Análise_quantitativa_de_Fundos_Imobiliários.ipynb

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    "colab": {
      "name": "Análise quantitativa de Fundos Imobiliários.ipynb",
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      "authorship_tag": "ABX9TyNDvYkVbDVGpV4SJa/3xIhl",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/andrejuniorba/Analises_estudos/blob/main/An%C3%A1lise_quantitativa_de_Fundos_Imobili%C3%A1rios.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------\n",
        "# **Análise quantitativa de Fundos de Investimentos Imobiliários**\n",
        "------"
      ],
      "metadata": {
        "id": "boiFTOQVLMno"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Bibliotecas para web scraping\n",
        "!pip install requests\n",
        "!pip install bs4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CG0uXnX8MufH",
        "outputId": "54ebbc68-8df5-4a31-eea8-42a6d5d9709e"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests) (2022.5.18.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests) (2.10)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: bs4 in /usr/local/lib/python3.7/dist-packages (0.0.1)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from bs4) (4.6.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#Configurar para mostrar todas as colunas\n",
        "pd.set_option('max_columns',26)"
      ],
      "metadata": {
        "id": "xBE4pmBkOKhd"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Web Scraping**\n",
        "---"
      ],
      "metadata": {
        "id": "0WBEuhfIO6xz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_url = requests.get('https://www.fundsexplorer.com.br/ranking')\n",
        "dados_url"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqY5ICIOQhVl",
        "outputId": "5671677d-5575-4900-e295-9b3f996ba134"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Response [200]>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificar a extração\n",
        "#dados_url.text"
      ],
      "metadata": {
        "id": "eJG0FgVsQ6_6"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Organizar o html extraído do site\n",
        "dados_bs4 = BeautifulSoup(dados_url.text,'html.parser')\n",
        "#Apresentar uma amostra organizada devido ao tamanho\n",
        "#dados_bs4.head()"
      ],
      "metadata": {
        "id": "pRS9o4U4RZDk"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Extraindo a tabela de acordo ao nome 'id' que é dado no código HTML\n",
        "dados_tabela = dados_bs4.findAll(attrs = {'id':'scroll-wrapper'})"
      ],
      "metadata": {
        "id": "fDr1fMevTLuw"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Indexando pelo início\n",
        "#dados_tabela[0]"
      ],
      "metadata": {
        "id": "bZ7PkT89UWeJ"
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tabela = dados_tabela[0].findAll('table')"
      ],
      "metadata": {
        "id": "w5G8XQXcUXqE"
      },
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Forçando a leitura como strings e indexando\n",
        "df_FIIs = pd.read_html(str(dados_tabela[0]))[0]\n",
        "#Apresentando as 5 primeiras linhas do DF\n",
        "df_FIIs.head()"
      ],
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          "height": 392
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        "id": "zrLgl8Vud_uu",
        "outputId": "9bfefa92-c676-4c08-ffa7-d4f89675c053"
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      "execution_count": 145,
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          "output_type": "execute_result",
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              "  Códigodo fundo                Setor Preço Atual  Liquidez Diária Dividendo  \\\n",
              "0         FIVN11            Shoppings     R$ 2,20           1553.0   R$ 0,00   \n",
              "1         BZLI11  Títulos e Val. Mob.    R$ 14,70          44041.0   R$ 0,00   \n",
              "2         XTED11   Lajes Corporativas     R$ 6,19             78.0   R$ 0,00   \n",
              "3         ALMI11   Lajes Corporativas   R$ 930,00             46.0   R$ 0,00   \n",
              "4         PABY11              Híbrido     R$ 7,46             14.0   R$ 0,00   \n",
              "\n",
              "  DividendYield DY (3M)Acumulado DY (6M)Acumulado DY (12M)Acumulado  \\\n",
              "0         0,00%            0,00%            0,00%             0,00%   \n",
              "1         0,00%            0,00%            0,00%             0,00%   \n",
              "2         0,00%            0,00%            0,00%             0,00%   \n",
              "3         0,00%            0,00%            0,00%             0,00%   \n",
              "4         0,00%            0,00%            0,00%             0,00%   \n",
              "\n",
              "  DY (3M)Média DY (6M)Média DY (12M)Média DY Ano Variação Preço  \\\n",
              "0        0,00%        0,00%         0,00%    NaN          0,00%   \n",
              "1        0,00%        0,00%         0,00%  0,00%          0,68%   \n",
              "2        0,00%        0,00%         0,00%  0,00%          1,10%   \n",
              "3        0,00%        0,00%         0,00%  0,00%          2,27%   \n",
              "4        0,00%        0,00%         0,00%  0,00%        -19,84%   \n",
              "\n",
              "  Rentab.Período Rentab.Acumulada     PatrimônioLíq.          VPA  P/VPA  \\\n",
              "0          0,00%            0,00%   R$ 65.678.487,99      R$ 6,98   32.0   \n",
              "1          0,68%            1,14%  R$ 431.076.817,02     R$ 10,70  137.0   \n",
              "2          1,10%          -37,83%   R$ 27.305.951,24     R$ 13,89   45.0   \n",
              "3          2,27%          -28,29%  R$ 249.632.973,43  R$ 2.245,37   41.0   \n",
              "4        -19,84%          -31,62%   R$ -9.546.407,61    R$ -12,59  -59.0   \n",
              "\n",
              "  DYPatrimonial VariaçãoPatrimonial Rentab. Patr.no Período  \\\n",
              "0           NaN                 NaN                     NaN   \n",
              "1           NaN                 NaN                     NaN   \n",
              "2           NaN                 NaN                     NaN   \n",
              "3           NaN                 NaN                     NaN   \n",
              "4           NaN                 NaN                     NaN   \n",
              "\n",
              "  Rentab. Patr.Acumulada VacânciaFísica VacânciaFinanceira  QuantidadeAtivos  \n",
              "0                    NaN         56,00%                NaN                 1  \n",
              "1                    NaN            NaN                NaN                 0  \n",
              "2                    NaN          0,00%            100,00%                 1  \n",
              "3                    NaN         64,05%                NaN                 1  \n",
              "4                    NaN            NaN                NaN                 1  "
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              "      <th>Códigodo fundo</th>\n",
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              "      <th>DY (6M)Média</th>\n",
              "      <th>DY (12M)Média</th>\n",
              "      <th>DY Ano</th>\n",
              "      <th>Variação Preço</th>\n",
              "      <th>Rentab.Período</th>\n",
              "      <th>Rentab.Acumulada</th>\n",
              "      <th>PatrimônioLíq.</th>\n",
              "      <th>VPA</th>\n",
              "      <th>P/VPA</th>\n",
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              "      <th>VacânciaFísica</th>\n",
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              "      <th>QuantidadeAtivos</th>\n",
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              "      <th>0</th>\n",
              "      <td>FIVN11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>R$ 2,20</td>\n",
              "      <td>1553.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>R$ 65.678.487,99</td>\n",
              "      <td>R$ 6,98</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56,00%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BZLI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>R$ 14,70</td>\n",
              "      <td>44041.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>1,14%</td>\n",
              "      <td>R$ 431.076.817,02</td>\n",
              "      <td>R$ 10,70</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>R$ 6,19</td>\n",
              "      <td>78.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>-37,83%</td>\n",
              "      <td>R$ 27.305.951,24</td>\n",
              "      <td>R$ 13,89</td>\n",
              "      <td>45.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>100,00%</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>R$ 930,00</td>\n",
              "      <td>46.0</td>\n",
              "      <td>R$ 0,00</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>-28,29%</td>\n",
              "      <td>R$ 249.632.973,43</td>\n",
              "      <td>R$ 2.245,37</td>\n",
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              "      <td>64,05%</td>\n",
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              "      <th>4</th>\n",
              "      <td>PABY11</td>\n",
              "      <td>Híbrido</td>\n",
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              "            '<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": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificando as colunas\n",
        "df_FIIs.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GcSEcLqzfNUW",
        "outputId": "47fd2e9d-c3e7-4f99-aac4-43ef825ef9bd"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Códigodo fundo', 'Setor', 'Preço Atual', 'Liquidez Diária',\n",
              "       'Dividendo', 'DividendYield', 'DY (3M)Acumulado', 'DY (6M)Acumulado',\n",
              "       'DY (12M)Acumulado', 'DY (3M)Média', 'DY (6M)Média', 'DY (12M)Média',\n",
              "       'DY Ano', 'Variação Preço', 'Rentab.Período', 'Rentab.Acumulada',\n",
              "       'PatrimônioLíq.', 'VPA', 'P/VPA', 'DYPatrimonial',\n",
              "       'VariaçãoPatrimonial', 'Rentab. Patr.no Período',\n",
              "       'Rentab. Patr.Acumulada', 'VacânciaFísica', 'VacânciaFinanceira',\n",
              "       'QuantidadeAtivos'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificar os fundos imobiliários negociados no Brasil em ordem alfabética\n",
        "str(list(df_FIIs['Códigodo fundo'].sort_values()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "FdMTdiodtz7w",
        "outputId": "31741baa-0017-4891-d118-2934822b3f1e"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"['ABCP11', 'AFHI11', 'AFOF11', 'AIEC11', 'ALMI11', 'ALZR11', 'APTO11', 'ARCT11', 'ARRI11', 'ATSA11', 'BARI11', 'BBFI11B', 'BBFO11', 'BBGO11', 'BBIM11', 'BBPO11', 'BBRC11', 'BCFF11', 'BCIA11', 'BCRI11', 'BICE11', 'BICR11', 'BIME11', 'BLCA11', 'BLCP11', 'BLMC11', 'BLMG11', 'BLMO11', 'BLMR11', 'BMLC11', 'BNFS11', 'BPFF11', 'BPML11', 'BRCO11', 'BRCR11', 'BREV11', 'BRLA11', 'BTAL11', 'BTCR11', 'BTLG11', 'BTRA11', 'BTSG11', 'BTWR11', 'BZLI11', 'CACR11', 'CARE11', 'CBOP11', 'CCRF11', 'CEOC11', 'CJCT11', 'CNES11', 'CORM11', 'CPFF11', 'CPTS11', 'CRFF11', 'CTXT11', 'CVBI11', 'CXAG11', 'CXCE11B', 'CXCI11', 'CXCO11', 'CXRI11', 'CXTL11', 'CYCR11', 'DEVA11', 'DRIT11B', 'DVFF11', 'EDFO11B', 'EDGA11', 'EQIR11', 'ERCR11', 'ERPA11', 'EURO11', 'EVBI11', 'FAED11', 'FAMB11B', 'FATN11', 'FCFL11', 'FEXC11', 'FIGS11', 'FIIB11', 'FIIP11B', 'FISC11', 'FIVN11', 'FLCR11', 'FLMA11', 'FLRP11', 'FMOF11', 'FPAB11', 'FVPQ11', 'GALG11', 'GAME11', 'GCFF11', 'GCRA11', 'GCRI11', 'GESE11B', 'GGRC11', 'GSFI11', 'GTLG11', 'GTWR11', 'HAAA11', 'HABT11', 'HBRH11', 'HCHG11', 'HCRI11', 'HCTR11', 'HFOF11', 'HGBS11', 'HGCR11', 'HGFF11', 'HGIC11', 'HGLG11', 'HGPO11', 'HGRE11', 'HGRS11', 'HGRU11', 'HLOG11', 'HOSI11', 'HPDP11', 'HRDF11', 'HREC11', 'HSAF11', 'HSLG11', 'HSML11', 'HSRE11', 'HTMX11', 'HUSC11', 'IBCR11', 'IBFF11', 'IDFI11', 'IRDM11', 'IRIM11', 'ITIP11', 'ITIT11', 'JFLL11', 'JGPX11', 'JPPA11', 'JRDM11', 'JSAF11', 'JSRE11', 'KFOF11', 'KINP11', 'KISU11', 'KNCA11', 'KNCR11', 'KNHY11', 'KNIP11', 'KNRE11', 'KNRI11', 'KNSC11', 'LASC11', 'LFTT11', 'LGCP11', 'LUGG11', 'LVBI11', 'MALL11', 'MATV11', 'MAXR11', 'MBRF11', 'MCCI11', 'MCHF11', 'MCHY11', 'MFAI11', 'MFII11', 'MGCR11', 'MGFF11', 'MGHT11', 'MGLG11', 'MORC11', 'MORE11', 'MXRF11', 'NAVT11', 'NCHB11', 'NEWL11', 'NEWU11', 'NSLU11', 'NVHO11', 'NVIF11B', 'ONEF11', 'ORPD11', 'OUFF11', 'OUJP11', 'OULG11', 'OURE11', 'PABY11', 'PATC11', 'PATL11', 'PLCR11', 'PLOG11', 'PLRI11', 'PORD11', 'PQAG11', 'PQDP11', 'PRSV11', 'PVBI11', 'QAGR11', 'QAMI11', 'QIRI11', 'RBCO11', 'RBDS11', 'RBED11', 'RBFF11', 'RBGS11', 'RBHG11', 'RBHY11', 'RBIR11', 'RBLG11', 'RBRD11', 'RBRF11', 'RBRL11', 'RBRP11', 'RBRR11', 'RBRS11', 'RBRY11', 'RBVA11', 'RBVO11', 'RCRB11', 'RDPD11', 'RECR11', 'RECT11', 'RECX11', 'RELG11', 'RFOF11', 'RMAI11', 'RNDP11', 'RNGO11', 'RRCI11', 'RVBI11', 'RZAG11', 'RZAK11', 'RZTR11', 'SADI11', 'SARE11', 'SCPF11', 'SDIL11', 'SEQR11', 'SHPH11', 'SNCI11', 'SNFF11', 'SPTW11', 'SPVJ11', 'SRVD11', 'STRX11', 'TEPP11', 'TGAR11', 'TORD11', 'TRNT11', 'TRXF11', 'URPR11', 'VCJR11', 'VCRR11', 'VGHF11', 'VGIA11', 'VGIP11', 'VGIR11', 'VIFI11', 'VILG11', 'VINO11', 'VISC11', 'VIUR11', 'VJFD11', 'VLOL11', 'VOTS11', 'VRTA11', 'VSHO11', 'VSLH11', 'VTLT11', 'VVPR11', 'VXXV11', 'WPLZ11', 'WTSP11B', 'XPCA11', 'XPCI11', 'XPCM11', 'XPHT11', 'XPIN11', 'XPLG11', 'XPML11', 'XPPR11', 'XPSF11', 'XTED11', 'YUFI11B']\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Organização dos dados**\n",
        "---"
      ],
      "metadata": {
        "id": "q7dUoxgKuuL7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Backup do Dataframe original\n",
        "df_FIIs_backup = df_FIIs.copy()"
      ],
      "metadata": {
        "id": "laXrrvr3uyfk"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "1UWXgFH4vIqb",
        "outputId": "5afd3da1-adc2-4480-f883-e50edefb258c"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Códigodo fundo                Setor Preço Atual  Liquidez Diária Dividendo  \\\n",
              "0         FIVN11            Shoppings     R$ 2,20           1553.0   R$ 0,00   \n",
              "1         BZLI11  Títulos e Val. Mob.    R$ 14,70          44041.0   R$ 0,00   \n",
              "2         XTED11   Lajes Corporativas     R$ 6,19             78.0   R$ 0,00   \n",
              "3         ALMI11   Lajes Corporativas   R$ 930,00             46.0   R$ 0,00   \n",
              "4         PABY11              Híbrido     R$ 7,46             14.0   R$ 0,00   \n",
              "\n",
              "  DividendYield DY (3M)Acumulado DY (6M)Acumulado DY (12M)Acumulado  \\\n",
              "0         0,00%            0,00%            0,00%             0,00%   \n",
              "1         0,00%            0,00%            0,00%             0,00%   \n",
              "2         0,00%            0,00%            0,00%             0,00%   \n",
              "3         0,00%            0,00%            0,00%             0,00%   \n",
              "4         0,00%            0,00%            0,00%             0,00%   \n",
              "\n",
              "  DY (3M)Média DY (6M)Média DY (12M)Média DY Ano Variação Preço  \\\n",
              "0        0,00%        0,00%         0,00%    NaN          0,00%   \n",
              "1        0,00%        0,00%         0,00%  0,00%          0,68%   \n",
              "2        0,00%        0,00%         0,00%  0,00%          1,10%   \n",
              "3        0,00%        0,00%         0,00%  0,00%          2,27%   \n",
              "4        0,00%        0,00%         0,00%  0,00%        -19,84%   \n",
              "\n",
              "  Rentab.Período Rentab.Acumulada     PatrimônioLíq.          VPA  P/VPA  \\\n",
              "0          0,00%            0,00%   R$ 65.678.487,99      R$ 6,98   32.0   \n",
              "1          0,68%            1,14%  R$ 431.076.817,02     R$ 10,70  137.0   \n",
              "2          1,10%          -37,83%   R$ 27.305.951,24     R$ 13,89   45.0   \n",
              "3          2,27%          -28,29%  R$ 249.632.973,43  R$ 2.245,37   41.0   \n",
              "4        -19,84%          -31,62%   R$ -9.546.407,61    R$ -12,59  -59.0   \n",
              "\n",
              "  DYPatrimonial VariaçãoPatrimonial Rentab. Patr.no Período  \\\n",
              "0           NaN                 NaN                     NaN   \n",
              "1           NaN                 NaN                     NaN   \n",
              "2           NaN                 NaN                     NaN   \n",
              "3           NaN                 NaN                     NaN   \n",
              "4           NaN                 NaN                     NaN   \n",
              "\n",
              "  Rentab. Patr.Acumulada VacânciaFísica VacânciaFinanceira  QuantidadeAtivos  \n",
              "0                    NaN         56,00%                NaN                 1  \n",
              "1                    NaN            NaN                NaN                 0  \n",
              "2                    NaN          0,00%            100,00%                 1  \n",
              "3                    NaN         64,05%                NaN                 1  \n",
              "4                    NaN            NaN                NaN                 1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-124deb4d-0c6d-46b3-a2eb-0e3780ae204a\">\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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Códigodo fundo</th>\n",
              "      <th>Setor</th>\n",
              "      <th>Preço Atual</th>\n",
              "      <th>Liquidez Diária</th>\n",
              "      <th>Dividendo</th>\n",
              "      <th>DividendYield</th>\n",
              "      <th>DY (3M)Acumulado</th>\n",
              "      <th>DY (6M)Acumulado</th>\n",
              "      <th>DY (12M)Acumulado</th>\n",
              "      <th>DY (3M)Média</th>\n",
              "      <th>DY (6M)Média</th>\n",
              "      <th>DY (12M)Média</th>\n",
              "      <th>DY Ano</th>\n",
              "      <th>Variação Preço</th>\n",
              "      <th>Rentab.Período</th>\n",
              "      <th>Rentab.Acumulada</th>\n",
              "      <th>PatrimônioLíq.</th>\n",
              "      <th>VPA</th>\n",
              "      <th>P/VPA</th>\n",
