1960,\n        \"max\": 1960,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1960\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Sep\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82,\n        \"min\": 390,\n        \"max\": 606,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          508\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oExRT3lck2qx"
      },
      "source": [
        "Responda:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMC0xBa0kSFZ"
      },
      "source": [
        " - Qual é a granularidade temporal da base de dados: **?**\n",
        " - Qual é o intervalo de tempo (min/max): **?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# criando um coluna para calcular a granularidade\n",
        "flights['ano-mes'] = flights['year'].astype(str) + '-' + flights['month'].astype(str).str.zfill(2)\n",
        "flights['ano-mes'] = pd.to_datetime(flights['ano-mes'], format='%Y-%b')\n",
        "gran_voo = flights.set_index(keys=['ano-mes'])\n",
        "granularidade = pd.infer_freq(gran_voo.index)\n",
        "print(f'A granularidade desse dataset é {granularidade}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSXLxIiU5ToO",
        "outputId": "1d9be675-c24b-40ac-c212-cc8dc8ec0162"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A granularidade desse dataset é MS\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "intervalo = (flights['ano-mes'].max()-flights['ano-mes'].min())\n",
        "print(f'O intervalo total de tempo é de {flights.index.max()} meses, sendo um total de {intervalo.days} dias ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxKgVb8E9uIo",
        "outputId": "96105d18-1d51-4604-c1d5-27bdda3e2dbc"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O intervalo total de tempo é de 143 meses, sendo um total de 4352 dias \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ln5XPZzijGVO"
      },
      "source": [
        "### **2.2. Transformação**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tranforme o texto da coluna `month` para sua representação numérica, exemplo: `Jan` para `1` e `Dec` para `12`."
      ],
      "metadata": {
        "id": "tdEeJbwBCuhS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# criando um df com um dict dos meses para recolocar\n",
        "df_mes = pd.DataFrame({'mes':\n",
        " ['Jan','Fev','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']})\n",
        "flights['month'] = flights['month'].replace(\n",
        "    {'Jan': '01','Feb': '02','Mar': '03','Apr': '04',\n",
        "     'May': '05','Jun': '06','Jul': '07','Aug': '08',\n",
        "     'Sep': '09','Oct': '10', 'Nov': '11', 'Dec': '12'})"
      ],
      "metadata": {
        "id": "HzE9oBQPDCYS"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flights.head(12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "fswXvrLoRanz",
        "outputId": "bb956798-93dc-44e9-e9b1-f2ab53462644"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year month  passengers    ano-mes\n",
              "0   1949    01         112 1949-01-01\n",
              "1   1949    02         118 1949-02-01\n",
              "2   1949    03         132 1949-03-01\n",
              "3   1949    04         129 1949-04-01\n",
              "4   1949    05         121 1949-05-01\n",
              "5   1949    06         135 1949-06-01\n",
              "6   1949    07         148 1949-07-01\n",
              "7   1949    08         148 1949-08-01\n",
              "8   1949    09         136 1949-09-01\n",
              "9   1949    10         119 1949-10-01\n",
              "10  1949    11         104 1949-11-01\n",
              "11  1949    12         118 1949-12-01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e213244-9bd1-41cd-99f8-19bd7e0be108\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "      margin-bottom: 4px;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
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              "        document.querySelector('#df-2e213244-9bd1-41cd-99f8-19bd7e0be108 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e213244-9bd1-41cd-99f8-19bd7e0be108');\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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bec2d582-d25c-4812-858a-d399c4f65ccd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "flights",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"11\",\n          \"10\",\n          \"01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119,\n        \"min\": 104,\n        \"max\": 622,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293,\n          340,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ano-mes\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1949-01-01 00:00:00\",\n        \"max\": \"1960-12-01 00:00:00\",\n        \"num_unique_values\": 144,\n        \"samples\": [\n          \"1958-10-01 00:00:00\",\n          \"1950-08-01 00:00:00\",\n          \"1955-11-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crie uma chave temporal `year-month` no formato `YYYY-MM` através da concatenação das colunas `year` e `month`, exemplo: `1949-01`."
