20220330_Ciclos_de_Mercado_sazonalidade_anual.ipynb

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        "<a href=\"https://colab.research.google.com/github/Trading-com-Dados/desenvolvimento_interno/blob/main/20220330_Ciclos_de_Mercado_sazonalidade_anual.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "<img src=\"https://tradingcomdados.com/images/logotipo/logotipo-trading-com-dados.svg\" width=\"300\">"
      ]
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    {
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        "id": "H7j0UCOJ0EMX"
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      "source": [
        "---\n",
        "# **Ciclos de Mercado: avaliando a sazonalidade anual do IBOV**\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Contexto:**\n",
        "\n",
        "Você provavelmente já ouviu a expressão \"*Sell in May and go away!*\", que em uma tradução literal seria algo como \"*Venda em maio e vá embora!*\". O fato é que além de uma rima legal, essa expressão traz um contexto de sazonalidade de mercado muito interessante e que, sem dúvida, merece nossa atenção.<p>\n",
        "Estudos mostram que a expressão original é ainda mais curiosa: \"*Sell in May, go away, and come back on St. Leger’s Day*\" = Venda em maio, vá embora e volte no dia de 'Saint Leger' (15 de setembro). A estratégia envolve o lucro a partir na sazonalindade anual do mercado. Basicamente, você venderia todas as suas ações em maio, quando o mercado historicamente apresenta um desempenho abaixo do esperado. No mesmo ano, entre outubro e novembro, você os compra novamente, quando o mercado de ações começa a subir novamente.<p>\n",
        "Em um artigo intitulado \"*The Halloween Indicator, 'Sell in May and Go Away': Another Puzzle*\", publicado na American Economic Review (2002), os autores Jacobsen and Bouman avaliam e documentam esse padrão para 36 de 37 mercados avaliados (incluindo países desenvolvidos e emergentes). Eles reportam que esse \"indicador Halloween\" é particularmente forte na Europa, e surpreendentemente pode ser observado desde 1694 no mercado do UK:<p>\n",
        ">[...] we find this inherited wisdom to be true in 36 of the 37 developed and emerging markets studied in our sample. The Sell in May effect tends to be particularly strong in European countries and is robust over time. Sample evidence, for instance, shows that in the UK the effect has been noticeable since 1694.\n",
        "\n",
        "Por fim, embora tenham testado diversos preditores que pudessem explicar esse padrão, não chegaram a nenhuma conclusão sobre o que poderia de fato explicar esse \"quebra-cabeça\":\n",
        ">[...] While we have examined a number of possible explanations, none of these appears to convincingly explain the puzzle.\n",
        "\n",
        "Nós, da Trading com Dados, apresentamos aqui um código exemplificando o padrão no IBOV. E em seus ativos preferidos, quer testar?"
      ],
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LYsJ_x8_waa"
      },
      "source": [
        "# 1. Bibliotecas utilizadas"
      ]
    },
    {
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          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 19.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
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            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70\n"
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      "source": [
        "!pip install yfinance"
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    {
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      "execution_count": 28,
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        "id": "wRVARobC6jIH"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import plotly.express as px\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LzlBKluaAKi7"
      },
      "source": [
        "# 2. Obtenção e organização do conjunto de dados para o estudo"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1. Extração dos dados e criação de variáveis úteis"
      ],
      "metadata": {
        "id": "oV7re6TGEIP-"
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    },
    {
      "cell_type": "markdown",
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      "source": [
        "Neste código iremos explorar apenas os resultados mensais para fins de simplificar a quantidade de dados e os resultados."
      ]
    },
    {
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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      ],
      "source": [
        "# Cotações mensais do IBOV cobrindo o máximo de tempo possível, incluindo as últimas cotações do mês atual\n",
        "\n",
        "OHLC_IBOV = yf.download('^BVSP',period='max',interval='1mo')"
      ]
    },
    {
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              "                     Open           High            Low          Close  \\\n",
              "Date                                                                     \n",
              "1993-05-01      24.100000      35.599998      23.799999      34.900002   \n",
              "1993-06-01      35.400002      54.799999      35.400002      53.700001   \n",
              "1993-07-01      53.700001      67.400002      51.700001      65.599998   \n",
              "1993-08-01      65.599998     100.099998      60.599998      96.400002   \n",
              "1993-09-01      96.400002     150.300003      96.400002     146.600006   \n",
              "...                   ...            ...            ...            ...   \n",
              "2021-12-01  101916.000000  109493.000000  100727.000000  104822.000000   \n",
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              "2022-03-01  113143.000000  120900.000000  107781.000000  120260.000000   \n",
              "2022-03-30       0.000000       0.000000       0.000000  120259.757812   \n",
              "\n",
              "                Adj Close     Volume  \n",
              "Date                                  \n",
              "1993-05-01      34.900002          0  \n",
              "1993-06-01      53.700001          0  \n",
              "1993-07-01      65.599998          0  \n",
              "1993-08-01      96.400002          0  \n",
              "1993-09-01     146.600006          0  \n",
              "...                   ...        ...  \n",
              "2021-12-01  104822.000000  234055900  \n",
              "2022-01-01  112388.000000  245216600  \n",
              "2022-02-01  113142.000000  224030100  \n",
              "2022-03-01  120260.000000  257186900  \n",
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              "      <td>96.400002</td>\n",
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      ],
      "source": [
        "# Podemos notar que os primeiros dados acessados são de maio/1993\n",
        "\n",
        "OHLC_IBOV"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observamos os 4 parametros típicos das cotações OHLC, assim como Adj Close e Volume <p>\n",
        "No caso do IBOV, o Adj Close não faz sentido já que é o mesmo valor do Close (o que seria diferente se estivessemos falando de algum papel específico do IBOV) <p>\n",
        "Vamos aproveitar e calcular o parâmetro **Percent Change** que já exploramos em outros códigos<p>\n",
        "Considerando o nosso estudo, o Percent Change pode ser avaliado como um resultado para aquele mês, já que é calculado da seguinte forma:"
      ],
      "metadata": {
        "id": "rNsrbZlt8jlG"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "$$\\frac{Close_{atual} - Close_{anterior}}{Close_{anterior}} \\times  100 $$"
      ],
      "metadata": {
        "id": "FsqN7N6Y9CsD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Usaremos a função pct_change do pandas que retorna o resultado da mesma fórmula acima, porém sem multiplicar por 100.<p>\n",
        "Isso mesmo, chama-se percent change mas não é expresso em % <p>\n",
        "Criaremos a coluna 'Resultado_mes' multiplicando o resultado por 100 para termos o resultado em % como estamos acostumados (ao menos aqui no Brasil)"
      ],
      "metadata": {
        "id": "hFF8AT05_frG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "OHLC_IBOV['Resultado_mes'] = OHLC_IBOV['Close'].pct_change()*100\n",
        "OHLC_IBOV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "9WcP8zenAb7V",
        "outputId": "b7d7aa86-21c0-4734-e935-71d5b5015964"
      },
      "execution_count": 5,
      "outputs": [
        {
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              "                     Open           High            Low          Close  \\\n",
              "Date                                                                     \n",
              "1993-05-01      24.100000      35.599998      23.799999      34.900002   \n",
              "1993-06-01      35.400002      54.799999      35.400002      53.700001   \n",
              "1993-07-01      53.700001      67.400002      51.700001      65.599998   \n",
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              "...                   ...            ...            ...            ...   \n",
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              "2022-03-30       0.000000       0.000000       0.000000  120259.757812   \n",
              "\n",
              "                Adj Close     Volume  Resultado_mes  \n",
              "Date                                                 \n",
              "1993-05-01      34.900002          0            NaN  \n",
              "1993-06-01      53.700001          0      53.868190  \n",
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              "1993-08-01      96.400002          0      46.951225  \n",
              "1993-09-01     146.600006          0      52.074693  \n",
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          "metadata": {},
          "execution_count": 5
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      ]
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        "outputId": "813ca6fb-01f1-4158-8b00-61a07fc5cd95"
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          "data": {
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              "                     Open           High            Low          Close  \\\n",
              "Date                                                                     \n",
              "1994-01-01     375.500000     741.700012     373.399994     740.599976   \n",
              "1994-02-01     740.599976    1116.000000     740.599976    1053.900024   \n",
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              "1994-05-01    1708.400024    2516.899902    1325.500000    2467.199951   \n",
              "...                   ...            ...            ...            ...   \n",
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              "      <td>100727.000000</td>\n",
              "      <td>104822.000000</td>\n",
              "      <td>104822.000000</td>\n",
              "      <td>234055900</td>\n",
              "      <td>2.852377</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>336 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c11e57f0-789d-4993-b084-1135276b7e45')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c11e57f0-789d-4993-b084-1135276b7e45 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-c11e57f0-789d-4993-b084-1135276b7e45');\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": 6
        }
      ],
      "source": [
        "# Para fins didáticos, vamos excluir todas as cotações antes de janeiro de 1994 e também do ano atual (2022).\n",
        "# Assim, temos um conjunto de dados é composto por cotações mensais ao longo de 28 anos, totalizando 336 meses\n",
        "\n",
        "dataset_IBOV = OHLC_IBOV.loc['1994-01-01':'2021-12-31']\n",
        "dataset_IBOV"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Uma rápida visualização do preço de fechamento em nosso dataset\n",
        "\n",
        "dataset_IBOV['Close'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "yszVxIxF3uRb",
        "outputId": "b4dcb6fd-fa33-4656-ed8a-ef405b621372"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6393e04fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "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": [
        "# E agora dos resultados percent change\n",
        "\n",
        "dataset_IBOV['Resultado_mes'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "tD9sXIK_3MaZ",
        "outputId": "781cde7e-735c-4506-fd1b-bfae08bb2668"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6393cf2d10>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "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": "markdown",
      "source": [
        "Na figura acima, observamos que nos primeiros anos temos uma grande variação nos resultados ('ganhos' e 'perdas' em % que superam até a crise de 2008 e COVID-19) <p>\n",
        "Nessas horas um bom pesquisador precisa fazer decisões importantes <p>\n",
        "Por exemplo, veja nos outputs acima que, em 1994, os meses de jan, fev e mar apresentaram resultados de 97%, 42% e 44%, respectivamente<p>\n",
        "Será que este tipo de resultado ajudaria a entender o padrão que queremos testar ou apenas produzir um ruído que atrapalhe as estatísticas?<p>\n",
        "Existem diversas técnicas que poderíamos usar para decidir sobre a melhor forma de filtragem dos dados. Para simplificar, vamos utilizar os dados a partir de 2000, que parecem já apresentar variações mais dentro do padrão das que observamos até hoje"
      ],
      "metadata": {
        "id": "ekRCXUnOFp5M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtrar a partir de 2000. Nosso conjunto de dados então compreende 22 anos (2000 a 2021), totalizando 264 meses\n",
        "\n",
        "dataset_IBOV = dataset_IBOV.loc['2000-01-01':'2021-12-31']\n",
        "dataset_IBOV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "Ryya-ab5EkGM",
        "outputId": "b23765aa-0188-484f-b7d5-65dcf3ae22e5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-01-01   17098.0   18099.0   15350.0   16388.0    16388.0          0   \n",
              "2000-02-01   16389.0   18886.0   16389.0   17660.0    17660.0          0   \n",
              "2000-03-01   17668.0   19047.0   16922.0   17820.0    17820.0          0   \n",
              "2000-04-01   17826.0   17826.0   14029.0   15538.0    15538.0          0   \n",
              "2000-05-01   15596.0   15944.0   13556.0   14957.0    14957.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-08-01  121803.0  124536.0  114801.0  118781.0   118781.0  201788600   \n",
              "2021-09-01  118793.0  119942.0  107520.0  110979.0   110979.0  231540400   \n",
              "2021-10-01  110980.0  114927.0  102854.0  103501.0   103501.0  243860400   \n",
              "2021-10-31  103514.0  108669.0  100075.0  101915.0   101915.0  236342500   \n",
              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
              "            Resultado_mes  \n",
              "Date                       \n",
              "2000-01-01      -4.118886  \n",
              "2000-02-01       7.761777  \n",
              "2000-03-01       0.906002  \n",
              "2000-04-01     -12.805836  \n",
              "2000-05-01      -3.739220  \n",
              "...                   ...  \n",
              "2021-08-01      -2.479454  \n",
              "2021-09-01      -6.568391  \n",
              "2021-10-01      -6.738212  \n",
              "2021-10-31      -1.532352  \n",
              "2021-12-01       2.852377  \n",
              "\n",
              "[264 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e282c5d-bd64-4a81-a479-b36229cb4acc\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Resultado_mes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000-01-01</th>\n",
              "      <td>17098.0</td>\n",
              "      <td>18099.0</td>\n",
              "      <td>15350.0</td>\n",
              "      <td>16388.0</td>\n",
              "      <td>16388.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-4.118886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>16389.0</td>\n",
              "      <td>18886.0</td>\n",
              "      <td>16389.0</td>\n",
              "      <td>17660.0</td>\n",
              "      <td>17660.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.761777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-01</th>\n",
              "      <td>17668.0</td>\n",
              "      <td>19047.0</td>\n",
              "      <td>16922.0</td>\n",
              "      <td>17820.0</td>\n",
              "      <td>17820.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.906002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-01</th>\n",
              "      <td>17826.0</td>\n",
              "      <td>17826.0</td>\n",
              "      <td>14029.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-12.805836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>15596.0</td>\n",
              "      <td>15944.0</td>\n",
              "      <td>13556.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.739220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-01</th>\n",
              "      <td>121803.0</td>\n",
              "      <td>124536.0</td>\n",
              "      <td>114801.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>201788600</td>\n",
              "      <td>-2.479454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>118793.0</td>\n",
              "      <td>119942.0</td>\n",
              "      <td>107520.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>231540400</td>\n",
              "      <td>-6.568391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>110980.0</td>\n",
              "      <td>114927.0</td>\n",
              "      <td>102854.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>243860400</td>\n",
              "      <td>-6.738212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-31</th>\n",
              "      <td>103514.0</td>\n",
              "      <td>108669.0</td>\n",
              "      <td>100075.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>236342500</td>\n",
              "      <td>-1.532352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-01</th>\n",
              "      <td>101916.0</td>\n",
              "      <td>109493.0</td>\n",
              "      <td>100727.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>234055900</td>\n",
              "      <td>2.852377</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e282c5d-bd64-4a81-a479-b36229cb4acc')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1e282c5d-bd64-4a81-a479-b36229cb4acc');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checar a ocorrência de NAs é um passo fundamental durante a organização de qq conjunto de dados\n",
        "# Uma das formas pode ser usando o .isna\n",
        "\n",
        "dataset_IBOV.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0hja9b6TmIP",
        "outputId": "108c4e12-c50e-441c-fe2b-b5750abf3fe2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Open             0\n",
              "High             0\n",
              "Low              0\n",
              "Close            0\n",
              "Adj Close        0\n",
              "Volume           0\n",
              "Resultado_mes    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Como a análise envolve os meses (nossa unidade amostral) vamos criar algumas outras colunas no nosso dataframe\n",
        "# Primeiro calcular uma coluna que indique o mês do ano usando um método da biblioteca pandas e um método que transforma a data em strings desejadas (ex., mês, dia da semana...)\n",
        "# Aproveitar e colocar uma para o ano e o mês também como numérico (para alguma análise de classificação posterior, já que a alfabetica não faria sentido)\n",
        "\n",
        "dataset_IBOV[\"Mes\"] = pd.to_datetime(dataset_IBOV.index).strftime('%B')\n",
        "dataset_IBOV[\"Ano\"] = pd.to_datetime(dataset_IBOV.index).strftime('%Y')\n",
        "dataset_IBOV[\"Mes_num\"] = pd.to_datetime(dataset_IBOV.index).strftime('%m')\n",
        "dataset_IBOV\n",
        "\n",
        "# Nesse caso, não se preocupe com a msg do output. Elas não são erros, mas advertências sobre o uso do índice do df (nesse caso, nossas datas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 786
        },
        "id": "zAiapVGjbpL1",
        "outputId": "c34b9ac8-55a0-439d-9834-1377a14f2248"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: 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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: 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",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: 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",
            "  import sys\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-01-01   17098.0   18099.0   15350.0   16388.0    16388.0          0   \n",
              "2000-02-01   16389.0   18886.0   16389.0   17660.0    17660.0          0   \n",
              "2000-03-01   17668.0   19047.0   16922.0   17820.0    17820.0          0   \n",
              "2000-04-01   17826.0   17826.0   14029.0   15538.0    15538.0          0   \n",
              "2000-05-01   15596.0   15944.0   13556.0   14957.0    14957.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-08-01  121803.0  124536.0  114801.0  118781.0   118781.0  201788600   \n",
              "2021-09-01  118793.0  119942.0  107520.0  110979.0   110979.0  231540400   \n",
              "2021-10-01  110980.0  114927.0  102854.0  103501.0   103501.0  243860400   \n",
              "2021-10-31  103514.0  108669.0  100075.0  101915.0   101915.0  236342500   \n",
              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
              "            Resultado_mes        Mes   Ano Mes_num  \n",
              "Date                                                \n",
              "2000-01-01      -4.118886    January  2000      01  \n",
              "2000-02-01       7.761777   February  2000      02  \n",
              "2000-03-01       0.906002      March  2000      03  \n",
              "2000-04-01     -12.805836      April  2000      04  \n",
              "2000-05-01      -3.739220        May  2000      05  \n",
              "...                   ...        ...   ...     ...  \n",
              "2021-08-01      -2.479454     August  2021      08  \n",
              "2021-09-01      -6.568391  September  2021      09  \n",
              "2021-10-01      -6.738212    October  2021      10  \n",
              "2021-10-31      -1.532352    October  2021      10  \n",
              "2021-12-01       2.852377   December  2021      12  \n",
              "\n",
              "[264 rows x 10 columns]"
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              "      <th>2000-03-01</th>\n",
              "      <td>17668.0</td>\n",
              "      <td>19047.0</td>\n",
              "      <td>16922.0</td>\n",
              "      <td>17820.0</td>\n",
              "      <td>17820.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>2000</td>\n",
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              "      <th>2000-04-01</th>\n",
              "      <td>17826.0</td>\n",
              "      <td>17826.0</td>\n",
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              "      <td>15538.0</td>\n",
              "      <td>15538.0</td>\n",
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              "      <td>-12.805836</td>\n",
              "      <td>April</td>\n",
              "      <td>2000</td>\n",
              "      <td>04</td>\n",
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              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>15596.0</td>\n",
              "      <td>15944.0</td>\n",
              "      <td>13556.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.739220</td>\n",
              "      <td>May</td>\n",
              "      <td>2000</td>\n",