              "      <th>DYPatrimonial</th>\n",
              "      <th>VariaçãoPatrimonial</th>\n",
              "      <th>Rentab. Patr.no Período</th>\n",
              "      <th>Rentab. Patr.Acumulada</th>\n",
              "      <th>VacânciaFísica</th>\n",
              "      <th>VacânciaFinanceira</th>\n",
              "      <th>QuantidadeAtivos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FIVN11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>R$ 2,20</td>\n",
              "      <td>1553.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>R$ 65.678.487,99</td>\n",
              "      <td>R$ 6,98</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56,00%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BZLI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>R$ 14,70</td>\n",
              "      <td>44041.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>1,14%</td>\n",
              "      <td>R$ 431.076.817,02</td>\n",
              "      <td>R$ 10,70</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>R$ 6,19</td>\n",
              "      <td>78.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>-37,83%</td>\n",
              "      <td>R$ 27.305.951,24</td>\n",
              "      <td>R$ 13,89</td>\n",
              "      <td>45.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>100,00%</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>R$ 930,00</td>\n",
              "      <td>46.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>-28,29%</td>\n",
              "      <td>R$ 249.632.973,43</td>\n",
              "      <td>R$ 2.245,37</td>\n",
              "      <td>41.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,05%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PABY11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>R$ 7,46</td>\n",
              "      <td>14.0</td>\n",
              "      <td>R$ 0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>-19,84%</td>\n",
              "      <td>-19,84%</td>\n",
              "      <td>-31,62%</td>\n",
              "      <td>R$ -9.546.407,61</td>\n",
              "      <td>R$ -12,59</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-124deb4d-0c6d-46b3-a2eb-0e3780ae204a')\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\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-124deb4d-0c6d-46b3-a2eb-0e3780ae204a 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-124deb4d-0c6d-46b3-a2eb-0e3780ae204a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs['Preço Atual']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ZWhr9iXvOWh",
        "outputId": "a90db1e9-3992-4f8a-84e7-1397cba0caaf"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        R$ 2,20\n",
              "1       R$ 14,70\n",
              "2        R$ 6,19\n",
              "3      R$ 930,00\n",
              "4        R$ 7,46\n",
              "         ...    \n",
              "277    R$ 379,19\n",
              "278     R$ 87,02\n",
              "279     R$ 75,80\n",
              "280     R$ 76,42\n",
              "281    R$ 115,33\n",
              "Name: Preço Atual, Length: 282, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Encontrar a string R$ e substituir por um valor vazio\n",
        "df_FIIs = df_FIIs.replace(to_replace = r'^R\\$ ', value = '', regex = True)"
      ],
      "metadata": {
        "id": "MpTLZDK1vlyU"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "PkRSHT-2xu9u",
        "outputId": "edb912a6-c3c4-450d-edc4-c9ac7f14c1f6"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Códigodo fundo                Setor Preço Atual  Liquidez Diária Dividendo  \\\n",
              "0         FIVN11            Shoppings        2,20           1553.0      0,00   \n",
              "1         BZLI11  Títulos e Val. Mob.       14,70          44041.0      0,00   \n",
              "2         XTED11   Lajes Corporativas        6,19             78.0      0,00   \n",
              "3         ALMI11   Lajes Corporativas      930,00             46.0      0,00   \n",
              "4         PABY11              Híbrido        7,46             14.0      0,00   \n",
              "\n",
              "  DividendYield DY (3M)Acumulado DY (6M)Acumulado DY (12M)Acumulado  \\\n",
              "0         0,00%            0,00%            0,00%             0,00%   \n",
              "1         0,00%            0,00%            0,00%             0,00%   \n",
              "2         0,00%            0,00%            0,00%             0,00%   \n",
              "3         0,00%            0,00%            0,00%             0,00%   \n",
              "4         0,00%            0,00%            0,00%             0,00%   \n",
              "\n",
              "  DY (3M)Média DY (6M)Média DY (12M)Média DY Ano Variação Preço  \\\n",
              "0        0,00%        0,00%         0,00%    NaN          0,00%   \n",
              "1        0,00%        0,00%         0,00%  0,00%          0,68%   \n",
              "2        0,00%        0,00%         0,00%  0,00%          1,10%   \n",
              "3        0,00%        0,00%         0,00%  0,00%          2,27%   \n",
              "4        0,00%        0,00%         0,00%  0,00%        -19,84%   \n",
              "\n",
              "  Rentab.Período Rentab.Acumulada  PatrimônioLíq.       VPA  P/VPA  \\\n",
              "0          0,00%            0,00%   65.678.487,99      6,98   32.0   \n",
              "1          0,68%            1,14%  431.076.817,02     10,70  137.0   \n",
              "2          1,10%          -37,83%   27.305.951,24     13,89   45.0   \n",
              "3          2,27%          -28,29%  249.632.973,43  2.245,37   41.0   \n",
              "4        -19,84%          -31,62%   -9.546.407,61    -12,59  -59.0   \n",
              "\n",
              "  DYPatrimonial VariaçãoPatrimonial Rentab. Patr.no Período  \\\n",
              "0           NaN                 NaN                     NaN   \n",
              "1           NaN                 NaN                     NaN   \n",
              "2           NaN                 NaN                     NaN   \n",
              "3           NaN                 NaN                     NaN   \n",
              "4           NaN                 NaN                     NaN   \n",
              "\n",
              "  Rentab. Patr.Acumulada VacânciaFísica VacânciaFinanceira  QuantidadeAtivos  \n",
              "0                    NaN         56,00%                NaN                 1  \n",
              "1                    NaN            NaN                NaN                 0  \n",
              "2                    NaN          0,00%            100,00%                 1  \n",
              "3                    NaN         64,05%                NaN                 1  \n",
              "4                    NaN            NaN                NaN                 1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5658f48a-bd0f-4451-acbc-b4c7317fb6c4\">\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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Códigodo fundo</th>\n",
              "      <th>Setor</th>\n",
              "      <th>Preço Atual</th>\n",
              "      <th>Liquidez Diária</th>\n",
              "      <th>Dividendo</th>\n",
              "      <th>DividendYield</th>\n",
              "      <th>DY (3M)Acumulado</th>\n",
              "      <th>DY (6M)Acumulado</th>\n",
              "      <th>DY (12M)Acumulado</th>\n",
              "      <th>DY (3M)Média</th>\n",
              "      <th>DY (6M)Média</th>\n",
              "      <th>DY (12M)Média</th>\n",
              "      <th>DY Ano</th>\n",
              "      <th>Variação Preço</th>\n",
              "      <th>Rentab.Período</th>\n",
              "      <th>Rentab.Acumulada</th>\n",
              "      <th>PatrimônioLíq.</th>\n",
              "      <th>VPA</th>\n",
              "      <th>P/VPA</th>\n",
              "      <th>DYPatrimonial</th>\n",
              "      <th>VariaçãoPatrimonial</th>\n",
              "      <th>Rentab. Patr.no Período</th>\n",
              "      <th>Rentab. Patr.Acumulada</th>\n",
              "      <th>VacânciaFísica</th>\n",
              "      <th>VacânciaFinanceira</th>\n",
              "      <th>QuantidadeAtivos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FIVN11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>2,20</td>\n",
              "      <td>1553.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>65.678.487,99</td>\n",
              "      <td>6,98</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56,00%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BZLI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>14,70</td>\n",
              "      <td>44041.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>1,14%</td>\n",
              "      <td>431.076.817,02</td>\n",
              "      <td>10,70</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>6,19</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>-37,83%</td>\n",
              "      <td>27.305.951,24</td>\n",
              "      <td>13,89</td>\n",
              "      <td>45.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>100,00%</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>930,00</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>-28,29%</td>\n",
              "      <td>249.632.973,43</td>\n",
              "      <td>2.245,37</td>\n",
              "      <td>41.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,05%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PABY11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>7,46</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>-19,84%</td>\n",
              "      <td>-19,84%</td>\n",
              "      <td>-31,62%</td>\n",
              "      <td>-9.546.407,61</td>\n",
              "      <td>-12,59</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5658f48a-bd0f-4451-acbc-b4c7317fb6c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\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-5658f48a-bd0f-4451-acbc-b4c7317fb6c4');\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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            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs['PatrimônioLíq.']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V588HJW8xxo5",
        "outputId": "beca24b3-05e8-4595-8c4b-9eeb4374ba3b"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         65.678.487,99\n",
              "1        431.076.817,02\n",
              "2         27.305.951,24\n",
              "3        249.632.973,43\n",
              "4         -9.546.407,61\n",
              "             ...       \n",
              "277       50.992.934,89\n",
              "278      195.230.258,40\n",
              "279      376.309.193,73\n",
              "280      489.344.835,30\n",
              "281    2.187.502.617,24\n",
              "Name: PatrimônioLíq., Length: 282, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Retirar o separador de milhar que está sendo um ponto\n",
        "df_FIIs['PatrimônioLíq.'] = df_FIIs['PatrimônioLíq.'].str.replace('.','')\n",
        "df_FIIs['Preço Atual'] = df_FIIs['Preço Atual'].str.replace('.','')\n",
        "df_FIIs['Dividendo'] = df_FIIs['Dividendo'].str.replace('.','')\n",
        "df_FIIs['VPA'] = df_FIIs['VPA'].str.replace('.','')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "go_gUWOfyz1_",
        "outputId": "34cb134f-aae2-45f0-dbcf-2d73fa655728"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning:\n",
            "\n",
            "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning:\n",
            "\n",
            "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: FutureWarning:\n",
            "\n",
            "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning:\n",
            "\n",
            "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "\n"
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        "df_FIIs.head()"
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      "execution_count": 155,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Códigodo fundo                Setor Preço Atual  Liquidez Diária Dividendo  \\\n",
              "0         FIVN11            Shoppings        2,20           1553.0      0,00   \n",
              "1         BZLI11  Títulos e Val. Mob.       14,70          44041.0      0,00   \n",
              "2         XTED11   Lajes Corporativas        6,19             78.0      0,00   \n",
              "3         ALMI11   Lajes Corporativas      930,00             46.0      0,00   \n",
              "4         PABY11              Híbrido        7,46             14.0      0,00   \n",
              "\n",
              "  DividendYield DY (3M)Acumulado DY (6M)Acumulado DY (12M)Acumulado  \\\n",
              "0         0,00%            0,00%            0,00%             0,00%   \n",
              "1         0,00%            0,00%            0,00%             0,00%   \n",
              "2         0,00%            0,00%            0,00%             0,00%   \n",
              "3         0,00%            0,00%            0,00%             0,00%   \n",
              "4         0,00%            0,00%            0,00%             0,00%   \n",
              "\n",
              "  DY (3M)Média DY (6M)Média DY (12M)Média DY Ano Variação Preço  \\\n",
              "0        0,00%        0,00%         0,00%    NaN          0,00%   \n",
              "1        0,00%        0,00%         0,00%  0,00%          0,68%   \n",
              "2        0,00%        0,00%         0,00%  0,00%          1,10%   \n",
              "3        0,00%        0,00%         0,00%  0,00%          2,27%   \n",
              "4        0,00%        0,00%         0,00%  0,00%        -19,84%   \n",
              "\n",
              "  Rentab.Período Rentab.Acumulada PatrimônioLíq.      VPA  P/VPA  \\\n",
              "0          0,00%            0,00%    65678487,99     6,98   32.0   \n",
              "1          0,68%            1,14%   431076817,02    10,70  137.0   \n",
              "2          1,10%          -37,83%    27305951,24    13,89   45.0   \n",
              "3          2,27%          -28,29%   249632973,43  2245,37   41.0   \n",
              "4        -19,84%          -31,62%    -9546407,61   -12,59  -59.0   \n",
              "\n",
              "  DYPatrimonial VariaçãoPatrimonial Rentab. Patr.no Período  \\\n",
              "0           NaN                 NaN                     NaN   \n",
              "1           NaN                 NaN                     NaN   \n",
              "2           NaN                 NaN                     NaN   \n",
              "3           NaN                 NaN                     NaN   \n",
              "4           NaN                 NaN                     NaN   \n",
              "\n",
              "  Rentab. Patr.Acumulada VacânciaFísica VacânciaFinanceira  QuantidadeAtivos  \n",
              "0                    NaN         56,00%                NaN                 1  \n",
              "1                    NaN            NaN                NaN                 0  \n",
              "2                    NaN          0,00%            100,00%                 1  \n",
              "3                    NaN         64,05%                NaN                 1  \n",
              "4                    NaN            NaN                NaN                 1  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Códigodo fundo</th>\n",
              "      <th>Setor</th>\n",
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              "      <th>DY (12M)Média</th>\n",
              "      <th>DY Ano</th>\n",
              "      <th>Variação Preço</th>\n",
              "      <th>Rentab.Período</th>\n",
              "      <th>Rentab.Acumulada</th>\n",
              "      <th>PatrimônioLíq.</th>\n",
              "      <th>VPA</th>\n",
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              "      <th>QuantidadeAtivos</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FIVN11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>2,20</td>\n",
              "      <td>1553.0</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>65678487,99</td>\n",
              "      <td>6,98</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56,00%</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BZLI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>14,70</td>\n",
              "      <td>44041.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>0,68%</td>\n",
              "      <td>1,14%</td>\n",
              "      <td>431076817,02</td>\n",
              "      <td>10,70</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>6,19</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>1,10%</td>\n",
              "      <td>-37,83%</td>\n",
              "      <td>27305951,24</td>\n",
              "      <td>13,89</td>\n",
              "      <td>45.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>100,00%</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>930,00</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0,00</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>2,27%</td>\n",
              "      <td>-28,29%</td>\n",
              "      <td>249632973,43</td>\n",
              "      <td>2245,37</td>\n",
              "      <td>41.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64,05%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PABY11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>7,46</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0,00</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
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              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>0,00%</td>\n",
              "      <td>-19,84%</td>\n",
              "      <td>-19,84%</td>\n",
              "      <td>-31,62%</td>\n",
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              "      <td>-12,59</td>\n",
              "      <td>-59.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Modificar o separador vírgula por ponto\n",
        "df_FIIs['PatrimônioLíq.'] = df_FIIs['PatrimônioLíq.'].str.replace(',','.')\n",
        "df_FIIs['Preço Atual'] = df_FIIs['Preço Atual'].str.replace(',','.')\n",
        "df_FIIs['Dividendo'] = df_FIIs['Dividendo'].str.replace(',','.')\n",
        "df_FIIs['VPA'] = df_FIIs['VPA'].str.replace(',','.')"
