DesafioInferencia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of DesafioInferencia.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1TdBPNq-O0VhdstwPrB_EBBOiXYxYsEf7",
      "authorship_tag": "ABX9TyPYKzDzpqHKs5I9kyqQHnKs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/liviaspereira/desafio_inferencia/blob/main/DesafioInferencia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Lendo os Banco de dados**"
      ],
      "metadata": {
        "id": "geffzJu2cFRD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "df1 = pd.read_csv(\"https://raw.githubusercontent.com/liviaspereira/desafio_inferencia/main/DEMO_PHQ.csv\")\n",
        "df2 = pd.read_csv(\"https://raw.githubusercontent.com/liviaspereira/desafio_inferencia/main/PAG_HEI.csv\")\n"
      ],
      "metadata": {
        "id": "INbJ1uMUw6cl"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este método **\".info\"** *imprime* informações sobre um DataFrame incluindo o dtype de índice e dtypes de coluna, valores não nulos e uso de memória."
      ],
      "metadata": {
        "id": "rvJ2X_Lyx8EW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.info()\n",
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4ucI7EiyBUY",
        "outputId": "3e68bffd-7366-4544-fe8b-194a20d0385d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5334 entries, 0 to 5333\n",
            "Data columns (total 15 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   SEQN      5334 non-null   int64  \n",
            " 1   DPQ010    4836 non-null   float64\n",
            " 2   DPQ020    4836 non-null   float64\n",
            " 3   DPQ030    4836 non-null   float64\n",
            " 4   DPQ040    4836 non-null   float64\n",
            " 5   DPQ050    4836 non-null   float64\n",
            " 6   DPQ060    4836 non-null   float64\n",
            " 7   DPQ070    4836 non-null   float64\n",
            " 8   DPQ080    4836 non-null   float64\n",
            " 9   DPQ090    4836 non-null   float64\n",
            " 10  RIAGENDR  5334 non-null   int64  \n",
            " 11  RIDAGEYR  5334 non-null   int64  \n",
            " 12  RIDRETH1  5334 non-null   int64  \n",
            " 13  DMDEDUC   5334 non-null   int64  \n",
            " 14  INDFMINC  5287 non-null   float64\n",
            "dtypes: float64(10), int64(5)\n",
            "memory usage: 625.2 KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9424 entries, 0 to 9423\n",
            "Data columns (total 17 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   SEQN                      9424 non-null   int64  \n",
            " 1   PAG_MINW                  7150 non-null   float64\n",
            " 2   ADHERENCE                 7150 non-null   float64\n",
            " 3   HEI2015C1_TOTALVEG        8549 non-null   float64\n",
            " 4   HEI2015C2_GREEN_AND_BEAN  8549 non-null   float64\n",
            " 5   HEI2015C3_TOTALFRUIT      8549 non-null   float64\n",
            " 6   HEI2015C4_WHOLEFRUIT      8549 non-null   float64\n",
            " 7   HEI2015C5_WHOLEGRAIN      8549 non-null   float64\n",
            " 8   HEI2015C6_TOTALDAIRY      8549 non-null   float64\n",
            " 9   HEI2015C7_TOTPROT         8549 non-null   float64\n",
            " 10  HEI2015C8_SEAPLANT_PROT   8549 non-null   float64\n",
            " 11  HEI2015C9_FATTYACID       8549 non-null   float64\n",
            " 12  HEI2015C10_SODIUM         8549 non-null   float64\n",
            " 13  HEI2015C11_REFINEDGRAIN   8549 non-null   float64\n",
            " 14  HEI2015C12_SFAT           8549 non-null   float64\n",
            " 15  HEI2015C13_ADDSUG         8549 non-null   float64\n",
            " 16  HEI2015_TOTAL_SCORE       8549 non-null   float64\n",
            "dtypes: float64(16), int64(1)\n",
            "memory usage: 1.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "id": "Obro9Rehy0XN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "94b75669-e6cc-4275-e199-17cd5b61a1e8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SEQN  DPQ010  DPQ020  DPQ030  DPQ040  DPQ050  DPQ060  DPQ070  DPQ080  \\\n",
              "0     31130     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
              "1     31131     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2     31132     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "3     31134     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "4     31139     0.0     0.0     0.0     0.0     3.0     1.0     0.0     0.0   \n",
              "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "5329  41466     0.0     0.0     1.0     1.0     0.0     0.0     0.0     0.0   \n",
              "5330  41468     0.0     2.0     0.0     1.0     1.0     2.0     1.0     3.0   \n",
              "5331  41469     1.0     0.0     0.0     1.0     0.0     0.0     0.0     0.0   \n",
              "5332  41472     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "5333  41473     0.0     0.0     2.0     1.0     0.0     1.0     0.0     0.0   \n",
              "\n",
              "      DPQ090  RIAGENDR  RIDAGEYR  RIDRETH1  DMDEDUC  INDFMINC  \n",
              "0        NaN         2        85         3        4       4.0  \n",
              "1        0.0         2        44         4        4      11.0  \n",
              "2        0.0         1        70         3        5      11.0  \n",
              "3        0.0         1        73         3        3      12.0  \n",
              "4        0.0         2        18         2        3      11.0  \n",
              "...      ...       ...       ...       ...      ...       ...  \n",
              "5329     0.0         2        58         5        2       3.0  \n",
              "5330     0.0         2        66         1        1       8.0  \n",
              "5331     0.0         1        19         4        4       2.0  \n",
              "5332     0.0         1        34         3        4       7.0  \n",
              "5333     0.0         1        21         1        2       6.0  \n",
              "\n",
              "[5334 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0dee5d8b-7138-4da4-a656-de65c2c9adb2\">\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>SEQN</th>\n",
              "      <th>DPQ010</th>\n",
              "      <th>DPQ020</th>\n",
              "      <th>DPQ030</th>\n",
              "      <th>DPQ040</th>\n",
              "      <th>DPQ050</th>\n",
              "      <th>DPQ060</th>\n",
              "      <th>DPQ070</th>\n",
              "      <th>DPQ080</th>\n",
              "      <th>DPQ090</th>\n",
              "      <th>RIAGENDR</th>\n",
              "      <th>RIDAGEYR</th>\n",
              "      <th>RIDRETH1</th>\n",
              "      <th>DMDEDUC</th>\n",
              "      <th>INDFMINC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31130</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>85</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31131</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31132</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31134</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31139</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>11.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5329</th>\n",