              "      <td>05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2021-08-01</th>\n",
              "      <td>121803.0</td>\n",
              "      <td>124536.0</td>\n",
              "      <td>114801.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>201788600</td>\n",
              "      <td>-2.479454</td>\n",
              "      <td>August</td>\n",
              "      <td>2021</td>\n",
              "      <td>08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>118793.0</td>\n",
              "      <td>119942.0</td>\n",
              "      <td>107520.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>231540400</td>\n",
              "      <td>-6.568391</td>\n",
              "      <td>September</td>\n",
              "      <td>2021</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>110980.0</td>\n",
              "      <td>114927.0</td>\n",
              "      <td>102854.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>243860400</td>\n",
              "      <td>-6.738212</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-31</th>\n",
              "      <td>103514.0</td>\n",
              "      <td>108669.0</td>\n",
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              "      <td>101915.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>236342500</td>\n",
              "      <td>-1.532352</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-01</th>\n",
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              "      <td>104822.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>234055900</td>\n",
              "      <td>2.852377</td>\n",
              "      <td>December</td>\n",
              "      <td>2021</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264 rows × 10 columns</p>\n",
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              "\n",
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              "          const element = document.querySelector('#df-8bf7fbd2-1c25-47ef-88aa-d8f6f6f705d2');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Por fim, deixar apenas 2 casas decimais para o resultado mensal\n",
        "\n",
        "dataset_IBOV['Resultado_mes'] = round(dataset_IBOV['Resultado_mes'],2)\n",
        "dataset_IBOV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "6I53F9b-Pv0b",
        "outputId": "b96dcaba-655f-4ab1-daa1-b541d0d43cc9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: 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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
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              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
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              "...              ...       ...       ...       ...        ...        ...   \n",
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              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
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              "...                   ...        ...   ...     ...  \n",
              "2021-08-01          -2.48     August  2021      08  \n",
              "2021-09-01          -6.57  September  2021      09  \n",
              "2021-10-01          -6.74    October  2021      10  \n",
              "2021-10-31          -1.53    October  2021      10  \n",
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              "      \n",
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              "          const element = document.querySelector('#df-42f2b46a-ed24-43b7-b283-48936f6ee4db');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2. Corrigindo um problema pontual nos dados"
      ],
      "metadata": {
        "id": "xPYbW6i0FEW-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se você for observador e estiver acostumado com dataframes, talvez já conseguiu notar que o mes de novembro na penultima linha não aparece.<p>\n",
        "Temos 2 outubros, sendo que o segundo (penultima linha) está indicado como 31/out<p>\n",
        "Pelo que parece, está relacionado apenas ao parâmetro *interval=1mo* para o IBOV na yfinance"
      ],
      "metadata": {
        "id": "uu70mpAGzqNd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Agora uma tabela dinâmica (pivot table), podemos ver se isso acontece em outros meses\n",
        "# Aplicamos uma contagem de células por meses\n",
        "\n",
        "pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes\"], columns=[],aggfunc='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "oNLomGiXKtsa",
        "outputId": "9c29dfb7-0243-43ee-d9c3-3b3a841f8c48"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Resultado_mes\n",
              "Mes                     \n",
              "April                 22\n",
              "August                22\n",
              "December              22\n",
              "February              22\n",
              "January               22\n",
              "July                  22\n",
              "June                  22\n",
              "March                 22\n",
              "May                   22\n",
              "November              18\n",
              "October               26\n",
              "September             22"
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              "      <th>October</th>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>September</th>\n",
              "      <td>22</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vemos que esse erro parece ocorrer em 4 anos\n",
        "# Explorando um poouco mais\n",
        "\n",
        "pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Ano\"], columns=[\"Mes_num\"],aggfunc='count')\n",
        "\n",
        "# De fato, ocorreu de 2018 a 2021"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "CY4AzmB8eMZ8",
        "outputId": "fe879456-8314-42e3-f7d9-c5a411515be7"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mes_num   01   02   03   04   05   06   07   08   09   10   11   12\n",
              "Ano                                                                \n",
              "2000     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2001     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2002     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2003     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2004     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2005     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2006     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2007     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2008     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2009     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2010     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2011     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2012     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2013     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2014     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2015     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2016     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2017     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0\n",
              "2018     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  2.0  NaN  1.0\n",
              "2019     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  2.0  NaN  1.0\n",
              "2020     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  2.0  NaN  1.0\n",
              "2021     1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  2.0  NaN  1.0"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>Mes_num</th>\n",
              "      <th>01</th>\n",
              "      <th>02</th>\n",
              "      <th>03</th>\n",
              "      <th>04</th>\n",
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              "      <th>09</th>\n",
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              "      <th>12</th>\n",
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              "    <tr>\n",
              "      <th>Ano</th>\n",
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              "      <th></th>\n",
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              "      <th>2000</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>2009</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>2012</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>2013</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b59d92c3-dbae-4bda-90fa-dab6bf269475')\"\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b59d92c3-dbae-4bda-90fa-dab6bf269475');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para não deixar tudo muito extenso, fizemos uma checagem com diversas outras fontes de dados de cotações. Trata-se de um erro de data, sendo que os valores estão corretos. A data '2021-10-31' deveria ser '2021-11-01' para todos os 4 anos<p>\n",
        "Vamos então apenas substituir as 4 datas já que os valores OHLC estão iguais aos relacionados ao mes de novembro em outras fontes"
      ],
      "metadata": {
        "id": "_uCyw9551d3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Como dito, fazer de forma rápida, em uma única célula de código, para não ficar mt extenso e fugirmos do nosso objetivo que é testar a hipótese do \"Indicador de Halloween\"\n",
        "\n",
        "dataset_IBOV['Data'] = pd.to_datetime(dataset_IBOV.index)\n",
        "dataset_IBOV = dataset_IBOV.replace('2021-10-31','2021-11-01')\n",
        "dataset_IBOV = dataset_IBOV.replace('2020-10-31','2020-11-01')\n",
        "dataset_IBOV = dataset_IBOV.replace('2019-10-31','2019-11-01')\n",
        "dataset_IBOV = dataset_IBOV.replace('2018-10-31','2018-11-01')\n",
        "dataset_IBOV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "GH5lioMo21N1",
        "outputId": "e5e782a9-d41e-4d63-b1b2-db61a1643da5"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: 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",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-01-01   17098.0   18099.0   15350.0   16388.0    16388.0          0   \n",
              "2000-02-01   16389.0   18886.0   16389.0   17660.0    17660.0          0   \n",
              "2000-03-01   17668.0   19047.0   16922.0   17820.0    17820.0          0   \n",
              "2000-04-01   17826.0   17826.0   14029.0   15538.0    15538.0          0   \n",
              "2000-05-01   15596.0   15944.0   13556.0   14957.0    14957.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-08-01  121803.0  124536.0  114801.0  118781.0   118781.0  201788600   \n",
              "2021-09-01  118793.0  119942.0  107520.0  110979.0   110979.0  231540400   \n",
              "2021-10-01  110980.0  114927.0  102854.0  103501.0   103501.0  243860400   \n",
              "2021-10-31  103514.0  108669.0  100075.0  101915.0   101915.0  236342500   \n",
              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
              "            Resultado_mes        Mes   Ano Mes_num       Data  \n",
              "Date                                                           \n",
              "2000-01-01          -4.12    January  2000      01 2000-01-01  \n",
              "2000-02-01           7.76   February  2000      02 2000-02-01  \n",
              "2000-03-01           0.91      March  2000      03 2000-03-01  \n",
              "2000-04-01         -12.81      April  2000      04 2000-04-01  \n",
              "2000-05-01          -3.74        May  2000      05 2000-05-01  \n",
              "...                   ...        ...   ...     ...        ...  \n",
              "2021-08-01          -2.48     August  2021      08 2021-08-01  \n",
              "2021-09-01          -6.57  September  2021      09 2021-09-01  \n",
              "2021-10-01          -6.74    October  2021      10 2021-10-01  \n",
              "2021-10-31          -1.53    October  2021      10 2021-11-01  \n",
              "2021-12-01           2.85   December  2021      12 2021-12-01  \n",
              "\n",
              "[264 rows x 11 columns]"
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              "      <th>2000-01-01</th>\n",
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              "      <td>16389.0</td>\n",
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              "      <td>17660.0</td>\n",
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              "      <th>2000-03-01</th>\n",
              "      <td>17668.0</td>\n",
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              "      <th>2000-04-01</th>\n",
              "      <td>17826.0</td>\n",
              "      <td>17826.0</td>\n",
              "      <td>14029.0</td>\n",
              "      <td>15538.0</td>\n",
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              "      <td>-12.81</td>\n",
              "      <td>April</td>\n",
              "      <td>2000</td>\n",
              "      <td>04</td>\n",
              "      <td>2000-04-01</td>\n",
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              "      <th>2000-05-01</th>\n",
              "      <td>15596.0</td>\n",
              "      <td>15944.0</td>\n",
              "      <td>13556.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.74</td>\n",
              "      <td>May</td>\n",
              "      <td>2000</td>\n",
              "      <td>05</td>\n",
              "      <td>2000-05-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>121803.0</td>\n",
              "      <td>124536.0</td>\n",
              "      <td>114801.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>201788600</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>August</td>\n",
              "      <td>2021</td>\n",
              "      <td>08</td>\n",
              "      <td>2021-08-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>118793.0</td>\n",
              "      <td>119942.0</td>\n",
              "      <td>107520.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>231540400</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>September</td>\n",
              "      <td>2021</td>\n",
              "      <td>09</td>\n",
              "      <td>2021-09-01</td>\n",
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              "      <th>2021-10-01</th>\n",
              "      <td>110980.0</td>\n",
              "      <td>114927.0</td>\n",
              "      <td>102854.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>243860400</td>\n",
              "      <td>-6.74</td>\n",
              "      <td>October</td>\n",
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              "      <td>10</td>\n",
              "      <td>2021-10-01</td>\n",
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              "      <td>103514.0</td>\n",
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              "      <td>236342500</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>October</td>\n",
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              "      <td>10</td>\n",
              "      <td>2021-11-01</td>\n",
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              "      <td>12</td>\n",
              "      <td>2021-12-01</td>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c033c9ad-c3c8-47fc-8703-54bf7d367fc5');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criamos uma estrutura de repetição que preencherá as listas (inicialmente vazias) com o mes referente à coluna Data que foi corrigida\n",
        "\n",
        "Mes = []\n",
        "Mes_num = []\n",
        "\n",
        "for i in range(0,len(dataset_IBOV.index)):\n",
        "  mes_linha = dataset_IBOV['Data'][i].strftime('%B')\n",
        "  mes_num_linha = dataset_IBOV['Data'][i].strftime('%m')\n",
        "  Mes.append(mes_linha)\n",
        "  Mes_num.append(mes_num_linha)"
      ],
      "metadata": {
        "id": "Ul126Bpv_JCS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Nesse caso deixaremos Mes final para comparar (apenas visualizaçao, poderia já substituir direto, como faremos com Mes_num)\n",
        "\n",
        "dataset_IBOV['Mes_final'] = Mes\n",
        "dataset_IBOV['Mes_num'] = Mes_num\n",
        "dataset_IBOV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "THc65PF8GW_B",
        "outputId": "f5451c4c-baf4-4fb6-be81-9ab61651a608"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-01-01   17098.0   18099.0   15350.0   16388.0    16388.0          0   \n",
              "2000-02-01   16389.0   18886.0   16389.0   17660.0    17660.0          0   \n",
              "2000-03-01   17668.0   19047.0   16922.0   17820.0    17820.0          0   \n",
              "2000-04-01   17826.0   17826.0   14029.0   15538.0    15538.0          0   \n",
              "2000-05-01   15596.0   15944.0   13556.0   14957.0    14957.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-08-01  121803.0  124536.0  114801.0  118781.0   118781.0  201788600   \n",
              "2021-09-01  118793.0  119942.0  107520.0  110979.0   110979.0  231540400   \n",
              "2021-10-01  110980.0  114927.0  102854.0  103501.0   103501.0  243860400   \n",
              "2021-10-31  103514.0  108669.0  100075.0  101915.0   101915.0  236342500   \n",
              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
              "            Resultado_mes        Mes   Ano Mes_num       Data  Mes_final  \n",
              "Date                                                                      \n",
              "2000-01-01          -4.12    January  2000      01 2000-01-01    January  \n",
              "2000-02-01           7.76   February  2000      02 2000-02-01   February  \n",
              "2000-03-01           0.91      March  2000      03 2000-03-01      March  \n",
              "2000-04-01         -12.81      April  2000      04 2000-04-01      April  \n",
              "2000-05-01          -3.74        May  2000      05 2000-05-01        May  \n",
              "...                   ...        ...   ...     ...        ...        ...  \n",
              "2021-08-01          -2.48     August  2021      08 2021-08-01     August  \n",
              "2021-09-01          -6.57  September  2021      09 2021-09-01  September  \n",
              "2021-10-01          -6.74    October  2021      10 2021-10-01    October  \n",
              "2021-10-31          -1.53    October  2021      11 2021-11-01   November  \n",
              "2021-12-01           2.85   December  2021      12 2021-12-01   December  \n",
              "\n",
              "[264 rows x 12 columns]"
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              "      <td>17660.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>02</td>\n",
              "      <td>2000-02-01</td>\n",
              "      <td>February</td>\n",
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              "      <th>2000-03-01</th>\n",
              "      <td>17668.0</td>\n",
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              "      <td>2000-03-01</td>\n",
              "      <td>March</td>\n",
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              "      <th>2000-04-01</th>\n",
              "      <td>17826.0</td>\n",
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              "      <td>14029.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-12.81</td>\n",
              "      <td>April</td>\n",
              "      <td>2000</td>\n",
              "      <td>04</td>\n",
              "      <td>2000-04-01</td>\n",
              "      <td>April</td>\n",
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              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>15596.0</td>\n",
              "      <td>15944.0</td>\n",
              "      <td>13556.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.74</td>\n",
              "      <td>May</td>\n",
              "      <td>2000</td>\n",
              "      <td>05</td>\n",
              "      <td>2000-05-01</td>\n",
              "      <td>May</td>\n",
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              "      <td>121803.0</td>\n",
              "      <td>124536.0</td>\n",
              "      <td>114801.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>201788600</td>\n",
              "      <td>-2.48</td>\n",
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              "      <td>2021</td>\n",
              "      <td>08</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>August</td>\n",
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              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>118793.0</td>\n",
              "      <td>119942.0</td>\n",
              "      <td>107520.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>231540400</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>September</td>\n",
              "      <td>2021</td>\n",
              "      <td>09</td>\n",
              "      <td>2021-09-01</td>\n",
              "      <td>September</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>110980.0</td>\n",
              "      <td>114927.0</td>\n",
              "      <td>102854.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>243860400</td>\n",
              "      <td>-6.74</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>October</td>\n",
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              "      <th>2021-10-31</th>\n",
              "      <td>103514.0</td>\n",
              "      <td>108669.0</td>\n",
              "      <td>100075.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>236342500</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>11</td>\n",
              "      <td>2021-11-01</td>\n",
              "      <td>November</td>\n",
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              "    <tr>\n",
              "      <th>2021-12-01</th>\n",
              "      <td>101916.0</td>\n",
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              "      <td>104822.0</td>\n",
              "      <td>234055900</td>\n",
              "      <td>2.85</td>\n",
              "      <td>December</td>\n",
              "      <td>2021</td>\n",
              "      <td>12</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>December</td>\n",
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              "  </tbody>\n",
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              "<p>264 rows × 12 columns</p>\n",
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              "        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-d32f0904-e949-4363-8888-0b90711bfa76');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Agora sim, temos o dataset pronto para explorarmos as nossos padrões de ciclos de mercado\n",
        "\n",
        "pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Ano\"], columns=[\"Mes_num\"],aggfunc='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "YIqleFNF3yNE",
        "outputId": "320931a9-ff6e-4254-a114-085ace422055"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mes_num  01  02  03  04  05  06  07  08  09  10  11  12\n",
              "Ano                                                    \n",
              "2000      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2001      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2002      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2003      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2004      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2005      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2006      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2007      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2008      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2009      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2010      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2011      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2012      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2013      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2014      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2015      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2016      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2017      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2018      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2019      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2020      1   1   1   1   1   1   1   1   1   1   1   1\n",