      ],
      "metadata": {
        "id": "uaPB7TmH0T0C"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs['PatrimônioLíq.']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hc_E7tSh1JBo",
        "outputId": "20f6d6c8-83dd-4839-db3c-fc5b82f23da0"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        65678487.99\n",
              "1       431076817.02\n",
              "2        27305951.24\n",
              "3       249632973.43\n",
              "4        -9546407.61\n",
              "           ...      \n",
              "277      50992934.89\n",
              "278     195230258.40\n",
              "279     376309193.73\n",
              "280     489344835.30\n",
              "281    2187502617.24\n",
              "Name: PatrimônioLíq., Length: 282, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Transformar as variáveis para float\n",
        "df_FIIs['PatrimônioLíq.'] = df_FIIs['PatrimônioLíq.'].astype(float)\n",
        "df_FIIs['Preço Atual'] = df_FIIs['Preço Atual'].astype(float)\n",
        "df_FIIs['Dividendo'] = df_FIIs['Dividendo'].astype(float)\n",
        "df_FIIs['VPA'] = df_FIIs['VPA'].astype(float)"
      ],
      "metadata": {
        "id": "WMjUBiC01rAY"
      },
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Valor máximo\n",
        "np.max(df_FIIs['Preço Atual'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mSvPdZFZ2KHI",
        "outputId": "2ae52cda-072d-4f78-d796-3ac9be433637"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "76000.0"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.min(df_FIIs['Preço Atual'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8vH17yi2WV0",
        "outputId": "0b74e3b2-448f-4ec8-ee31-7b98bd8f50a0"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Encontrar a string que terminam com %\n",
        "df_FIIs = df_FIIs.replace(to_replace = r'%$', value = '', regex = True)\n"
      ],
      "metadata": {
        "id": "EnhImpS12-9_"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs['DividendYield'] = df_FIIs['DividendYield'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY (3M)Acumulado'] = df_FIIs['DY (3M)Acumulado'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY (6M)Acumulado'] = df_FIIs['DY (6M)Acumulado'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY (12M)Acumulado'] = df_FIIs['DY (12M)Acumulado'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY (3M)Média'] = df_FIIs['DY (3M)Média'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY (6M)Média'] = df_FIIs['DY (6M)Média'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY (12M)Média'] = df_FIIs['DY (12M)Média'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DY Ano'] = df_FIIs['DY Ano'].str.replace(',','.').astype(float)\n",
        "df_FIIs['Variação Preço'] = df_FIIs['Variação Preço'].str.replace(',','.').astype(float)\n",
        "df_FIIs['Rentab.Período'] = df_FIIs['Rentab.Período'].str.replace(',','.').astype(float)\n",
        "df_FIIs['Rentab.Acumulada'] = df_FIIs['Rentab.Acumulada'].str.replace(',','.').astype(float)\n",
        "df_FIIs['DYPatrimonial'] = df_FIIs['DYPatrimonial'].str.replace(',','.').astype(float)\n",
        "df_FIIs['VariaçãoPatrimonial'] = df_FIIs['VariaçãoPatrimonial'].str.replace(',','.').astype(float)\n",
        "df_FIIs['Rentab. Patr.no Período'] = df_FIIs['Rentab. Patr.no Período'].str.replace(',','.').astype(float)\n",
        "df_FIIs['Rentab. Patr.Acumulada'] = df_FIIs['Rentab. Patr.Acumulada'].str.replace(',','.').astype(float)\n",
        "df_FIIs['VacânciaFísica'] = df_FIIs['VacânciaFísica'].str.replace(',','.').astype(float)\n",
        "df_FIIs['VacânciaFinanceira'] = df_FIIs['VacânciaFinanceira'].str.replace(',','.').astype(float)\n",
        "\n"
      ],
      "metadata": {
        "id": "0Tv9njuHM1Pg"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs.head()"
      ],
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        },
        "id": "riHfd7PM3T-P",
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      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Códigodo fundo                Setor  Preço Atual  Liquidez Diária  \\\n",
              "0         FIVN11            Shoppings         2.20           1553.0   \n",
              "1         BZLI11  Títulos e Val. Mob.        14.70          44041.0   \n",
              "2         XTED11   Lajes Corporativas         6.19             78.0   \n",
              "3         ALMI11   Lajes Corporativas       930.00             46.0   \n",
              "4         PABY11              Híbrido         7.46             14.0   \n",
              "\n",
              "   Dividendo  DividendYield  DY (3M)Acumulado  DY (6M)Acumulado  \\\n",
              "0        0.0            0.0               0.0               0.0   \n",
              "1        0.0            0.0               0.0               0.0   \n",
              "2        0.0            0.0               0.0               0.0   \n",
              "3        0.0            0.0               0.0               0.0   \n",
              "4        0.0            0.0               0.0               0.0   \n",
              "\n",
              "   DY (12M)Acumulado  DY (3M)Média  DY (6M)Média  DY (12M)Média  DY Ano  \\\n",
              "0                0.0           0.0           0.0            0.0     NaN   \n",
              "1                0.0           0.0           0.0            0.0     0.0   \n",
              "2                0.0           0.0           0.0            0.0     0.0   \n",
              "3                0.0           0.0           0.0            0.0     0.0   \n",
              "4                0.0           0.0           0.0            0.0     0.0   \n",
              "\n",
              "   Variação Preço  Rentab.Período  Rentab.Acumulada  PatrimônioLíq.      VPA  \\\n",
              "0            0.00            0.00              0.00    6.567849e+07     6.98   \n",
              "1            0.68            0.68              1.14    4.310768e+08    10.70   \n",
              "2            1.10            1.10            -37.83    2.730595e+07    13.89   \n",
              "3            2.27            2.27            -28.29    2.496330e+08  2245.37   \n",
              "4          -19.84          -19.84            -31.62   -9.546408e+06   -12.59   \n",
              "\n",
              "   P/VPA  DYPatrimonial  VariaçãoPatrimonial  Rentab. Patr.no Período  \\\n",
              "0   32.0            NaN                  NaN                      NaN   \n",
              "1  137.0            NaN                  NaN                      NaN   \n",
              "2   45.0            NaN                  NaN                      NaN   \n",
              "3   41.0            NaN                  NaN                      NaN   \n",
              "4  -59.0            NaN                  NaN                      NaN   \n",
              "\n",
              "   Rentab. Patr.Acumulada  VacânciaFísica  VacânciaFinanceira  \\\n",
              "0                     NaN           56.00                 NaN   \n",
              "1                     NaN             NaN                 NaN   \n",
              "2                     NaN            0.00               100.0   \n",
              "3                     NaN           64.05                 NaN   \n",
              "4                     NaN             NaN                 NaN   \n",
              "\n",
              "   QuantidadeAtivos  \n",
              "0                 1  \n",
              "1                 0  \n",
              "2                 1  \n",
              "3                 1  \n",
              "4                 1  "
            ],
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              "      <th>Códigodo fundo</th>\n",
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              "      <th>PatrimônioLíq.</th>\n",
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              "      <th>0</th>\n",
              "      <td>FIVN11</td>\n",
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              "      <td>6.98</td>\n",
              "      <td>32.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BZLI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>14.70</td>\n",
              "      <td>44041.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.68</td>\n",
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              "      <td>1.14</td>\n",
              "      <td>4.310768e+08</td>\n",
              "      <td>10.70</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>6.19</td>\n",
              "      <td>78.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>930.00</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>64.05</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PABY11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>7.46</td>\n",
              "      <td>14.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Quais setores exigem no DF (passa dentro de um conjunto para evitar repetições)\n",
        "set(df_FIIs['Setor'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_AsEKEZ3qD9",
        "outputId": "80cd1c08-99c1-4200-aa32-dda6d595f631"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Hospital',\n",
              " 'Hotel',\n",
              " 'Híbrido',\n",
              " 'Lajes Corporativas',\n",
              " 'Logística',\n",
              " 'Outros',\n",
              " 'Residencial',\n",
              " 'Shoppings',\n",
              " 'Títulos e Val. Mob.',\n",
              " nan}"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Maior DividendYield anual em porcentagem\n",
        "np.max(df_FIIs['DividendYield'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "veRwKjdfNc1z",
        "outputId": "92f8679b-c8cf-43f4-fe0e-cbc51abf4906"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.9"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osrHiNQ2RK7C",
        "outputId": "45fdce29-1057-443e-882c-ac2e6c487171"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 282 entries, 0 to 281\n",
            "Data columns (total 26 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Códigodo fundo           282 non-null    object \n",
            " 1   Setor                    278 non-null    object \n",
            " 2   Preço Atual              274 non-null    float64\n",
            " 3   Liquidez Diária          274 non-null    float64\n",
            " 4   Dividendo                282 non-null    float64\n",
            " 5   DividendYield            276 non-null    float64\n",
            " 6   DY (3M)Acumulado         276 non-null    float64\n",
            " 7   DY (6M)Acumulado         276 non-null    float64\n",
            " 8   DY (12M)Acumulado        276 non-null    float64\n",
            " 9   DY (3M)Média             276 non-null    float64\n",
            " 10  DY (6M)Média             276 non-null    float64\n",
            " 11  DY (12M)Média            276 non-null    float64\n",
            " 12  DY Ano                   262 non-null    float64\n",
            " 13  Variação Preço           276 non-null    float64\n",
            " 14  Rentab.Período           276 non-null    float64\n",
            " 15  Rentab.Acumulada         276 non-null    float64\n",
            " 16  PatrimônioLíq.           282 non-null    float64\n",
            " 17  VPA                      282 non-null    float64\n",
            " 18  P/VPA                    274 non-null    float64\n",
            " 19  DYPatrimonial            247 non-null    float64\n",
            " 20  VariaçãoPatrimonial      247 non-null    float64\n",
            " 21  Rentab. Patr.no Período  247 non-null    float64\n",
            " 22  Rentab. Patr.Acumulada   247 non-null    float64\n",
            " 23  VacânciaFísica           123 non-null    float64\n",
            " 24  VacânciaFinanceira       30 non-null     float64\n",
            " 25  QuantidadeAtivos         282 non-null    int64  \n",
            "dtypes: float64(23), int64(1), object(2)\n",
            "memory usage: 57.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Contagem de valores NaN de cada variável (em porcentagem)\n",
        "#Cabe avaliar qual das colunas seria desnecessário considerando a porcentagem alta em relação ao NaN\n",
        "df_FIIs.isna().sum() / len(df_FIIs) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8R-2o8fROs5",
        "outputId": "ad5682e7-f6e9-4861-a1da-7aada1f04927"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Códigodo fundo              0.000000\n",
              "Setor                       1.418440\n",
              "Preço Atual                 2.836879\n",
              "Liquidez Diária             2.836879\n",
              "Dividendo                   0.000000\n",
              "DividendYield               2.127660\n",
              "DY (3M)Acumulado            2.127660\n",
              "DY (6M)Acumulado            2.127660\n",
              "DY (12M)Acumulado           2.127660\n",
              "DY (3M)Média                2.127660\n",
              "DY (6M)Média                2.127660\n",
              "DY (12M)Média               2.127660\n",
              "DY Ano                      7.092199\n",
              "Variação Preço              2.127660\n",
              "Rentab.Período              2.127660\n",
              "Rentab.Acumulada            2.127660\n",
              "PatrimônioLíq.              0.000000\n",
              "VPA                         0.000000\n",
              "P/VPA                       2.836879\n",
              "DYPatrimonial              12.411348\n",
              "VariaçãoPatrimonial        12.411348\n",
              "Rentab. Patr.no Período    12.411348\n",
              "Rentab. Patr.Acumulada     12.411348\n",
              "VacânciaFísica             56.382979\n",
              "VacânciaFinanceira         89.361702\n",
              "QuantidadeAtivos            0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSUSddXjTKBA",
        "outputId": "7688146f-08aa-4542-a7e9-0d4bcdf1c5c4"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(282, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Excluir as linhas nas quais não existe o preço atual, o DividendYield e Setor.\n",
        "df_FIIs.dropna(subset = ['Preço Atual', 'DividendYield', 'Setor'], inplace = True)"
      ],
      "metadata": {
        "id": "rfFtvl86Rkmd"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_FIIs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "II7fPZe6TOge",
        "outputId": "a71f4299-b7cd-435a-b3c1-1e334d2a6e75"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(264, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "# **Análise exploratória**\n",
        "---"
      ],
      "metadata": {
        "id": "C1GjHv5DTRQ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Quanto maior o DY, maior o preço? \n",
        "\n",
        "Não necessariamente. Podemos verificar no gráfico de dispersão (através dos outliers) que nem toda relação entre preço e DY segue esse parâmetro."