              "      <td>41466</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>58</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5330</th>\n",
              "      <td>41468</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5331</th>\n",
              "      <td>41469</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5332</th>\n",
              "      <td>41472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5333</th>\n",
              "      <td>41473</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5334 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0dee5d8b-7138-4da4-a656-de65c2c9adb2')\"\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-0dee5d8b-7138-4da4-a656-de65c2c9adb2 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-0dee5d8b-7138-4da4-a656-de65c2c9adb2');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2"
      ],
      "metadata": {
        "id": "VFHRJWH_2Smy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "9ce1d74f-53b5-4d04-9660-cb60ae066479"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SEQN     PAG_MINW  ADHERENCE  HEI2015C1_TOTALVEG  \\\n",
              "0     31128          NaN        NaN            3.083350   \n",
              "1     31129   285.450346        2.0            1.697761   \n",
              "2     31130     0.000000        1.0                 NaN   \n",
              "3     31131   264.711316        2.0            3.115103   \n",
              "4     31132  1353.348730        3.0            3.509081   \n",
              "...     ...          ...        ...                 ...   \n",
              "9419  41469   184.711316        2.0            2.555935   \n",
              "9420  41471   951.478060        3.0            2.674192   \n",
              "9421  41472    27.713626        1.0            2.457422   \n",
              "9422  41473   685.912240        3.0            3.907340   \n",
              "9423  41474     0.000000        1.0            1.238646   \n",
              "\n",
              "      HEI2015C2_GREEN_AND_BEAN  HEI2015C3_TOTALFRUIT  HEI2015C4_WHOLEFRUIT  \\\n",
              "0                     0.000000              3.927008              5.000000   \n",
              "1                     0.000000              3.731602              4.929186   \n",
              "2                          NaN                   NaN                   NaN   \n",
              "3                     0.000000              5.000000              1.320674   \n",
              "4                     0.000000              5.000000              5.000000   \n",
              "...                        ...                   ...                   ...   \n",
              "9419                  0.000000              2.613733              0.000000   \n",
              "9420                  3.769401              2.198817              0.517369   \n",
              "9421                  2.930799              2.311917              0.000000   \n",
              "9422                  5.000000              0.000000              0.000000   \n",
              "9423                  4.610515              0.688137              0.240848   \n",
              "\n",
              "      HEI2015C5_WHOLEGRAIN  HEI2015C6_TOTALDAIRY  HEI2015C7_TOTPROT  \\\n",
              "0                 0.000000              5.000000           4.251329   \n",
              "1                 0.527631              3.887809           4.693141   \n",
              "2                      NaN                   NaN                NaN   \n",
              "3                 0.000000              3.141062           4.071959   \n",
              "4                 5.000000              4.448512           5.000000   \n",
              "...                    ...                   ...                ...   \n",
              "9419              0.000000              2.151846           5.000000   \n",
              "9420              0.463168              4.662005           5.000000   \n",
              "9421              0.933303              5.000000           5.000000   \n",
              "9422              0.000000              5.000000           5.000000   \n",
              "9423              5.000000              5.000000           4.304982   \n",
              "\n",
              "      HEI2015C8_SEAPLANT_PROT  HEI2015C9_FATTYACID  HEI2015C10_SODIUM  \\\n",
              "0                    0.000000             2.487572           6.911882   \n",
              "1                    0.928562             3.740758           6.146441   \n",
              "2                         NaN                  NaN                NaN   \n",
              "3                    2.052399             0.684985           2.297100   \n",
              "4                    0.000000             2.050769           7.369489   \n",
              "...                       ...                  ...                ...   \n",
              "9419                 0.000000             9.344947           3.535211   \n",
              "9420                 1.191796             6.145146           3.189620   \n",
              "9421                 5.000000             2.090534           8.409844   \n",
              "9422                 5.000000             0.000000           7.458058   \n",
              "9423                 5.000000             2.162293           7.018993   \n",
              "\n",
              "      HEI2015C11_REFINEDGRAIN  HEI2015C12_SFAT  HEI2015C13_ADDSUG  \\\n",
              "0                   10.000000         0.818029           6.408092   \n",
              "1                    7.274979         8.006457           0.000000   \n",
              "2                         NaN              NaN                NaN   \n",
              "3                    9.512964         0.438428           3.919226   \n",
              "4                    8.887880         8.186877           9.099088   \n",
              "...                       ...              ...                ...   \n",
              "9419                 4.326307         5.269780           8.195564   \n",
              "9420                 3.766149         3.974279           7.758940   \n",
              "9421                 8.936854         3.063795           0.000000   \n",
              "9422                10.000000         1.676912           2.751168   \n",
              "9423                10.000000         7.541082           0.000000   \n",
              "\n",
              "      HEI2015_TOTAL_SCORE  \n",
              "0               47.887262  \n",
              "1               45.564326  \n",
              "2                     NaN  \n",
              "3               35.553899  \n",
              "4               63.551696  \n",
              "...                   ...  \n",
              "9419            42.993323  \n",
              "9420            45.310883  \n",
              "9421            46.134467  \n",
              "9422            45.793478  \n",
              "9423            52.805494  \n",