              "2021      1   1   1   1   1   1   1   1   1   1   1   1"
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              "      <th>06</th>\n",
              "      <th>07</th>\n",
              "      <th>08</th>\n",
              "      <th>09</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ano</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ebd4acc-dca1-45ff-bf95-82136fe35d12')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bW8SS7QW0EMg"
      },
      "source": [
        "# 3. Análises para testar o padrão do **Indicador de Halloween**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1. Médias e Medianas mensais"
      ],
      "metadata": {
        "id": "sx3A5mU2RLSI"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_Pn8urH0EMg"
      },
      "source": [
        "A primeira grande pergunta que nos vêm à cabeça poderia ser:\n",
        "\n",
        "**Qual a média de resultado de cada mês ao longo desses 22 anos?**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Faremos uma tabela dinâmica (pivot table) para calcular a média de resultado por mês do ano\n",
        "# Primeiro vamos definir a opção de apresentar numeros com 2 casas decimais\n",
        "\n",
        "resultado_geral = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes_num\"], columns=[],aggfunc=np.mean)\n",
        "round(resultado_geral,2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
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        "id": "9wqLyuq_VvEW",
        "outputId": "f2404c75-9f00-4789-f1c1-0c55e4e27ce0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Resultado_mes\n",
              "Mes_num               \n",
              "01                0.53\n",
              "02                1.19\n",
              "03               -0.08\n",
              "04                2.04\n",
              "05               -1.10\n",
              "06               -0.30\n",
              "07                1.56\n",
              "08                0.91\n",
              "09               -0.87\n",
              "10                2.21\n",
              "11                1.95\n",
              "12                3.34"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Resultado_mes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mes_num</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>0.53</td>\n",
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              "      <th>02</th>\n",
              "      <td>1.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>-0.08</td>\n",
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              "      <th>04</th>\n",
              "      <td>2.04</td>\n",
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              "    <tr>\n",
              "      <th>05</th>\n",
              "      <td>-1.10</td>\n",
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              "      <th>06</th>\n",
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              "      <td>0.91</td>\n",
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              "      <td>-0.87</td>\n",
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              "      <th>10</th>\n",
              "      <td>2.21</td>\n",
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              "      <th>11</th>\n",
              "      <td>1.95</td>\n",
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              "      <th>12</th>\n",
              "      <td>3.34</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64aae4f0-a02c-4ceb-a66a-b87333bcd012')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-64aae4f0-a02c-4ceb-a66a-b87333bcd012 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-64aae4f0-a02c-4ceb-a66a-b87333bcd012');\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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos deixar as coisas um pouco mais visuais nesse resultado\n",
        "# Formatação condicional (positivo verde, se 0 ou negativo vermelho)\n",
        "\n",
        "resultado_geral.style.format('{:.2f}', na_rep=\"\").applymap(lambda x: 'background-color : #e6ffe6' if x>0 else 'background-color : #ffe6e6')"
      ],
      "metadata": {
        "id": "k7eMHi6SMRnt",
        "outputId": "ba4c7e45-8e51-4e4f-fd80-7fbfd1904a5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f6393836750>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_72082_row0_col0, #T_72082_row1_col0, #T_72082_row3_col0, #T_72082_row6_col0, #T_72082_row7_col0, #T_72082_row9_col0, #T_72082_row10_col0, #T_72082_row11_col0 {\n",
              "  background-color: #e6ffe6;\n",
              "}\n",
              "#T_72082_row2_col0, #T_72082_row4_col0, #T_72082_row5_col0, #T_72082_row8_col0 {\n",
              "  background-color: #ffe6e6;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_72082_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Resultado_mes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Mes_num</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row0\" class=\"row_heading level0 row0\" >01</th>\n",
              "      <td id=\"T_72082_row0_col0\" class=\"data row0 col0\" >0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row1\" class=\"row_heading level0 row1\" >02</th>\n",
              "      <td id=\"T_72082_row1_col0\" class=\"data row1 col0\" >1.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row2\" class=\"row_heading level0 row2\" >03</th>\n",
              "      <td id=\"T_72082_row2_col0\" class=\"data row2 col0\" >-0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row3\" class=\"row_heading level0 row3\" >04</th>\n",
              "      <td id=\"T_72082_row3_col0\" class=\"data row3 col0\" >2.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row4\" class=\"row_heading level0 row4\" >05</th>\n",
              "      <td id=\"T_72082_row4_col0\" class=\"data row4 col0\" >-1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row5\" class=\"row_heading level0 row5\" >06</th>\n",
              "      <td id=\"T_72082_row5_col0\" class=\"data row5 col0\" >-0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row6\" class=\"row_heading level0 row6\" >07</th>\n",
              "      <td id=\"T_72082_row6_col0\" class=\"data row6 col0\" >1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row7\" class=\"row_heading level0 row7\" >08</th>\n",
              "      <td id=\"T_72082_row7_col0\" class=\"data row7 col0\" >0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row8\" class=\"row_heading level0 row8\" >09</th>\n",
              "      <td id=\"T_72082_row8_col0\" class=\"data row8 col0\" >-0.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
              "      <td id=\"T_72082_row9_col0\" class=\"data row9 col0\" >2.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
              "      <td id=\"T_72082_row10_col0\" class=\"data row10 col0\" >1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_72082_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
              "      <td id=\"T_72082_row11_col0\" class=\"data row11 col0\" >3.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Relembrando: \"Sell in May, go away, and come back on St. Leger’s Day\" = <u> Venda em maio, vá embora e volte no dia de 'Saint Leger' (15 de setembro)</u>"
      ],
      "metadata": {
        "id": "WlR3qZxPOmjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pelo menos a priori, o Indicador Halloween parece ser marcante para o IBOV\n",
        "# Veja que maio tem a menor média de resultado e os meses pós setembro as maiores altas\n",
        "# Vamos visualizar para facilitar\n",
        "\n",
        "medias_mensais = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes_num\"], columns=[],aggfunc=np.mean)\n",
        "medias_mensais.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "xAZndfr1N_tQ",
        "outputId": "0c7baeee-b072-473e-cb91-b98fae92cb98"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6393457b90>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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H/zw2LcHkiMRQ53J7SIyOYHSycRONjrhIbjlnLDfNH8NHeyp54dNDPPLBXh79cB8X5mZw3ZmjsdsUhUdquXd57oAmSa3CsESulEoA/gz8QGtd1/3zWusngCcAZs+eHbgV/aJXa3ZWYFMQYbOx7WgdK6bLL1DCXK4SD3lOR0DKfDabYuGkdBZOSudwdRMvbTjEa58fYZWrlJhIG47YSK6YnW34fYPJkESulIqkM4m/rLX+ixHXFIHzwc4KZowajrfDh6uk98kgIQKpzetjR1k9N5yVE/B7jUqJ46cXT+GupRN5Z+tRXtt0hEumZRq6scgMg45edR4P9jSwQ2v9P4MPSQRSRV0LLreHH18wCXdtM+9sPYrWOmROeRPhZ3d5PW1eH/kDnOgciNgoO1fOGcmVc0JrK/6pGLGzcz7wTWCxUqqw67+LDbiuCIAPdlUAsGhSOvlOB/UtXg5VyzJEYR7/js5gJvJwM+gRudb6Y0CGcyFi7c4KMh0xTMlMxNd1+FDxUQ85IdDOSoQnl9tDYkwEo1NCb0elVchZK0NIq7eDj/dUsWhyOkopJo5IJMpuOz4iEsIMrhIP+U6HlPcGQRL5ELLxQA2NbR0snpQOQFSEjUkZiX3a/SZEILR6O9hZVkd+P3d0ipNJIh9C1u6sIDrCxvzxqcdfy3MmUeyuC+gZz0Kcyu6yBto7tNTHB0kS+RChtWbtzgrOHJdCbJT9+Ot5Tgee5naO1DSbGJ0YqvxlvWnO0DzjxCokkQ8R+6saOVTdxOLJ6Se97h8JFR+V8ooIPpe7FkdsJCOTY80OJaRJIh8iPtj5r2WHJ5qUkUiETcmEpzCFyy0TnUaQRD5ErN1ZwcQRCYzsdpZFdISdiSNkwlMEX6u3g11l9TLRaQBJ5ENAXUs7Gw/UsKhbWcUv3+nA5fbIhKcIql1l9TLRaRBJ5EPAx3uq8Po0Syb3fExnXraD2qZ23LUy4SmCp6hEdnQaRRL5ELB2ZwWO2Ehmjup5ZcDxCU8pr4ggKnZ7GBYXSfZwmegcLEnkYc7n03y4q4JzJ6YRYe/52z05IxG7THiKICuSHZ2GkUQe5oq6mkgsOUV9HCAm0s6E9ARc7i8dIy9EQLS0d7C7vF7KKgaRRB7m1nY1kVgwMe2078t3OtgmE54iSHaW1eP16X43WxY9k0Qe5vxNJIbHR532ffnZDqob2yg9oZ+h6JtXNx7misc/oaJe/u76yl/GG2izZXEySeRhzN9Eovtuzp7kZnX+H0rq5P2jteaJdfv5/OAxrn1qAzWNbWaHFBJcJbUkx0fhHCYTnUaQRB7G/E0k+pLIp2YmYVOycqW/dpbVs7+ykctmODlU3cQ3n96Ap6nd7LAsz+WuI08mOg0jiTyM+ZtITM5I7PW9sVF2JqTLDs/+WlVUik3BT5dN4f++OYvd5fVc/+xGGlq9ZodmWf6JzmlSVjGMJPIw1ertYN2eKhZ3NZHoizynA5ccadtnWmvedZVy5rgUUhOiWTgpnT9cMxOX28NNz35OU5sk857sKK2jw6elPm4gSeRhauOBGpraOvpUVvHLcyZR1dBKeV1rACMLHztK69lf1ciy/Kzjr12Qm8FDV01n06Eabn1hMy3tHSZGaE3Hj66VFSuGkUQepvxNJM4al9r7m7v41/TKhGffrHIdxW5TXJB78tEHlxZk8d+XF/Dx3ipue3kLbV6fSRFaU1GJh5T4KDIdMWaHEjYkkYchfxOJs7o1kejN1CyZ8OwrrTWriko5c2wKKQnRX/r85bOyeeCreazdWcH3X/kCb4ckc79it4f8bJnoNJIk8jB0qiYSvYmLimBcWoIk8j7YXlrHweomlk3LPOV7vjFvNL+4ZCp/31bGv72xlQ6fzD00t8mOzkCIMDsAYbzjTST6mcihc8Jz/d4qo0MKO6uKSrvKKhmnfd9NZ4+hxdvBf/99F9ERNh68bBo229AdiW4vrcOn5cRDo8mIPAyt2VHBpBGJZA+P6/3N3eQ5HVTUt1JRJ7sUT0VrzSpXKWeNSyG5lx2zALctHM/3l0zg9U0l3PP2tiG9KshVUgsgzSQMJok8zNS1tPP5wVM3keiN9PDs3bajdRyqbmJZ/qnLKt3dtXQC3z53LC9+doj/enfHkE3mLncdqQnRZCTJRKeRpLQSZvxNJPpbH/ebmpWEUuAqqWPxKRpRDHUr+1hWOZFSirsvmkyr18eT6w4QE2nn386fFMAorcnlriXfmSQTnQaTRB5m1uw4fROJ3iRERzAmNV6WIJ6CfxPQ/PGpvR5E1p1Sil9cMpWW9g4eWbuXmEg7ty8aH6BIraepzcveigYuzOv7bzKibySRhxGfT/PP3RUsOE0Tib7IdzrYsL/GwMjCR7G7jsM1TXxvgAnYZlM88NV8Wr0+fr26cwL0lnPGGhylNW0/2jnRKVvzjSc18jDibyIx0LKKX77TQVldC5X1ssOzu5Wuo0TYFOfnDrzsZLcpfn35NC7Oz+D+VTt48bNDBkZoXf7f8mSi03iSyE+h1dvBf7xZzD+2lZkdSp+t3VHepyYSvcmTCc8e+TcBzR+fyrC4/pVVuouw23joqhksnZLOf7xZzOubjhgUpXW5SjykJ0YzQiY6DSeJvAfeDh93vlLIi58d4udvFofMeRlrd1Uwsw9NJHozNSsJgOISSeQncrk9lBxrPu0moP6IirDxh2tmcs6EVH7y5yLeKnQbcl2rcrk9sn48QCSRd+Pzae7+i4u/byvjshlOKupbQ2K0VF7XQrG7bsDLDk+UFBMpE549WFVUSqRdccHUvq9W6U1MpJ0nvjmbuTnJ/PD1rfy9uNSwa1tJY6uXvZUNUlYJEEnkJ9Ba85+rtvOnzSXcuWQCv72ygNmjh/O/H+6j1WvtUbl/N+eSKYNP5NBZXtl2VJox+2mtWdlVVnHERRp67dgoO0/fMIeCbAd3vPLF8e9lONleWoeWHZ0BI4n8BL9fs4dn1x/kxvk5/GDpBJRS3LFkAqWeFv682dq/9q7dWUGWI4ZJI3pvItEX+c4k3LXN0rqsy9YSD+7a5n5tAuqPhOgInr1xLpMyEvn2S5v5eE94HZNQ1FWmk0QeGJLIuzzz8QEeen8Pl8/K5j+WTT2+YeHcCakUjBzGYx/upd2iJ9i1ejv4eG8Vi/rRRKI3edLD8ySrio4SaVecb2BZpTtHbCQv3jSPsanx3PLC52w8ED5LQIvdHkYkRZMuE50BYUgiV0o9o5SqUEoVG3G9YHtj0xHuW7mdC3MzePCy/JMONVJK8f3F4yk51sxfv7DmqHzD/s4mEkaVVQBy/StXJJF3bQIq45wJaYaXVbobHh/FizfPwzkslhuf3cgXh48F9H7BUlRSS75zYJvURO+MGpE/B1xo0LWC6u/Fpfzkz0WcMyGV3399eo8baRZPTic3K4nHPthryXOl/U0kzhzb9yYSvXHERjI6JU4SOVB4pDagZZXu0hKjefmWM0hJiOb6ZzaG/PegodXL/qpGKasEkCGJXGv9ERByvweu21PJ918ppGDkMB6/dhbRET03YVBKccfi8RysbmJlkbVWFQy0iURfdPbwDO0kYgT/apWlU4N39kyGI4Y/fmseiTGRfPPpDewurw/avY22ze1Ba2ntFkhDtka++dAxbn1hM2PT4nnuhrnER5/+tILzp2YwaUQij6zdY6kGAfsqGzlc08TiKcYnmbwsByXHmjk2hCc8fb7Os1XOnZCGIzawZZXusofH8fIt84i027jmyQ3sr2wI6v2N4h8MSLPlwAlaIldK3aqU2qSU2lRZWRms2/ZoR2kdNz67kRFJ0bxw89w+1T1tNsX3Fo9nX2Ujf7PQWl//UrXBbsvviRxpC4UltRz1tBi2Cai/clLj+eO35qG15htPbeBITZMpcQyGy+0h0xFDWuKXW+IJYwQtkWutn9Baz9Zaz05LG9wW8sE4UNXIN5/eSFxUBC/dMo/0xL7Pol+cn8nYtHj+sHYvPouMytfu7Gwi4RwWa/i185xdOzzdQ3c9+aqiUqLstqCWVbobn57IS7fMo7m9g68/+RlHa5tNi2UgXG6PjMYDbEiVVko9zVz71AZ8WvPSLXP73UHHbuusle8sq+e9HeUBirLv/E0kFhu4WuVEw+KiGJkcG/KTbQN1vKwyMZWkmOCWVbqbkpnEizfNw9PUzjee2hAyHZzqW9rZX9koJx4GmFHLD18BPgUmKaVKlFI3G3FdI1U3tHLtUxuoa27nhZvmMj59YBtnLp2WxeiUOB5es8f0Li/rdg+uiURf5A/hCc8vjhyj1MSySnf52Q6eu2kOZZ4WHnh3h9nh9In/t7k8megMKKNWrXxda52ptY7UWmdrrZ824rpGqWtp5/pnN1JyrJmnrp89qF/zIuw2bl84nm1H6/hgl7lbqdfu7GwiMWNk4Nbn5mY5OFzThKepPWD3sKqVRaVERdhYGoCJ5IGaNTqZy2Y6+ce2cpravGaH0yv/b3Oy9DCwwr600tzWwS3PbWJnaT2PXzuLeWNTBn3Nr8504hwWy8Nr9po2Kvf5NB/uqmDhpME1kejNUJ3w9Pk0f3OVsWBiGokml1W6W16QRXN7B+9tN7+815sit4csRwypCTLRGUhhncjbvD6++/JmPj9Uw++umm7IyYAAkXYbty0aR+GRWtaZdCbG1pJaqhsH30SiN/lDdIfnlsPHKKtr4RKLlFVONCcnmUxHDG8XHjU7lF4Vuz1y4mEQhG0i7/Bpfvh6IR/uquSBr+RzaUGWode/fFY2mY4YHllrTq38g50VhjSR6M3w+Cicw2KHXJ3cX1ZZYqGyip/NprhkWiYf7amktsm6a/zrWto5IDs6gyIsE7nWmp+/WczKolL+/aLJXDNvlOH3iI6w850F4/j84DE+M6G/5ZqdFcwaPXzQnWr6Is+ZNKRG5D6f5m/FpSycmEZCLxvFzLK8wEl7h+ZvxdbtYHW8Pp4tZ6wEWtglcq01D/5tJ69sPMxtC8fx7QXjAnavq+aMJC0xmofX7AnYPXpSXtfCtqPGNJHoi3yng4PVTdS1DI0Jz82Hj1Fe12qZ1So9yXMmMSY13tLlFZccXRs0YZfIH/twH//30X6uPWMUP75gUkDvFRNp59vnjuXT/dVsOhi8UXkgd3P2xL/KZ9sQ2Ri0qqiUaIuWVfyUUlxakMVnB6opt+iacpfbg3NYLMmDbD0oehdWifzFzw7x69W7WDE9i/uW5xl2NvfpXDNvFCnxUTy8dm/A7+W3ZmcFzmGxhjWR6E3eEJrw7OjaBLRwknXLKn7LC7LQGssd5OYnPTqDJ2wS+VuFbn7xVjFLp6TzmysKTjpTPJDioiK45ZyxfLS7ksIjtQG/X6u3g/V7q1g0OS0o/1ABpCZEk+mIGRITnpsO1lBR38qyacZOjgfC+PQEcrOSeHur9cornqZ2DlU3yYqVIAmLRP7+9nJ++PpW5o1J5g/XzCQygOuqe/LNM0czLC6SR4JQK/c3kQhWWcUvz+kYEiPyVa6uskqQ/34HanlBFluP1HKoutHsUE7i33cgR9cGR8gn8k/3VXPbH7eQm5XEU9fPISbS2DO5+yIhOoKb549hzc6KgCe7QDSR6It8p4P9VY3Uh/GEZ4evcxXI4snpvR5rbBWXdC2rfcdio/LjR9dmSSIPhpBO5FuP1HLL858zOjmO526ca2pN8/r5OSTGRPCHANbK/U0k5o9PNbyJRG/8tc7tR8N3wvPzgzVU1lt7tUp3zmGxzMkZbrnyiqvEw8jkWIbLRGdQhGwi311ez/XPbiQ5obPHodkz40kxkdx4Vg5/31bGzrLAJDt/E4lgLTs8UW7XkbbhXCdfVVRKTKQt6GWrwVpekMXu8oaA/dwNhEx0BldIJvLD1U1c+9QGIu02Xrp5HhkOa3TmvunsMcRH2QM2Kg/2ssMTpSfGMCIpOmzr5CeWVeKiQqOs4ndxfiZ2m+Iti6wpr21q43BNkzRbDqKQS+QVdS1c+/QGWr0+Xrp5HqNT4s0O6bhhcVFcd1YOq1yl7K0wvi3Xmp3lTM4ITBOJvsh3OigO09LKxgM1VDW0sizf+qtVuktJiGb++FTe2XrU9KOV4V9H18qIPHhCKpEfa2zj2qc3UN3QyvM3zWVSRnDWUffHLWePISbCzqMfGDsqrz9DKHoAABcQSURBVGtpZ9PBY6aUVfzynA72VTbQ2Gr941P7a5XrKLGRdhZNNq971WAsL8ii5FgzWw4Hfglsb4rcnTFIIg+ekErkv3xnGwerm3jyutlMD+AZ3IORkhDNN+aN4q1CNwerjFsSFowmEr3Jy3KgNWwvDa9RubfDx99DtKzid0HuCKIibJZYvVLs9jAqOa5PvXCFMUIqkf982VSevn42Z40P7tK7/rr13LFE2m089qFxo/I1O8sZFhfYJhK98W/u8J+hES46yyptIbVapbvEmEgWT0pnZVEp3g6fqbEUlcjRtcEWUok8LTGacyZY/1ff9KQYvj53FH/Z4jak67nPp/nnrkoWTAxsE4nejEjq7IQebk0mVrpKO8sqk0JrtUp3K6ZnUdXQasppnH7HGtsoOdYsZZUgC6lEHkq+vWAsNqX433/uG/S1gtVEoi/yw2yHp7fDx+riMpZMSQ/62nyjLZqcTkJ0BG9vdZsWg395qjRbDi5J5AGS6YjlitnZvLHpCEdrmwd1rbVBaiLRF3lZSeytaAiJfpF9seFADdWNbZbsBNRfMZF2zs8dwd+Ky2j1dpgSgz+R50oiDypJ5AH03YXj0Br+b5Cj8rVBbCLRmzynA5+GHWEy4bmyqJS4KDsLQ7ys4re8IIv6Fi//3FVpyv1dJR5yUuJwxMpEZzBJIg+g7OFxXDbTySufH6FigGdGl3mC20SiN/5JrOIwOJvc2+Fj9bYylkwZYcoZPYEwf3wqyfFRpm3Zd7k9x489FsEjiTzAbl80ng6f5omP9g/o6z/Y1bmbc8lkazQ5yEiKITUhKiy26n+2v4aaxjaW5Yd+WcUv0m7j4vwM3t9RHvT1/tUNrbhrm+XEQxNIIg+w0SnxrCjI4uUNh6lqaO3316/taiIxcURCAKLrP6UUuVnhMeG5ynWU+Cg7CyeZP/dgpOUFTlrafby3vTyo9z1+4qGMyINOEnkQ3LZoPC3eDp5ad6BfX9fSHvwmEn2R73Swp6KBlnZzJtSM0N61CWjp1PApq/jNHj2cTEdM0MsrxZLITSOJPAjGpydwybQsXvz0IMca2/r8dRsOdDaRsEpZxS/P6aDDp0N6wvPTfdUca2rn4jAqq/jZbJ39PD/aXdmvn7fBKirxMCY1nqQYmegMNknkQfK9ReNpbOvg2fV9H5V/sLOCmEgbZ45LCWBk/fevCc/QLa+sKiolPspuiSWdgbC8IAtv14mOwVIsR9eaRhJ5kEzKSOTC3AyeXX8QT3PvXXa01qzZWc5Z41It96t/liOG4XGRITvh2d7hY/X2Ms4Lw7KKX25WEmPT4oO2OaiqoZWjnhZJ5CaRRB5EdywZT32rl+c/Odjre/dVNnCkptkSuzm7U0qR53TgCtEliJ/sq6a2qT0kGiwPlFKK5QVZbDhQQ5lnYEtf+8P/j7qcsWIOSeRBlJvlYOmUdJ7++AANvSwNW9vVRMIq68e7y3c62FNeH5ITnquKjpIQHcE5E6x9+NpgLS/IQmtYWRT4SU//QWq5WUkBv5f4MknkQXbH4gl4mtt54dODp33f2p0VpjaR6E2+04HXp9lVVm92KP3S3uFj9bbysC6r+I1NSyDPmRSUo21dbg9j0+JJlIlOU0giD7KCkcNYMDGNp9YdOOV5JZ7mdj4/eMySZRU//xKzUKuTr99bhae5Paw2AZ3O8oIstpZ4DD0bvyeuEo8clGUiSeQm+P6S8dQ0tvHHDYd7/Py6PZV0mNxEojfZw2NxxEaG3MqVVUWlJEZHcM7E8C6r+F3SNQ8QyFF5RX0LZXUtsn7cRJLITTBrdDJnjUvh8X/u77HGvHZnRWcTiVHDTYiub5RSXT08QyeRt3k7z1Y5L3cE0RHhXVbxyxoWy9ycZN4KYD9P/z/m07Kt2bVrKJBEbpI7Fk+gqqGVVzeePCrv8Gk+3FXJwolp2G3W2c3Zkzyng11l9aYdmdpf6/dWUdfiHTJlFb9Lp2ext6KBHaWBmc9wldShlEx0mkkSuUnOGJvM3JxkHv/n/pMS4daSWmoa2yy7WuVEec4k2js0u8sazA6lT1YWlZIYE8HZYb5apbuL8zKw21TAtuy73LWMS0sgPjo0+52GA0MSuVLqQqXULqXUXqXU3UZcM9wppbhjyXjK6lr40+aS469/YKEmEr3JD6EJzzavj39sL+P8qRlDpqzil5IQzdnjU3knQOUVl+zoNN2gE7lSyg48ClwETAW+rpSaOtjrDgVnj09l+shhPPbBPtq7Guau2VHB7NHJlmgi0ZtRyXEkxUSERCL/eG8l9S3esOgENBArpmfhrm1my+Fjhl63oq6F8rpWSeQmM2JEPhfYq7Xer7VuA14FVhhw3bCnlOLOJRNw1zbz1y1uyjwtbC+1ThOJ3vh3eG4LgQnPlUWlJMVEMH/80Cqr+J2fm0F0hI23C40tr8iOTmswIpE7gSMnfFzS9dpJlFK3KqU2KaU2VVaa04bKihZOSiPf6eDRD/fy3o7O86OtvOywu3yng52l9bR5fWaHckqt3g7e21bO+bkZREUMzWmhhOgIlkxJZ5WrFG+Hcd+rohIPNgVTM2Wi00xB+6nWWj+htZ6ttZ6dlmb9+m+wKKX43uLxHKpu4rf/2GWpJhJ9ket00NbhY3e5dXd4frynivpWL8uGaFnFb3lBFlUNbXy6v9qwa7rcHpnotAAjErkbGHnCx9ldr4k+Om/KCCZnJFLb1M7iyemWaiLRG39t1Mobg1YVleKIjWT+uKFZVvFbOCmdxOgIw8orWuvOiU4pq5jOiET+OTBBKTVGKRUFXA28bcB1hwybTfGDpRMAOD/XWk0kejM6OY7E6AjLbgxqae/gve3lXJA7YsiWVfxiIu2cn5vB37eVGbL2v7yulcp6mei0gkH/ZGutvcD3gNXADuB1rfW2wV53qLkwL5O1/7aAcyaEVtnJZlPkOpMse6Ttuq6ySjh2AhqI5dOzqG/x8uGuwc9TuY7v6JREbjZDhiha63e11hO11uO01g8Ycc2haGxa6NTGT5SX5WBHad3xJZRWsqroaGdZZYiuVulu/rgUUuKjDNkc5Cqp7ZrolERutqH9u6YwRH62gzavjz3l1trh2dLewfs7KrgwN4NIu/yoA0TYbVycn8n728t7PRO/Ny63hwnpicRGDa0NVlYkP91i0Pyn3lmtTv7R7koaZLXKlyyfnkWr18d72wfez9M/0SknHlqDJHIxaGNS4omPsltu5coqVynD4iIt17zabLNGDcc5LHZQq1fK6lqoamiT+rhFSCIXg2azKXKzHJbaqt/S3sH728ulrNIDm01xSUEm6/ZUcayxbUDXKOpq7SYjcmuQn3BhiDxn54SnkbsGB+OfuytpbOuQssopLC/IwuvTvFtcOqCvL3Z7sNuU7Oi0CEnkwhD52Um0tPvYVxnYlmJ9taqolOFxkZw5VsoqPZmamcS4tPgBl1eKSjxMSE+QiU6LkEQuDGGlI207V6uUc2FeJhFSVumRUorlBU42HqyhzNPSr6/VWlMsR9daivyUC0OMSU0gziITng+9v4emtg6WF2SZHYqlLZ+ehdawsqh/o/KjnhaqG9tka76FSCIXhvDXS80eka/eVsbj/9zH1+eOktUqvRiTGk++09HvzUGurolOGZFbhyRyYZg8p4PtR+vo8AWmyW9vDlQ18qPXtzIt28E9l0pvk75YXpBFUYmHA1V9n9twuWux2xRTZKLTMiSRC8PkOx00t3ewvzL4Ozyb2rx858XN2O2Kx74xk5hImYTri0sKMlEK3unHqNzlrmPiiET5O7YQSeTCMHkmTXhqrfnpX1zsrqjn4atnkD08Lqj3D2WZjljm5CTzVqG7T/08tda4SmqZJmUVS5FELgwzLi2emEhb0BP5S58d4s3Co9y1dCLnhkDTaqtZMT2LfZWNbC/t/QRLd20zx5rayZOJTkuRRC4ME2G3MTUziW1BPNJ2y+Fj3LdyO4smpfG9ReODdt9wcnFeJhE21adJT/9Ep4zIrUUSuTBUflczZl8QJjyrG1q5/eUtjEiK4XdXTcdmC53OSlYyPD6KcyaksnJraa/fN5fbQ4RNMSkjMUjRib6QRC4Mlet00NjWwf5+rIIYiA6f5vuvfkF1YxuPXzuLYXFRAb1fuFs+PQt3bTNbDh877ftcbg+TMmSi02okkQtDBauH52//sYv1e6u5f0WeHNxkgPOmZhAdYTtteUVrTVGJ7Oi0IknkwlAT0hOIjrAFNJG/t72cxz7cx9VzRnLlnJG9f4HoVUJ0BEunjOBdV+kpDz4rOdaMp7lddnRakCRyYagIu40pAdzhebCqkR++XkieM4lfLs8NyD2GqksLsqhqaOOTfdU9fr5IdnRaliRyYbg8ZxLbjtYZPuHZ3NbBd17ajE0p/vcbs6ROa7CFk9JIjI44ZXnF5fYQaZeJTiuSRC4Ml+900NDq5WC1cROeWmt+9qaLXeX1PHT1dEYmy6Yfo8VE2rkgL4PVxWW0tHd86fMudy2TMhKJjpB/QK1GErkw3L96eBq3nvzlDYf5yxY33188gUWT0g27rjjZ8oIs6lu9fLir8qTXO3d0esh3DjMpMnE6ksiF4SaOSCTKbtyEZ+GRWu57ZzsLJqZx55IJhlxT9OyscSmkJkTx9lb3Sa8frmmirsUr9XGLkkQuDBdptzE5M/H4LsDBqGls47aXNpOWGM1Dsukn4CLsNpblZ7JmRwX1Le3HX/dPXkuzZWuSRC4CIs/poPiop08HMZ1Kh09z56tfUNXQuelneLxs+gmG5dOzaPX6eG97+fHXXCUeouw2Jo6QiU4rkkQuAiLf6aC+xcvhmqYBX+Oh93ezbk8V967IlbXLQTRz1HCcw2JPWr3icnuYnJlIVISkDCuS74oIiMH28Fyzo5xH1u7lilnZXC2bfoJKKcWlBVl8vKeKmsa2zolOt0d20FqYJHIREBNGJBBpVwNK5Ierm7jrtUJys5L4z6/koZTUxYNteUEWXp/mXVcph6qbqG/xyomHFhZhdgAiPEVH2JmUkdjvlSst7Z2bfgDZ9GOiKZmJjE9P4O2tR0mKjQSQEbmFyYhcBEy+00Gxu67PE55aa/7jzWK2l9bx0NXTGZUim37MopRieUEWnx+s4b3t5URFyESnlUkiFwGT53TgaW6n5Fhzn97/6udHeGNzCd9fPJ7Fk0cEODrRm+UFWWjd2c9zSoZMdFqZfGdEwORl9X3Cs6iklnve2sY5E1K5c+nEQIcm+iAnNZ6CrtVCsmrI2iSRi4CZlJFIhK33Cc9jjW1896UtpCVG8/urZ2CXTT+WcWlBFiAnHlqdTHaKgImJtDNxxOknPDt8mh+8VkhlfStvfOdMkmXTj6VcPiub7aV1LJkipS4rkxG5CKjOCc9T7/B8eM0e/rm7knuWT6VgpBzIZDXD4qL4nyunk5oQbXYo4jQGlciVUlcopbYppXxKqdlGBSXCR162g2NN7bhrvzzh+cGuCh5eu4evzczmmrmjTIhOiPAw2BF5MXAZ8JEBsYgwlJeVBHy5h+eRmiZ+8GohkzOSuF82/QgxKINK5FrrHVrrXUYFI8LPlMwk7N0mPFvaO/juy5vxac3j184kNko2/QgxGDLZKQIqJtLOhPQEit3/ajLxy7e3Ueyu46nrZjM6Jd7E6IQID70mcqXU+0BGD5/6mdb6rb7eSCl1K3ArwKhRUg8dSvKdDtburEBrzRubSnj18yPcvmgcS6fKSgghjNBrItdaLzXiRlrrJ4AnAGbPnm1sV15haXlOB29sLuH9HRX8/K1izh6fyg/Pm2R2WEKEDVl+KALOf9jS7S9vISU+it9fPV02/QhhoMEuP/yqUqoEOBNYpZRabUxYIpxMzUzCpkCjeewbM0mRNclCGGpQk51a678CfzUoFhGmYqPsfHvBOCZnJDJj1HCzwxEi7MiqFREUP7lwstkhCBG2pEYuhBAhThK5EEKEOEnkQggR4iSRCyFEiJNELoQQIU4SuRBChDhJ5EIIEeIkkQshRIhTp2rBFdCbKlUJHBrgl6cCVQaGYzXh/HzybKErnJ8vlJ5ttNY6rfuLpiTywVBKbdJah21buXB+Pnm20BXOzxcOzyalFSGECHGSyIUQIsSFYiJ/wuwAAiycn0+eLXSF8/OF/LOFXI1cCCHEyUJxRC6EEOIEksiFECLEWTqRK6UuVErtUkrtVUrd3fXa97o+1kqpVLNjHIxTPN/TSqmtSqkipdSflFIJZsc5EKd4tueUUgeUUoVd/003O86BOMWzrTvhuY4qpd40O86BOsXzLVZKbVFKFSulnldKhWRTGqXUM0qpCqVU8QmvXaGU2qaU8imlQnMZotbakv8BdmAfMBaIArYCU4EZQA5wEEg1O84APF/SCe/5H+Bus2M18NmeAy43O75APFu39/wZuM7sWA3+3h0BJna95z7gZrNjHeDznQvMBIpPeG0KMAn4EJhtdowD+c/KI/K5wF6t9X6tdRvwKrBCa/2F1vqguaEZ4lTPVweglFJALBCKs9E9PpvJMRnltM+mlEoCFgOhOiLv6fm+BrRprXd3vee9rtdCjtb6I6Cm22s7tNa7TArJEFZO5E46RwF+JV2vhYtTPp9S6lmgDJgMPBL80AbtdN+7B7rKRr9TSkUHP7RB6+3n8ivAGv8/yCGop+fLACJOKDtcDowMdmDi1KycyIcsrfWNQBawA7jK5HCM9O90/uM0B0gGfmJuOAHxdeAVs4MwmAauBn6nlNoI1AMd5oYkTmTlRO7m5H/1s7teCxenfT6tdQf/+rU21PT4bFrrUt2pFXiWzl/jQ80pv29dk+9zgVUmxGWUU33vPtVan6O1ngt8BOzu8auFKaycyD8HJiilxiilougcEbxtckxG6vH5lFLj4XiNfDmw08QYB+pUz5YJx5/tK0Dxaa5hVaf7ubwcWKm1bjEtusE71fcuHaCrHPYT4HETYxTdWHYJkdbaq5T6HrCazpn0Z7TW25RS3wf+H511uyKl1Lta61vMjHUgeno+Oksp67omzBSdKwa+a16UA3Oa791apVQanc9WCHzHzDgH4lTP1vXpq4EHTQvOAKf53v1aKXUJnYO//9VarzU10AFSSr0CLARSlVIlwD10Tn4+AqQBq5RShVrrC8yLsv9ki74QQoQ4K5dWhBBC9IEkciGECHGSyIUQIsRJIhdCiBAniVwIIUKcJHIhhAhxkshFSOo6xvilEz6OUEpVKqVWmhmXEGaQRC5CVSOQp5SK7fr4PMLrCAch+kwSuQhl7wLLuv580mFVSqn4riYCG5VSXyilVnS9ntv1WmHXKYwTerqwUipHKbVDKfVkV9OBf/j/0VBKfeg/CVAplaqUOtj15xuUUm8qpd5TSh3saoLyw677f6aUSg7cX4UYyiSRi1D2KnC1UioGmAZsOOFzPwPWdh3ytAj4tVIqns5jAX6vtZ4OzKbzmNZTmQA8qrXOBWrp2wFmecBldJ7w+ADQpLWeAXwKXNefhxOiryx71ooQvdFaFymlcugcjb/b7dPnA8uVUj/q+jgGGEVnQv2ZUiob+IvWes9pbnFAa13Y9efNdHam6s0HWut6oF4p5QHe6XrdRec/NkIYThK5CHVvA7+h8yCklBNeV8DXeuj8skMptYHOksy7Sqlvn+YAqNYT/txBZ8cmAC//+m025jRf4zvhYx/y/zcRIFJaEaHuGeBerbWr2+urgTu6jsxFKTWj63/HAvu11g8DbzGwUfJBYFbXny8fSNBCGEkSuQhpWuuSrqTc3X8CkXQedbyt62OAK4FipVQhnfXsFwZw298A31VKfQGkDuDrhTCUHGMrhBAhTkbkQggR4mTyRQxpSqkUYE0Pn1qita4OdjxCDISUVoQQIsRJaUUIIUKcJHIhhAhxksiFECLESSIXQogQ9/8BFrx/Fe25RJwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# A média pode \"mascarar\" alguns resultados quando a distribuição não é normal. O uso da mediana ao invés da média pode ser uma alternativa para evitar o efeito dos 'outliers'\n",
        "# Usando a mediana, obtivemos um padrão gráfico semelhante, porém com algumas diferenças que são importantes em nossas análises\n",
        "# 1. Veja que janeiro passa a ser o mês com menor mediana\n",
        "# 2. Novembro não seria um mês tão bom quanto no grafico usando médias\n",
        "# 3. Set não seria tão ruim como no grafico usando médias\n",
        " \n",
        "medianas_mensais = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes_num\"], columns=[],aggfunc=np.median)\n",
        "medianas_mensais.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "Xp_Dffh8Qo-Q",
        "outputId": "64c48849-6b4f-4468-c243-7da2d1282700"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6393411710>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": "markdown",
      "source": [
        "### 3.2. Médias, medianas e somatório de resultados mensais ao longo de todo o período avaliado"
      ],
      "metadata": {
        "id": "3K56xMLnRdX_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Novamente, usando uma tabela dinâmica, porém com duas categorias de agrupamento: Ano em linhas e Mês em colunas\n",
        "# Nesse caso, o parametro usado no argumento aggfunc não importa já que é um valor apenas (poderia ser media, mediana, max...)\n",
        "\n",
        "result_mensais = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Ano\",], columns=[\"Mes_num\"],aggfunc=np.mean)\n",