      ],
      "metadata": {
        "id": "4kqcms9p3j02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "sffP1daW4A8D"
      },
      "execution_count": 171,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Gráfico de dispersão\n",
        "fig = px.scatter(df_FIIs, x = 'Preço Atual', y = 'DY (12M)Acumulado', color = 'Setor')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PN4l22sz4nAj",
        "outputId": "b0a6aa30-d590-441e-c079-12b0d274d83f"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "#Ajustando a escala para melhorar a visualização\n",
        "df_FIIs['log Preço Atual'] = np.log(df_FIIs['Preço Atual']+1)\n",
        "\n",
        "fig = px.scatter(df_FIIs, x = 'log Preço Atual', y = 'DY (12M)Acumulado', color = 'Setor')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "NYxtFHKC5ur0",
        "outputId": "a7e169ab-5f1e-4fa6-9acb-e7b4f01bc95e"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matriz de correlação levando em consideração as variáveis do DF."
      ],
      "metadata": {
        "id": "MMyRK5DW05KU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "É possível verificar que existem algumas correlações fortes com o preço e outras não."
      ],
      "metadata": {
        "id": "YMOppx6gUBXr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "0GNGKqLf1Lvn"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = (15,15))\n",
        "matriz_correlacao = df_FIIs.corr(method = 'spearman')\n",
        "\n",
        "#Configurações do gráfico\n",
        "plot = sns.heatmap(matriz_correlacao, annot = True, fmt = '.1f', linewidths = 0.7)\n",
        "\n",
        "#Aumentar o tamanho da fonte\n",
        "plt.rcParams.update({'font.size':10})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "id": "iDeLY9u51bzi",
        "outputId": "dbacb117-5c2e-4746-f8aa-20ecaa5d8484"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comparação entre a média e mediana dos DY(12M) a fim de entender essa relação e verificar os que tem melhor média de retorno dentre esses DY.\n",
        "\n",
        "Podemos destacar as três primeiras linhas como sendo os setores com as melhores médias de pagamento dos dividendos. Seria necessário verificar aqueles que estão dentro da denominação \"Outros\"."
      ],
      "metadata": {
        "id": "n25vpGSW3cIK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pivot_table_FIIs = pd.pivot_table(df_FIIs, values = 'DY (12M)Acumulado', index = ['Setor'], columns = [], aggfunc = ('mean', 'median'))\n",
        "#Ordenando pela maior média (sentido decrescente)\n",
        "pivot_table_FIIs.sort_values('mean', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "H7knHHyO82eZ",
        "outputId": "76f6df9e-ad72-4acc-98b4-330c0f122d6d"
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         mean  median\n",
              "Setor                                \n",
              "Outros               9.391176   9.990\n",
              "Hospital             8.563333   9.330\n",
              "Títulos e Val. Mob.  8.520889  10.360\n",
              "Logística            7.820455   8.115\n",
              "Híbrido              7.100455   7.960\n",
              "Lajes Corporativas   6.926667   7.260\n",
              "Hotel                6.826667   5.410\n",
              "Shoppings            5.252273   6.790\n",
              "Residencial          2.394286   0.000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-783ef1c5-4e6e-47ec-a844-edb239e687eb\">\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>mean</th>\n",
              "      <th>median</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Setor</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Outros</th>\n",
              "      <td>9.391176</td>\n",
              "      <td>9.990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hospital</th>\n",
              "      <td>8.563333</td>\n",
              "      <td>9.330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Títulos e Val. Mob.</th>\n",
              "      <td>8.520889</td>\n",
              "      <td>10.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logística</th>\n",
              "      <td>7.820455</td>\n",
              "      <td>8.115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Híbrido</th>\n",
              "      <td>7.100455</td>\n",
              "      <td>7.960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lajes Corporativas</th>\n",
              "      <td>6.926667</td>\n",
              "      <td>7.260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hotel</th>\n",
              "      <td>6.826667</td>\n",
              "      <td>5.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shoppings</th>\n",
              "      <td>5.252273</td>\n",
              "      <td>6.790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residencial</th>\n",
              "      <td>2.394286</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-783ef1c5-4e6e-47ec-a844-edb239e687eb')\"\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-783ef1c5-4e6e-47ec-a844-edb239e687eb 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-783ef1c5-4e6e-47ec-a844-edb239e687eb');\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": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fundos com P/VPA menor que 1, isto é, a empresa vale em bolsa menos do que o seu patrimônio líquido."
      ],
      "metadata": {
        "id": "TDpAxopCPZg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando a coluna apenas com os FIIs com P/VPA < 1\n",
        "df_FIIs['Fundos_VPA'] = df_FIIs['P/VPA'] < 100\n",
        "\n",
        "FII_VPA = df_FIIs[df_FIIs['Fundos_VPA']==True]\n",
        "FII_VPA"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "pAP78MrQNnQz",
        "outputId": "ca9b5765-302c-4b13-aa5b-195f5b905b3a"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Códigodo fundo                Setor  Preço Atual  Liquidez Diária  \\\n",
              "0           FIVN11            Shoppings         2.20           1553.0   \n",
              "2           XTED11   Lajes Corporativas         6.19             78.0   \n",
              "3           ALMI11   Lajes Corporativas       930.00             46.0   \n",
              "4           PABY11              Híbrido         7.46             14.0   \n",
              "8           HSML11            Shoppings        77.50          15523.0   \n",
              "..             ...                  ...          ...              ...   \n",
              "277         RNDP11  Títulos e Val. Mob.       379.19              4.0   \n",
              "278         PLCR11              Híbrido        87.02           2690.0   \n",
              "279         CXCO11   Lajes Corporativas        75.80           6905.0   \n",
              "280         PATL11              Híbrido        76.42           9681.0   \n",
              "281         HGRU11              Híbrido       115.33          26884.0   \n",
              "\n",
              "     Dividendo  DividendYield  DY (3M)Acumulado  DY (6M)Acumulado  \\\n",
              "0         0.00           0.00              0.00              0.00   \n",
              "2         0.00           0.00              0.00              0.00   \n",
              "3         0.00           0.00              0.00              0.00   \n",
              "4         0.00           0.00              0.00              0.00   \n",
              "8         0.60           0.74              2.27              4.37   \n",
              "..         ...            ...               ...               ...   \n",
              "277       4.26           1.12              2.84              4.83   \n",
              "278       1.10           1.26              3.63              7.16   \n",
              "279       0.73           0.94              2.64              5.16   \n",
              "280       0.58           0.75              2.27              4.69   \n",
              "281       0.82           0.71              2.12              4.09   \n",
              "\n",
              "     DY (12M)Acumulado  DY (3M)Média  DY (6M)Média  DY (12M)Média  DY Ano  \\\n",
              "0                 0.00          0.00          0.00           0.00     NaN   \n",
              "2                 0.00          0.00          0.00           0.00    0.00   \n",
              "3                 0.00          0.00          0.00           0.00    0.00   \n",
              "4                 0.00          0.00          0.00           0.00    0.00   \n",
              "8                 7.72          0.76          0.73           0.64    2.99   \n",
              "..                 ...           ...           ...            ...     ...   \n",
              "277               8.38          0.95          0.80           0.70    3.50   \n",
              "278              12.96          1.21          1.19           1.08    4.81   \n",
              "279               9.31          0.88          0.86           0.78    3.44   \n",
              "280               9.13          0.76          0.78           0.76    3.04   \n",
              "281               7.94          0.71          0.68           0.66    2.78   \n",
              "\n",
              "     ...  Rentab.Acumulada  PatrimônioLíq.      VPA  P/VPA  DYPatrimonial  \\\n",
              "0    ...              0.00    6.567849e+07     6.98   32.0            NaN   \n",
              "2    ...            -37.83    2.730595e+07    13.89   45.0            NaN   \n",
              "3    ...            -28.29    2.496330e+08  2245.37   41.0            NaN   \n",
              "4    ...            -31.62   -9.546408e+06   -12.59  -59.0            NaN   \n",
              "8    ...              9.17    1.479544e+09    93.76   83.0           0.64   \n",
              "..   ...               ...             ...      ...    ...            ...   \n",
              "277  ...            -34.41    5.099293e+07   474.44   80.0           0.90   \n",
              "278  ...              3.81    1.952303e+08    92.56   94.0           1.19   \n",
              "279  ...             -0.64    3.763092e+08    95.89   79.0           0.76   \n",
              "280  ...              4.88    4.893448e+08    98.03   78.0           0.59   \n",
              "281  ...              3.43    2.187503e+09   118.84   97.0           0.69   \n",
              "\n",
              "     VariaçãoPatrimonial  Rentab. Patr.no Período  Rentab. Patr.Acumulada  \\\n",
              "0                    NaN                      NaN                     NaN   \n",
              "2                    NaN                      NaN                     NaN   \n",
              "3                    NaN                      NaN                     NaN   \n",
              "4                    NaN                      NaN                     NaN   \n",
              "8                  -0.06                     0.58                    1.59   \n",
              "..                   ...                      ...                     ...   \n",
              "277               -29.72                   -29.09                  -27.55   \n",
              "278                -0.09                     1.10                    4.62   \n",
              "279                -0.13                     0.63                    2.53   \n",
              "280                -0.06                     0.54                    2.39   \n",
              "281                 0.01                     0.70                    2.78   \n",
              "\n",
              "     VacânciaFísica  VacânciaFinanceira  QuantidadeAtivos  log Preço Atual  \\\n",
              "0             56.00                 NaN                 1         1.163151   \n",
              "2              0.00               100.0                 1         1.972691   \n",
              "3             64.05                 NaN                 1         6.836259   \n",
              "4               NaN                 NaN                 1         2.135349   \n",
              "8              4.50                 NaN                 5         4.363099   \n",
              "..              ...                 ...               ...              ...   \n",
              "277             NaN                 NaN                 0         5.940671   \n",
              "278             NaN                 NaN                 0         4.477564   \n",
              "279             NaN                 NaN                 0         4.341205   \n",
              "280            0.00                 0.0                 4         4.349245   \n",
              "281            0.00                 0.0                17         4.756431   \n",
              "\n",
              "     Fundos_VPA  \n",
              "0          True  \n",
              "2          True  \n",
              "3          True  \n",
              "4          True  \n",
              "8          True  \n",
              "..          ...  \n",
              "277        True  \n",
              "278        True  \n",
              "279        True  \n",
              "280        True  \n",
              "281        True  \n",
              "\n",
              "[201 rows x 28 columns]"
            ],
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              "\n",
              "  <div id=\"df-a8c9b822-bcd7-4687-919e-6bb33d811df0\">\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",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Códigodo fundo</th>\n",
              "      <th>Setor</th>\n",
              "      <th>Preço Atual</th>\n",
              "      <th>Liquidez Diária</th>\n",
              "      <th>Dividendo</th>\n",
              "      <th>DividendYield</th>\n",
              "      <th>DY (3M)Acumulado</th>\n",
              "      <th>DY (6M)Acumulado</th>\n",
              "      <th>DY (12M)Acumulado</th>\n",
              "      <th>DY (3M)Média</th>\n",
              "      <th>DY (6M)Média</th>\n",
              "      <th>DY (12M)Média</th>\n",
              "      <th>DY Ano</th>\n",
              "      <th>...</th>\n",
              "      <th>Rentab.Acumulada</th>\n",
              "      <th>PatrimônioLíq.</th>\n",
              "      <th>VPA</th>\n",
              "      <th>P/VPA</th>\n",
              "      <th>DYPatrimonial</th>\n",
              "      <th>VariaçãoPatrimonial</th>\n",
              "      <th>Rentab. Patr.no Período</th>\n",
              "      <th>Rentab. Patr.Acumulada</th>\n",
              "      <th>VacânciaFísica</th>\n",
              "      <th>VacânciaFinanceira</th>\n",
              "      <th>QuantidadeAtivos</th>\n",
              "      <th>log Preço Atual</th>\n",
              "      <th>Fundos_VPA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FIVN11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1553.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.567849e+07</td>\n",
              "      <td>6.98</td>\n",
              "      <td>32.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>56.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1.163151</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XTED11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>6.19</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-37.83</td>\n",
              "      <td>2.730595e+07</td>\n",
              "      <td>13.89</td>\n",
              "      <td>45.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.972691</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALMI11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>930.00</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-28.29</td>\n",
              "      <td>2.496330e+08</td>\n",
              "      <td>2245.37</td>\n",
              "      <td>41.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>64.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>6.836259</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PABY11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>7.46</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>-31.62</td>\n",
              "      <td>-9.546408e+06</td>\n",
              "      <td>-12.59</td>\n",
              "      <td>-59.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2.135349</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>HSML11</td>\n",
              "      <td>Shoppings</td>\n",
              "      <td>77.50</td>\n",
              "      <td>15523.0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.74</td>\n",
              "      <td>2.27</td>\n",
              "      <td>4.37</td>\n",
              "      <td>7.72</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.64</td>\n",
              "      <td>2.99</td>\n",
              "      <td>...</td>\n",
              "      <td>9.17</td>\n",
              "      <td>1.479544e+09</td>\n",
              "      <td>93.76</td>\n",
              "      <td>83.0</td>\n",
              "      <td>0.64</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.58</td>\n",
              "      <td>1.59</td>\n",
              "      <td>4.50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>4.363099</td>\n",
              "      <td>True</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",
              "      <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>277</th>\n",
              "      <td>RNDP11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>379.19</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.26</td>\n",
              "      <td>1.12</td>\n",
              "      <td>2.84</td>\n",
              "      <td>4.83</td>\n",
              "      <td>8.38</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.70</td>\n",
              "      <td>3.50</td>\n",
              "      <td>...</td>\n",
              "      <td>-34.41</td>\n",
              "      <td>5.099293e+07</td>\n",
              "      <td>474.44</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>-29.72</td>\n",
              "      <td>-29.09</td>\n",
              "      <td>-27.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>5.940671</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>PLCR11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>87.02</td>\n",
              "      <td>2690.0</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.26</td>\n",
              "      <td>3.63</td>\n",
              "      <td>7.16</td>\n",
              "      <td>12.96</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.08</td>\n",
              "      <td>4.81</td>\n",
              "      <td>...</td>\n",
              "      <td>3.81</td>\n",
              "      <td>1.952303e+08</td>\n",
              "      <td>92.56</td>\n",
              "      <td>94.0</td>\n",
              "      <td>1.19</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>1.10</td>\n",
              "      <td>4.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.477564</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>CXCO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>75.80</td>\n",
              "      <td>6905.0</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.94</td>\n",
              "      <td>2.64</td>\n",
              "      <td>5.16</td>\n",
              "      <td>9.31</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.78</td>\n",
              "      <td>3.44</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.64</td>\n",
              "      <td>3.763092e+08</td>\n",
              "      <td>95.89</td>\n",
              "      <td>79.0</td>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.13</td>\n",
              "      <td>0.63</td>\n",
              "      <td>2.53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.341205</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>PATL11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>76.42</td>\n",
              "      <td>9681.0</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2.27</td>\n",
              "      <td>4.69</td>\n",
              "      <td>9.13</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.76</td>\n",
              "      <td>3.04</td>\n",
              "      <td>...</td>\n",
              "      <td>4.88</td>\n",
              "      <td>4.893448e+08</td>\n",
              "      <td>98.03</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.59</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.54</td>\n",
              "      <td>2.39</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>4.349245</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>HGRU11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>115.33</td>\n",
              "      <td>26884.0</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.71</td>\n",
              "      <td>2.12</td>\n",
              "      <td>4.09</td>\n",
              "      <td>7.94</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.66</td>\n",
              "      <td>2.78</td>\n",
              "      <td>...</td>\n",
              "      <td>3.43</td>\n",
              "      <td>2.187503e+09</td>\n",
              "      <td>118.84</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.70</td>\n",
              "      <td>2.78</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17</td>\n",
              "      <td>4.756431</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>201 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8c9b822-bcd7-4687-919e-6bb33d811df0')\"\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-a8c9b822-bcd7-4687-919e-6bb33d811df0 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-a8c9b822-bcd7-4687-919e-6bb33d811df0');\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": 177
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Fundos com P/VPA menor que 1 e DY anual maior ou igual a 10%"