      ],
      "metadata": {
        "id": "sGQpJX1g_-mx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flights['year-month'] = flights['year'].astype(str) + '-' + (flights['month'].astype(str)).apply(lambda x:'{:02}'.format(x))\n",
        "flights.drop('ano-mes',axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "jQuaU8f3WqNh"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Bbn5o6p3Web7",
        "outputId": "664dc5b2-73ab-46ad-96a7-69ae9716acda"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     year month  passengers year-month\n",
              "0    1949    01         112    1949-01\n",
              "1    1949    02         118    1949-02\n",
              "2    1949    03         132    1949-03\n",
              "3    1949    04         129    1949-04\n",
              "4    1949    05         121    1949-05\n",
              "..    ...   ...         ...        ...\n",
              "139  1960    08         606    1960-08\n",
              "140  1960    09         508    1960-09\n",
              "141  1960    10         461    1960-10\n",
              "142  1960    11         390    1960-11\n",
              "143  1960    12         432    1960-12\n",
              "\n",
              "[144 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-73d17a14-1206-4fc1-8583-266a950979fa\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>passengers</th>\n",
              "      <th>year-month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1949</td>\n",
              "      <td>01</td>\n",
              "      <td>112</td>\n",
              "      <td>1949-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1949</td>\n",
              "      <td>02</td>\n",
              "      <td>118</td>\n",
              "      <td>1949-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1949</td>\n",
              "      <td>03</td>\n",
              "      <td>132</td>\n",
              "      <td>1949-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1949</td>\n",
              "      <td>04</td>\n",
              "      <td>129</td>\n",
              "      <td>1949-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949</td>\n",
              "      <td>05</td>\n",
              "      <td>121</td>\n",
              "      <td>1949-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>1960</td>\n",
              "      <td>08</td>\n",
              "      <td>606</td>\n",
              "      <td>1960-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>1960</td>\n",
              "      <td>09</td>\n",
              "      <td>508</td>\n",
              "      <td>1960-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>1960</td>\n",
              "      <td>10</td>\n",
              "      <td>461</td>\n",
              "      <td>1960-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>1960</td>\n",
              "      <td>11</td>\n",
              "      <td>390</td>\n",
              "      <td>1960-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>1960</td>\n",
              "      <td>12</td>\n",
              "      <td>432</td>\n",
              "      <td>1960-12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>144 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73d17a14-1206-4fc1-8583-266a950979fa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-73d17a14-1206-4fc1-8583-266a950979fa 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-73d17a14-1206-4fc1-8583-266a950979fa');\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",
              "\n",
              "\n",
              "<div id=\"df-c51ff70e-7fc0-41f0-a590-2bb201bb2e31\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c51ff70e-7fc0-41f0-a590-2bb201bb2e31')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c51ff70e-7fc0-41f0-a590-2bb201bb2e31 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b9eb93cb-1dd5-4aa3-a410-63e9a962fb62\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('flights')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "flights",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"11\",\n          \"10\",\n          \"01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119,\n        \"min\": 104,\n        \"max\": 622,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293,\n          340,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year-month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 144,\n        \"samples\": [\n          \"1958-10\",\n          \"1950-08\",\n          \"1955-11\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reordene as colunas da seguinte forma: `year-month`, `year`, `month` e `passengers`."