        "result_mensais"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "OPeo8ZrmRdj_",
        "outputId": "fcc5d727-b1e7-46a0-a0d4-c0d1db7c977b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mes_num     01     02     03     04     05     06     07     08     09     10  \\\n",
              "Ano                                                                             \n",
              "2000     -4.12   7.76   0.91 -12.81  -3.74  11.84  -1.63   5.42  -8.18  -6.66   \n",
              "2001     15.82 -10.08  -9.14   3.32  -1.80  -0.61  -5.54  -6.64 -17.17   6.85   \n",
              "2002     -6.31  10.31  -5.54  -1.28  -1.71 -13.39 -12.35   6.34 -16.94  17.92   \n",
              "2003     -2.90  -6.03   9.66  11.38   6.89  -3.35   4.62  11.80   5.52  12.31   \n",
              "2004     -1.73  -0.44   1.78 -11.45  -0.32   8.21   5.62   2.09   1.94  -0.83   \n",
              "2005     -7.04  15.56  -5.43  -6.64   1.46  -0.62   3.96   7.69  12.62  -4.40   \n",
              "2006     14.73   0.59  -1.70   6.35  -9.50   0.28   1.22  -2.28   0.60   7.72   \n",
              "2007      0.38  -1.68   4.36   6.88   6.77   4.06  -0.38   0.84  10.67   8.03   \n",
              "2008     -6.88   6.72  -3.97  11.32   6.96 -10.43  -8.48  -6.43 -11.03 -24.80   \n",
              "2009      4.66  -2.84   7.18  15.55  12.49  -3.26   6.41   3.15   8.90   0.05   \n",
              "2010     -4.65   1.68   5.82  -4.04  -6.64  -3.35  10.80  -3.51   6.58   1.79   \n",
              "2011     -3.94   1.21   1.79  -3.58  -2.29  -3.43  -5.74  -3.96  -7.38  11.49   \n",
              "2012     11.13   4.34  -1.98  -4.17 -11.86  -0.25   3.20   1.72   3.71  -3.56   \n",
              "2013     -1.95  -3.91  -1.87  -0.78  -4.30 -11.31   1.64   3.68   4.66   3.66   \n",
              "2014     -7.51  -1.14   7.05   2.40  -0.75   3.76   5.00   9.78 -11.70   0.95   \n",
              "2015     -6.20   9.97  -0.84   9.93  -6.17   0.61  -4.17  -8.33  -3.36   1.80   \n",
              "2016     -6.79   5.91  16.97   7.70 -10.09   6.30  11.22   1.03   0.80  11.24   \n",
              "2017      7.38   3.08  -2.52   0.64  -4.12   0.30   4.80   7.46   4.88   0.02   \n",
              "2018     11.14   0.67  -0.13   0.88 -10.87  -5.20   8.87  -3.21   3.47  10.19   \n",
              "2019     10.82  -1.86  -0.18   0.98   0.70   4.06   0.84  -0.66   3.57   2.36   \n",
              "2020     -1.63  -8.43 -29.90  10.25   8.57   8.76   8.26  -3.44  -4.80  -0.69   \n",
              "2021     -2.77  -5.15   6.00   1.94   6.16   0.46  -3.94  -2.48  -6.57  -6.74   \n",
              "\n",
              "Mes_num     11     12  \n",
              "Ano                    \n",
              "2000    -10.63  14.84  \n",
              "2001     13.79   5.00  \n",
              "2002      3.35   7.22  \n",
              "2003     12.25  10.17  \n",
              "2004      9.01   4.25  \n",
              "2005      5.71   4.82  \n",
              "2006      6.80   6.06  \n",
              "2007     -3.54   1.40  \n",
              "2008     -1.77   2.61  \n",
              "2009      8.93   2.30  \n",
              "2010     -4.20   2.36  \n",
              "2011     -2.51  -0.21  \n",
              "2012      0.71   6.05  \n",
              "2013     -3.27  -1.86  \n",
              "2014      0.06  -8.52  \n",
              "2015     -1.63  -3.92  \n",
              "2016     -4.65  -2.71  \n",
              "2017     -3.15   6.16  \n",
              "2018      2.38  -1.81  \n",
              "2019      0.94   6.85  \n",
              "2020     15.90   9.57  \n",
              "2021     -1.53   2.85  "
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              "      <th>Mes_num</th>\n",
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              "      <th>03</th>\n",
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              "      <th>Ano</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2000</th>\n",
              "      <td>-4.12</td>\n",
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              "      <td>-12.81</td>\n",
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              "      <td>-8.18</td>\n",
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              "      <td>-10.63</td>\n",
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              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>15.82</td>\n",
              "      <td>-10.08</td>\n",
              "      <td>-9.14</td>\n",
              "      <td>3.32</td>\n",
              "      <td>-1.80</td>\n",
              "      <td>-0.61</td>\n",
              "      <td>-5.54</td>\n",
              "      <td>-6.64</td>\n",
              "      <td>-17.17</td>\n",
              "      <td>6.85</td>\n",
              "      <td>13.79</td>\n",
              "      <td>5.00</td>\n",
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              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>-6.31</td>\n",
              "      <td>10.31</td>\n",
              "      <td>-5.54</td>\n",
              "      <td>-1.28</td>\n",
              "      <td>-1.71</td>\n",
              "      <td>-13.39</td>\n",
              "      <td>-12.35</td>\n",
              "      <td>6.34</td>\n",
              "      <td>-16.94</td>\n",
              "      <td>17.92</td>\n",
              "      <td>3.35</td>\n",
              "      <td>7.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>-2.90</td>\n",
              "      <td>-6.03</td>\n",
              "      <td>9.66</td>\n",
              "      <td>11.38</td>\n",
              "      <td>6.89</td>\n",
              "      <td>-3.35</td>\n",
              "      <td>4.62</td>\n",
              "      <td>11.80</td>\n",
              "      <td>5.52</td>\n",
              "      <td>12.31</td>\n",
              "      <td>12.25</td>\n",
              "      <td>10.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>-1.73</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>1.78</td>\n",
              "      <td>-11.45</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>8.21</td>\n",
              "      <td>5.62</td>\n",
              "      <td>2.09</td>\n",
              "      <td>1.94</td>\n",
              "      <td>-0.83</td>\n",
              "      <td>9.01</td>\n",
              "      <td>4.25</td>\n",
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              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>-7.04</td>\n",
              "      <td>15.56</td>\n",
              "      <td>-5.43</td>\n",
              "      <td>-6.64</td>\n",
              "      <td>1.46</td>\n",
              "      <td>-0.62</td>\n",
              "      <td>3.96</td>\n",
              "      <td>7.69</td>\n",
              "      <td>12.62</td>\n",
              "      <td>-4.40</td>\n",
              "      <td>5.71</td>\n",
              "      <td>4.82</td>\n",
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              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>14.73</td>\n",
              "      <td>0.59</td>\n",
              "      <td>-1.70</td>\n",
              "      <td>6.35</td>\n",
              "      <td>-9.50</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.22</td>\n",
              "      <td>-2.28</td>\n",
              "      <td>0.60</td>\n",
              "      <td>7.72</td>\n",
              "      <td>6.80</td>\n",
              "      <td>6.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>0.38</td>\n",
              "      <td>-1.68</td>\n",
              "      <td>4.36</td>\n",
              "      <td>6.88</td>\n",
              "      <td>6.77</td>\n",
              "      <td>4.06</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>0.84</td>\n",
              "      <td>10.67</td>\n",
              "      <td>8.03</td>\n",
              "      <td>-3.54</td>\n",
              "      <td>1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>-6.88</td>\n",
              "      <td>6.72</td>\n",
              "      <td>-3.97</td>\n",
              "      <td>11.32</td>\n",
              "      <td>6.96</td>\n",
              "      <td>-10.43</td>\n",
              "      <td>-8.48</td>\n",
              "      <td>-6.43</td>\n",
              "      <td>-11.03</td>\n",
              "      <td>-24.80</td>\n",
              "      <td>-1.77</td>\n",
              "      <td>2.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>4.66</td>\n",
              "      <td>-2.84</td>\n",
              "      <td>7.18</td>\n",
              "      <td>15.55</td>\n",
              "      <td>12.49</td>\n",
              "      <td>-3.26</td>\n",
              "      <td>6.41</td>\n",
              "      <td>3.15</td>\n",
              "      <td>8.90</td>\n",
              "      <td>0.05</td>\n",
              "      <td>8.93</td>\n",
              "      <td>2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>-4.65</td>\n",
              "      <td>1.68</td>\n",
              "      <td>5.82</td>\n",
              "      <td>-4.04</td>\n",
              "      <td>-6.64</td>\n",
              "      <td>-3.35</td>\n",
              "      <td>10.80</td>\n",
              "      <td>-3.51</td>\n",
              "      <td>6.58</td>\n",
              "      <td>1.79</td>\n",
              "      <td>-4.20</td>\n",
              "      <td>2.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>-3.94</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.79</td>\n",
              "      <td>-3.58</td>\n",
              "      <td>-2.29</td>\n",
              "      <td>-3.43</td>\n",
              "      <td>-5.74</td>\n",
              "      <td>-3.96</td>\n",
              "      <td>-7.38</td>\n",
              "      <td>11.49</td>\n",
              "      <td>-2.51</td>\n",
              "      <td>-0.21</td>\n",
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              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>11.13</td>\n",
              "      <td>4.34</td>\n",
              "      <td>-1.98</td>\n",
              "      <td>-4.17</td>\n",
              "      <td>-11.86</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.72</td>\n",
              "      <td>3.71</td>\n",
              "      <td>-3.56</td>\n",
              "      <td>0.71</td>\n",
              "      <td>6.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>-1.95</td>\n",
              "      <td>-3.91</td>\n",
              "      <td>-1.87</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-4.30</td>\n",
              "      <td>-11.31</td>\n",
              "      <td>1.64</td>\n",
              "      <td>3.68</td>\n",
              "      <td>4.66</td>\n",
              "      <td>3.66</td>\n",
              "      <td>-3.27</td>\n",
              "      <td>-1.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>-7.51</td>\n",
              "      <td>-1.14</td>\n",
              "      <td>7.05</td>\n",
              "      <td>2.40</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>3.76</td>\n",
              "      <td>5.00</td>\n",
              "      <td>9.78</td>\n",
              "      <td>-11.70</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.06</td>\n",
              "      <td>-8.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>-6.20</td>\n",
              "      <td>9.97</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>9.93</td>\n",
              "      <td>-6.17</td>\n",
              "      <td>0.61</td>\n",
              "      <td>-4.17</td>\n",
              "      <td>-8.33</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>1.80</td>\n",
              "      <td>-1.63</td>\n",
              "      <td>-3.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>-6.79</td>\n",
              "      <td>5.91</td>\n",
              "      <td>16.97</td>\n",
              "      <td>7.70</td>\n",
              "      <td>-10.09</td>\n",
              "      <td>6.30</td>\n",
              "      <td>11.22</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.80</td>\n",
              "      <td>11.24</td>\n",
              "      <td>-4.65</td>\n",
              "      <td>-2.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>7.38</td>\n",
              "      <td>3.08</td>\n",
              "      <td>-2.52</td>\n",
              "      <td>0.64</td>\n",
              "      <td>-4.12</td>\n",
              "      <td>0.30</td>\n",
              "      <td>4.80</td>\n",
              "      <td>7.46</td>\n",
              "      <td>4.88</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-3.15</td>\n",
              "      <td>6.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>11.14</td>\n",
              "      <td>0.67</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-10.87</td>\n",
              "      <td>-5.20</td>\n",
              "      <td>8.87</td>\n",
              "      <td>-3.21</td>\n",
              "      <td>3.47</td>\n",
              "      <td>10.19</td>\n",
              "      <td>2.38</td>\n",
              "      <td>-1.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>10.82</td>\n",
              "      <td>-1.86</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.70</td>\n",
              "      <td>4.06</td>\n",
              "      <td>0.84</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>3.57</td>\n",
              "      <td>2.36</td>\n",
              "      <td>0.94</td>\n",
              "      <td>6.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>-1.63</td>\n",
              "      <td>-8.43</td>\n",
              "      <td>-29.90</td>\n",
              "      <td>10.25</td>\n",
              "      <td>8.57</td>\n",
              "      <td>8.76</td>\n",
              "      <td>8.26</td>\n",
              "      <td>-3.44</td>\n",
              "      <td>-4.80</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>15.90</td>\n",
              "      <td>9.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>-2.77</td>\n",
              "      <td>-5.15</td>\n",
              "      <td>6.00</td>\n",
              "      <td>1.94</td>\n",
              "      <td>6.16</td>\n",
              "      <td>0.46</td>\n",
              "      <td>-3.94</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>-6.74</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>2.85</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Um pouco de cor para facilitar\n",
        "# Nota algum mes com destaque positivo/negativo?\n",
        "\n",
        "result_mensais.style.format('{:.2f}', na_rep=\"\").applymap(lambda x: 'background-color : #e6ffe6' if x>0 else 'background-color : #ffe6e6')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        },
        "id": "5c_kG903Jvu3",
        "outputId": "bd2a50a7-a220-4909-a4e2-108a63268e64"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Mes_num</th>\n",
              "      <th class=\"col_heading level0 col0\" >01</th>\n",
              "      <th class=\"col_heading level0 col1\" >02</th>\n",
              "      <th class=\"col_heading level0 col2\" >03</th>\n",
              "      <th class=\"col_heading level0 col3\" >04</th>\n",
              "      <th class=\"col_heading level0 col4\" >05</th>\n",
              "      <th class=\"col_heading level0 col5\" >06</th>\n",
              "      <th class=\"col_heading level0 col6\" >07</th>\n",
              "      <th class=\"col_heading level0 col7\" >08</th>\n",
              "      <th class=\"col_heading level0 col8\" >09</th>\n",
              "      <th class=\"col_heading level0 col9\" >10</th>\n",
              "      <th class=\"col_heading level0 col10\" >11</th>\n",
              "      <th class=\"col_heading level0 col11\" >12</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Ano</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "      <th class=\"blank col7\" >&nbsp;</th>\n",
              "      <th class=\"blank col8\" >&nbsp;</th>\n",
              "      <th class=\"blank col9\" >&nbsp;</th>\n",
              "      <th class=\"blank col10\" >&nbsp;</th>\n",
              "      <th class=\"blank col11\" >&nbsp;</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row0\" class=\"row_heading level0 row0\" >2000</th>\n",
              "      <td id=\"T_c8b90_row0_col0\" class=\"data row0 col0\" >-4.12</td>\n",
              "      <td id=\"T_c8b90_row0_col1\" class=\"data row0 col1\" >7.76</td>\n",
              "      <td id=\"T_c8b90_row0_col2\" class=\"data row0 col2\" >0.91</td>\n",
              "      <td id=\"T_c8b90_row0_col3\" class=\"data row0 col3\" >-12.81</td>\n",
              "      <td id=\"T_c8b90_row0_col4\" class=\"data row0 col4\" >-3.74</td>\n",
              "      <td id=\"T_c8b90_row0_col5\" class=\"data row0 col5\" >11.84</td>\n",
              "      <td id=\"T_c8b90_row0_col6\" class=\"data row0 col6\" >-1.63</td>\n",
              "      <td id=\"T_c8b90_row0_col7\" class=\"data row0 col7\" >5.42</td>\n",
              "      <td id=\"T_c8b90_row0_col8\" class=\"data row0 col8\" >-8.18</td>\n",
              "      <td id=\"T_c8b90_row0_col9\" class=\"data row0 col9\" >-6.66</td>\n",
              "      <td id=\"T_c8b90_row0_col10\" class=\"data row0 col10\" >-10.63</td>\n",
              "      <td id=\"T_c8b90_row0_col11\" class=\"data row0 col11\" >14.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row1\" class=\"row_heading level0 row1\" >2001</th>\n",
              "      <td id=\"T_c8b90_row1_col0\" class=\"data row1 col0\" >15.82</td>\n",
              "      <td id=\"T_c8b90_row1_col1\" class=\"data row1 col1\" >-10.08</td>\n",
              "      <td id=\"T_c8b90_row1_col2\" class=\"data row1 col2\" >-9.14</td>\n",
              "      <td id=\"T_c8b90_row1_col3\" class=\"data row1 col3\" >3.32</td>\n",
              "      <td id=\"T_c8b90_row1_col4\" class=\"data row1 col4\" >-1.80</td>\n",
              "      <td id=\"T_c8b90_row1_col5\" class=\"data row1 col5\" >-0.61</td>\n",
              "      <td id=\"T_c8b90_row1_col6\" class=\"data row1 col6\" >-5.54</td>\n",
              "      <td id=\"T_c8b90_row1_col7\" class=\"data row1 col7\" >-6.64</td>\n",
              "      <td id=\"T_c8b90_row1_col8\" class=\"data row1 col8\" >-17.17</td>\n",
              "      <td id=\"T_c8b90_row1_col9\" class=\"data row1 col9\" >6.85</td>\n",
              "      <td id=\"T_c8b90_row1_col10\" class=\"data row1 col10\" >13.79</td>\n",
              "      <td id=\"T_c8b90_row1_col11\" class=\"data row1 col11\" >5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row2\" class=\"row_heading level0 row2\" >2002</th>\n",
              "      <td id=\"T_c8b90_row2_col0\" class=\"data row2 col0\" >-6.31</td>\n",
              "      <td id=\"T_c8b90_row2_col1\" class=\"data row2 col1\" >10.31</td>\n",
              "      <td id=\"T_c8b90_row2_col2\" class=\"data row2 col2\" >-5.54</td>\n",
              "      <td id=\"T_c8b90_row2_col3\" class=\"data row2 col3\" >-1.28</td>\n",
              "      <td id=\"T_c8b90_row2_col4\" class=\"data row2 col4\" >-1.71</td>\n",
              "      <td id=\"T_c8b90_row2_col5\" class=\"data row2 col5\" >-13.39</td>\n",
              "      <td id=\"T_c8b90_row2_col6\" class=\"data row2 col6\" >-12.35</td>\n",
              "      <td id=\"T_c8b90_row2_col7\" class=\"data row2 col7\" >6.34</td>\n",
              "      <td id=\"T_c8b90_row2_col8\" class=\"data row2 col8\" >-16.94</td>\n",
              "      <td id=\"T_c8b90_row2_col9\" class=\"data row2 col9\" >17.92</td>\n",
              "      <td id=\"T_c8b90_row2_col10\" class=\"data row2 col10\" >3.35</td>\n",
              "      <td id=\"T_c8b90_row2_col11\" class=\"data row2 col11\" >7.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row3\" class=\"row_heading level0 row3\" >2003</th>\n",
              "      <td id=\"T_c8b90_row3_col0\" class=\"data row3 col0\" >-2.90</td>\n",
              "      <td id=\"T_c8b90_row3_col1\" class=\"data row3 col1\" >-6.03</td>\n",
              "      <td id=\"T_c8b90_row3_col2\" class=\"data row3 col2\" >9.66</td>\n",
              "      <td id=\"T_c8b90_row3_col3\" class=\"data row3 col3\" >11.38</td>\n",
              "      <td id=\"T_c8b90_row3_col4\" class=\"data row3 col4\" >6.89</td>\n",
              "      <td id=\"T_c8b90_row3_col5\" class=\"data row3 col5\" >-3.35</td>\n",
              "      <td id=\"T_c8b90_row3_col6\" class=\"data row3 col6\" >4.62</td>\n",
              "      <td id=\"T_c8b90_row3_col7\" class=\"data row3 col7\" >11.80</td>\n",
              "      <td id=\"T_c8b90_row3_col8\" class=\"data row3 col8\" >5.52</td>\n",
              "      <td id=\"T_c8b90_row3_col9\" class=\"data row3 col9\" >12.31</td>\n",
              "      <td id=\"T_c8b90_row3_col10\" class=\"data row3 col10\" >12.25</td>\n",
              "      <td id=\"T_c8b90_row3_col11\" class=\"data row3 col11\" >10.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row4\" class=\"row_heading level0 row4\" >2004</th>\n",
              "      <td id=\"T_c8b90_row4_col0\" class=\"data row4 col0\" >-1.73</td>\n",
              "      <td id=\"T_c8b90_row4_col1\" class=\"data row4 col1\" >-0.44</td>\n",
              "      <td id=\"T_c8b90_row4_col2\" class=\"data row4 col2\" >1.78</td>\n",
              "      <td id=\"T_c8b90_row4_col3\" class=\"data row4 col3\" >-11.45</td>\n",
              "      <td id=\"T_c8b90_row4_col4\" class=\"data row4 col4\" >-0.32</td>\n",
              "      <td id=\"T_c8b90_row4_col5\" class=\"data row4 col5\" >8.21</td>\n",
              "      <td id=\"T_c8b90_row4_col6\" class=\"data row4 col6\" >5.62</td>\n",
              "      <td id=\"T_c8b90_row4_col7\" class=\"data row4 col7\" >2.09</td>\n",
              "      <td id=\"T_c8b90_row4_col8\" class=\"data row4 col8\" >1.94</td>\n",
              "      <td id=\"T_c8b90_row4_col9\" class=\"data row4 col9\" >-0.83</td>\n",
              "      <td id=\"T_c8b90_row4_col10\" class=\"data row4 col10\" >9.01</td>\n",
              "      <td id=\"T_c8b90_row4_col11\" class=\"data row4 col11\" >4.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row5\" class=\"row_heading level0 row5\" >2005</th>\n",
              "      <td id=\"T_c8b90_row5_col0\" class=\"data row5 col0\" >-7.04</td>\n",
              "      <td id=\"T_c8b90_row5_col1\" class=\"data row5 col1\" >15.56</td>\n",
              "      <td id=\"T_c8b90_row5_col2\" class=\"data row5 col2\" >-5.43</td>\n",
              "      <td id=\"T_c8b90_row5_col3\" class=\"data row5 col3\" >-6.64</td>\n",
              "      <td id=\"T_c8b90_row5_col4\" class=\"data row5 col4\" >1.46</td>\n",
              "      <td id=\"T_c8b90_row5_col5\" class=\"data row5 col5\" >-0.62</td>\n",
              "      <td id=\"T_c8b90_row5_col6\" class=\"data row5 col6\" >3.96</td>\n",
              "      <td id=\"T_c8b90_row5_col7\" class=\"data row5 col7\" >7.69</td>\n",
              "      <td id=\"T_c8b90_row5_col8\" class=\"data row5 col8\" >12.62</td>\n",
              "      <td id=\"T_c8b90_row5_col9\" class=\"data row5 col9\" >-4.40</td>\n",
              "      <td id=\"T_c8b90_row5_col10\" class=\"data row5 col10\" >5.71</td>\n",
              "      <td id=\"T_c8b90_row5_col11\" class=\"data row5 col11\" >4.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row6\" class=\"row_heading level0 row6\" >2006</th>\n",
              "      <td id=\"T_c8b90_row6_col0\" class=\"data row6 col0\" >14.73</td>\n",
              "      <td id=\"T_c8b90_row6_col1\" class=\"data row6 col1\" >0.59</td>\n",
              "      <td id=\"T_c8b90_row6_col2\" class=\"data row6 col2\" >-1.70</td>\n",
              "      <td id=\"T_c8b90_row6_col3\" class=\"data row6 col3\" >6.35</td>\n",
              "      <td id=\"T_c8b90_row6_col4\" class=\"data row6 col4\" >-9.50</td>\n",
              "      <td id=\"T_c8b90_row6_col5\" class=\"data row6 col5\" >0.28</td>\n",
              "      <td id=\"T_c8b90_row6_col6\" class=\"data row6 col6\" >1.22</td>\n",
              "      <td id=\"T_c8b90_row6_col7\" class=\"data row6 col7\" >-2.28</td>\n",
              "      <td id=\"T_c8b90_row6_col8\" class=\"data row6 col8\" >0.60</td>\n",