      ],
      "metadata": {
        "id": "TY1SfSC2Rf_2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Selecionar_FII_VPA = FII_VPA[FII_VPA['DY (12M)Acumulado'] >= 10]\n",
        "Selecionar_FII_VPA"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "H15ikKT4SWJq",
        "outputId": "75ce04a0-2835-48c1-f815-0b400d58c704"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Códigodo fundo                Setor  Preço Atual  Liquidez Diária  \\\n",
              "10          SPTW11   Lajes Corporativas        44.00           6623.0   \n",
              "17          PLRI11  Títulos e Val. Mob.        37.40            452.0   \n",
              "19          PORD11  Títulos e Val. Mob.        94.50           8149.0   \n",
              "28          RRCI11  Títulos e Val. Mob.        90.70           1646.0   \n",
              "29          BBFO11  Títulos e Val. Mob.        73.80           1700.0   \n",
              "31          HBRH11              Híbrido        81.79           1236.0   \n",
              "34          AIEC11   Lajes Corporativas        79.44           2566.0   \n",
              "35          HSAF11  Títulos e Val. Mob.        94.12           3269.0   \n",
              "38          RFOF11  Títulos e Val. Mob.        70.72           3967.0   \n",
              "42          MGHT11                Hotel        71.12           3575.0   \n",
              "55          ITIP11  Títulos e Val. Mob.        82.90           1238.0   \n",
              "60          BBRC11               Outros        97.78           1205.0   \n",
              "64          RBHG11  Títulos e Val. Mob.        89.89           1147.0   \n",
              "66         BBFI11B   Lajes Corporativas      1929.90             90.0   \n",
              "67          RBVA11               Outros        92.37           5776.0   \n",
              "70          BBPO11   Lajes Corporativas        82.00          17804.0   \n",
              "76         CXCE11B               Outros        38.45            872.0   \n",
              "80          HGIC11  Títulos e Val. Mob.       108.00            152.0   \n",
              "88          JPPA11  Títulos e Val. Mob.       103.66          13583.0   \n",
              "92          BARI11  Títulos e Val. Mob.       100.15           3818.0   \n",
              "97          VOTS11  Títulos e Val. Mob.        95.65              8.0   \n",
              "103         RDPD11  Títulos e Val. Mob.        53.94             32.0   \n",
              "113         TGAR11               Outros       115.16          16431.0   \n",
              "115         NEWL11              Híbrido        93.50            583.0   \n",
              "117         MORE11  Títulos e Val. Mob.        67.73           4563.0   \n",
              "134         MGCR11  Títulos e Val. Mob.        85.30           1981.0   \n",
              "137         NEWU11   Lajes Corporativas        37.92             92.0   \n",
              "139        FAMB11B   Lajes Corporativas       820.00             42.0   \n",
              "143         MXRF11              Híbrido         9.82         426921.0   \n",
              "144         NAVT11  Títulos e Val. Mob.        74.98            707.0   \n",
              "146         GCFF11  Títulos e Val. Mob.        72.84           1099.0   \n",
              "151         OUFF11  Títulos e Val. Mob.        65.47           2365.0   \n",
              "152         BLMC11               Outros        95.00             18.0   \n",
              "153         RECX11               Outros        81.19             92.0   \n",
              "162         OURE11  Títulos e Val. Mob.        76.77           3328.0   \n",
              "187         BLMR11  Títulos e Val. Mob.         7.61          91839.0   \n",
              "195         SEQR11               Outros        63.10           2478.0   \n",
              "200         VSLH11               Outros         9.10          48403.0   \n",
              "201         TORD11               Outros         9.19          45849.0   \n",
              "202         HCTR11               Outros       110.80          62093.0   \n",
              "203         BLMG11            Logística        80.28           7256.0   \n",
              "208         FEXC11  Títulos e Val. Mob.        90.29           7397.0   \n",
              "210         FAED11               Outros       143.99            471.0   \n",
              "217         RELG11              Híbrido        74.00           3323.0   \n",
              "220         MBRF11               Outros       707.01             79.0   \n",
              "229         RVBI11  Títulos e Val. Mob.        74.10           4277.0   \n",
              "230         CPFF11              Híbrido        69.19           8613.0   \n",
              "242         MFAI11              Híbrido        71.18            552.0   \n",
              "248         XPCM11   Lajes Corporativas        16.42           2737.0   \n",
              "250         MFII11              Híbrido        97.09           2635.0   \n",
              "253         OUJP11  Títulos e Val. Mob.        94.99           2865.0   \n",
              "256         XPCI11               Outros        94.95          20773.0   \n",
              "263         CBOP11   Lajes Corporativas        62.40            320.0   \n",
              "266         XPSF11               Outros         7.11         124998.0   \n",
              "271         BPFF11  Títulos e Val. Mob.        63.62           3592.0   \n",
              "273         PLOG11            Logística        70.16           1911.0   \n",
              "278         PLCR11              Híbrido        87.02           2690.0   \n",
              "\n",
              "     Dividendo  DividendYield  DY (3M)Acumulado  DY (6M)Acumulado  \\\n",
              "10        0.40           0.90              2.71              5.30   \n",
              "17        0.21           0.52              4.06              7.82   \n",
              "19        1.41           1.49              3.95              7.54   \n",
              "28        1.30           1.41              4.25              7.95   \n",
              "29        0.77           1.05              3.03              5.96   \n",
              "31        0.71           0.85              2.68              6.11   \n",
              "34        0.72           0.90              3.35              5.84   \n",
              "35        1.15           1.21              3.60              7.00   \n",
              "38        0.67           0.94              2.78              5.43   \n",
              "42        0.80           1.01              2.91              5.67   \n",
              "55        1.02           1.23              3.51              7.11   \n",
              "60        0.94           0.95              2.87              5.63   \n",
              "64        1.15           1.26              3.77              7.83   \n",
              "66       23.91           1.16              3.82              7.84   \n",
              "67        0.92           0.93              2.84              5.59   \n",
              "70        0.89           1.04              3.02              5.91   \n",
              "76        0.35           0.89              2.64              5.19   \n",
              "80        0.99           0.90              2.86              5.90   \n",
              "88        1.80           1.74              4.81              8.71   \n",
              "92        1.30           1.27              3.41              6.94   \n",
              "97        1.35           1.44              4.05              6.87   \n",
              "103       0.84           1.48              3.60              6.27   \n",
              "113       1.44           1.20              3.40              6.42   \n",
              "115       0.80           0.80              2.38              5.28   \n",
              "117       0.70           1.03              2.95              5.69   \n",
              "134       1.15           1.31              3.64              7.02   \n",
              "137       0.00           0.00              0.00              0.56   \n",
              "139       9.16           0.56              2.17              4.78   \n",
              "143       0.11           1.11              3.18              5.87   \n",
              "144       0.85           1.16              3.34              6.00   \n",
              "146       0.75           1.03              3.06              5.98   \n",
              "151       0.72           1.11              3.18              6.05   \n",
              "152       1.20           1.25              3.54              7.00   \n",
              "153       0.70           0.88              2.89              6.21   \n",
              "162       0.95           1.19              3.25              6.72   \n",
              "187       0.08           1.01              3.04              5.92   \n",
              "195       0.63           0.97              2.77              5.34   \n",
              "200       0.11           1.18              3.65              7.07   \n",
              "201       0.12           1.29              2.81              5.28   \n",
              "202       1.75           1.65              3.97              7.82   \n",
              "203       0.80           0.94              2.78              5.54   \n",
              "208       1.10           1.20              3.50              6.72   \n",
              "210       1.48           1.02              3.04              5.54   \n",
              "217       0.90           1.21              3.04              6.05   \n",
              "220      10.00           1.42              4.10              8.18   \n",
              "229       0.75           0.97              2.86              5.60   \n",
              "230       0.60           0.85              2.58              5.09   \n",
              "242       0.44           0.53              3.55              7.09   \n",
              "248       0.18           1.03              3.00              5.24   \n",
              "250       1.11           1.07              3.29              6.66   \n",
              "253       1.30           1.36              4.16              7.59   \n",
              "256       1.18           1.22              3.56              6.69   \n",
              "263       0.49           0.77              2.40              8.00   \n",
              "266       0.68           0.93              2.80              5.45   \n",
              "271       0.65           1.00              2.86              5.55   \n",
              "273       0.75           1.01              3.20              6.21   \n",
              "278       1.10           1.26              3.63              7.16   \n",
              "\n",
              "     DY (12M)Acumulado  DY (3M)Média  DY (6M)Média  DY (12M)Média  DY Ano  \\\n",
              "10               16.30          0.90          0.88           1.36    3.59   \n",
              "17               10.95          1.35          1.30           0.91    6.80   \n",
              "19               13.24          1.32          1.26           1.10    5.20   \n",
              "28               13.62          1.42          1.32           1.13    5.56   \n",
              "29               10.31          1.01          0.99           0.86    4.14   \n",
              "31               10.40          0.89          1.02           0.87    3.51   \n",
              "34               10.22          1.12          0.97           0.85    4.13   \n",
              "35               13.07          1.20          1.17           1.09    4.77   \n",
              "38               10.13          0.93          0.91           0.84    3.66   \n",
              "42               10.20          0.97          0.94           0.85    3.82   \n",
              "55               12.89          1.17          1.19           1.07    4.76   \n",
              "60               10.35          0.96          0.94           0.86    3.77   \n",
              "64               13.41          1.26          1.31           1.12    4.84   \n",
              "66               14.14          1.27          1.31           1.18    4.87   \n",
              "67               10.75          0.95          0.93           0.90    3.77   \n",
              "70               11.25          1.01          0.99           0.94    3.97   \n",
              "76               12.16          0.88          0.86           1.01    3.45   \n",
              "80               10.52          0.95          0.98           0.88    3.79   \n",
              "88               14.92          1.60          1.45           1.24    6.04   \n",
              "92               13.27          1.14          1.16           1.11    4.46   \n",
              "97               11.20          1.35          1.14           0.93    5.10   \n",
              "103              10.85          1.20          1.05           0.90    4.59   \n",
              "113              11.60          1.13          1.07           0.97    4.35   \n",
              "115              10.16          0.79          0.88           0.85    3.16   \n",
              "117              10.50          0.98          0.95           0.88    3.87   \n",
              "134              12.17          1.21          1.17           1.01    4.77   \n",
              "137              16.63          0.00          0.09           1.39    0.61   \n",
              "139              10.43          0.72          0.80           0.87    8.82   \n",
              "143              10.39          1.06          0.98           0.87    4.16   \n",
              "144              10.29          1.11          1.00           0.86    4.37   \n",
              "146              11.11          1.02          1.00           0.93    3.99   \n",
              "151              11.30          1.06          1.01           0.94    4.21   \n",
              "152              12.40          1.18          1.17           1.03    4.66   \n",
              "153              11.73          0.96          1.03           0.98    3.95   \n",
              "162              13.01          1.08          1.12           1.08    4.12   \n",
              "187              10.90          1.01          0.99           0.91    3.98   \n",
              "195              10.31          0.92          0.89           0.86    3.64   \n",
              "200              15.36          1.22          1.18           1.28    4.77   \n",
              "201              11.05          0.94          0.88           0.92    3.64   \n",
              "202              15.45          1.32          1.30           1.29    5.25   \n",
              "203              11.23          0.93          0.92           0.94    3.63   \n",
              "208              11.57          1.17          1.12           0.96    4.57   \n",
              "210              10.11          1.01          0.92           0.84    3.93   \n",
              "217              11.12          1.01          1.01           0.93    3.91   \n",
              "220              13.54          1.37          1.36           1.13    6.68   \n",
              "229              10.66          0.95          0.93           0.89    3.78   \n",
              "230              10.51          0.86          0.85           0.88    3.57   \n",
              "242              11.84          1.18          1.18           0.99    4.86   \n",
              "248              10.16          1.00          0.87           0.85    3.78   \n",
              "250              12.49          1.10          1.11           1.04    4.36   \n",
              "253              12.80          1.39          1.26           1.07    5.37   \n",
              "256              12.53          1.19          1.12           1.04    4.68   \n",
              "263              12.26          0.80          1.33           1.02    3.07   \n",
              "266              10.43          0.93          0.91           0.87    3.61   \n",
              "271              10.18          0.95          0.93           0.85    3.73   \n",
              "273              11.14          1.07          1.04           0.93    4.24   \n",
              "278              12.96          1.21          1.19           1.08    4.81   \n",
              "\n",
              "     ...  Rentab.Acumulada  PatrimônioLíq.      VPA  P/VPA  DYPatrimonial  \\\n",
              "10   ...              1.30    9.506514e+07    52.87   83.0           0.76   \n",
              "17   ...             12.89    3.721710e+07    39.00   96.0           0.53   \n",
              "19   ...              6.72    3.698597e+08    99.20   95.0           1.42   \n",
              "28   ...             13.74    4.726013e+07    98.10   92.0           1.33   \n",
              "29   ...              5.70    3.431596e+08    85.71   86.0           0.90   \n",
              "31   ...             -4.78    3.137782e+08   113.19   72.0           0.63   \n",
              "34   ...              6.58    4.885024e+08   101.24   78.0           0.71   \n",
              "35   ...              5.57    1.684045e+08    94.95   99.0           1.21   \n",
              "38   ...              0.84    9.129462e+07    84.68   84.0           0.79   \n",
              "42   ...             -6.67    1.297777e+08    98.78   72.0           0.81   \n",
              "55   ...              7.01    6.536910e+07    87.37   95.0           1.17   \n",
              "60   ...              2.58    1.718291e+08   108.07   90.0           0.87   \n",
              "64   ...              8.06    1.666678e+08    96.47   93.0           1.19   \n",
              "66   ...             -5.38    3.707626e+08  2852.02   68.0           1.12   \n",
              "67   ...              4.31    1.193342e+09   102.98   90.0           0.89   \n",
              "70   ...             -5.10    1.526612e+09    95.89   86.0           0.93   \n",
              "76   ...            -10.26    1.110487e+08    64.83   59.0           0.54   \n",
              "80   ...              7.77    3.118663e+07   112.20   96.0           0.88   \n",
              "88   ...             12.90    6.455450e+07   106.88   97.0           1.68   \n",
              "92   ...              2.08    4.743034e+08   102.35   98.0           1.27   \n",
              "97   ...             17.08    8.095816e+07    97.79   98.0           1.38   \n",
              "103  ...              4.87    6.452890e+07    59.86   90.0           1.40   \n",
              "113  ...              1.97    1.447640e+09   128.00   90.0           1.12   \n",
              "115  ...              0.86    2.367494e+08   116.45   80.0           0.69   \n",
              "117  ...             -7.71    2.029528e+08    87.51   77.0           0.80   \n",
              "134  ...              4.43    1.351998e+08    95.06   90.0           1.05   \n",
              "137  ...            -31.62    4.814503e+07    64.89   58.0            NaN   \n",
              "139  ...            -47.80    3.253447e+08  3104.43   26.0            NaN   \n",
              "143  ...             13.97    2.302694e+09    10.19   96.0           0.98   \n",
              "144  ...              4.79    9.502605e+07    90.11   83.0           0.94   \n",
              "146  ...             -5.52    3.009670e+07    88.89   82.0           0.88   \n",
              "151  ...              3.64    1.444772e+08    79.63   82.0           0.90   \n",
              "152  ...              2.37    3.921653e+07    98.16   97.0           1.17   \n",
              "153  ...            -12.58    1.890990e+07    86.62   94.0           0.92   \n",
              "162  ...             -3.62    9.975426e+07    92.61   83.0           1.03   \n",
              "187  ...             -3.12    2.140527e+08     8.38   91.0           0.89   \n",
              "195  ...             -6.67    1.547155e+08    96.18   66.0           0.65   \n",
              "200  ...              0.56    3.032310e+08    10.15   90.0           1.08   \n",
              "201  ...             -0.39    4.663733e+08    13.31   69.0           0.90   \n",
              "202  ...            -10.57    2.644863e+09   119.76   93.0           1.46   \n",
              "203  ...             -6.38    3.684345e+08    96.69   83.0           0.83   \n",
              "208  ...              7.63    5.717035e+08    93.67   96.0           1.12   \n",
              "210  ...              4.12    1.388235e+08   216.18   67.0           0.71   \n",
              "217  ...             -4.12    1.615510e+08   121.05   61.0           0.74   \n",
              "220  ...             -5.67    1.137176e+08  1118.56   63.0           0.85   \n",
              "229  ...             -1.03    1.260013e+08    87.50   85.0           0.86   \n",
              "230  ...              2.73    4.211661e+08    77.28   90.0           0.78   \n",
              "242  ...             16.86    2.338973e+07    77.15   92.0           0.57   \n",
              "248  ...             -5.06    1.298543e+08    53.78   31.0           0.33   \n",
              "250  ...             -1.59    4.343143e+08   104.73   93.0           1.06   \n",
              "253  ...             10.34    3.310427e+08   101.78   93.0           1.28   \n",
              "256  ...              3.65    6.704597e+08    97.34   98.0           1.21   \n",
              "263  ...            -10.34    1.263803e+08    89.31   70.0           0.55   \n",
              "266  ...             -4.19    3.787900e+08    87.48    8.0           0.78   \n",
              "271  ...             -1.96    3.515101e+08    78.25   81.0           0.83   \n",
              "273  ...              2.86    7.515549e+07   100.21   70.0           0.75   \n",