              "\n",
              "[9424 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a27ddb1e-1c1b-4b29-9043-c7b0acc68b16\">\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>SEQN</th>\n",
              "      <th>PAG_MINW</th>\n",
              "      <th>ADHERENCE</th>\n",
              "      <th>HEI2015C1_TOTALVEG</th>\n",
              "      <th>HEI2015C2_GREEN_AND_BEAN</th>\n",
              "      <th>HEI2015C3_TOTALFRUIT</th>\n",
              "      <th>HEI2015C4_WHOLEFRUIT</th>\n",
              "      <th>HEI2015C5_WHOLEGRAIN</th>\n",
              "      <th>HEI2015C6_TOTALDAIRY</th>\n",
              "      <th>HEI2015C7_TOTPROT</th>\n",
              "      <th>HEI2015C8_SEAPLANT_PROT</th>\n",
              "      <th>HEI2015C9_FATTYACID</th>\n",
              "      <th>HEI2015C10_SODIUM</th>\n",
              "      <th>HEI2015C11_REFINEDGRAIN</th>\n",
              "      <th>HEI2015C12_SFAT</th>\n",
              "      <th>HEI2015C13_ADDSUG</th>\n",
              "      <th>HEI2015_TOTAL_SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31128</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.083350</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.927008</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.251329</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.487572</td>\n",
              "      <td>6.911882</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.818029</td>\n",
              "      <td>6.408092</td>\n",
              "      <td>47.887262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31129</td>\n",
              "      <td>285.450346</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.697761</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.731602</td>\n",
              "      <td>4.929186</td>\n",
              "      <td>0.527631</td>\n",
              "      <td>3.887809</td>\n",
              "      <td>4.693141</td>\n",
              "      <td>0.928562</td>\n",
              "      <td>3.740758</td>\n",
              "      <td>6.146441</td>\n",
              "      <td>7.274979</td>\n",
              "      <td>8.006457</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.564326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31130</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</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>NaN</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>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31131</td>\n",
              "      <td>264.711316</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.115103</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.320674</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.141062</td>\n",
              "      <td>4.071959</td>\n",
              "      <td>2.052399</td>\n",
              "      <td>0.684985</td>\n",
              "      <td>2.297100</td>\n",
              "      <td>9.512964</td>\n",
              "      <td>0.438428</td>\n",
              "      <td>3.919226</td>\n",
              "      <td>35.553899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31132</td>\n",
              "      <td>1353.348730</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.509081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.448512</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.050769</td>\n",
              "      <td>7.369489</td>\n",
              "      <td>8.887880</td>\n",
              "      <td>8.186877</td>\n",
              "      <td>9.099088</td>\n",
              "      <td>63.551696</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9419</th>\n",
              "      <td>41469</td>\n",
              "      <td>184.711316</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.555935</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.613733</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.151846</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.344947</td>\n",
              "      <td>3.535211</td>\n",
              "      <td>4.326307</td>\n",
              "      <td>5.269780</td>\n",
              "      <td>8.195564</td>\n",
              "      <td>42.993323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9420</th>\n",
              "      <td>41471</td>\n",
              "      <td>951.478060</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.674192</td>\n",
              "      <td>3.769401</td>\n",
              "      <td>2.198817</td>\n",
              "      <td>0.517369</td>\n",
              "      <td>0.463168</td>\n",
              "      <td>4.662005</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.191796</td>\n",
              "      <td>6.145146</td>\n",
              "      <td>3.189620</td>\n",
              "      <td>3.766149</td>\n",
              "      <td>3.974279</td>\n",
              "      <td>7.758940</td>\n",
              "      <td>45.310883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9421</th>\n",
              "      <td>41472</td>\n",
              "      <td>27.713626</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.457422</td>\n",
              "      <td>2.930799</td>\n",
              "      <td>2.311917</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.933303</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.090534</td>\n",
              "      <td>8.409844</td>\n",
              "      <td>8.936854</td>\n",
              "      <td>3.063795</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.134467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9422</th>\n",
              "      <td>41473</td>\n",
              "      <td>685.912240</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.907340</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.458058</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.676912</td>\n",
              "      <td>2.751168</td>\n",
              "      <td>45.793478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9423</th>\n",
              "      <td>41474</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.238646</td>\n",
              "      <td>4.610515</td>\n",
              "      <td>0.688137</td>\n",
              "      <td>0.240848</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.304982</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.162293</td>\n",
              "      <td>7.018993</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.541082</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.805494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9424 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a27ddb1e-1c1b-4b29-9043-c7b0acc68b16')\"\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-a27ddb1e-1c1b-4b29-9043-c7b0acc68b16 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-a27ddb1e-1c1b-4b29-9043-c7b0acc68b16');\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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Combinar os dois bancos de dados fornecidos, utilizando a variável SEQN como chave única. O banco de dados final deve conter 5334 observações dos adultos respondentes do NHANES."