              "      <td id=\"T_c8b90_row6_col9\" class=\"data row6 col9\" >7.72</td>\n",
              "      <td id=\"T_c8b90_row6_col10\" class=\"data row6 col10\" >6.80</td>\n",
              "      <td id=\"T_c8b90_row6_col11\" class=\"data row6 col11\" >6.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row7\" class=\"row_heading level0 row7\" >2007</th>\n",
              "      <td id=\"T_c8b90_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
              "      <td id=\"T_c8b90_row7_col1\" class=\"data row7 col1\" >-1.68</td>\n",
              "      <td id=\"T_c8b90_row7_col2\" class=\"data row7 col2\" >4.36</td>\n",
              "      <td id=\"T_c8b90_row7_col3\" class=\"data row7 col3\" >6.88</td>\n",
              "      <td id=\"T_c8b90_row7_col4\" class=\"data row7 col4\" >6.77</td>\n",
              "      <td id=\"T_c8b90_row7_col5\" class=\"data row7 col5\" >4.06</td>\n",
              "      <td id=\"T_c8b90_row7_col6\" class=\"data row7 col6\" >-0.38</td>\n",
              "      <td id=\"T_c8b90_row7_col7\" class=\"data row7 col7\" >0.84</td>\n",
              "      <td id=\"T_c8b90_row7_col8\" class=\"data row7 col8\" >10.67</td>\n",
              "      <td id=\"T_c8b90_row7_col9\" class=\"data row7 col9\" >8.03</td>\n",
              "      <td id=\"T_c8b90_row7_col10\" class=\"data row7 col10\" >-3.54</td>\n",
              "      <td id=\"T_c8b90_row7_col11\" class=\"data row7 col11\" >1.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row8\" class=\"row_heading level0 row8\" >2008</th>\n",
              "      <td id=\"T_c8b90_row8_col0\" class=\"data row8 col0\" >-6.88</td>\n",
              "      <td id=\"T_c8b90_row8_col1\" class=\"data row8 col1\" >6.72</td>\n",
              "      <td id=\"T_c8b90_row8_col2\" class=\"data row8 col2\" >-3.97</td>\n",
              "      <td id=\"T_c8b90_row8_col3\" class=\"data row8 col3\" >11.32</td>\n",
              "      <td id=\"T_c8b90_row8_col4\" class=\"data row8 col4\" >6.96</td>\n",
              "      <td id=\"T_c8b90_row8_col5\" class=\"data row8 col5\" >-10.43</td>\n",
              "      <td id=\"T_c8b90_row8_col6\" class=\"data row8 col6\" >-8.48</td>\n",
              "      <td id=\"T_c8b90_row8_col7\" class=\"data row8 col7\" >-6.43</td>\n",
              "      <td id=\"T_c8b90_row8_col8\" class=\"data row8 col8\" >-11.03</td>\n",
              "      <td id=\"T_c8b90_row8_col9\" class=\"data row8 col9\" >-24.80</td>\n",
              "      <td id=\"T_c8b90_row8_col10\" class=\"data row8 col10\" >-1.77</td>\n",
              "      <td id=\"T_c8b90_row8_col11\" class=\"data row8 col11\" >2.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row9\" class=\"row_heading level0 row9\" >2009</th>\n",
              "      <td id=\"T_c8b90_row9_col0\" class=\"data row9 col0\" >4.66</td>\n",
              "      <td id=\"T_c8b90_row9_col1\" class=\"data row9 col1\" >-2.84</td>\n",
              "      <td id=\"T_c8b90_row9_col2\" class=\"data row9 col2\" >7.18</td>\n",
              "      <td id=\"T_c8b90_row9_col3\" class=\"data row9 col3\" >15.55</td>\n",
              "      <td id=\"T_c8b90_row9_col4\" class=\"data row9 col4\" >12.49</td>\n",
              "      <td id=\"T_c8b90_row9_col5\" class=\"data row9 col5\" >-3.26</td>\n",
              "      <td id=\"T_c8b90_row9_col6\" class=\"data row9 col6\" >6.41</td>\n",
              "      <td id=\"T_c8b90_row9_col7\" class=\"data row9 col7\" >3.15</td>\n",
              "      <td id=\"T_c8b90_row9_col8\" class=\"data row9 col8\" >8.90</td>\n",
              "      <td id=\"T_c8b90_row9_col9\" class=\"data row9 col9\" >0.05</td>\n",
              "      <td id=\"T_c8b90_row9_col10\" class=\"data row9 col10\" >8.93</td>\n",
              "      <td id=\"T_c8b90_row9_col11\" class=\"data row9 col11\" >2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row10\" class=\"row_heading level0 row10\" >2010</th>\n",
              "      <td id=\"T_c8b90_row10_col0\" class=\"data row10 col0\" >-4.65</td>\n",
              "      <td id=\"T_c8b90_row10_col1\" class=\"data row10 col1\" >1.68</td>\n",
              "      <td id=\"T_c8b90_row10_col2\" class=\"data row10 col2\" >5.82</td>\n",
              "      <td id=\"T_c8b90_row10_col3\" class=\"data row10 col3\" >-4.04</td>\n",
              "      <td id=\"T_c8b90_row10_col4\" class=\"data row10 col4\" >-6.64</td>\n",
              "      <td id=\"T_c8b90_row10_col5\" class=\"data row10 col5\" >-3.35</td>\n",
              "      <td id=\"T_c8b90_row10_col6\" class=\"data row10 col6\" >10.80</td>\n",
              "      <td id=\"T_c8b90_row10_col7\" class=\"data row10 col7\" >-3.51</td>\n",
              "      <td id=\"T_c8b90_row10_col8\" class=\"data row10 col8\" >6.58</td>\n",
              "      <td id=\"T_c8b90_row10_col9\" class=\"data row10 col9\" >1.79</td>\n",
              "      <td id=\"T_c8b90_row10_col10\" class=\"data row10 col10\" >-4.20</td>\n",
              "      <td id=\"T_c8b90_row10_col11\" class=\"data row10 col11\" >2.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row11\" class=\"row_heading level0 row11\" >2011</th>\n",
              "      <td id=\"T_c8b90_row11_col0\" class=\"data row11 col0\" >-3.94</td>\n",
              "      <td id=\"T_c8b90_row11_col1\" class=\"data row11 col1\" >1.21</td>\n",
              "      <td id=\"T_c8b90_row11_col2\" class=\"data row11 col2\" >1.79</td>\n",
              "      <td id=\"T_c8b90_row11_col3\" class=\"data row11 col3\" >-3.58</td>\n",
              "      <td id=\"T_c8b90_row11_col4\" class=\"data row11 col4\" >-2.29</td>\n",
              "      <td id=\"T_c8b90_row11_col5\" class=\"data row11 col5\" >-3.43</td>\n",
              "      <td id=\"T_c8b90_row11_col6\" class=\"data row11 col6\" >-5.74</td>\n",
              "      <td id=\"T_c8b90_row11_col7\" class=\"data row11 col7\" >-3.96</td>\n",
              "      <td id=\"T_c8b90_row11_col8\" class=\"data row11 col8\" >-7.38</td>\n",
              "      <td id=\"T_c8b90_row11_col9\" class=\"data row11 col9\" >11.49</td>\n",
              "      <td id=\"T_c8b90_row11_col10\" class=\"data row11 col10\" >-2.51</td>\n",
              "      <td id=\"T_c8b90_row11_col11\" class=\"data row11 col11\" >-0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row12\" class=\"row_heading level0 row12\" >2012</th>\n",
              "      <td id=\"T_c8b90_row12_col0\" class=\"data row12 col0\" >11.13</td>\n",
              "      <td id=\"T_c8b90_row12_col1\" class=\"data row12 col1\" >4.34</td>\n",
              "      <td id=\"T_c8b90_row12_col2\" class=\"data row12 col2\" >-1.98</td>\n",
              "      <td id=\"T_c8b90_row12_col3\" class=\"data row12 col3\" >-4.17</td>\n",
              "      <td id=\"T_c8b90_row12_col4\" class=\"data row12 col4\" >-11.86</td>\n",
              "      <td id=\"T_c8b90_row12_col5\" class=\"data row12 col5\" >-0.25</td>\n",
              "      <td id=\"T_c8b90_row12_col6\" class=\"data row12 col6\" >3.20</td>\n",
              "      <td id=\"T_c8b90_row12_col7\" class=\"data row12 col7\" >1.72</td>\n",
              "      <td id=\"T_c8b90_row12_col8\" class=\"data row12 col8\" >3.71</td>\n",
              "      <td id=\"T_c8b90_row12_col9\" class=\"data row12 col9\" >-3.56</td>\n",
              "      <td id=\"T_c8b90_row12_col10\" class=\"data row12 col10\" >0.71</td>\n",
              "      <td id=\"T_c8b90_row12_col11\" class=\"data row12 col11\" >6.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row13\" class=\"row_heading level0 row13\" >2013</th>\n",
              "      <td id=\"T_c8b90_row13_col0\" class=\"data row13 col0\" >-1.95</td>\n",
              "      <td id=\"T_c8b90_row13_col1\" class=\"data row13 col1\" >-3.91</td>\n",
              "      <td id=\"T_c8b90_row13_col2\" class=\"data row13 col2\" >-1.87</td>\n",
              "      <td id=\"T_c8b90_row13_col3\" class=\"data row13 col3\" >-0.78</td>\n",
              "      <td id=\"T_c8b90_row13_col4\" class=\"data row13 col4\" >-4.30</td>\n",
              "      <td id=\"T_c8b90_row13_col5\" class=\"data row13 col5\" >-11.31</td>\n",
              "      <td id=\"T_c8b90_row13_col6\" class=\"data row13 col6\" >1.64</td>\n",
              "      <td id=\"T_c8b90_row13_col7\" class=\"data row13 col7\" >3.68</td>\n",
              "      <td id=\"T_c8b90_row13_col8\" class=\"data row13 col8\" >4.66</td>\n",
              "      <td id=\"T_c8b90_row13_col9\" class=\"data row13 col9\" >3.66</td>\n",
              "      <td id=\"T_c8b90_row13_col10\" class=\"data row13 col10\" >-3.27</td>\n",
              "      <td id=\"T_c8b90_row13_col11\" class=\"data row13 col11\" >-1.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row14\" class=\"row_heading level0 row14\" >2014</th>\n",
              "      <td id=\"T_c8b90_row14_col0\" class=\"data row14 col0\" >-7.51</td>\n",
              "      <td id=\"T_c8b90_row14_col1\" class=\"data row14 col1\" >-1.14</td>\n",
              "      <td id=\"T_c8b90_row14_col2\" class=\"data row14 col2\" >7.05</td>\n",
              "      <td id=\"T_c8b90_row14_col3\" class=\"data row14 col3\" >2.40</td>\n",
              "      <td id=\"T_c8b90_row14_col4\" class=\"data row14 col4\" >-0.75</td>\n",
              "      <td id=\"T_c8b90_row14_col5\" class=\"data row14 col5\" >3.76</td>\n",
              "      <td id=\"T_c8b90_row14_col6\" class=\"data row14 col6\" >5.00</td>\n",
              "      <td id=\"T_c8b90_row14_col7\" class=\"data row14 col7\" >9.78</td>\n",
              "      <td id=\"T_c8b90_row14_col8\" class=\"data row14 col8\" >-11.70</td>\n",
              "      <td id=\"T_c8b90_row14_col9\" class=\"data row14 col9\" >0.95</td>\n",
              "      <td id=\"T_c8b90_row14_col10\" class=\"data row14 col10\" >0.06</td>\n",
              "      <td id=\"T_c8b90_row14_col11\" class=\"data row14 col11\" >-8.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row15\" class=\"row_heading level0 row15\" >2015</th>\n",
              "      <td id=\"T_c8b90_row15_col0\" class=\"data row15 col0\" >-6.20</td>\n",
              "      <td id=\"T_c8b90_row15_col1\" class=\"data row15 col1\" >9.97</td>\n",
              "      <td id=\"T_c8b90_row15_col2\" class=\"data row15 col2\" >-0.84</td>\n",
              "      <td id=\"T_c8b90_row15_col3\" class=\"data row15 col3\" >9.93</td>\n",
              "      <td id=\"T_c8b90_row15_col4\" class=\"data row15 col4\" >-6.17</td>\n",
              "      <td id=\"T_c8b90_row15_col5\" class=\"data row15 col5\" >0.61</td>\n",
              "      <td id=\"T_c8b90_row15_col6\" class=\"data row15 col6\" >-4.17</td>\n",
              "      <td id=\"T_c8b90_row15_col7\" class=\"data row15 col7\" >-8.33</td>\n",
              "      <td id=\"T_c8b90_row15_col8\" class=\"data row15 col8\" >-3.36</td>\n",
              "      <td id=\"T_c8b90_row15_col9\" class=\"data row15 col9\" >1.80</td>\n",
              "      <td id=\"T_c8b90_row15_col10\" class=\"data row15 col10\" >-1.63</td>\n",
              "      <td id=\"T_c8b90_row15_col11\" class=\"data row15 col11\" >-3.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row16\" class=\"row_heading level0 row16\" >2016</th>\n",
              "      <td id=\"T_c8b90_row16_col0\" class=\"data row16 col0\" >-6.79</td>\n",
              "      <td id=\"T_c8b90_row16_col1\" class=\"data row16 col1\" >5.91</td>\n",
              "      <td id=\"T_c8b90_row16_col2\" class=\"data row16 col2\" >16.97</td>\n",
              "      <td id=\"T_c8b90_row16_col3\" class=\"data row16 col3\" >7.70</td>\n",
              "      <td id=\"T_c8b90_row16_col4\" class=\"data row16 col4\" >-10.09</td>\n",
              "      <td id=\"T_c8b90_row16_col5\" class=\"data row16 col5\" >6.30</td>\n",
              "      <td id=\"T_c8b90_row16_col6\" class=\"data row16 col6\" >11.22</td>\n",
              "      <td id=\"T_c8b90_row16_col7\" class=\"data row16 col7\" >1.03</td>\n",
              "      <td id=\"T_c8b90_row16_col8\" class=\"data row16 col8\" >0.80</td>\n",
              "      <td id=\"T_c8b90_row16_col9\" class=\"data row16 col9\" >11.24</td>\n",
              "      <td id=\"T_c8b90_row16_col10\" class=\"data row16 col10\" >-4.65</td>\n",
              "      <td id=\"T_c8b90_row16_col11\" class=\"data row16 col11\" >-2.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row17\" class=\"row_heading level0 row17\" >2017</th>\n",
              "      <td id=\"T_c8b90_row17_col0\" class=\"data row17 col0\" >7.38</td>\n",
              "      <td id=\"T_c8b90_row17_col1\" class=\"data row17 col1\" >3.08</td>\n",
              "      <td id=\"T_c8b90_row17_col2\" class=\"data row17 col2\" >-2.52</td>\n",
              "      <td id=\"T_c8b90_row17_col3\" class=\"data row17 col3\" >0.64</td>\n",
              "      <td id=\"T_c8b90_row17_col4\" class=\"data row17 col4\" >-4.12</td>\n",
              "      <td id=\"T_c8b90_row17_col5\" class=\"data row17 col5\" >0.30</td>\n",
              "      <td id=\"T_c8b90_row17_col6\" class=\"data row17 col6\" >4.80</td>\n",
              "      <td id=\"T_c8b90_row17_col7\" class=\"data row17 col7\" >7.46</td>\n",
              "      <td id=\"T_c8b90_row17_col8\" class=\"data row17 col8\" >4.88</td>\n",
              "      <td id=\"T_c8b90_row17_col9\" class=\"data row17 col9\" >0.02</td>\n",
              "      <td id=\"T_c8b90_row17_col10\" class=\"data row17 col10\" >-3.15</td>\n",
              "      <td id=\"T_c8b90_row17_col11\" class=\"data row17 col11\" >6.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row18\" class=\"row_heading level0 row18\" >2018</th>\n",
              "      <td id=\"T_c8b90_row18_col0\" class=\"data row18 col0\" >11.14</td>\n",
              "      <td id=\"T_c8b90_row18_col1\" class=\"data row18 col1\" >0.67</td>\n",
              "      <td id=\"T_c8b90_row18_col2\" class=\"data row18 col2\" >-0.13</td>\n",
              "      <td id=\"T_c8b90_row18_col3\" class=\"data row18 col3\" >0.88</td>\n",
              "      <td id=\"T_c8b90_row18_col4\" class=\"data row18 col4\" >-10.87</td>\n",
              "      <td id=\"T_c8b90_row18_col5\" class=\"data row18 col5\" >-5.20</td>\n",
              "      <td id=\"T_c8b90_row18_col6\" class=\"data row18 col6\" >8.87</td>\n",
              "      <td id=\"T_c8b90_row18_col7\" class=\"data row18 col7\" >-3.21</td>\n",
              "      <td id=\"T_c8b90_row18_col8\" class=\"data row18 col8\" >3.47</td>\n",
              "      <td id=\"T_c8b90_row18_col9\" class=\"data row18 col9\" >10.19</td>\n",
              "      <td id=\"T_c8b90_row18_col10\" class=\"data row18 col10\" >2.38</td>\n",
              "      <td id=\"T_c8b90_row18_col11\" class=\"data row18 col11\" >-1.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row19\" class=\"row_heading level0 row19\" >2019</th>\n",
              "      <td id=\"T_c8b90_row19_col0\" class=\"data row19 col0\" >10.82</td>\n",
              "      <td id=\"T_c8b90_row19_col1\" class=\"data row19 col1\" >-1.86</td>\n",
              "      <td id=\"T_c8b90_row19_col2\" class=\"data row19 col2\" >-0.18</td>\n",
              "      <td id=\"T_c8b90_row19_col3\" class=\"data row19 col3\" >0.98</td>\n",
              "      <td id=\"T_c8b90_row19_col4\" class=\"data row19 col4\" >0.70</td>\n",
              "      <td id=\"T_c8b90_row19_col5\" class=\"data row19 col5\" >4.06</td>\n",
              "      <td id=\"T_c8b90_row19_col6\" class=\"data row19 col6\" >0.84</td>\n",
              "      <td id=\"T_c8b90_row19_col7\" class=\"data row19 col7\" >-0.66</td>\n",
              "      <td id=\"T_c8b90_row19_col8\" class=\"data row19 col8\" >3.57</td>\n",
              "      <td id=\"T_c8b90_row19_col9\" class=\"data row19 col9\" >2.36</td>\n",
              "      <td id=\"T_c8b90_row19_col10\" class=\"data row19 col10\" >0.94</td>\n",
              "      <td id=\"T_c8b90_row19_col11\" class=\"data row19 col11\" >6.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row20\" class=\"row_heading level0 row20\" >2020</th>\n",
              "      <td id=\"T_c8b90_row20_col0\" class=\"data row20 col0\" >-1.63</td>\n",
              "      <td id=\"T_c8b90_row20_col1\" class=\"data row20 col1\" >-8.43</td>\n",
              "      <td id=\"T_c8b90_row20_col2\" class=\"data row20 col2\" >-29.90</td>\n",
              "      <td id=\"T_c8b90_row20_col3\" class=\"data row20 col3\" >10.25</td>\n",
              "      <td id=\"T_c8b90_row20_col4\" class=\"data row20 col4\" >8.57</td>\n",
              "      <td id=\"T_c8b90_row20_col5\" class=\"data row20 col5\" >8.76</td>\n",
              "      <td id=\"T_c8b90_row20_col6\" class=\"data row20 col6\" >8.26</td>\n",
              "      <td id=\"T_c8b90_row20_col7\" class=\"data row20 col7\" >-3.44</td>\n",
              "      <td id=\"T_c8b90_row20_col8\" class=\"data row20 col8\" >-4.80</td>\n",
              "      <td id=\"T_c8b90_row20_col9\" class=\"data row20 col9\" >-0.69</td>\n",
              "      <td id=\"T_c8b90_row20_col10\" class=\"data row20 col10\" >15.90</td>\n",
              "      <td id=\"T_c8b90_row20_col11\" class=\"data row20 col11\" >9.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_c8b90_level0_row21\" class=\"row_heading level0 row21\" >2021</th>\n",
              "      <td id=\"T_c8b90_row21_col0\" class=\"data row21 col0\" >-2.77</td>\n",
              "      <td id=\"T_c8b90_row21_col1\" class=\"data row21 col1\" >-5.15</td>\n",
              "      <td id=\"T_c8b90_row21_col2\" class=\"data row21 col2\" >6.00</td>\n",
              "      <td id=\"T_c8b90_row21_col3\" class=\"data row21 col3\" >1.94</td>\n",
              "      <td id=\"T_c8b90_row21_col4\" class=\"data row21 col4\" >6.16</td>\n",
              "      <td id=\"T_c8b90_row21_col5\" class=\"data row21 col5\" >0.46</td>\n",
              "      <td id=\"T_c8b90_row21_col6\" class=\"data row21 col6\" >-3.94</td>\n",
              "      <td id=\"T_c8b90_row21_col7\" class=\"data row21 col7\" >-2.48</td>\n",
              "      <td id=\"T_c8b90_row21_col8\" class=\"data row21 col8\" >-6.57</td>\n",
              "      <td id=\"T_c8b90_row21_col9\" class=\"data row21 col9\" >-6.74</td>\n",
              "      <td id=\"T_c8b90_row21_col10\" class=\"data row21 col10\" >-1.53</td>\n",
              "      <td id=\"T_c8b90_row21_col11\" class=\"data row21 col11\" >2.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualização gráfica por boxplots\n",
        "# Lembrando que os boxplots trazem médias não paramétricas (mediana, quartis, amplitude...)\n",
        "\n",
        "fig = px.box(dataset_IBOV, x=\"Mes_num\", y=\"Resultado_mes\")\n",
        "fig.add_hline(y=0)\n",
        "fig.show()"
      ],
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        "outputId": "d6773d68-cfba-4799-bd8f-9a7dadc2ebe4"
      },
      "execution_count": 25,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "# Produzir uma tabela mais completa: números de meses avaliados, média, mediana, máx e mín\n",
        "\n",
        "Tabela_01 = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes_num\"], aggfunc=['count',np.mean,np.median,np.max,np.min])\n",
        "Tabela_01"
      ],
      "metadata": {
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          "height": 487
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        "id": "JzqpIhwORXDE",
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      "execution_count": 45,
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          "data": {
            "text/plain": [
              "                count          mean        median          amax          amin\n",
              "        Resultado_mes Resultado_mes Resultado_mes Resultado_mes Resultado_mes\n",
              "Mes_num                                                                      \n",
              "01                 22      0.529091        -2.360         15.82         -7.51\n",
              "02                 22      1.192727         0.630         15.56        -10.08\n",
              "03                 22     -0.076364        -0.155         16.97        -29.90\n",
              "04                 22      2.035000         1.460         15.55        -12.81\n",
              "05                 22     -1.098182        -1.755         12.49        -11.86\n",
              "06                 22     -0.298182         0.015         11.84        -13.39\n",
              "07                 22      1.555909         2.420         11.22        -12.35\n",
              "08                 22      0.911818         0.935         11.80         -8.33\n",
              "09                 22     -0.873182         1.370         12.62        -17.17\n",
              "10                 22      2.213636         1.795         17.92        -24.80\n",
              "11                 22      1.952273         0.385         15.90        -10.63\n",
              "12                 22      3.340000         3.550         14.84         -8.52"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>amax</th>\n",
              "      <th>amin</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>Resultado_mes</th>\n",
              "      <th>Resultado_mes</th>\n",
              "      <th>Resultado_mes</th>\n",
              "      <th>Resultado_mes</th>\n",
              "      <th>Resultado_mes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mes_num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>22</td>\n",
              "      <td>0.529091</td>\n",
              "      <td>-2.360</td>\n",
              "      <td>15.82</td>\n",
              "      <td>-7.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02</th>\n",
              "      <td>22</td>\n",
              "      <td>1.192727</td>\n",
              "      <td>0.630</td>\n",
              "      <td>15.56</td>\n",
              "      <td>-10.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>22</td>\n",
              "      <td>-0.076364</td>\n",
              "      <td>-0.155</td>\n",
              "      <td>16.97</td>\n",
              "      <td>-29.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04</th>\n",
              "      <td>22</td>\n",
              "      <td>2.035000</td>\n",
              "      <td>1.460</td>\n",
              "      <td>15.55</td>\n",
              "      <td>-12.81</td>\n",
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              "      <td>22</td>\n",
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              "      <td>-1.755</td>\n",
              "      <td>12.49</td>\n",
              "      <td>-11.86</td>\n",
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              "    <tr>\n",
              "      <th>06</th>\n",
              "      <td>22</td>\n",
              "      <td>-0.298182</td>\n",
              "      <td>0.015</td>\n",
              "      <td>11.84</td>\n",
              "      <td>-13.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07</th>\n",
              "      <td>22</td>\n",
              "      <td>1.555909</td>\n",
              "      <td>2.420</td>\n",
              "      <td>11.22</td>\n",
              "      <td>-12.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08</th>\n",
              "      <td>22</td>\n",
              "      <td>0.911818</td>\n",
              "      <td>0.935</td>\n",
              "      <td>11.80</td>\n",
              "      <td>-8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>09</th>\n",
              "      <td>22</td>\n",
              "      <td>-0.873182</td>\n",
              "      <td>1.370</td>\n",
              "      <td>12.62</td>\n",
              "      <td>-17.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>22</td>\n",
              "      <td>2.213636</td>\n",
              "      <td>1.795</td>\n",
              "      <td>17.92</td>\n",
              "      <td>-24.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>22</td>\n",
              "      <td>1.952273</td>\n",
              "      <td>0.385</td>\n",
              "      <td>15.90</td>\n",
              "      <td>-10.63</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>22</td>\n",