      ],
      "metadata": {
        "id": "NCmjAdF6CZRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flights = flights[['year-month', 'year', 'month', 'passengers']]"
      ],
      "metadata": {
        "id": "pCCM8aYdCZRd"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "67JUvPq_fmHW",
        "outputId": "505c2c51-04de-4ded-efb5-351fa7857b4b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year-month  year month  passengers\n",
              "0      1949-01  1949    01         112\n",
              "1      1949-02  1949    02         118\n",
              "2      1949-03  1949    03         132\n",
              "3      1949-04  1949    04         129\n",
              "4      1949-05  1949    05         121\n",
              "..         ...   ...   ...         ...\n",
              "139    1960-08  1960    08         606\n",
              "140    1960-09  1960    09         508\n",
              "141    1960-10  1960    10         461\n",
              "142    1960-11  1960    11         390\n",
              "143    1960-12  1960    12         432\n",
              "\n",
              "[144 rows x 4 columns]"
            ],
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              "      <td>1949-01</td>\n",
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              "      <td>112</td>\n",
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              "      <td>1949-04</td>\n",
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              "      <td>129</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1949-05</td>\n",
              "      <td>1949</td>\n",
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              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>1960-09</td>\n",
              "      <td>1960</td>\n",
              "      <td>09</td>\n",
              "      <td>508</td>\n",
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              "      <th>141</th>\n",
              "      <td>1960-10</td>\n",
              "      <td>1960</td>\n",
              "      <td>10</td>\n",
              "      <td>461</td>\n",
              "    </tr>\n",
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              "      <th>142</th>\n",
              "      <td>1960-11</td>\n",
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              "      <td>390</td>\n",
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              "      <td>12</td>\n",
              "      <td>432</td>\n",
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              "  </tbody>\n",
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              "<p>144 rows × 4 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "flights",
              "summary": "{\n  \"name\": \"flights\",\n  \"rows\": 144,\n  \"fields\": [\n    {\n      \"column\": \"year-month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 144,\n        \"samples\": [\n          \"1958-10\",\n          \"1950-08\",\n          \"1955-11\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1949,\n        \"max\": 1960,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1959,\n          1958,\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"11\",\n          \"10\",\n          \"01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passengers\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 119,\n        \"min\": 104,\n        \"max\": 622,\n        \"num_unique_values\": 118,\n        \"samples\": [\n          293,\n          340,\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2.3. Carregamento**"
      ],
      "metadata": {
        "id": "wsinLrPJgnuV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Persista o DataFrame em um arquivo com o nome `flights.csv`, no formato CSV. Garanta que o índice será descartado."
      ],
      "metadata": {
        "id": "S3ENEYSIgqZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "flights.to_csv('flights.csv', index=False)"
      ],
      "metadata": {
        "id": "PMDNWZ34CUdL"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDQqSdrWHY78"
      },
      "source": [
        "## 3\\. Dashboard"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conduza os items **3.1** e **3.2** no [Google Data Studio](https://datastudio.google.com/)."
      ],
      "metadata": {
        "id": "amqTtJr7DeWR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.1. Fonte de dados**"
      ],
      "metadata": {
        "id": "4AwXNpjIgrqM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crie a fonte de dados `flights-data` através do *upload* do arquivo `flights.csv`."
      ],
      "metadata": {
        "id": "szmM1yQ7gui8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3.2. Relatório**"
      ],
      "metadata": {
        "id": "CnvcmCFBgvzk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crie o relatório `flights-report` utilizando a fonte de dados `flights-data`. Edite o relatório conforme sua criatividade (texto, cores, etc.). Compartilhe o relatório e copie o link abaixo:"
      ],
      "metadata": {
        "id": "XDTIM7V6FQp9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " - KPI"
      ],
      "metadata": {
        "id": "vk5F8LI9gvzl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crie um filtro a nível de página com a dimensão `year`. Adicione um `scorecard` com uma `métrica` que represente a `soma dos passageiros em um determinado ano`. Note que o valor do `scorecard` deve ser dinâmico, ou seja, deve se alterar com o filtro temporal `year`."
      ],
      "metadata": {
        "id": "v8IDxOBGD1ON"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " - EDA"
      ],
      "metadata": {
        "id": "0bUaJeRmD1py"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adicione um gráfico de série temporal (*time series chart*) utilizando com dimensão a coluna `year-month` e como métrica a soma da dimensão `passengers`. Note que o valor do gráfico deve ser dinâmico, ou seja, deve se alterar com o filtro temporal `year`."
      ],
      "metadata": {
        "id": "5p8ArqxjD2me"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " - Link"
      ],
      "metadata": {
        "id": "_QSgjyrgclwp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Screenshot 2024-05-10 at 12-28-22 flights.pdf.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "KRqyzYuwHlVE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " > Link do relatório: **https://lookerstudio.google.com/s/grpcrUheHdg**"
      ],
      "metadata": {
        "id": "EhyWI0-6cXNm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waxn4B2UDHyl"
      },
      "source": [
        "---"
      ]
    }
  ]
}