      ],
      "metadata": {
        "id": "3V2h3PCL2U8q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df1.merge(df2, on = 'SEQN')"
      ],
      "metadata": {
        "id": "wzJmB2oF2iyJ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.info()"
      ],
      "metadata": {
        "id": "oZ_yLty12maV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62b04b44-7fbf-4390-cf52-c4f382902356"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5334 entries, 0 to 5333\n",
            "Data columns (total 31 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   SEQN                      5334 non-null   int64  \n",
            " 1   DPQ010                    4836 non-null   float64\n",
            " 2   DPQ020                    4836 non-null   float64\n",
            " 3   DPQ030                    4836 non-null   float64\n",
            " 4   DPQ040                    4836 non-null   float64\n",
            " 5   DPQ050                    4836 non-null   float64\n",
            " 6   DPQ060                    4836 non-null   float64\n",
            " 7   DPQ070                    4836 non-null   float64\n",
            " 8   DPQ080                    4836 non-null   float64\n",
            " 9   DPQ090                    4836 non-null   float64\n",
            " 10  RIAGENDR                  5334 non-null   int64  \n",
            " 11  RIDAGEYR                  5334 non-null   int64  \n",
            " 12  RIDRETH1                  5334 non-null   int64  \n",
            " 13  DMDEDUC                   5334 non-null   int64  \n",
            " 14  INDFMINC                  5287 non-null   float64\n",
            " 15  PAG_MINW                  5334 non-null   float64\n",
            " 16  ADHERENCE                 5334 non-null   float64\n",
            " 17  HEI2015C1_TOTALVEG        5060 non-null   float64\n",
            " 18  HEI2015C2_GREEN_AND_BEAN  5060 non-null   float64\n",
            " 19  HEI2015C3_TOTALFRUIT      5060 non-null   float64\n",
            " 20  HEI2015C4_WHOLEFRUIT      5060 non-null   float64\n",
            " 21  HEI2015C5_WHOLEGRAIN      5060 non-null   float64\n",
            " 22  HEI2015C6_TOTALDAIRY      5060 non-null   float64\n",
            " 23  HEI2015C7_TOTPROT         5060 non-null   float64\n",
            " 24  HEI2015C8_SEAPLANT_PROT   5060 non-null   float64\n",
            " 25  HEI2015C9_FATTYACID       5060 non-null   float64\n",
            " 26  HEI2015C10_SODIUM         5060 non-null   float64\n",
            " 27  HEI2015C11_REFINEDGRAIN   5060 non-null   float64\n",
            " 28  HEI2015C12_SFAT           5060 non-null   float64\n",
            " 29  HEI2015C13_ADDSUG         5060 non-null   float64\n",
            " 30  HEI2015_TOTAL_SCORE       5060 non-null   float64\n",
            "dtypes: float64(26), int64(5)\n",
            "memory usage: 1.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Jp7PI4ZNPUk8",
        "outputId": "d922a4f7-f80e-49d6-b50b-a58464051628"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    SEQN  DPQ010  DPQ020  DPQ030  DPQ040  DPQ050  DPQ060  DPQ070  DPQ080  \\\n",
              "0  31130     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
              "1  31131     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2  31132     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "3  31134     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "4  31139     0.0     0.0     0.0     0.0     3.0     1.0     0.0     0.0   \n",
              "\n",
              "   DPQ090  ...  HEI2015C5_WHOLEGRAIN  HEI2015C6_TOTALDAIRY  HEI2015C7_TOTPROT  \\\n",
              "0     NaN  ...                   NaN                   NaN                NaN   \n",
              "1     0.0  ...              0.000000              3.141062           4.071959   \n",
              "2     0.0  ...              5.000000              4.448512           5.000000   \n",
              "3     0.0  ...              2.217331              2.313900           5.000000   \n",
              "4     0.0  ...              0.211544              0.000000           4.088849   \n",
              "\n",
              "   HEI2015C8_SEAPLANT_PROT  HEI2015C9_FATTYACID  HEI2015C10_SODIUM  \\\n",
              "0                      NaN                  NaN                NaN   \n",
              "1                 2.052399             0.684985           2.297100   \n",
              "2                 0.000000             2.050769           7.369489   \n",
              "3                 1.268648             2.958985           0.000000   \n",
              "4                 4.221442            10.000000           2.845774   \n",
              "\n",
              "   HEI2015C11_REFINEDGRAIN  HEI2015C12_SFAT  HEI2015C13_ADDSUG  \\\n",
              "0                      NaN              NaN                NaN   \n",
              "1                 9.512964         0.438428           3.919226   \n",
              "2                 8.887880         8.186877           9.099088   \n",
              "3                 5.627488         5.863078           5.878686   \n",
              "4                 0.000000        10.000000           9.658507   \n",
              "\n",
              "   HEI2015_TOTAL_SCORE  \n",
              "0                  NaN  \n",
              "1            35.553899  \n",
              "2            63.551696  \n",
              "3            42.886826  \n",
              "4            47.074393  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c727eff-e607-4376-b9c8-c3cb4cc41689\">\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>SEQN</th>\n",
              "      <th>DPQ010</th>\n",
              "      <th>DPQ020</th>\n",
              "      <th>DPQ030</th>\n",
              "      <th>DPQ040</th>\n",
              "      <th>DPQ050</th>\n",
              "      <th>DPQ060</th>\n",
              "      <th>DPQ070</th>\n",
              "      <th>DPQ080</th>\n",
              "      <th>DPQ090</th>\n",
              "      <th>...</th>\n",
              "      <th>HEI2015C5_WHOLEGRAIN</th>\n",
              "      <th>HEI2015C6_TOTALDAIRY</th>\n",