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              "      <td>3.550</td>\n",
              "      <td>14.84</td>\n",
              "      <td>-8.52</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Podemos remover o índice 1 das colunas (Resultado_mes) que é desnecessário\n",
        "# Assim, transformamos um multi index de colunas para index de colunas\n",
        "\n",
        "Tabela_01.columns = Tabela_01.columns.droplevel(1)\n",
        "Tabela_01"
      ],
      "metadata": {
        "id": "hlb6P0VxyfgY",
        "outputId": "c80d5bd1-6999-4e19-9d56-a8214a96cef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         count      mean  median   amax   amin\n",
              "Mes_num                                       \n",
              "01          22  0.529091  -2.360  15.82  -7.51\n",
              "02          22  1.192727   0.630  15.56 -10.08\n",
              "03          22 -0.076364  -0.155  16.97 -29.90\n",
              "04          22  2.035000   1.460  15.55 -12.81\n",
              "05          22 -1.098182  -1.755  12.49 -11.86\n",
              "06          22 -0.298182   0.015  11.84 -13.39\n",
              "07          22  1.555909   2.420  11.22 -12.35\n",
              "08          22  0.911818   0.935  11.80  -8.33\n",
              "09          22 -0.873182   1.370  12.62 -17.17\n",
              "10          22  2.213636   1.795  17.92 -24.80\n",
              "11          22  1.952273   0.385  15.90 -10.63\n",
              "12          22  3.340000   3.550  14.84  -8.52"
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              "          const element = document.querySelector('#df-4878cad3-9b0e-407d-8a45-0db0fa999531');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Produzir uma tabela por ordem decrescente de resultado médio\n",
        "# Aqui usaremos os heatmaps da biblioteca seaborn\n",
        "\n",
        "Tabela_01_b = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes_num\",\"Mes_final\"], aggfunc=[np.mean])\n",
        "Tabela_01_b.columns = Tabela_01_b.columns.droplevel(1)\n",
        "Tabela_01_b = Tabela_01_b.sort_values(by=\"mean\",ascending=False)\n",
        "\n",
        "cm = sns.diverging_palette(20,130, as_cmap=True)\n",
        "Tabela_01_b.style.format('{:.2f}', na_rep=\"\").background_gradient(cmap=cm,subset=[\"mean\"])"
      ],
      "metadata": {
        "id": "minw2fXnvYV-",
        "outputId": "41a501a3-82c1-425c-b2ab-8fb6af474fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "execution_count": 47,
      "outputs": [
        {
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              "      <th class=\"blank\" >&nbsp;</th>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >mean</th>\n",
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              "      <th class=\"index_name level0\" >Mes_num</th>\n",
              "      <th class=\"index_name level1\" >Mes_final</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
              "      <th id=\"T_a287c_level1_row0\" class=\"row_heading level1 row0\" >December</th>\n",
              "      <td id=\"T_a287c_row0_col0\" class=\"data row0 col0\" >3.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row1\" class=\"row_heading level0 row1\" >10</th>\n",
              "      <th id=\"T_a287c_level1_row1\" class=\"row_heading level1 row1\" >October</th>\n",
              "      <td id=\"T_a287c_row1_col0\" class=\"data row1 col0\" >2.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row2\" class=\"row_heading level0 row2\" >04</th>\n",
              "      <th id=\"T_a287c_level1_row2\" class=\"row_heading level1 row2\" >April</th>\n",
              "      <td id=\"T_a287c_row2_col0\" class=\"data row2 col0\" >2.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row3\" class=\"row_heading level0 row3\" >11</th>\n",
              "      <th id=\"T_a287c_level1_row3\" class=\"row_heading level1 row3\" >November</th>\n",
              "      <td id=\"T_a287c_row3_col0\" class=\"data row3 col0\" >1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row4\" class=\"row_heading level0 row4\" >07</th>\n",
              "      <th id=\"T_a287c_level1_row4\" class=\"row_heading level1 row4\" >July</th>\n",
              "      <td id=\"T_a287c_row4_col0\" class=\"data row4 col0\" >1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row5\" class=\"row_heading level0 row5\" >02</th>\n",
              "      <th id=\"T_a287c_level1_row5\" class=\"row_heading level1 row5\" >February</th>\n",
              "      <td id=\"T_a287c_row5_col0\" class=\"data row5 col0\" >1.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row6\" class=\"row_heading level0 row6\" >08</th>\n",
              "      <th id=\"T_a287c_level1_row6\" class=\"row_heading level1 row6\" >August</th>\n",
              "      <td id=\"T_a287c_row6_col0\" class=\"data row6 col0\" >0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row7\" class=\"row_heading level0 row7\" >01</th>\n",
              "      <th id=\"T_a287c_level1_row7\" class=\"row_heading level1 row7\" >January</th>\n",
              "      <td id=\"T_a287c_row7_col0\" class=\"data row7 col0\" >0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row8\" class=\"row_heading level0 row8\" >03</th>\n",
              "      <th id=\"T_a287c_level1_row8\" class=\"row_heading level1 row8\" >March</th>\n",
              "      <td id=\"T_a287c_row8_col0\" class=\"data row8 col0\" >-0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row9\" class=\"row_heading level0 row9\" >06</th>\n",
              "      <th id=\"T_a287c_level1_row9\" class=\"row_heading level1 row9\" >June</th>\n",
              "      <td id=\"T_a287c_row9_col0\" class=\"data row9 col0\" >-0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row10\" class=\"row_heading level0 row10\" >09</th>\n",
              "      <th id=\"T_a287c_level1_row10\" class=\"row_heading level1 row10\" >September</th>\n",
              "      <td id=\"T_a287c_row10_col0\" class=\"data row10 col0\" >-0.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a287c_level0_row11\" class=\"row_heading level0 row11\" >05</th>\n",
              "      <th id=\"T_a287c_level1_row11\" class=\"row_heading level1 row11\" >May</th>\n",
              "      <td id=\"T_a287c_row11_col0\" class=\"data row11 col0\" >-1.10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Produzir uma tabela por ordem decrescente de resultado da mediana\n",
        "\n",
        "Tabela_01_c = pd.pivot_table(dataset_IBOV, values=\"Resultado_mes\", index=[\"Mes_num\",\"Mes_final\"], aggfunc=[np.median])\n",
        "Tabela_01_c.columns = Tabela_01_c.columns.droplevel(1)\n",
        "Tabela_01_c = Tabela_01_c.sort_values(by=\"median\",ascending=False)\n",
        "\n",
        "cm = sns.diverging_palette(20,130, as_cmap=True)\n",
        "Tabela_01_c.style.format('{:.2f}', na_rep=\"\").background_gradient(cmap=cm,subset=[\"median\"])"
      ],
      "metadata": {
        "id": "s_EM6JLjyx3l",
        "outputId": "586307f2-cc9d-4840-878c-e1e91a78beae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f63925f7290>"
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              "}\n",
              "</style>\n",
              "<table id=\"T_b4599_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank\" >&nbsp;</th>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Mes_num</th>\n",
              "      <th class=\"index_name level1\" >Mes_final</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row0\" class=\"row_heading level0 row0\" >12</th>\n",
              "      <th id=\"T_b4599_level1_row0\" class=\"row_heading level1 row0\" >December</th>\n",
              "      <td id=\"T_b4599_row0_col0\" class=\"data row0 col0\" >3.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row1\" class=\"row_heading level0 row1\" >07</th>\n",
              "      <th id=\"T_b4599_level1_row1\" class=\"row_heading level1 row1\" >July</th>\n",
              "      <td id=\"T_b4599_row1_col0\" class=\"data row1 col0\" >2.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row2\" class=\"row_heading level0 row2\" >10</th>\n",
              "      <th id=\"T_b4599_level1_row2\" class=\"row_heading level1 row2\" >October</th>\n",
              "      <td id=\"T_b4599_row2_col0\" class=\"data row2 col0\" >1.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row3\" class=\"row_heading level0 row3\" >04</th>\n",
              "      <th id=\"T_b4599_level1_row3\" class=\"row_heading level1 row3\" >April</th>\n",
              "      <td id=\"T_b4599_row3_col0\" class=\"data row3 col0\" >1.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row4\" class=\"row_heading level0 row4\" >09</th>\n",
              "      <th id=\"T_b4599_level1_row4\" class=\"row_heading level1 row4\" >September</th>\n",
              "      <td id=\"T_b4599_row4_col0\" class=\"data row4 col0\" >1.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row5\" class=\"row_heading level0 row5\" >08</th>\n",
              "      <th id=\"T_b4599_level1_row5\" class=\"row_heading level1 row5\" >August</th>\n",
              "      <td id=\"T_b4599_row5_col0\" class=\"data row5 col0\" >0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row6\" class=\"row_heading level0 row6\" >02</th>\n",
              "      <th id=\"T_b4599_level1_row6\" class=\"row_heading level1 row6\" >February</th>\n",
              "      <td id=\"T_b4599_row6_col0\" class=\"data row6 col0\" >0.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row7\" class=\"row_heading level0 row7\" >11</th>\n",
              "      <th id=\"T_b4599_level1_row7\" class=\"row_heading level1 row7\" >November</th>\n",
              "      <td id=\"T_b4599_row7_col0\" class=\"data row7 col0\" >0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row8\" class=\"row_heading level0 row8\" >06</th>\n",
              "      <th id=\"T_b4599_level1_row8\" class=\"row_heading level1 row8\" >June</th>\n",
              "      <td id=\"T_b4599_row8_col0\" class=\"data row8 col0\" >0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row9\" class=\"row_heading level0 row9\" >03</th>\n",
              "      <th id=\"T_b4599_level1_row9\" class=\"row_heading level1 row9\" >March</th>\n",
              "      <td id=\"T_b4599_row9_col0\" class=\"data row9 col0\" >-0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row10\" class=\"row_heading level0 row10\" >05</th>\n",
              "      <th id=\"T_b4599_level1_row10\" class=\"row_heading level1 row10\" >May</th>\n",
              "      <td id=\"T_b4599_row10_col0\" class=\"data row10 col0\" >-1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b4599_level0_row11\" class=\"row_heading level0 row11\" >01</th>\n",
              "      <th id=\"T_b4599_level1_row11\" class=\"row_heading level1 row11\" >January</th>\n",
              "      <td id=\"T_b4599_row11_col0\" class=\"data row11 col0\" >-2.36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Bv0zYNZ_hmd"
      },
      "source": [
        "### 3.3. Separando meses positivos / negativos\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Primeiro criaremos uma estrutura de repetição para classificar os resultados mensais em positivos x negativos\n",
        "\n",
        "ls_resultados_mensais = dataset_IBOV['Resultado_mes']\n",
        "Resultado_bin = []\n",
        "\n",
        "for i in ls_resultados_mensais:\n",
        "  if i > 0:\n",
        "    Resultado_bin.append('positivo')\n",
        "  else:\n",
        "    Resultado_bin.append('negativo')"
      ],
      "metadata": {
        "id": "SXuHs9YMU22L"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Criamos uma nova coluna no nosso conjunto de dados \"Resultado_bin\", indicando se o mês foi positivo ou negativo\n",
        "\n",
        "dataset_IBOV['Resultado_bin'] = Resultado_bin\n",
        "dataset_IBOV"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "ofZNEg3RV5Di",
        "outputId": "9b084e46-bdc1-4fb2-e46d-14c199df9250"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-01-01   17098.0   18099.0   15350.0   16388.0    16388.0          0   \n",
              "2000-02-01   16389.0   18886.0   16389.0   17660.0    17660.0          0   \n",
              "2000-03-01   17668.0   19047.0   16922.0   17820.0    17820.0          0   \n",
              "2000-04-01   17826.0   17826.0   14029.0   15538.0    15538.0          0   \n",
              "2000-05-01   15596.0   15944.0   13556.0   14957.0    14957.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-08-01  121803.0  124536.0  114801.0  118781.0   118781.0  201788600   \n",
              "2021-09-01  118793.0  119942.0  107520.0  110979.0   110979.0  231540400   \n",
              "2021-10-01  110980.0  114927.0  102854.0  103501.0   103501.0  243860400   \n",
              "2021-10-31  103514.0  108669.0  100075.0  101915.0   101915.0  236342500   \n",
              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
              "            Resultado_mes        Mes   Ano Mes_num       Data  Mes_final  \\\n",
              "Date                                                                       \n",
              "2000-01-01          -4.12    January  2000      01 2000-01-01    January   \n",
              "2000-02-01           7.76   February  2000      02 2000-02-01   February   \n",
              "2000-03-01           0.91      March  2000      03 2000-03-01      March   \n",
              "2000-04-01         -12.81      April  2000      04 2000-04-01      April   \n",
              "2000-05-01          -3.74        May  2000      05 2000-05-01        May   \n",
              "...                   ...        ...   ...     ...        ...        ...   \n",
              "2021-08-01          -2.48     August  2021      08 2021-08-01     August   \n",
              "2021-09-01          -6.57  September  2021      09 2021-09-01  September   \n",
              "2021-10-01          -6.74    October  2021      10 2021-10-01    October   \n",
              "2021-10-31          -1.53    October  2021      11 2021-11-01   November   \n",
              "2021-12-01           2.85   December  2021      12 2021-12-01   December   \n",
              "\n",
              "           Resultado_bin  \n",
              "Date                      \n",
              "2000-01-01      negativo  \n",
              "2000-02-01      positivo  \n",
              "2000-03-01      positivo  \n",
              "2000-04-01      negativo  \n",
              "2000-05-01      negativo  \n",
              "...                  ...  \n",
              "2021-08-01      negativo  \n",
              "2021-09-01      negativo  \n",
              "2021-10-01      negativo  \n",
              "2021-10-31      negativo  \n",
              "2021-12-01      positivo  \n",
              "\n",
              "[264 rows x 13 columns]"
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              "      <th></th>\n",
              "      <th>Open</th>\n",
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              "      <th>Low</th>\n",
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              "      <th>Adj Close</th>\n",
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              "      <th>Date</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2000-01-01</th>\n",
              "      <td>17098.0</td>\n",
              "      <td>18099.0</td>\n",
              "      <td>15350.0</td>\n",
              "      <td>16388.0</td>\n",
              "      <td>16388.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-4.12</td>\n",
              "      <td>January</td>\n",
              "      <td>2000</td>\n",
              "      <td>01</td>\n",
              "      <td>2000-01-01</td>\n",
              "      <td>January</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-02-01</th>\n",
              "      <td>16389.0</td>\n",
              "      <td>18886.0</td>\n",
              "      <td>16389.0</td>\n",
              "      <td>17660.0</td>\n",
              "      <td>17660.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.76</td>\n",
              "      <td>February</td>\n",
              "      <td>2000</td>\n",
              "      <td>02</td>\n",
              "      <td>2000-02-01</td>\n",
              "      <td>February</td>\n",
              "      <td>positivo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-03-01</th>\n",
              "      <td>17668.0</td>\n",
              "      <td>19047.0</td>\n",
              "      <td>16922.0</td>\n",
              "      <td>17820.0</td>\n",
              "      <td>17820.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.91</td>\n",
              "      <td>March</td>\n",
              "      <td>2000</td>\n",
              "      <td>03</td>\n",
              "      <td>2000-03-01</td>\n",
              "      <td>March</td>\n",
              "      <td>positivo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-04-01</th>\n",
              "      <td>17826.0</td>\n",
              "      <td>17826.0</td>\n",
              "      <td>14029.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-12.81</td>\n",
              "      <td>April</td>\n",
              "      <td>2000</td>\n",
              "      <td>04</td>\n",
              "      <td>2000-04-01</td>\n",
              "      <td>April</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>15596.0</td>\n",
              "      <td>15944.0</td>\n",
              "      <td>13556.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.74</td>\n",
              "      <td>May</td>\n",
              "      <td>2000</td>\n",
              "      <td>05</td>\n",
              "      <td>2000-05-01</td>\n",
              "      <td>May</td>\n",
              "      <td>negativo</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>2021-08-01</th>\n",
              "      <td>121803.0</td>\n",
              "      <td>124536.0</td>\n",
              "      <td>114801.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>201788600</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>August</td>\n",
              "      <td>2021</td>\n",
              "      <td>08</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>August</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>118793.0</td>\n",
              "      <td>119942.0</td>\n",
              "      <td>107520.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>231540400</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>September</td>\n",
              "      <td>2021</td>\n",
              "      <td>09</td>\n",
              "      <td>2021-09-01</td>\n",
              "      <td>September</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>110980.0</td>\n",
              "      <td>114927.0</td>\n",
              "      <td>102854.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>243860400</td>\n",
              "      <td>-6.74</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>October</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-31</th>\n",
              "      <td>103514.0</td>\n",
              "      <td>108669.0</td>\n",
              "      <td>100075.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>236342500</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>11</td>\n",
              "      <td>2021-11-01</td>\n",
              "      <td>November</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-01</th>\n",
              "      <td>101916.0</td>\n",
              "      <td>109493.0</td>\n",
              "      <td>100727.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>234055900</td>\n",
              "      <td>2.85</td>\n",
              "      <td>December</td>\n",
              "      <td>2021</td>\n",
              "      <td>12</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>December</td>\n",
              "      <td>positivo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>264 rows × 13 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtramos os meses positivos\n",
        "\n",
        "dataset_IBOV_positivos = dataset_IBOV[dataset_IBOV.Resultado_bin == 'positivo']\n",
        "dataset_IBOV_positivos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "0fJAuYAcWx3j",
        "outputId": "214ad86c-1597-4b41-d0b1-daa74e71032b"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-02-01   16389.0   18886.0   16389.0   17660.0    17660.0          0   \n",
              "2000-03-01   17668.0   19047.0   16922.0   17820.0    17820.0          0   \n",
              "2000-06-01   14967.0   17396.0   14967.0   16728.0    16728.0          0   \n",
              "2000-08-01   16475.0   17926.0   16073.0   17347.0    17347.0          0   \n",
              "2000-12-01   13303.0   15415.0   13242.0   15259.0    15259.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-03-01  110036.0  117249.0  107319.0  116634.0   116634.0  215718800   \n",
              "2021-04-01  116644.0  121974.0  114991.0  118894.0   118894.0  158707300   \n",
              "2021-05-01  118951.0  126216.0  117631.0  126216.0   126216.0  171080700   \n",
              "2021-06-01  126218.0  131190.0  126184.0  126802.0   126802.0  197840500   \n",
              "2021-12-01  101916.0  109493.0  100727.0  104822.0   104822.0  234055900   \n",
              "\n",
              "            Resultado_mes       Mes   Ano Mes_num       Data Mes_final  \\\n",
              "Date                                                                     \n",
              "2000-02-01           7.76  February  2000      02 2000-02-01  February   \n",
              "2000-03-01           0.91     March  2000      03 2000-03-01     March   \n",
              "2000-06-01          11.84      June  2000      06 2000-06-01      June   \n",
              "2000-08-01           5.42    August  2000      08 2000-08-01    August   \n",
              "2000-12-01          14.84  December  2000      12 2000-12-01  December   \n",
              "...                   ...       ...   ...     ...        ...       ...   \n",
              "2021-03-01           6.00     March  2021      03 2021-03-01     March   \n",
              "2021-04-01           1.94     April  2021      04 2021-04-01     April   \n",
              "2021-05-01           6.16       May  2021      05 2021-05-01       May   \n",
              "2021-06-01           0.46      June  2021      06 2021-06-01      June   \n",
              "2021-12-01           2.85  December  2021      12 2021-12-01  December   \n",
              "\n",
              "           Resultado_bin  \n",
              "Date                      \n",
              "2000-02-01      positivo  \n",
              "2000-03-01      positivo  \n",
              "2000-06-01      positivo  \n",
              "2000-08-01      positivo  \n",
              "2000-12-01      positivo  \n",
              "...                  ...  \n",
              "2021-03-01      positivo  \n",
              "2021-04-01      positivo  \n",
              "2021-05-01      positivo  \n",
              "2021-06-01      positivo  \n",
              "2021-12-01      positivo  \n",
              "\n",
              "[145 rows x 13 columns]"
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              "      <td>March</td>\n",
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              "      <th>2000-06-01</th>\n",
              "      <td>14967.0</td>\n",
              "      <td>17396.0</td>\n",
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              "      <td>16728.0</td>\n",
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              "      <td>June</td>\n",
              "      <td>positivo</td>\n",
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              "      <th>2000-08-01</th>\n",
              "      <td>16475.0</td>\n",
              "      <td>17926.0</td>\n",
              "      <td>16073.0</td>\n",
              "      <td>17347.0</td>\n",
              "      <td>17347.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.42</td>\n",
              "      <td>August</td>\n",
              "      <td>2000</td>\n",
              "      <td>08</td>\n",