              "278  ...              3.81    1.952303e+08    92.56   94.0           1.19   \n",
              "\n",
              "     VariaçãoPatrimonial  Rentab. Patr.no Período  Rentab. Patr.Acumulada  \\\n",
              "10                  0.06                     0.81                    3.30   \n",
              "17                 -1.58                    -1.06                   -0.98   \n",
              "19                  0.03                     1.45                    4.92   \n",
              "28                  0.38                     1.72                    5.92   \n",
              "29                  0.28                     1.18                    2.46   \n",
              "31                  0.61                     1.24                    3.22   \n",
              "34                  0.02                     0.74                    2.64   \n",
              "35                  0.40                     1.61                    4.24   \n",
              "38                  1.28                     2.08                    0.24   \n",
              "42                  0.18                     0.99                    1.81   \n",
              "55                 -0.53                     0.63                    2.71   \n",
              "60                  0.10                     0.97                    3.88   \n",
              "64                 -0.30                     0.88                    4.61   \n",
              "66                  0.13                     1.25                    2.11   \n",
              "67                 -0.02                     0.87                    3.50   \n",
              "70                  0.02                     0.95                    3.83   \n",
              "76                  0.04                     0.58                    2.27   \n",
              "80                  1.25                     2.14                    7.86   \n",
              "88                  0.44                     2.13                    6.83   \n",
              "92                  0.36                     1.63                    5.44   \n",
              "97                  0.08                     1.46                    4.69   \n",
              "103                 0.47                     1.88                    5.45   \n",
              "113                -1.58                    -0.47                    1.07   \n",
              "115                 0.19                     0.88                    3.37   \n",
              "117                -2.21                    -1.43                   -0.66   \n",
              "134                -0.02                     1.04                    2.10   \n",
              "137                  NaN                      NaN                     NaN   \n",
              "139                  NaN                      NaN                     NaN   \n",
              "143                -0.02                     0.96                    2.22   \n",
              "144                -0.93                     0.00                    0.82   \n",
              "146                -2.44                    -1.58                   -0.77   \n",
              "151                 1.36                     2.28                    3.49   \n",
              "152                 1.29                     2.48                    0.96   \n",
              "153                -1.55                    -0.64                    0.48   \n",
              "162                 0.00                     1.02                    3.91   \n",
              "187                 1.35                     2.25                    2.58   \n",
              "195                -0.25                     0.40                    2.76   \n",
              "200                 1.40                     2.50                    7.37   \n",
              "201                -1.44                    -0.55                    1.24   \n",
              "202                -0.22                     1.24                    7.16   \n",
              "203                -2.50                    -1.69                   -1.84   \n",
              "208                 1.09                     2.23                    3.68   \n",
              "210                -0.01                     0.70                    1.30   \n",
              "217                -0.27                     0.47                    1.98   \n",
              "220                 0.05                     0.90                    3.66   \n",
              "229                 0.36                     1.22                    3.21   \n",
              "230                -0.84                    -0.07                    0.84   \n",
              "242                -4.23                    -3.69                   -2.71   \n",
              "248                -0.56                    -0.23                   -0.45   \n",
              "250                -1.34                    -0.30                    0.42   \n",
              "253                -0.12                     1.15                    5.78   \n",
              "256                 0.98                     2.20                    7.18   \n",
              "263                -0.57                    -0.03                    0.85   \n",
              "266                 1.16                     1.95                    2.34   \n",
              "271                 0.53                     1.37                    1.99   \n",
              "273                -0.01                     0.74                    3.05   \n",
              "278                -0.09                     1.10                    4.62   \n",
              "\n",
              "     VacânciaFísica  VacânciaFinanceira  QuantidadeAtivos  log Preço Atual  \\\n",
              "10             0.00                 NaN                 1         3.806662   \n",
              "17              NaN                 NaN                 0         3.648057   \n",
              "19              NaN                 NaN                 0         4.559126   \n",
              "28              NaN                 NaN                 0         4.518522   \n",
              "29              NaN                 NaN                 0         4.314818   \n",
              "31              NaN                 NaN                 3         4.416307   \n",
              "34             0.00                 NaN                 2         4.387512   \n",
              "35              NaN                 NaN                 0         4.555139   \n",
              "38              NaN                 NaN                 0         4.272770   \n",
              "42              NaN                 NaN                 0         4.278331   \n",
              "55              NaN                 NaN                 0         4.429626   \n",
              "60             5.37                1.08                20         4.592895   \n",
              "64              NaN                 NaN                 0         4.509650   \n",
              "66            39.11                 NaN                 2         7.565741   \n",
              "67             0.70                 NaN                72         4.536570   \n",
              "70             0.00                0.00                64         4.418841   \n",
              "76             0.00                 NaN                 1         3.675034   \n",
              "80              NaN                 NaN                 0         4.691348   \n",
              "88              NaN                 NaN                 0         4.650717   \n",
              "92              NaN                 NaN                 0         4.616605   \n",
              "97              NaN                 NaN                 0         4.571096   \n",
              "103             NaN                 NaN                 0         4.006242   \n",
              "113             NaN                 NaN                 0         4.754969   \n",
              "115            0.00                 NaN                 2         4.548600   \n",
              "117             NaN                 NaN                 0         4.230186   \n",
              "134             NaN                 NaN                 0         4.457830   \n",
              "137           63.20                 NaN                 3         3.661508   \n",
              "139          100.00                 NaN                 1         6.710523   \n",
              "143             NaN                 NaN                 0         2.381396   \n",
              "144             NaN                 NaN                 0         4.330470   \n",
              "146             NaN                 NaN                 0         4.301901   \n",
              "151             NaN                 NaN                 0         4.196751   \n",
              "152             NaN                 NaN                 0         4.564348   \n",
              "153             NaN                 NaN                 0         4.409034   \n",
              "162             NaN                 NaN                 0         4.353756   \n",
              "187             NaN                 NaN                 0         2.152924   \n",
              "195            0.00                 NaN                 0         4.160444   \n",
              "200             NaN                 NaN                 0         2.312535   \n",
              "201             NaN                 NaN                 0         2.321407   \n",
              "202             NaN                 NaN                 0         4.716712   \n",
              "203            0.00                0.00                 4         4.397900   \n",
              "208             NaN                 NaN                 0         4.514041   \n",
              "210            0.00                 NaN                 3         4.976665   \n",
              "217           10.40               10.40                 3         4.317488   \n",
              "220            0.00                 NaN                 2         6.562458   \n",
              "229             NaN                 NaN                 0         4.318821   \n",
              "230             NaN                 NaN                 0         4.251206   \n",
              "242             NaN                 NaN                 0         4.279163   \n",
              "248          100.00                 NaN                 1         2.857619   \n",
              "250             NaN                 NaN                 9         4.585885   \n",
              "253             NaN                 NaN                 0         4.564244   \n",
              "256             NaN                 NaN                 0         4.563827   \n",
              "263           33.59               30.79                 1         4.149464   \n",
              "266             NaN                 NaN                 0         2.093098   \n",
              "271             NaN                 NaN                 0         4.168524   \n",
              "273           20.40                 NaN                 1         4.264931   \n",
              "278             NaN                 NaN                 0         4.477564   \n",
              "\n",
              "     Fundos_VPA  \n",
              "10         True  \n",
              "17         True  \n",
              "19         True  \n",
              "28         True  \n",
              "29         True  \n",
              "31         True  \n",
              "34         True  \n",
              "35         True  \n",
              "38         True  \n",
              "42         True  \n",
              "55         True  \n",
              "60         True  \n",
              "64         True  \n",
              "66         True  \n",
              "67         True  \n",
              "70         True  \n",
              "76         True  \n",
              "80         True  \n",
              "88         True  \n",
              "92         True  \n",
              "97         True  \n",
              "103        True  \n",
              "113        True  \n",
              "115        True  \n",
              "117        True  \n",
              "134        True  \n",
              "137        True  \n",
              "139        True  \n",
              "143        True  \n",
              "144        True  \n",
              "146        True  \n",
              "151        True  \n",
              "152        True  \n",
              "153        True  \n",
              "162        True  \n",
              "187        True  \n",
              "195        True  \n",
              "200        True  \n",
              "201        True  \n",
              "202        True  \n",
              "203        True  \n",
              "208        True  \n",
              "210        True  \n",
              "217        True  \n",
              "220        True  \n",
              "229        True  \n",
              "230        True  \n",
              "242        True  \n",
              "248        True  \n",
              "250        True  \n",
              "253        True  \n",
              "256        True  \n",
              "263        True  \n",
              "266        True  \n",
              "271        True  \n",
              "273        True  \n",
              "278        True  \n",
              "\n",
              "[57 rows x 28 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-d274c692-ecfd-4dab-8774-1a94d01ece2d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Códigodo fundo</th>\n",
              "      <th>Setor</th>\n",
              "      <th>Preço Atual</th>\n",
              "      <th>Liquidez Diária</th>\n",
              "      <th>Dividendo</th>\n",
              "      <th>DividendYield</th>\n",
              "      <th>DY (3M)Acumulado</th>\n",
              "      <th>DY (6M)Acumulado</th>\n",
              "      <th>DY (12M)Acumulado</th>\n",
              "      <th>DY (3M)Média</th>\n",
              "      <th>DY (6M)Média</th>\n",
              "      <th>DY (12M)Média</th>\n",
              "      <th>DY Ano</th>\n",
              "      <th>...</th>\n",
              "      <th>Rentab.Acumulada</th>\n",
              "      <th>PatrimônioLíq.</th>\n",
              "      <th>VPA</th>\n",
              "      <th>P/VPA</th>\n",
              "      <th>DYPatrimonial</th>\n",
              "      <th>VariaçãoPatrimonial</th>\n",
              "      <th>Rentab. Patr.no Período</th>\n",
              "      <th>Rentab. Patr.Acumulada</th>\n",
              "      <th>VacânciaFísica</th>\n",
              "      <th>VacânciaFinanceira</th>\n",
              "      <th>QuantidadeAtivos</th>\n",
              "      <th>log Preço Atual</th>\n",
              "      <th>Fundos_VPA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>SPTW11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>44.00</td>\n",
              "      <td>6623.0</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.90</td>\n",
              "      <td>2.71</td>\n",
              "      <td>5.30</td>\n",
              "      <td>16.30</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.36</td>\n",
              "      <td>3.59</td>\n",
              "      <td>...</td>\n",
              "      <td>1.30</td>\n",
              "      <td>9.506514e+07</td>\n",
              "      <td>52.87</td>\n",
              "      <td>83.0</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.81</td>\n",
              "      <td>3.30</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>3.806662</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>PLRI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>37.40</td>\n",
              "      <td>452.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.52</td>\n",
              "      <td>4.06</td>\n",
              "      <td>7.82</td>\n",
              "      <td>10.95</td>\n",
              "      <td>1.35</td>\n",
              "      <td>1.30</td>\n",
              "      <td>0.91</td>\n",
              "      <td>6.80</td>\n",
              "      <td>...</td>\n",
              "      <td>12.89</td>\n",
              "      <td>3.721710e+07</td>\n",
              "      <td>39.00</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.53</td>\n",
              "      <td>-1.58</td>\n",
              "      <td>-1.06</td>\n",
              "      <td>-0.98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3.648057</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>PORD11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.50</td>\n",
              "      <td>8149.0</td>\n",
              "      <td>1.41</td>\n",
              "      <td>1.49</td>\n",
              "      <td>3.95</td>\n",
              "      <td>7.54</td>\n",
              "      <td>13.24</td>\n",
              "      <td>1.32</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1.10</td>\n",
              "      <td>5.20</td>\n",
              "      <td>...</td>\n",
              "      <td>6.72</td>\n",
              "      <td>3.698597e+08</td>\n",
              "      <td>99.20</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.42</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.45</td>\n",
              "      <td>4.92</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.559126</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>RRCI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>90.70</td>\n",
              "      <td>1646.0</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.41</td>\n",
              "      <td>4.25</td>\n",
              "      <td>7.95</td>\n",
              "      <td>13.62</td>\n",
              "      <td>1.42</td>\n",
              "      <td>1.32</td>\n",
              "      <td>1.13</td>\n",
              "      <td>5.56</td>\n",
              "      <td>...</td>\n",
              "      <td>13.74</td>\n",
              "      <td>4.726013e+07</td>\n",
              "      <td>98.10</td>\n",
              "      <td>92.0</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.38</td>\n",
              "      <td>1.72</td>\n",
              "      <td>5.92</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.518522</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>BBFO11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>73.80</td>\n",
              "      <td>1700.0</td>\n",
              "      <td>0.77</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.03</td>\n",
              "      <td>5.96</td>\n",
              "      <td>10.31</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.86</td>\n",
              "      <td>4.14</td>\n",
              "      <td>...</td>\n",
              "      <td>5.70</td>\n",
              "      <td>3.431596e+08</td>\n",
              "      <td>85.71</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.18</td>\n",
              "      <td>2.46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.314818</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>HBRH11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>81.79</td>\n",
              "      <td>1236.0</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.85</td>\n",
              "      <td>2.68</td>\n",
              "      <td>6.11</td>\n",
              "      <td>10.40</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.87</td>\n",
              "      <td>3.51</td>\n",
              "      <td>...</td>\n",
              "      <td>-4.78</td>\n",
              "      <td>3.137782e+08</td>\n",
              "      <td>113.19</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.61</td>\n",
              "      <td>1.24</td>\n",
              "      <td>3.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>4.416307</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>AIEC11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>79.44</td>\n",
              "      <td>2566.0</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.90</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.84</td>\n",
              "      <td>10.22</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.85</td>\n",
              "      <td>4.13</td>\n",
              "      <td>...</td>\n",
              "      <td>6.58</td>\n",
              "      <td>4.885024e+08</td>\n",
              "      <td>101.24</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.74</td>\n",
              "      <td>2.64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>4.387512</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>HSAF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.12</td>\n",
              "      <td>3269.0</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.21</td>\n",
              "      <td>3.60</td>\n",
              "      <td>7.00</td>\n",
              "      <td>13.07</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.09</td>\n",
              "      <td>4.77</td>\n",
              "      <td>...</td>\n",
              "      <td>5.57</td>\n",
              "      <td>1.684045e+08</td>\n",
              "      <td>94.95</td>\n",
              "      <td>99.0</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.61</td>\n",
              "      <td>4.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.555139</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>RFOF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>70.72</td>\n",
              "      <td>3967.0</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.94</td>\n",
              "      <td>2.78</td>\n",
              "      <td>5.43</td>\n",
              "      <td>10.13</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.84</td>\n",