              "      <th>HEI2015C7_TOTPROT</th>\n",
              "      <th>HEI2015C8_SEAPLANT_PROT</th>\n",
              "      <th>HEI2015C9_FATTYACID</th>\n",
              "      <th>HEI2015C10_SODIUM</th>\n",
              "      <th>HEI2015C11_REFINEDGRAIN</th>\n",
              "      <th>HEI2015C12_SFAT</th>\n",
              "      <th>HEI2015C13_ADDSUG</th>\n",
              "      <th>HEI2015_TOTAL_SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31130</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31131</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.141062</td>\n",
              "      <td>4.071959</td>\n",
              "      <td>2.052399</td>\n",
              "      <td>0.684985</td>\n",
              "      <td>2.297100</td>\n",
              "      <td>9.512964</td>\n",
              "      <td>0.438428</td>\n",
              "      <td>3.919226</td>\n",
              "      <td>35.553899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31132</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.448512</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.050769</td>\n",
              "      <td>7.369489</td>\n",
              "      <td>8.887880</td>\n",
              "      <td>8.186877</td>\n",
              "      <td>9.099088</td>\n",
              "      <td>63.551696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31134</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.217331</td>\n",
              "      <td>2.313900</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.268648</td>\n",
              "      <td>2.958985</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.627488</td>\n",
              "      <td>5.863078</td>\n",
              "      <td>5.878686</td>\n",
              "      <td>42.886826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31139</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.211544</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.088849</td>\n",
              "      <td>4.221442</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.845774</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.658507</td>\n",
              "      <td>47.074393</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c727eff-e607-4376-b9c8-c3cb4cc41689')\"\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-4c727eff-e607-4376-b9c8-c3cb4cc41689 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-4c727eff-e607-4376-b9c8-c3cb4cc41689');\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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Crie a variável phq9 - escore Patient Health Questionnaire-9 como a soma das variáveis DPQ010 a DPQ090"
      ],
      "metadata": {
        "id": "dzUFSk9e3_vw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "  df3[\"phq9\"] = df3[[\"DPQ010\", \n",
        "                   \"DPQ020\", \n",
        "                   \"DPQ030\", \n",
        "                   \"DPQ040\", \n",
        "                   \"DPQ050\", \n",
        "                   \"DPQ060\", \n",
        "                   \"DPQ070\", \n",
        "                   \"DPQ080\", \n",
        "                   \"DPQ090\"]].sum(axis = 'columns', skipna = False)\n",
        "\n",
        "  df3[[\"DPQ010\", \n",
        "     \"DPQ020\", \n",
        "     \"DPQ030\", \n",
        "     \"DPQ040\",\n",
        "     \"DPQ050\", \n",
        "     \"DPQ060\", \n",
        "     \"DPQ070\", \n",
        "     \"DPQ080\", \n",
        "     \"DPQ090\",\n",
        "     \"phq9\"]].head()"
      ],
      "metadata": {
        "id": "gX7RKRI54C-c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "be8d2b93-4061-41cc-82df-ed8fe2c47c96"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   DPQ010  DPQ020  DPQ030  DPQ040  DPQ050  DPQ060  DPQ070  DPQ080  DPQ090  \\\n",
              "0     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
              "1     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "3     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "4     0.0     0.0     0.0     0.0     3.0     1.0     0.0     0.0     0.0   \n",
              "\n",
              "   phq9  \n",
              "0   NaN  \n",
              "1   0.0  \n",
              "2   0.0  \n",
              "3   0.0  \n",
              "4   4.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-723d4659-66af-4e28-9c51-d779666f5271\">\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>DPQ010</th>\n",
              "      <th>DPQ020</th>\n",
              "      <th>DPQ030</th>\n",
              "      <th>DPQ040</th>\n",
              "      <th>DPQ050</th>\n",
              "      <th>DPQ060</th>\n",
              "      <th>DPQ070</th>\n",
              "      <th>DPQ080</th>\n",
              "      <th>DPQ090</th>\n",
              "      <th>phq9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-723d4659-66af-4e28-9c51-d779666f5271')\"\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-723d4659-66af-4e28-9c51-d779666f5271 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-723d4659-66af-4e28-9c51-d779666f5271');\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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criar uma variável phq_grp - grupo de sintomas de depressão que assume \n",
        "0 (“sem sintomas”) se phq9 < 5, \n",
        "1 (“sintomas leves”) se 5 <= phq9 < 10, \n",
        "2 (“sintomas moderados”) se 10 <= phq9 < 15, \n",
        "3 (“sintomas moderadamente severos”) se 15 <= phq9 < 19 e\n",
        "4 (“sintomas severos”) se phq9 >= 20"
      ],
      "metadata": {
        "id": "yQB5S42hcR3t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conditions = [\n",
        "  (df3['phq9'].isna()),\n",
        "  (df3['phq9'] <= 5),\n",
        "  (df3['phq9'] > 5) & (df3['phq9'] <= 9),\n",
        "  (df3['phq9'] > 9) & (df3['phq9'] <= 14),\n",
        "  (df3['phq9'] > 14) & (df3['phq9'] <= 19),\n",
        "  (df3['phq9'] > 19)\n",
        "    ]\n",
        "values = [np.nan, 0, 1, 2, 3, 4]\n",
        "\n",
        "df3[\"phq_grp\"] = np.select(conditions, values)\n",
        "\n",
        "df3[\"phq_grp2\"] = df3[\"phq_grp\"]\n"
      ],