              "      <td>2000-08-01</td>\n",
              "      <td>August</td>\n",
              "      <td>positivo</td>\n",
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              "      <th>2000-12-01</th>\n",
              "      <td>13303.0</td>\n",
              "      <td>15415.0</td>\n",
              "      <td>13242.0</td>\n",
              "      <td>15259.0</td>\n",
              "      <td>15259.0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.84</td>\n",
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              "      <td>2000</td>\n",
              "      <td>12</td>\n",
              "      <td>2000-12-01</td>\n",
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              "      <td>107319.0</td>\n",
              "      <td>116634.0</td>\n",
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              "      <td>6.00</td>\n",
              "      <td>March</td>\n",
              "      <td>2021</td>\n",
              "      <td>03</td>\n",
              "      <td>2021-03-01</td>\n",
              "      <td>March</td>\n",
              "      <td>positivo</td>\n",
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              "      <td>121974.0</td>\n",
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              "      <td>1.94</td>\n",
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              "      <td>2021</td>\n",
              "      <td>04</td>\n",
              "      <td>2021-04-01</td>\n",
              "      <td>April</td>\n",
              "      <td>positivo</td>\n",
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              "      <td>126216.0</td>\n",
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              "      <td>6.16</td>\n",
              "      <td>May</td>\n",
              "      <td>2021</td>\n",
              "      <td>05</td>\n",
              "      <td>2021-05-01</td>\n",
              "      <td>May</td>\n",
              "      <td>positivo</td>\n",
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              "      <td>126802.0</td>\n",
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              "      <td>0.46</td>\n",
              "      <td>June</td>\n",
              "      <td>2021</td>\n",
              "      <td>06</td>\n",
              "      <td>2021-06-01</td>\n",
              "      <td>June</td>\n",
              "      <td>positivo</td>\n",
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              "      <th>2021-12-01</th>\n",
              "      <td>101916.0</td>\n",
              "      <td>109493.0</td>\n",
              "      <td>100727.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>104822.0</td>\n",
              "      <td>234055900</td>\n",
              "      <td>2.85</td>\n",
              "      <td>December</td>\n",
              "      <td>2021</td>\n",
              "      <td>12</td>\n",
              "      <td>2021-12-01</td>\n",
              "      <td>December</td>\n",
              "      <td>positivo</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Produzir uma tabela similar à anterior (números de meses avaliados, somatório dos resultados, média, mediana, máx e mín dos resultados), mas dessa vez apenas considerando os meses positivos\n",
        "\n",
        "Tabela_02 = pd.pivot_table(dataset_IBOV_positivos, values=\"Resultado_mes\", index=[\"Mes_num\"], aggfunc=['count',np.mean,np.median,np.max,np.min])\n",
        "Tabela_02.columns = Tabela_02.columns.droplevel(1)\n",
        "Tabela_02"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "Y1iDWM2YXTd5",
        "outputId": "e47cf35d-d59c-4a00-fd28-15da05765234"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         count      mean  median   amax  amin\n",
              "Mes_num                                      \n",
              "01           8  9.507500  10.975  15.82  0.38\n",
              "02          12  5.650000   5.125  15.56  0.59\n",
              "03          10  6.152000   5.910  16.97  0.91\n",
              "04          14  6.394286   6.615  15.55  0.64\n",
              "05           8  6.250000   6.830  12.49  0.70\n",
              "06          11  4.421818   4.060  11.84  0.28\n",
              "07          14  5.461429   4.900  11.22  0.84\n",
              "08          12  5.083333   4.550  11.80  0.84\n",
              "09          13  5.224615   4.660  12.62  0.60\n",
              "10          15  6.425333   6.850  17.92  0.02\n",
              "11          12  6.652500   6.255  15.90  0.06\n",
              "12          16  5.781875   5.525  14.84  1.40"
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              "      <td>17.92</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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          },
          "metadata": {},
          "execution_count": 52
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Agora, filtramos os meses negativos\n",
        "\n",
        "dataset_IBOV_negativos = dataset_IBOV[dataset_IBOV.Resultado_bin == 'negativo']\n",
        "dataset_IBOV_negativos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
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        "id": "2PDNCoIUX3Xa",
        "outputId": "70b7034e-b0c2-4630-bd37-b5ccdf65cb44"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Open      High       Low     Close  Adj Close     Volume  \\\n",
              "Date                                                                       \n",
              "2000-01-01   17098.0   18099.0   15350.0   16388.0    16388.0          0   \n",
              "2000-04-01   17826.0   17826.0   14029.0   15538.0    15538.0          0   \n",
              "2000-05-01   15596.0   15944.0   13556.0   14957.0    14957.0          0   \n",
              "2000-07-01   16747.0   17795.0   16390.0   16455.0    16455.0          0   \n",
              "2000-09-01   17360.0   17716.0   15657.0   15928.0    15928.0          0   \n",
              "...              ...       ...       ...       ...        ...        ...   \n",
              "2021-07-01  126802.0  129620.0  121748.0  121801.0   121801.0  152098400   \n",
              "2021-08-01  121803.0  124536.0  114801.0  118781.0   118781.0  201788600   \n",
              "2021-09-01  118793.0  119942.0  107520.0  110979.0   110979.0  231540400   \n",
              "2021-10-01  110980.0  114927.0  102854.0  103501.0   103501.0  243860400   \n",
              "2021-10-31  103514.0  108669.0  100075.0  101915.0   101915.0  236342500   \n",
              "\n",
              "            Resultado_mes        Mes   Ano Mes_num       Data  Mes_final  \\\n",
              "Date                                                                       \n",
              "2000-01-01          -4.12    January  2000      01 2000-01-01    January   \n",
              "2000-04-01         -12.81      April  2000      04 2000-04-01      April   \n",
              "2000-05-01          -3.74        May  2000      05 2000-05-01        May   \n",
              "2000-07-01          -1.63       July  2000      07 2000-07-01       July   \n",
              "2000-09-01          -8.18  September  2000      09 2000-09-01  September   \n",
              "...                   ...        ...   ...     ...        ...        ...   \n",
              "2021-07-01          -3.94       July  2021      07 2021-07-01       July   \n",
              "2021-08-01          -2.48     August  2021      08 2021-08-01     August   \n",
              "2021-09-01          -6.57  September  2021      09 2021-09-01  September   \n",
              "2021-10-01          -6.74    October  2021      10 2021-10-01    October   \n",
              "2021-10-31          -1.53    October  2021      11 2021-11-01   November   \n",
              "\n",
              "           Resultado_bin  \n",
              "Date                      \n",
              "2000-01-01      negativo  \n",
              "2000-04-01      negativo  \n",
              "2000-05-01      negativo  \n",
              "2000-07-01      negativo  \n",
              "2000-09-01      negativo  \n",
              "...                  ...  \n",
              "2021-07-01      negativo  \n",
              "2021-08-01      negativo  \n",
              "2021-09-01      negativo  \n",
              "2021-10-01      negativo  \n",
              "2021-10-31      negativo  \n",
              "\n",
              "[119 rows x 13 columns]"
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              "      <td>14029.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>15538.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-12.81</td>\n",
              "      <td>April</td>\n",
              "      <td>2000</td>\n",
              "      <td>04</td>\n",
              "      <td>2000-04-01</td>\n",
              "      <td>April</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-05-01</th>\n",
              "      <td>15596.0</td>\n",
              "      <td>15944.0</td>\n",
              "      <td>13556.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>14957.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-3.74</td>\n",
              "      <td>May</td>\n",
              "      <td>2000</td>\n",
              "      <td>05</td>\n",
              "      <td>2000-05-01</td>\n",
              "      <td>May</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-07-01</th>\n",
              "      <td>16747.0</td>\n",
              "      <td>17795.0</td>\n",
              "      <td>16390.0</td>\n",
              "      <td>16455.0</td>\n",
              "      <td>16455.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.63</td>\n",
              "      <td>July</td>\n",
              "      <td>2000</td>\n",
              "      <td>07</td>\n",
              "      <td>2000-07-01</td>\n",
              "      <td>July</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000-09-01</th>\n",
              "      <td>17360.0</td>\n",
              "      <td>17716.0</td>\n",
              "      <td>15657.0</td>\n",
              "      <td>15928.0</td>\n",
              "      <td>15928.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-8.18</td>\n",
              "      <td>September</td>\n",
              "      <td>2000</td>\n",
              "      <td>09</td>\n",
              "      <td>2000-09-01</td>\n",
              "      <td>September</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-01</th>\n",
              "      <td>126802.0</td>\n",
              "      <td>129620.0</td>\n",
              "      <td>121748.0</td>\n",
              "      <td>121801.0</td>\n",
              "      <td>121801.0</td>\n",
              "      <td>152098400</td>\n",
              "      <td>-3.94</td>\n",
              "      <td>July</td>\n",
              "      <td>2021</td>\n",
              "      <td>07</td>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>July</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-08-01</th>\n",
              "      <td>121803.0</td>\n",
              "      <td>124536.0</td>\n",
              "      <td>114801.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>118781.0</td>\n",
              "      <td>201788600</td>\n",
              "      <td>-2.48</td>\n",
              "      <td>August</td>\n",
              "      <td>2021</td>\n",
              "      <td>08</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>August</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-01</th>\n",
              "      <td>118793.0</td>\n",
              "      <td>119942.0</td>\n",
              "      <td>107520.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>110979.0</td>\n",
              "      <td>231540400</td>\n",
              "      <td>-6.57</td>\n",
              "      <td>September</td>\n",
              "      <td>2021</td>\n",
              "      <td>09</td>\n",
              "      <td>2021-09-01</td>\n",
              "      <td>September</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-01</th>\n",
              "      <td>110980.0</td>\n",
              "      <td>114927.0</td>\n",
              "      <td>102854.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>103501.0</td>\n",
              "      <td>243860400</td>\n",
              "      <td>-6.74</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>10</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>October</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-10-31</th>\n",
              "      <td>103514.0</td>\n",
              "      <td>108669.0</td>\n",
              "      <td>100075.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>101915.0</td>\n",
              "      <td>236342500</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>October</td>\n",
              "      <td>2021</td>\n",
              "      <td>11</td>\n",
              "      <td>2021-11-01</td>\n",
              "      <td>November</td>\n",
              "      <td>negativo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>119 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17800b5a-ab41-4aa0-bd5a-2e963c3bc586')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    [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-17800b5a-ab41-4aa0-bd5a-2e963c3bc586 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-17800b5a-ab41-4aa0-bd5a-2e963c3bc586');\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": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Produzir uma tabela similar à anterior (números de meses avaliados, somatório dos resultados, média, mediana, máx e mín dos resultados), mas dessa vez apenas considerando os meses positivos\n",
        "\n",
        "Tabela_03 = pd.pivot_table(dataset_IBOV_negativos, values=\"Resultado_mes\", index=[\"Mes_num\"], aggfunc=['count',np.mean,np.median,np.max,np.min])\n",
        "Tabela_03.columns = Tabela_03.columns.droplevel(1)\n",
        "Tabela_03"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "6Pu1SNROYUFy",
        "outputId": "cfa3f18b-b54f-4a47-ac7c-67b4735418f6"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         count      mean  median  amax   amin\n",
              "Mes_num                                      \n",
              "01          14 -4.601429  -4.385 -1.63  -7.51\n",
              "02          10 -4.156000  -3.375 -0.44 -10.08\n",
              "03          12 -5.266667  -2.250 -0.13 -29.90\n",
              "04           8 -5.593750  -4.105 -0.78 -12.81\n",
              "05          14 -5.297143  -4.210 -0.32 -11.86\n",
              "06          11 -5.018182  -3.350 -0.25 -13.39\n",
              "07           8 -5.278750  -4.855 -0.38 -12.35\n",
              "08          10 -4.094000  -3.475 -0.66  -8.33\n",
              "09           9 -9.681111  -8.180 -3.36 -17.17\n",
              "10           7 -6.811429  -4.400 -0.69 -24.80\n",
              "11          10 -3.688000  -3.210 -1.53 -10.63\n",
              "12           6 -3.171667  -2.285 -0.21  -8.52"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1750c50e-a853-407f-93d6-3346764014dd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>median</th>\n",
              "      <th>amax</th>\n",
              "      <th>amin</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mes_num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>14</td>\n",
              "      <td>-4.601429</td>\n",
              "      <td>-4.385</td>\n",
              "      <td>-1.63</td>\n",
              "      <td>-7.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02</th>\n",
              "      <td>10</td>\n",
              "      <td>-4.156000</td>\n",
              "      <td>-3.375</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>-10.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>12</td>\n",
              "      <td>-5.266667</td>\n",
              "      <td>-2.250</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-29.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04</th>\n",
              "      <td>8</td>\n",
              "      <td>-5.593750</td>\n",
              "      <td>-4.105</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-12.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05</th>\n",
              "      <td>14</td>\n",
              "      <td>-5.297143</td>\n",
              "      <td>-4.210</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-11.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06</th>\n",
              "      <td>11</td>\n",
              "      <td>-5.018182</td>\n",
              "      <td>-3.350</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-13.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07</th>\n",
              "      <td>8</td>\n",
              "      <td>-5.278750</td>\n",
              "      <td>-4.855</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-12.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08</th>\n",
              "      <td>10</td>\n",
              "      <td>-4.094000</td>\n",
              "      <td>-3.475</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>-8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>09</th>\n",
              "      <td>9</td>\n",
              "      <td>-9.681111</td>\n",
              "      <td>-8.180</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>-17.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>7</td>\n",
              "      <td>-6.811429</td>\n",
              "      <td>-4.400</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-24.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>-3.688000</td>\n",
              "      <td>-3.210</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>-10.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>6</td>\n",
              "      <td>-3.171667</td>\n",
              "      <td>-2.285</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-8.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1750c50e-a853-407f-93d6-3346764014dd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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-1750c50e-a853-407f-93d6-3346764014dd 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-1750c50e-a853-407f-93d6-3346764014dd');\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": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.4. Organização dos resultados"
      ],
      "metadata": {
        "id": "EQhno8VWN8UR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Listar os nomes das colunas das três tabelas de resultados\n",
        "\n",
        "print(Tabela_01.columns)\n",
        "print(Tabela_02.columns)\n",
        "print(Tabela_03.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9Y8xHjbPpKh",
        "outputId": "f604fc36-eb6c-46c1-901c-33d4846cf91f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['count', 'mean', 'median', 'amax', 'amin'], dtype='object')\n",
            "Index(['count', 'mean', 'median', 'amax', 'amin'], dtype='object')\n",
            "Index(['count', 'mean', 'median', 'amax', 'amin'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Primeiro, renomear as colunas antes de concatenar tudo\n",
        "\n",
        "Tabela_01.rename(columns={'count':'qtd_meses_todos','mean':'media_todos','median':'mediana_todos','amax':'max_todos','amin':'min_todos'},inplace=True)\n",
        "Tabela_02.rename(columns={'count':'qtd_meses_pos','mean':'media_pos','median':'mediana_pos','amax':'max_pos','amin':'min_pos'},inplace=True)\n",
        "Tabela_03.rename(columns={'count':'qtd_meses_neg','mean':'media_neg','median':'mediana_neg','amax':'max_neg','amin':'min_neg'},inplace=True)"
      ],
      "metadata": {
        "id": "vcC_uCHUPbPi"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inserir os nomes dos meses para concatenar todas as tabelas de resultados\n",
        "\n",
        "Tabela_meses = pd.DataFrame(dataset_IBOV['Mes_final'].unique())\n",
        "Tabela_meses.rename(columns={0:'Mes'},inplace=True)\n",
        "Tabela_meses.index = Tabela_01.index"
      ],
      "metadata": {
        "id": "91GoNynzejkQ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Concatenar as Tabelas\n",
        "\n",
        "Tabela_final = pd.concat([Tabela_meses,Tabela_01,Tabela_02,Tabela_03],axis=1)\n",
        "Tabela_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "tPxa4EuKWvGk",
        "outputId": "e1125b1e-6480-4e12-cbc3-035ebfeb1d9f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Mes  qtd_meses_todos  media_todos  mediana_todos  max_todos  \\\n",
              "Mes_num                                                                      \n",
              "01         January               22     0.529091         -2.360      15.82   \n",
              "02        February               22     1.192727          0.630      15.56   \n",
              "03           March               22    -0.076364         -0.155      16.97   \n",
              "04           April               22     2.035000          1.460      15.55   \n",
              "05             May               22    -1.098182         -1.755      12.49   \n",
              "06            June               22    -0.298182          0.015      11.84   \n",
              "07            July               22     1.555909          2.420      11.22   \n",
              "08          August               22     0.911818          0.935      11.80   \n",
              "09       September               22    -0.873182          1.370      12.62   \n",
              "10         October               22     2.213636          1.795      17.92   \n",
              "11        November               22     1.952273          0.385      15.90   \n",
              "12        December               22     3.340000          3.550      14.84   \n",
              "\n",
              "         min_todos  qtd_meses_pos  media_pos  mediana_pos  max_pos  min_pos  \\\n",
              "Mes_num                                                                       \n",
              "01           -7.51              8   9.507500       10.975    15.82     0.38   \n",
              "02          -10.08             12   5.650000        5.125    15.56     0.59   \n",
              "03          -29.90             10   6.152000        5.910    16.97     0.91   \n",
              "04          -12.81             14   6.394286        6.615    15.55     0.64   \n",
              "05          -11.86              8   6.250000        6.830    12.49     0.70   \n",
              "06          -13.39             11   4.421818        4.060    11.84     0.28   \n",
              "07          -12.35             14   5.461429        4.900    11.22     0.84   \n",
              "08           -8.33             12   5.083333        4.550    11.80     0.84   \n",
              "09          -17.17             13   5.224615        4.660    12.62     0.60   \n",
              "10          -24.80             15   6.425333        6.850    17.92     0.02   \n",
              "11          -10.63             12   6.652500        6.255    15.90     0.06   \n",
              "12           -8.52             16   5.781875        5.525    14.84     1.40   \n",
              "\n",
              "         qtd_meses_neg  media_neg  mediana_neg  max_neg  min_neg  \n",
              "Mes_num                                                           \n",
              "01                  14  -4.601429       -4.385    -1.63    -7.51  \n",
              "02                  10  -4.156000       -3.375    -0.44   -10.08  \n",
              "03                  12  -5.266667       -2.250    -0.13   -29.90  \n",
              "04                   8  -5.593750       -4.105    -0.78   -12.81  \n",
              "05                  14  -5.297143       -4.210    -0.32   -11.86  \n",
              "06                  11  -5.018182       -3.350    -0.25   -13.39  \n",
              "07                   8  -5.278750       -4.855    -0.38   -12.35  \n",
              "08                  10  -4.094000       -3.475    -0.66    -8.33  \n",
              "09                   9  -9.681111       -8.180    -3.36   -17.17  \n",