              "      <td>3.66</td>\n",
              "      <td>...</td>\n",
              "      <td>0.84</td>\n",
              "      <td>9.129462e+07</td>\n",
              "      <td>84.68</td>\n",
              "      <td>84.0</td>\n",
              "      <td>0.79</td>\n",
              "      <td>1.28</td>\n",
              "      <td>2.08</td>\n",
              "      <td>0.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.272770</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>MGHT11</td>\n",
              "      <td>Hotel</td>\n",
              "      <td>71.12</td>\n",
              "      <td>3575.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.01</td>\n",
              "      <td>2.91</td>\n",
              "      <td>5.67</td>\n",
              "      <td>10.20</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.85</td>\n",
              "      <td>3.82</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.67</td>\n",
              "      <td>1.297777e+08</td>\n",
              "      <td>98.78</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.81</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.278331</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>ITIP11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>82.90</td>\n",
              "      <td>1238.0</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.23</td>\n",
              "      <td>3.51</td>\n",
              "      <td>7.11</td>\n",
              "      <td>12.89</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.07</td>\n",
              "      <td>4.76</td>\n",
              "      <td>...</td>\n",
              "      <td>7.01</td>\n",
              "      <td>6.536910e+07</td>\n",
              "      <td>87.37</td>\n",
              "      <td>95.0</td>\n",
              "      <td>1.17</td>\n",
              "      <td>-0.53</td>\n",
              "      <td>0.63</td>\n",
              "      <td>2.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.429626</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>BBRC11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>97.78</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>2.87</td>\n",
              "      <td>5.63</td>\n",
              "      <td>10.35</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.77</td>\n",
              "      <td>...</td>\n",
              "      <td>2.58</td>\n",
              "      <td>1.718291e+08</td>\n",
              "      <td>108.07</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.97</td>\n",
              "      <td>3.88</td>\n",
              "      <td>5.37</td>\n",
              "      <td>1.08</td>\n",
              "      <td>20</td>\n",
              "      <td>4.592895</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>RBHG11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>89.89</td>\n",
              "      <td>1147.0</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.26</td>\n",
              "      <td>3.77</td>\n",
              "      <td>7.83</td>\n",
              "      <td>13.41</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1.31</td>\n",
              "      <td>1.12</td>\n",
              "      <td>4.84</td>\n",
              "      <td>...</td>\n",
              "      <td>8.06</td>\n",
              "      <td>1.666678e+08</td>\n",
              "      <td>96.47</td>\n",
              "      <td>93.0</td>\n",
              "      <td>1.19</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>0.88</td>\n",
              "      <td>4.61</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.509650</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>BBFI11B</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>1929.90</td>\n",
              "      <td>90.0</td>\n",
              "      <td>23.91</td>\n",
              "      <td>1.16</td>\n",
              "      <td>3.82</td>\n",
              "      <td>7.84</td>\n",
              "      <td>14.14</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.31</td>\n",
              "      <td>1.18</td>\n",
              "      <td>4.87</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.38</td>\n",
              "      <td>3.707626e+08</td>\n",
              "      <td>2852.02</td>\n",
              "      <td>68.0</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.11</td>\n",
              "      <td>39.11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>7.565741</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>RBVA11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>92.37</td>\n",
              "      <td>5776.0</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.93</td>\n",
              "      <td>2.84</td>\n",
              "      <td>5.59</td>\n",
              "      <td>10.75</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.90</td>\n",
              "      <td>3.77</td>\n",
              "      <td>...</td>\n",
              "      <td>4.31</td>\n",
              "      <td>1.193342e+09</td>\n",
              "      <td>102.98</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.89</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.87</td>\n",
              "      <td>3.50</td>\n",
              "      <td>0.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72</td>\n",
              "      <td>4.536570</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>BBPO11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>82.00</td>\n",
              "      <td>17804.0</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.04</td>\n",
              "      <td>3.02</td>\n",
              "      <td>5.91</td>\n",
              "      <td>11.25</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.94</td>\n",
              "      <td>3.97</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.10</td>\n",
              "      <td>1.526612e+09</td>\n",
              "      <td>95.89</td>\n",
              "      <td>86.0</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.95</td>\n",
              "      <td>3.83</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>64</td>\n",
              "      <td>4.418841</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>CXCE11B</td>\n",
              "      <td>Outros</td>\n",
              "      <td>38.45</td>\n",
              "      <td>872.0</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.89</td>\n",
              "      <td>2.64</td>\n",
              "      <td>5.19</td>\n",
              "      <td>12.16</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.86</td>\n",
              "      <td>1.01</td>\n",
              "      <td>3.45</td>\n",
              "      <td>...</td>\n",
              "      <td>-10.26</td>\n",
              "      <td>1.110487e+08</td>\n",
              "      <td>64.83</td>\n",
              "      <td>59.0</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.58</td>\n",
              "      <td>2.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>3.675034</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>HGIC11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>108.00</td>\n",
              "      <td>152.0</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.90</td>\n",
              "      <td>2.86</td>\n",
              "      <td>5.90</td>\n",
              "      <td>10.52</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.88</td>\n",
              "      <td>3.79</td>\n",
              "      <td>...</td>\n",
              "      <td>7.77</td>\n",
              "      <td>3.118663e+07</td>\n",
              "      <td>112.20</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.25</td>\n",
              "      <td>2.14</td>\n",
              "      <td>7.86</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.691348</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>JPPA11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>103.66</td>\n",
              "      <td>13583.0</td>\n",
              "      <td>1.80</td>\n",
              "      <td>1.74</td>\n",
              "      <td>4.81</td>\n",
              "      <td>8.71</td>\n",
              "      <td>14.92</td>\n",
              "      <td>1.60</td>\n",
              "      <td>1.45</td>\n",
              "      <td>1.24</td>\n",
              "      <td>6.04</td>\n",
              "      <td>...</td>\n",
              "      <td>12.90</td>\n",
              "      <td>6.455450e+07</td>\n",
              "      <td>106.88</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1.68</td>\n",
              "      <td>0.44</td>\n",
              "      <td>2.13</td>\n",
              "      <td>6.83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.650717</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>BARI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>100.15</td>\n",
              "      <td>3818.0</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.27</td>\n",
              "      <td>3.41</td>\n",
              "      <td>6.94</td>\n",
              "      <td>13.27</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.11</td>\n",
              "      <td>4.46</td>\n",
              "      <td>...</td>\n",
              "      <td>2.08</td>\n",
              "      <td>4.743034e+08</td>\n",
              "      <td>102.35</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.63</td>\n",
              "      <td>5.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.616605</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>VOTS11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>95.65</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.35</td>\n",
              "      <td>1.44</td>\n",
              "      <td>4.05</td>\n",
              "      <td>6.87</td>\n",
              "      <td>11.20</td>\n",
              "      <td>1.35</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.93</td>\n",
              "      <td>5.10</td>\n",
              "      <td>...</td>\n",
              "      <td>17.08</td>\n",
              "      <td>8.095816e+07</td>\n",
              "      <td>97.79</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.46</td>\n",
              "      <td>4.69</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.571096</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>RDPD11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>53.94</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.84</td>\n",
              "      <td>1.48</td>\n",
              "      <td>3.60</td>\n",
              "      <td>6.27</td>\n",
              "      <td>10.85</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.90</td>\n",
              "      <td>4.59</td>\n",
              "      <td>...</td>\n",
              "      <td>4.87</td>\n",
              "      <td>6.452890e+07</td>\n",
              "      <td>59.86</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.88</td>\n",
              "      <td>5.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.006242</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>TGAR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>115.16</td>\n",
              "      <td>16431.0</td>\n",
              "      <td>1.44</td>\n",
              "      <td>1.20</td>\n",
              "      <td>3.40</td>\n",
              "      <td>6.42</td>\n",
              "      <td>11.60</td>\n",
              "      <td>1.13</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.97</td>\n",
              "      <td>4.35</td>\n",
              "      <td>...</td>\n",
              "      <td>1.97</td>\n",
              "      <td>1.447640e+09</td>\n",
              "      <td>128.00</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.12</td>\n",
              "      <td>-1.58</td>\n",
              "      <td>-0.47</td>\n",
              "      <td>1.07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.754969</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>NEWL11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>93.50</td>\n",
              "      <td>583.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.80</td>\n",
              "      <td>2.38</td>\n",
              "      <td>5.28</td>\n",
              "      <td>10.16</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.85</td>\n",
              "      <td>3.16</td>\n",
              "      <td>...</td>\n",
              "      <td>0.86</td>\n",
              "      <td>2.367494e+08</td>\n",
              "      <td>116.45</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.88</td>\n",
              "      <td>3.37</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>4.548600</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>MORE11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>67.73</td>\n",
              "      <td>4563.0</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.03</td>\n",
              "      <td>2.95</td>\n",
              "      <td>5.69</td>\n",
              "      <td>10.50</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.88</td>\n",
              "      <td>3.87</td>\n",
              "      <td>...</td>\n",
              "      <td>-7.71</td>\n",
              "      <td>2.029528e+08</td>\n",
              "      <td>87.51</td>\n",
              "      <td>77.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>-2.21</td>\n",
              "      <td>-1.43</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.230186</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>MGCR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>85.30</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.31</td>\n",
              "      <td>3.64</td>\n",
              "      <td>7.02</td>\n",
              "      <td>12.17</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.01</td>\n",
              "      <td>4.77</td>\n",
              "      <td>...</td>\n",
              "      <td>4.43</td>\n",
              "      <td>1.351998e+08</td>\n",
              "      <td>95.06</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.05</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1.04</td>\n",
              "      <td>2.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.457830</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>NEWU11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>37.92</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.56</td>\n",
              "      <td>16.63</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.61</td>\n",
              "      <td>...</td>\n",
              "      <td>-31.62</td>\n",
              "      <td>4.814503e+07</td>\n",
              "      <td>64.89</td>\n",
              "      <td>58.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63.20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>3.661508</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>FAMB11B</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>820.00</td>\n",
              "      <td>42.0</td>\n",
              "      <td>9.16</td>\n",
              "      <td>0.56</td>\n",
              "      <td>2.17</td>\n",
              "      <td>4.78</td>\n",
              "      <td>10.43</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.87</td>\n",
              "      <td>8.82</td>\n",
              "      <td>...</td>\n",
              "      <td>-47.80</td>\n",
              "      <td>3.253447e+08</td>\n",
              "      <td>3104.43</td>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>6.710523</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>MXRF11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>9.82</td>\n",
              "      <td>426921.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.11</td>\n",
              "      <td>3.18</td>\n",
              "      <td>5.87</td>\n",
              "      <td>10.39</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.87</td>\n",
              "      <td>4.16</td>\n",
              "      <td>...</td>\n",
              "      <td>13.97</td>\n",
              "      <td>2.302694e+09</td>\n",
              "      <td>10.19</td>\n",
              "      <td>96.0</td>\n",
              "      <td>0.98</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>0.96</td>\n",
              "      <td>2.22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2.381396</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>NAVT11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>74.98</td>\n",
              "      <td>707.0</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1.16</td>\n",
              "      <td>3.34</td>\n",
              "      <td>6.00</td>\n",
              "      <td>10.29</td>\n",
              "      <td>1.11</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.86</td>\n",
              "      <td>4.37</td>\n",
              "      <td>...</td>\n",
              "      <td>4.79</td>\n",
              "      <td>9.502605e+07</td>\n",
              "      <td>90.11</td>\n",
              "      <td>83.0</td>\n",
              "      <td>0.94</td>\n",
              "      <td>-0.93</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.330470</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>GCFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>72.84</td>\n",
              "      <td>1099.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.06</td>\n",
              "      <td>5.98</td>\n",
              "      <td>11.11</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.93</td>\n",
              "      <td>3.99</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.52</td>\n",
              "      <td>3.009670e+07</td>\n",
              "      <td>88.89</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.88</td>\n",
              "      <td>-2.44</td>\n",
              "      <td>-1.58</td>\n",
              "      <td>-0.77</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.301901</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>OUFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>65.47</td>\n",
              "      <td>2365.0</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.11</td>\n",
              "      <td>3.18</td>\n",
              "      <td>6.05</td>\n",
              "      <td>11.30</td>\n",
              "      <td>1.06</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.94</td>\n",
              "      <td>4.21</td>\n",
              "      <td>...</td>\n",
              "      <td>3.64</td>\n",
              "      <td>1.444772e+08</td>\n",
              "      <td>79.63</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.36</td>\n",
              "      <td>2.28</td>\n",
              "      <td>3.49</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.196751</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>BLMC11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>95.00</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1.25</td>\n",
              "      <td>3.54</td>\n",
              "      <td>7.00</td>\n",
              "      <td>12.40</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.03</td>\n",
              "      <td>4.66</td>\n",
              "      <td>...</td>\n",
              "      <td>2.37</td>\n",
              "      <td>3.921653e+07</td>\n",
              "      <td>98.16</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.29</td>\n",
              "      <td>2.48</td>\n",
              "      <td>0.96</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.564348</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>RECX11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>81.19</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.88</td>\n",
              "      <td>2.89</td>\n",
              "      <td>6.21</td>\n",
              "      <td>11.73</td>\n",
              "      <td>0.96</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.98</td>\n",
              "      <td>3.95</td>\n",
              "      <td>...</td>\n",
              "      <td>-12.58</td>\n",
              "      <td>1.890990e+07</td>\n",
              "      <td>86.62</td>\n",
              "      <td>94.0</td>\n",
              "      <td>0.92</td>\n",
              "      <td>-1.55</td>\n",
              "      <td>-0.64</td>\n",
              "      <td>0.48</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.409034</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>OURE11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>76.77</td>\n",
              "      <td>3328.0</td>\n",
              "      <td>0.95</td>\n",
              "      <td>1.19</td>\n",
              "      <td>3.25</td>\n",
              "      <td>6.72</td>\n",
              "      <td>13.01</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.08</td>\n",
              "      <td>4.12</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.62</td>\n",
              "      <td>9.975426e+07</td>\n",
              "      <td>92.61</td>\n",
              "      <td>83.0</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.02</td>\n",
              "      <td>3.91</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.353756</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>BLMR11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>7.61</td>\n",
              "      <td>91839.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.01</td>\n",
              "      <td>3.04</td>\n",
              "      <td>5.92</td>\n",
              "      <td>10.90</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.91</td>\n",
              "      <td>3.98</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.12</td>\n",
              "      <td>2.140527e+08</td>\n",
              "      <td>8.38</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.35</td>\n",