      "metadata": {
        "id": "9455gwsjcVlE"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Novas variáveis a partir da variável phq_grp:"
      ],
      "metadata": {
        "id": "R6U7dVlY18QI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Agrupando phq_grp em 0 = Sem sintomas, 1 = Sintomas leves, 2 = Sintomas moderados a severos\n",
        "\n",
        "df3[\"phq_grp2\"] = df3[\"phq_grp\"].replace([3, 4], 2)\n",
        "\n",
        "df3[[\"phq_grp2\"]].value_counts(sort = False) # Avaliando frequencia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7kPmlzp1dK1",
        "outputId": "c94dcc65-1e5e-49a6-b3c9-0185696e61dc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "phq_grp2\n",
              "0.0         4013\n",
              "1.0          495\n",
              "2.0          328\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3"
      ],
      "metadata": {
        "id": "BLEdu48rtIGK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "d6da2734-2f46-4f85-9783-a3d77abba969"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SEQN  DPQ010  DPQ020  DPQ030  DPQ040  DPQ050  DPQ060  DPQ070  DPQ080  \\\n",
              "0     31130     NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
              "1     31131     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "2     31132     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "3     31134     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "4     31139     0.0     0.0     0.0     0.0     3.0     1.0     0.0     0.0   \n",
              "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
              "5329  41466     0.0     0.0     1.0     1.0     0.0     0.0     0.0     0.0   \n",
              "5330  41468     0.0     2.0     0.0     1.0     1.0     2.0     1.0     3.0   \n",
              "5331  41469     1.0     0.0     0.0     1.0     0.0     0.0     0.0     0.0   \n",
              "5332  41472     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "5333  41473     0.0     0.0     2.0     1.0     0.0     1.0     0.0     0.0   \n",
              "\n",
              "      DPQ090  ...  HEI2015C8_SEAPLANT_PROT  HEI2015C9_FATTYACID  \\\n",
              "0        NaN  ...                      NaN                  NaN   \n",
              "1        0.0  ...                 2.052399             0.684985   \n",
              "2        0.0  ...                 0.000000             2.050769   \n",
              "3        0.0  ...                 1.268648             2.958985   \n",
              "4        0.0  ...                 4.221442            10.000000   \n",
              "...      ...  ...                      ...                  ...   \n",
              "5329     0.0  ...                 4.672204             4.633477   \n",
              "5330     0.0  ...                 2.344264             4.780282   \n",
              "5331     0.0  ...                 0.000000             9.344947   \n",
              "5332     0.0  ...                 5.000000             2.090534   \n",
              "5333     0.0  ...                 5.000000             0.000000   \n",
              "\n",
              "      HEI2015C10_SODIUM  HEI2015C11_REFINEDGRAIN  HEI2015C12_SFAT  \\\n",
              "0                   NaN                      NaN              NaN   \n",
              "1              2.297100                 9.512964         0.438428   \n",
              "2              7.369489                 8.887880         8.186877   \n",
              "3              0.000000                 5.627488         5.863078   \n",
              "4              2.845774                 0.000000        10.000000   \n",
              "...                 ...                      ...              ...   \n",
              "5329           0.000000                 6.580006         8.393244   \n",
              "5330           3.134216                 1.986137         5.850269   \n",
              "5331           3.535211                 4.326307         5.269780   \n",
              "5332           8.409844                 8.936854         3.063795   \n",
              "5333           7.458058                10.000000         1.676912   \n",
              "\n",
              "      HEI2015C13_ADDSUG  HEI2015_TOTAL_SCORE  phq9  phq_grp  phq_grp2  \n",
              "0                   NaN                  NaN   NaN      NaN       NaN  \n",
              "1              3.919226            35.553899   0.0      0.0       0.0  \n",
              "2              9.099088            63.551696   0.0      0.0       0.0  \n",
              "3              5.878686            42.886826   0.0      0.0       0.0  \n",
              "4              9.658507            47.074393   4.0      0.0       0.0  \n",
              "...                 ...                  ...   ...      ...       ...  \n",
              "5329           9.360990            59.949915   2.0      0.0       0.0  \n",
              "5330          10.000000            57.747869  10.0      2.0       2.0  \n",
              "5331           8.195564            42.993323   2.0      0.0       0.0  \n",
              "5332           0.000000            46.134467   0.0      0.0       0.0  \n",
              "5333           2.751168            45.793478   4.0      0.0       0.0  \n",
              "\n",
              "[5334 rows x 34 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6244a0db-be61-4b68-8e02-95e91a837c94\">\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>SEQN</th>\n",
              "      <th>DPQ010</th>\n",
              "      <th>DPQ020</th>\n",
              "      <th>DPQ030</th>\n",
              "      <th>DPQ040</th>\n",
              "      <th>DPQ050</th>\n",
              "      <th>DPQ060</th>\n",
              "      <th>DPQ070</th>\n",
              "      <th>DPQ080</th>\n",
              "      <th>DPQ090</th>\n",
              "      <th>...</th>\n",
              "      <th>HEI2015C8_SEAPLANT_PROT</th>\n",
              "      <th>HEI2015C9_FATTYACID</th>\n",
              "      <th>HEI2015C10_SODIUM</th>\n",
              "      <th>HEI2015C11_REFINEDGRAIN</th>\n",