              "10                   7  -6.811429       -4.400    -0.69   -24.80  \n",
              "11                  10  -3.688000       -3.210    -1.53   -10.63  \n",
              "12                   6  -3.171667       -2.285    -0.21    -8.52  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79b8cc92-9dd1-44a1-a714-ef560ed4d7da\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mes</th>\n",
              "      <th>qtd_meses_todos</th>\n",
              "      <th>media_todos</th>\n",
              "      <th>mediana_todos</th>\n",
              "      <th>max_todos</th>\n",
              "      <th>min_todos</th>\n",
              "      <th>qtd_meses_pos</th>\n",
              "      <th>media_pos</th>\n",
              "      <th>mediana_pos</th>\n",
              "      <th>max_pos</th>\n",
              "      <th>min_pos</th>\n",
              "      <th>qtd_meses_neg</th>\n",
              "      <th>media_neg</th>\n",
              "      <th>mediana_neg</th>\n",
              "      <th>max_neg</th>\n",
              "      <th>min_neg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mes_num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>January</td>\n",
              "      <td>22</td>\n",
              "      <td>0.529091</td>\n",
              "      <td>-2.360</td>\n",
              "      <td>15.82</td>\n",
              "      <td>-7.51</td>\n",
              "      <td>8</td>\n",
              "      <td>9.507500</td>\n",
              "      <td>10.975</td>\n",
              "      <td>15.82</td>\n",
              "      <td>0.38</td>\n",
              "      <td>14</td>\n",
              "      <td>-4.601429</td>\n",
              "      <td>-4.385</td>\n",
              "      <td>-1.63</td>\n",
              "      <td>-7.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02</th>\n",
              "      <td>February</td>\n",
              "      <td>22</td>\n",
              "      <td>1.192727</td>\n",
              "      <td>0.630</td>\n",
              "      <td>15.56</td>\n",
              "      <td>-10.08</td>\n",
              "      <td>12</td>\n",
              "      <td>5.650000</td>\n",
              "      <td>5.125</td>\n",
              "      <td>15.56</td>\n",
              "      <td>0.59</td>\n",
              "      <td>10</td>\n",
              "      <td>-4.156000</td>\n",
              "      <td>-3.375</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>-10.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>March</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.076364</td>\n",
              "      <td>-0.155</td>\n",
              "      <td>16.97</td>\n",
              "      <td>-29.90</td>\n",
              "      <td>10</td>\n",
              "      <td>6.152000</td>\n",
              "      <td>5.910</td>\n",
              "      <td>16.97</td>\n",
              "      <td>0.91</td>\n",
              "      <td>12</td>\n",
              "      <td>-5.266667</td>\n",
              "      <td>-2.250</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-29.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04</th>\n",
              "      <td>April</td>\n",
              "      <td>22</td>\n",
              "      <td>2.035000</td>\n",
              "      <td>1.460</td>\n",
              "      <td>15.55</td>\n",
              "      <td>-12.81</td>\n",
              "      <td>14</td>\n",
              "      <td>6.394286</td>\n",
              "      <td>6.615</td>\n",
              "      <td>15.55</td>\n",
              "      <td>0.64</td>\n",
              "      <td>8</td>\n",
              "      <td>-5.593750</td>\n",
              "      <td>-4.105</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-12.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05</th>\n",
              "      <td>May</td>\n",
              "      <td>22</td>\n",
              "      <td>-1.098182</td>\n",
              "      <td>-1.755</td>\n",
              "      <td>12.49</td>\n",
              "      <td>-11.86</td>\n",
              "      <td>8</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>6.830</td>\n",
              "      <td>12.49</td>\n",
              "      <td>0.70</td>\n",
              "      <td>14</td>\n",
              "      <td>-5.297143</td>\n",
              "      <td>-4.210</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-11.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06</th>\n",
              "      <td>June</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.298182</td>\n",
              "      <td>0.015</td>\n",
              "      <td>11.84</td>\n",
              "      <td>-13.39</td>\n",
              "      <td>11</td>\n",
              "      <td>4.421818</td>\n",
              "      <td>4.060</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.28</td>\n",
              "      <td>11</td>\n",
              "      <td>-5.018182</td>\n",
              "      <td>-3.350</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-13.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07</th>\n",
              "      <td>July</td>\n",
              "      <td>22</td>\n",
              "      <td>1.555909</td>\n",
              "      <td>2.420</td>\n",
              "      <td>11.22</td>\n",
              "      <td>-12.35</td>\n",
              "      <td>14</td>\n",
              "      <td>5.461429</td>\n",
              "      <td>4.900</td>\n",
              "      <td>11.22</td>\n",
              "      <td>0.84</td>\n",
              "      <td>8</td>\n",
              "      <td>-5.278750</td>\n",
              "      <td>-4.855</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-12.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08</th>\n",
              "      <td>August</td>\n",
              "      <td>22</td>\n",
              "      <td>0.911818</td>\n",
              "      <td>0.935</td>\n",
              "      <td>11.80</td>\n",
              "      <td>-8.33</td>\n",
              "      <td>12</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>4.550</td>\n",
              "      <td>11.80</td>\n",
              "      <td>0.84</td>\n",
              "      <td>10</td>\n",
              "      <td>-4.094000</td>\n",
              "      <td>-3.475</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>-8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>09</th>\n",
              "      <td>September</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.873182</td>\n",
              "      <td>1.370</td>\n",
              "      <td>12.62</td>\n",
              "      <td>-17.17</td>\n",
              "      <td>13</td>\n",
              "      <td>5.224615</td>\n",
              "      <td>4.660</td>\n",
              "      <td>12.62</td>\n",
              "      <td>0.60</td>\n",
              "      <td>9</td>\n",
              "      <td>-9.681111</td>\n",
              "      <td>-8.180</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>-17.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>October</td>\n",
              "      <td>22</td>\n",
              "      <td>2.213636</td>\n",
              "      <td>1.795</td>\n",
              "      <td>17.92</td>\n",
              "      <td>-24.80</td>\n",
              "      <td>15</td>\n",
              "      <td>6.425333</td>\n",
              "      <td>6.850</td>\n",
              "      <td>17.92</td>\n",
              "      <td>0.02</td>\n",
              "      <td>7</td>\n",
              "      <td>-6.811429</td>\n",
              "      <td>-4.400</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-24.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>November</td>\n",
              "      <td>22</td>\n",
              "      <td>1.952273</td>\n",
              "      <td>0.385</td>\n",
              "      <td>15.90</td>\n",
              "      <td>-10.63</td>\n",
              "      <td>12</td>\n",
              "      <td>6.652500</td>\n",
              "      <td>6.255</td>\n",
              "      <td>15.90</td>\n",
              "      <td>0.06</td>\n",
              "      <td>10</td>\n",
              "      <td>-3.688000</td>\n",
              "      <td>-3.210</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>-10.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>December</td>\n",
              "      <td>22</td>\n",
              "      <td>3.340000</td>\n",
              "      <td>3.550</td>\n",
              "      <td>14.84</td>\n",
              "      <td>-8.52</td>\n",
              "      <td>16</td>\n",
              "      <td>5.781875</td>\n",
              "      <td>5.525</td>\n",
              "      <td>14.84</td>\n",
              "      <td>1.40</td>\n",
              "      <td>6</td>\n",
              "      <td>-3.171667</td>\n",
              "      <td>-2.285</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-8.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mais dois parâmetros, para avaliarmos a qtd de meses positivos vs negativos em %\n",
        "\n",
        "Tabela_final['Porc_meses_pos'] = Tabela_final['qtd_meses_pos']/Tabela_final['qtd_meses_todos']\n",
        "Tabela_final['Porc_meses_neg'] = Tabela_final['qtd_meses_neg']/Tabela_final['qtd_meses_todos']"
      ],
      "metadata": {
        "id": "dPoZUcYpXlCN"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Tabela_final"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "VyGc3Y7JX7Ft",
        "outputId": "ec160db4-2601-488c-b436-6c342d1a6c90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Mes  qtd_meses_todos  media_todos  mediana_todos  max_todos  \\\n",
              "Mes_num                                                                      \n",
              "01         January               22     0.529091         -2.360      15.82   \n",
              "02        February               22     1.192727          0.630      15.56   \n",
              "03           March               22    -0.076364         -0.155      16.97   \n",
              "04           April               22     2.035000          1.460      15.55   \n",
              "05             May               22    -1.098182         -1.755      12.49   \n",
              "06            June               22    -0.298182          0.015      11.84   \n",
              "07            July               22     1.555909          2.420      11.22   \n",
              "08          August               22     0.911818          0.935      11.80   \n",
              "09       September               22    -0.873182          1.370      12.62   \n",
              "10         October               22     2.213636          1.795      17.92   \n",
              "11        November               22     1.952273          0.385      15.90   \n",
              "12        December               22     3.340000          3.550      14.84   \n",
              "\n",
              "         min_todos  qtd_meses_pos  media_pos  mediana_pos  max_pos  min_pos  \\\n",
              "Mes_num                                                                       \n",
              "01           -7.51              8   9.507500       10.975    15.82     0.38   \n",
              "02          -10.08             12   5.650000        5.125    15.56     0.59   \n",
              "03          -29.90             10   6.152000        5.910    16.97     0.91   \n",
              "04          -12.81             14   6.394286        6.615    15.55     0.64   \n",
              "05          -11.86              8   6.250000        6.830    12.49     0.70   \n",
              "06          -13.39             11   4.421818        4.060    11.84     0.28   \n",
              "07          -12.35             14   5.461429        4.900    11.22     0.84   \n",
              "08           -8.33             12   5.083333        4.550    11.80     0.84   \n",
              "09          -17.17             13   5.224615        4.660    12.62     0.60   \n",
              "10          -24.80             15   6.425333        6.850    17.92     0.02   \n",
              "11          -10.63             12   6.652500        6.255    15.90     0.06   \n",
              "12           -8.52             16   5.781875        5.525    14.84     1.40   \n",
              "\n",
              "         qtd_meses_neg  media_neg  mediana_neg  max_neg  min_neg  \\\n",
              "Mes_num                                                            \n",
              "01                  14  -4.601429       -4.385    -1.63    -7.51   \n",
              "02                  10  -4.156000       -3.375    -0.44   -10.08   \n",
              "03                  12  -5.266667       -2.250    -0.13   -29.90   \n",
              "04                   8  -5.593750       -4.105    -0.78   -12.81   \n",
              "05                  14  -5.297143       -4.210    -0.32   -11.86   \n",
              "06                  11  -5.018182       -3.350    -0.25   -13.39   \n",
              "07                   8  -5.278750       -4.855    -0.38   -12.35   \n",
              "08                  10  -4.094000       -3.475    -0.66    -8.33   \n",
              "09                   9  -9.681111       -8.180    -3.36   -17.17   \n",
              "10                   7  -6.811429       -4.400    -0.69   -24.80   \n",
              "11                  10  -3.688000       -3.210    -1.53   -10.63   \n",
              "12                   6  -3.171667       -2.285    -0.21    -8.52   \n",
              "\n",
              "         Porc_meses_pos  Porc_meses_neg  \n",
              "Mes_num                                  \n",
              "01             0.363636        0.636364  \n",
              "02             0.545455        0.454545  \n",
              "03             0.454545        0.545455  \n",
              "04             0.636364        0.363636  \n",
              "05             0.363636        0.636364  \n",
              "06             0.500000        0.500000  \n",
              "07             0.636364        0.363636  \n",
              "08             0.545455        0.454545  \n",
              "09             0.590909        0.409091  \n",
              "10             0.681818        0.318182  \n",
              "11             0.545455        0.454545  \n",
              "12             0.727273        0.272727  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mes</th>\n",
              "      <th>qtd_meses_todos</th>\n",
              "      <th>media_todos</th>\n",
              "      <th>mediana_todos</th>\n",
              "      <th>max_todos</th>\n",
              "      <th>min_todos</th>\n",
              "      <th>qtd_meses_pos</th>\n",
              "      <th>media_pos</th>\n",
              "      <th>mediana_pos</th>\n",
              "      <th>max_pos</th>\n",
              "      <th>min_pos</th>\n",
              "      <th>qtd_meses_neg</th>\n",
              "      <th>media_neg</th>\n",
              "      <th>mediana_neg</th>\n",
              "      <th>max_neg</th>\n",
              "      <th>min_neg</th>\n",
              "      <th>Porc_meses_pos</th>\n",
              "      <th>Porc_meses_neg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mes_num</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>January</td>\n",
              "      <td>22</td>\n",
              "      <td>0.529091</td>\n",
              "      <td>-2.360</td>\n",
              "      <td>15.82</td>\n",
              "      <td>-7.51</td>\n",
              "      <td>8</td>\n",
              "      <td>9.507500</td>\n",
              "      <td>10.975</td>\n",
              "      <td>15.82</td>\n",
              "      <td>0.38</td>\n",
              "      <td>14</td>\n",
              "      <td>-4.601429</td>\n",
              "      <td>-4.385</td>\n",
              "      <td>-1.63</td>\n",
              "      <td>-7.51</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.636364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02</th>\n",
              "      <td>February</td>\n",
              "      <td>22</td>\n",
              "      <td>1.192727</td>\n",
              "      <td>0.630</td>\n",
              "      <td>15.56</td>\n",
              "      <td>-10.08</td>\n",
              "      <td>12</td>\n",
              "      <td>5.650000</td>\n",
              "      <td>5.125</td>\n",
              "      <td>15.56</td>\n",
              "      <td>0.59</td>\n",
              "      <td>10</td>\n",
              "      <td>-4.156000</td>\n",
              "      <td>-3.375</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>-10.08</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>March</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.076364</td>\n",
              "      <td>-0.155</td>\n",
              "      <td>16.97</td>\n",
              "      <td>-29.90</td>\n",
              "      <td>10</td>\n",
              "      <td>6.152000</td>\n",
              "      <td>5.910</td>\n",
              "      <td>16.97</td>\n",
              "      <td>0.91</td>\n",
              "      <td>12</td>\n",
              "      <td>-5.266667</td>\n",
              "      <td>-2.250</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>-29.90</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04</th>\n",
              "      <td>April</td>\n",
              "      <td>22</td>\n",
              "      <td>2.035000</td>\n",
              "      <td>1.460</td>\n",
              "      <td>15.55</td>\n",
              "      <td>-12.81</td>\n",
              "      <td>14</td>\n",
              "      <td>6.394286</td>\n",
              "      <td>6.615</td>\n",
              "      <td>15.55</td>\n",
              "      <td>0.64</td>\n",
              "      <td>8</td>\n",
              "      <td>-5.593750</td>\n",
              "      <td>-4.105</td>\n",
              "      <td>-0.78</td>\n",
              "      <td>-12.81</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.363636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05</th>\n",
              "      <td>May</td>\n",
              "      <td>22</td>\n",
              "      <td>-1.098182</td>\n",
              "      <td>-1.755</td>\n",
              "      <td>12.49</td>\n",
              "      <td>-11.86</td>\n",
              "      <td>8</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>6.830</td>\n",
              "      <td>12.49</td>\n",
              "      <td>0.70</td>\n",
              "      <td>14</td>\n",
              "      <td>-5.297143</td>\n",
              "      <td>-4.210</td>\n",
              "      <td>-0.32</td>\n",
              "      <td>-11.86</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>0.636364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06</th>\n",
              "      <td>June</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.298182</td>\n",
              "      <td>0.015</td>\n",
              "      <td>11.84</td>\n",
              "      <td>-13.39</td>\n",
              "      <td>11</td>\n",
              "      <td>4.421818</td>\n",
              "      <td>4.060</td>\n",
              "      <td>11.84</td>\n",
              "      <td>0.28</td>\n",
              "      <td>11</td>\n",
              "      <td>-5.018182</td>\n",
              "      <td>-3.350</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>-13.39</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07</th>\n",
              "      <td>July</td>\n",
              "      <td>22</td>\n",
              "      <td>1.555909</td>\n",
              "      <td>2.420</td>\n",
              "      <td>11.22</td>\n",
              "      <td>-12.35</td>\n",
              "      <td>14</td>\n",
              "      <td>5.461429</td>\n",
              "      <td>4.900</td>\n",
              "      <td>11.22</td>\n",
              "      <td>0.84</td>\n",
              "      <td>8</td>\n",
              "      <td>-5.278750</td>\n",
              "      <td>-4.855</td>\n",
              "      <td>-0.38</td>\n",
              "      <td>-12.35</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.363636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08</th>\n",
              "      <td>August</td>\n",
              "      <td>22</td>\n",
              "      <td>0.911818</td>\n",
              "      <td>0.935</td>\n",
              "      <td>11.80</td>\n",
              "      <td>-8.33</td>\n",
              "      <td>12</td>\n",
              "      <td>5.083333</td>\n",
              "      <td>4.550</td>\n",
              "      <td>11.80</td>\n",
              "      <td>0.84</td>\n",
              "      <td>10</td>\n",
              "      <td>-4.094000</td>\n",
              "      <td>-3.475</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>-8.33</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>09</th>\n",
              "      <td>September</td>\n",
              "      <td>22</td>\n",
              "      <td>-0.873182</td>\n",
              "      <td>1.370</td>\n",
              "      <td>12.62</td>\n",
              "      <td>-17.17</td>\n",
              "      <td>13</td>\n",
              "      <td>5.224615</td>\n",
              "      <td>4.660</td>\n",
              "      <td>12.62</td>\n",
              "      <td>0.60</td>\n",
              "      <td>9</td>\n",
              "      <td>-9.681111</td>\n",
              "      <td>-8.180</td>\n",
              "      <td>-3.36</td>\n",
              "      <td>-17.17</td>\n",
              "      <td>0.590909</td>\n",
              "      <td>0.409091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>October</td>\n",
              "      <td>22</td>\n",
              "      <td>2.213636</td>\n",
              "      <td>1.795</td>\n",
              "      <td>17.92</td>\n",
              "      <td>-24.80</td>\n",
              "      <td>15</td>\n",
              "      <td>6.425333</td>\n",
              "      <td>6.850</td>\n",
              "      <td>17.92</td>\n",
              "      <td>0.02</td>\n",
              "      <td>7</td>\n",
              "      <td>-6.811429</td>\n",
              "      <td>-4.400</td>\n",
              "      <td>-0.69</td>\n",
              "      <td>-24.80</td>\n",
              "      <td>0.681818</td>\n",
              "      <td>0.318182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>November</td>\n",
              "      <td>22</td>\n",
              "      <td>1.952273</td>\n",
              "      <td>0.385</td>\n",
              "      <td>15.90</td>\n",
              "      <td>-10.63</td>\n",
              "      <td>12</td>\n",
              "      <td>6.652500</td>\n",
              "      <td>6.255</td>\n",
              "      <td>15.90</td>\n",
              "      <td>0.06</td>\n",
              "      <td>10</td>\n",
              "      <td>-3.688000</td>\n",
              "      <td>-3.210</td>\n",
              "      <td>-1.53</td>\n",
              "      <td>-10.63</td>\n",
              "      <td>0.545455</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>December</td>\n",
              "      <td>22</td>\n",
              "      <td>3.340000</td>\n",
              "      <td>3.550</td>\n",
              "      <td>14.84</td>\n",
              "      <td>-8.52</td>\n",
              "      <td>16</td>\n",
              "      <td>5.781875</td>\n",
              "      <td>5.525</td>\n",
              "      <td>14.84</td>\n",
              "      <td>1.40</td>\n",
              "      <td>6</td>\n",
              "      <td>-3.171667</td>\n",
              "      <td>-2.285</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-8.52</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.272727</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          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": 76
        }
      ]
    }
  ],
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      "name": "20220328_Ciclos_de_Mercado_sazonalidade_anual.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
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