              "      <td>2.25</td>\n",
              "      <td>2.58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2.152924</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>SEQR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>63.10</td>\n",
              "      <td>2478.0</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.97</td>\n",
              "      <td>2.77</td>\n",
              "      <td>5.34</td>\n",
              "      <td>10.31</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.64</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.67</td>\n",
              "      <td>1.547155e+08</td>\n",
              "      <td>96.18</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.65</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.40</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.160444</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>VSLH11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>9.10</td>\n",
              "      <td>48403.0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.18</td>\n",
              "      <td>3.65</td>\n",
              "      <td>7.07</td>\n",
              "      <td>15.36</td>\n",
              "      <td>1.22</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.28</td>\n",
              "      <td>4.77</td>\n",
              "      <td>...</td>\n",
              "      <td>0.56</td>\n",
              "      <td>3.032310e+08</td>\n",
              "      <td>10.15</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.40</td>\n",
              "      <td>2.50</td>\n",
              "      <td>7.37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2.312535</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>TORD11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>9.19</td>\n",
              "      <td>45849.0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>1.29</td>\n",
              "      <td>2.81</td>\n",
              "      <td>5.28</td>\n",
              "      <td>11.05</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.92</td>\n",
              "      <td>3.64</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>4.663733e+08</td>\n",
              "      <td>13.31</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>-1.44</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>1.24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2.321407</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>HCTR11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>110.80</td>\n",
              "      <td>62093.0</td>\n",
              "      <td>1.75</td>\n",
              "      <td>1.65</td>\n",
              "      <td>3.97</td>\n",
              "      <td>7.82</td>\n",
              "      <td>15.45</td>\n",
              "      <td>1.32</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.29</td>\n",
              "      <td>5.25</td>\n",
              "      <td>...</td>\n",
              "      <td>-10.57</td>\n",
              "      <td>2.644863e+09</td>\n",
              "      <td>119.76</td>\n",
              "      <td>93.0</td>\n",
              "      <td>1.46</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>1.24</td>\n",
              "      <td>7.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.716712</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>203</th>\n",
              "      <td>BLMG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>80.28</td>\n",
              "      <td>7256.0</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.94</td>\n",
              "      <td>2.78</td>\n",
              "      <td>5.54</td>\n",
              "      <td>11.23</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.94</td>\n",
              "      <td>3.63</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.38</td>\n",
              "      <td>3.684345e+08</td>\n",
              "      <td>96.69</td>\n",
              "      <td>83.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>-2.50</td>\n",
              "      <td>-1.69</td>\n",
              "      <td>-1.84</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>4</td>\n",
              "      <td>4.397900</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>FEXC11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>90.29</td>\n",
              "      <td>7397.0</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.20</td>\n",
              "      <td>3.50</td>\n",
              "      <td>6.72</td>\n",
              "      <td>11.57</td>\n",
              "      <td>1.17</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.96</td>\n",
              "      <td>4.57</td>\n",
              "      <td>...</td>\n",
              "      <td>7.63</td>\n",
              "      <td>5.717035e+08</td>\n",
              "      <td>93.67</td>\n",
              "      <td>96.0</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.09</td>\n",
              "      <td>2.23</td>\n",
              "      <td>3.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.514041</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>FAED11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>143.99</td>\n",
              "      <td>471.0</td>\n",
              "      <td>1.48</td>\n",
              "      <td>1.02</td>\n",
              "      <td>3.04</td>\n",
              "      <td>5.54</td>\n",
              "      <td>10.11</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.84</td>\n",
              "      <td>3.93</td>\n",
              "      <td>...</td>\n",
              "      <td>4.12</td>\n",
              "      <td>1.388235e+08</td>\n",
              "      <td>216.18</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.71</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.70</td>\n",
              "      <td>1.30</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>4.976665</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>RELG11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>74.00</td>\n",
              "      <td>3323.0</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.21</td>\n",
              "      <td>3.04</td>\n",
              "      <td>6.05</td>\n",
              "      <td>11.12</td>\n",
              "      <td>1.01</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.93</td>\n",
              "      <td>3.91</td>\n",
              "      <td>...</td>\n",
              "      <td>-4.12</td>\n",
              "      <td>1.615510e+08</td>\n",
              "      <td>121.05</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.74</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.98</td>\n",
              "      <td>10.40</td>\n",
              "      <td>10.40</td>\n",
              "      <td>3</td>\n",
              "      <td>4.317488</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>MBRF11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>707.01</td>\n",
              "      <td>79.0</td>\n",
              "      <td>10.00</td>\n",
              "      <td>1.42</td>\n",
              "      <td>4.10</td>\n",
              "      <td>8.18</td>\n",
              "      <td>13.54</td>\n",
              "      <td>1.37</td>\n",
              "      <td>1.36</td>\n",
              "      <td>1.13</td>\n",
              "      <td>6.68</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.67</td>\n",
              "      <td>1.137176e+08</td>\n",
              "      <td>1118.56</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.90</td>\n",
              "      <td>3.66</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>6.562458</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>RVBI11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>74.10</td>\n",
              "      <td>4277.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.97</td>\n",
              "      <td>2.86</td>\n",
              "      <td>5.60</td>\n",
              "      <td>10.66</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.89</td>\n",
              "      <td>3.78</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.03</td>\n",
              "      <td>1.260013e+08</td>\n",
              "      <td>87.50</td>\n",
              "      <td>85.0</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.22</td>\n",
              "      <td>3.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.318821</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>CPFF11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>69.19</td>\n",
              "      <td>8613.0</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.85</td>\n",
              "      <td>2.58</td>\n",
              "      <td>5.09</td>\n",
              "      <td>10.51</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.88</td>\n",
              "      <td>3.57</td>\n",
              "      <td>...</td>\n",
              "      <td>2.73</td>\n",
              "      <td>4.211661e+08</td>\n",
              "      <td>77.28</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.78</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.84</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.251206</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>MFAI11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>71.18</td>\n",
              "      <td>552.0</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.53</td>\n",
              "      <td>3.55</td>\n",
              "      <td>7.09</td>\n",
              "      <td>11.84</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.99</td>\n",
              "      <td>4.86</td>\n",
              "      <td>...</td>\n",
              "      <td>16.86</td>\n",
              "      <td>2.338973e+07</td>\n",
              "      <td>77.15</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.57</td>\n",
              "      <td>-4.23</td>\n",
              "      <td>-3.69</td>\n",
              "      <td>-2.71</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.279163</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>XPCM11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>16.42</td>\n",
              "      <td>2737.0</td>\n",
              "      <td>0.18</td>\n",
              "      <td>1.03</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.24</td>\n",
              "      <td>10.16</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.85</td>\n",
              "      <td>3.78</td>\n",
              "      <td>...</td>\n",
              "      <td>-5.06</td>\n",
              "      <td>1.298543e+08</td>\n",
              "      <td>53.78</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.56</td>\n",
              "      <td>-0.23</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>100.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2.857619</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>MFII11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>97.09</td>\n",
              "      <td>2635.0</td>\n",
              "      <td>1.11</td>\n",
              "      <td>1.07</td>\n",
              "      <td>3.29</td>\n",
              "      <td>6.66</td>\n",
              "      <td>12.49</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.11</td>\n",
              "      <td>1.04</td>\n",
              "      <td>4.36</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.59</td>\n",
              "      <td>4.343143e+08</td>\n",
              "      <td>104.73</td>\n",
              "      <td>93.0</td>\n",
              "      <td>1.06</td>\n",
              "      <td>-1.34</td>\n",
              "      <td>-0.30</td>\n",
              "      <td>0.42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>4.585885</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>OUJP11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>94.99</td>\n",
              "      <td>2865.0</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.36</td>\n",
              "      <td>4.16</td>\n",
              "      <td>7.59</td>\n",
              "      <td>12.80</td>\n",
              "      <td>1.39</td>\n",
              "      <td>1.26</td>\n",
              "      <td>1.07</td>\n",
              "      <td>5.37</td>\n",
              "      <td>...</td>\n",
              "      <td>10.34</td>\n",
              "      <td>3.310427e+08</td>\n",
              "      <td>101.78</td>\n",
              "      <td>93.0</td>\n",
              "      <td>1.28</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>1.15</td>\n",
              "      <td>5.78</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.564244</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>XPCI11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>94.95</td>\n",
              "      <td>20773.0</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.22</td>\n",
              "      <td>3.56</td>\n",
              "      <td>6.69</td>\n",
              "      <td>12.53</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.12</td>\n",
              "      <td>1.04</td>\n",
              "      <td>4.68</td>\n",
              "      <td>...</td>\n",
              "      <td>3.65</td>\n",
              "      <td>6.704597e+08</td>\n",
              "      <td>97.34</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.98</td>\n",
              "      <td>2.20</td>\n",
              "      <td>7.18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.563827</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>263</th>\n",
              "      <td>CBOP11</td>\n",
              "      <td>Lajes Corporativas</td>\n",
              "      <td>62.40</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.77</td>\n",
              "      <td>2.40</td>\n",
              "      <td>8.00</td>\n",
              "      <td>12.26</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.33</td>\n",
              "      <td>1.02</td>\n",
              "      <td>3.07</td>\n",
              "      <td>...</td>\n",
              "      <td>-10.34</td>\n",
              "      <td>1.263803e+08</td>\n",
              "      <td>89.31</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.55</td>\n",
              "      <td>-0.57</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.85</td>\n",
              "      <td>33.59</td>\n",
              "      <td>30.79</td>\n",
              "      <td>1</td>\n",
              "      <td>4.149464</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>266</th>\n",
              "      <td>XPSF11</td>\n",
              "      <td>Outros</td>\n",
              "      <td>7.11</td>\n",
              "      <td>124998.0</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.93</td>\n",
              "      <td>2.80</td>\n",
              "      <td>5.45</td>\n",
              "      <td>10.43</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.87</td>\n",
              "      <td>3.61</td>\n",
              "      <td>...</td>\n",
              "      <td>-4.19</td>\n",
              "      <td>3.787900e+08</td>\n",
              "      <td>87.48</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.78</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2.093098</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>271</th>\n",
              "      <td>BPFF11</td>\n",
              "      <td>Títulos e Val. Mob.</td>\n",
              "      <td>63.62</td>\n",
              "      <td>3592.0</td>\n",
              "      <td>0.65</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.86</td>\n",
              "      <td>5.55</td>\n",
              "      <td>10.18</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.85</td>\n",
              "      <td>3.73</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.96</td>\n",
              "      <td>3.515101e+08</td>\n",
              "      <td>78.25</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.37</td>\n",
              "      <td>1.99</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.168524</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>273</th>\n",
              "      <td>PLOG11</td>\n",
              "      <td>Logística</td>\n",
              "      <td>70.16</td>\n",
              "      <td>1911.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.01</td>\n",
              "      <td>3.20</td>\n",
              "      <td>6.21</td>\n",
              "      <td>11.14</td>\n",
              "      <td>1.07</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.93</td>\n",
              "      <td>4.24</td>\n",
              "      <td>...</td>\n",
              "      <td>2.86</td>\n",
              "      <td>7.515549e+07</td>\n",
              "      <td>100.21</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.74</td>\n",
              "      <td>3.05</td>\n",
              "      <td>20.40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>4.264931</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>278</th>\n",
              "      <td>PLCR11</td>\n",
              "      <td>Híbrido</td>\n",
              "      <td>87.02</td>\n",
              "      <td>2690.0</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.26</td>\n",
              "      <td>3.63</td>\n",
              "      <td>7.16</td>\n",
              "      <td>12.96</td>\n",
              "      <td>1.21</td>\n",
              "      <td>1.19</td>\n",
              "      <td>1.08</td>\n",
              "      <td>4.81</td>\n",
              "      <td>...</td>\n",
              "      <td>3.81</td>\n",
              "      <td>1.952303e+08</td>\n",
              "      <td>92.56</td>\n",
              "      <td>94.0</td>\n",
              "      <td>1.19</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>1.10</td>\n",
              "      <td>4.62</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>4.477564</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>57 rows × 28 columns</p>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Selecionando os Fundos levando em consideração o setor e o DY anual."
      ],
      "metadata": {
        "id": "UvcgNALIcaNC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "selecionar_setores = ['Códigodo fundo','Setor']"
      ],
      "metadata": {
        "id": "8n9btRYbai3s"
      },
      "execution_count": 179,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "microdados_Setores = Selecionar_FII_VPA.filter(items = selecionar_setores)\n",
        "microdados_Setores.head()"
      ],
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      "execution_count": 180,
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        {
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            "text/plain": [
              "   Códigodo fundo                Setor\n",
              "10         SPTW11   Lajes Corporativas\n",
              "17         PLRI11  Títulos e Val. Mob.\n",
              "19         PORD11  Títulos e Val. Mob.\n",
              "28         RRCI11  Títulos e Val. Mob.\n",
              "29         BBFO11  Títulos e Val. Mob."
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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Verificando a quantidade de Fundos por setores."
      ],
      "metadata": {
        "id": "26cCOPUzcDDn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "microdados_Setores.groupby('Setor').count()"
      ],
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      "execution_count": 181,
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        {
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            "text/plain": [
              "                     Códigodo fundo\n",
              "Setor                              \n",
              "Hotel                             1\n",
              "Híbrido                           8\n",
              "Lajes Corporativas                8\n",
              "Logística                         2\n",
              "Outros                           14\n",
              "Títulos e Val. Mob.              24"
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      "source": [
        "dados = Selecionar_FII_VPA[['Códigodo fundo', 'Setor', 'DY (12M)Acumulado']]\n",
        "dados.head()"
      ],
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              "   Códigodo fundo                Setor  DY (12M)Acumulado\n",
              "10         SPTW11   Lajes Corporativas              16.30\n",
              "17         PLRI11  Títulos e Val. Mob.              10.95\n",
              "19         PORD11  Títulos e Val. Mob.              13.24\n",
              "28         RRCI11  Títulos e Val. Mob.              13.62\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          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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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualização dos maiores DY(anual) por setor"
      ],
      "metadata": {
        "id": "vicu4mlbsqFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install plotly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIG8zSg4sCHq",
        "outputId": "46a01ad9-1749-4e29-b546-cfe1105b826e"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.plotting.backend = 'plotly'"
      ],
      "metadata": {
        "id": "Y2BUkJGpsBJp"
      },
      "execution_count": 184,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Selecionar_FII_VPA.plot(x = 'Códigodo fundo',y ='DY (12M)Acumulado', kind = 'hist', title = 'Código do fundo x Dividendos(anual) x Setor', color = 'Setor')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "8rPGIA7zVhwj",
        "outputId": "af3cb44d-8ba1-4c1e-ce6a-d779fc0ef8fb"
      },
      "execution_count": 185,
      "outputs": [
        {
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