              "      <th>HEI2015C12_SFAT</th>\n",
              "      <th>HEI2015C13_ADDSUG</th>\n",
              "      <th>HEI2015_TOTAL_SCORE</th>\n",
              "      <th>phq9</th>\n",
              "      <th>phq_grp</th>\n",
              "      <th>phq_grp2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31130</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31131</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.052399</td>\n",
              "      <td>0.684985</td>\n",
              "      <td>2.297100</td>\n",
              "      <td>9.512964</td>\n",
              "      <td>0.438428</td>\n",
              "      <td>3.919226</td>\n",
              "      <td>35.553899</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31132</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.050769</td>\n",
              "      <td>7.369489</td>\n",
              "      <td>8.887880</td>\n",
              "      <td>8.186877</td>\n",
              "      <td>9.099088</td>\n",
              "      <td>63.551696</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31134</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.268648</td>\n",
              "      <td>2.958985</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.627488</td>\n",
              "      <td>5.863078</td>\n",
              "      <td>5.878686</td>\n",
              "      <td>42.886826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31139</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.221442</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.845774</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.658507</td>\n",
              "      <td>47.074393</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5329</th>\n",
              "      <td>41466</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.672204</td>\n",
              "      <td>4.633477</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.580006</td>\n",
              "      <td>8.393244</td>\n",
              "      <td>9.360990</td>\n",
              "      <td>59.949915</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5330</th>\n",
              "      <td>41468</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.344264</td>\n",
              "      <td>4.780282</td>\n",
              "      <td>3.134216</td>\n",
              "      <td>1.986137</td>\n",
              "      <td>5.850269</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>57.747869</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5331</th>\n",
              "      <td>41469</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.344947</td>\n",
              "      <td>3.535211</td>\n",
              "      <td>4.326307</td>\n",
              "      <td>5.269780</td>\n",
              "      <td>8.195564</td>\n",
              "      <td>42.993323</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5332</th>\n",
              "      <td>41472</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.090534</td>\n",
              "      <td>8.409844</td>\n",
              "      <td>8.936854</td>\n",
              "      <td>3.063795</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.134467</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5333</th>\n",
              "      <td>41473</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.458058</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.676912</td>\n",
              "      <td>2.751168</td>\n",
              "      <td>45.793478</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5334 rows × 34 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6244a0db-be61-4b68-8e02-95e91a837c94')\"\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-6244a0db-be61-4b68-8e02-95e91a837c94 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-6244a0db-be61-4b68-8e02-95e91a837c94');\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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Avaliando o percentual de casos faltantes (missing):"
      ],
      "metadata": {
        "id": "owXa_kisI4hU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3.isnull().mean().round(4)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WS0a6dS9I7_v",
        "outputId": "97fe01d8-fafa-4820-a49c-ee2ab115595a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SEQN                        0.00\n",
              "DPQ010                      9.34\n",
              "DPQ020                      9.34\n",
              "DPQ030                      9.34\n",
              "DPQ040                      9.34\n",
              "DPQ050                      9.34\n",
              "DPQ060                      9.34\n",
              "DPQ070                      9.34\n",
              "DPQ080                      9.34\n",
              "DPQ090                      9.34\n",
              "RIAGENDR                    0.00\n",
              "RIDAGEYR                    0.00\n",
              "RIDRETH1                    0.00\n",
              "DMDEDUC                     0.00\n",
              "INDFMINC                    0.88\n",
              "PAG_MINW                    0.00\n",
              "ADHERENCE                   0.00\n",
              "HEI2015C1_TOTALVEG          5.14\n",
              "HEI2015C2_GREEN_AND_BEAN    5.14\n",
              "HEI2015C3_TOTALFRUIT        5.14\n",
              "HEI2015C4_WHOLEFRUIT        5.14\n",
              "HEI2015C5_WHOLEGRAIN        5.14\n",
              "HEI2015C6_TOTALDAIRY        5.14\n",
              "HEI2015C7_TOTPROT           5.14\n",
              "HEI2015C8_SEAPLANT_PROT     5.14\n",
              "HEI2015C9_FATTYACID         5.14\n",
              "HEI2015C10_SODIUM           5.14\n",
              "HEI2015C11_REFINEDGRAIN     5.14\n",
              "HEI2015C12_SFAT             5.14\n",
              "HEI2015C13_ADDSUG           5.14\n",
              "HEI2015_TOTAL_SCORE         5.14\n",
              "phq9                        9.34\n",
              "phq_grp                     9.34\n",
              "phq_grp2                    9.34\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O percentual de missing é bem baixo, dessa forma, a principio não se torna preocupante "
      ],
      "metadata": {
        "id": "7xKYCdxyN8Uj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3.drop(columns=['SEQN']).describe(percentiles = [.25, .5, .75, .95, .99]).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "rOBdI1roONko",
        "outputId": "61a9df1f-102d-4990-8e21-df7ddddb8e2b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        DPQ010   DPQ020   DPQ030   DPQ040   DPQ050   DPQ060   DPQ070   DPQ080  \\\n",
              "count  4836.00  4836.00  4836.00  4836.00  4836.00  4836.00  4836.00  4836.00   \n",
              "mean      0.31     0.31     0.55     0.72     0.32     0.21     0.23     0.15   \n",
              "std       0.76     0.71     0.93     0.91     0.74     0.62     0.64     0.59   \n",
              "min       0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "25%       0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "50%       0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "75%       0.00     0.00     1.00     1.00     0.00     0.00     0.00     0.00   \n",
              "95%       2.00     2.00     3.00     3.00     2.00     1.00     1.00     1.00   \n",
              "99%       3.00     3.00     3.00     3.00     3.00     3.00     3.00     3.00   \n",
              "max       9.00     9.00     9.00     9.00     9.00     9.00     9.00     9.00   \n",
              "\n",
              "        DPQ090  RIAGENDR  ...  HEI2015C8_SEAPLANT_PROT  HEI2015C9_FATTYACID  \\\n",
              "count  4836.00   5334.00  ...                  5060.00              5060.00   \n",
              "mean      0.05      1.52  ...                     2.59                 4.71   \n",
              "std       0.33      0.50  ...                     2.17                 3.24   \n",
              "min       0.00      1.00  ...                     0.00                 0.00   \n",
              "25%       0.00      1.00  ...                     0.00                 2.02   \n",
              "50%       0.00      2.00  ...                     2.59                 4.43   \n",
              "75%       0.00      2.00  ...                     5.00                 7.27   \n",
              "95%       0.00      2.00  ...                     5.00                10.00   \n",
              "99%       1.00      2.00  ...                     5.00                10.00   \n",
              "max       9.00      2.00  ...                     5.00                10.00   \n",
              "\n",
              "       HEI2015C10_SODIUM  HEI2015C11_REFINEDGRAIN  HEI2015C12_SFAT  \\\n",
              "count            5060.00                  5060.00          5060.00   \n",
              "mean                4.76                     5.80             6.07   \n",
              "std                 3.42                     3.50             3.18   \n",
              "min                 0.00                     0.00             0.00   \n",
              "25%                 1.59                     2.93             3.70   \n",
              "50%                 4.83                     6.23             6.41   \n",
              "75%                 7.65                     9.25             9.00   \n",
              "95%                10.00                    10.00            10.00   \n",
              "99%                10.00                    10.00            10.00   \n",
              "max                10.00                    10.00            10.00   \n",
              "\n",
              "       HEI2015C13_ADDSUG  HEI2015_TOTAL_SCORE     phq9  phq_grp  phq_grp2  \n",
              "count            5060.00              5060.00  4836.00  4836.00   4836.00  \n",
              "mean                6.22                49.97     2.85     0.27      0.24  \n",
              "std                 3.33                12.32     4.02     0.69      0.56  \n",
              "min                 0.00                16.06     0.00     0.00      0.00  \n",
              "25%                 3.72                41.03     0.00     0.00      0.00  \n",
              "50%                 6.86                49.51     1.00     0.00      0.00  \n",
              "75%                 9.45                58.17     4.00     0.00      0.00  \n",
              "95%                10.00                71.19    11.00     2.00      2.00  \n",
              "99%                10.00                79.86    18.00     3.00      2.00  \n",
              "max                10.00                89.53    42.00     4.00      2.00  \n",
              "\n",
              "[10 rows x 33 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5a82608-9f00-4e42-9b0b-ca192fa41ac7\">\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>DPQ010</th>\n",
              "      <th>DPQ020</th>\n",
              "      <th>DPQ030</th>\n",
              "      <th>DPQ040</th>\n",
              "      <th>DPQ050</th>\n",
              "      <th>DPQ060</th>\n",
              "      <th>DPQ070</th>\n",
              "      <th>DPQ080</th>\n",
              "      <th>DPQ090</th>\n",
              "      <th>RIAGENDR</th>\n",
              "      <th>...</th>\n",
              "      <th>HEI2015C8_SEAPLANT_PROT</th>\n",
              "      <th>HEI2015C9_FATTYACID</th>\n",
              "      <th>HEI2015C10_SODIUM</th>\n",
              "      <th>HEI2015C11_REFINEDGRAIN</th>\n",
              "      <th>HEI2015C12_SFAT</th>\n",
              "      <th>HEI2015C13_ADDSUG</th>\n",
              "      <th>HEI2015_TOTAL_SCORE</th>\n",
              "      <th>phq9</th>\n",
              "      <th>phq_grp</th>\n",
              "      <th>phq_grp2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>5334.00</td>\n",
              "      <td>...</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "      <td>4836.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.52</td>\n",
              "      <td>...</td>\n",
              "      <td>2.59</td>\n",
              "      <td>4.71</td>\n",
              "      <td>4.76</td>\n",
              "      <td>5.80</td>\n",
              "      <td>6.07</td>\n",
              "      <td>6.22</td>\n",
              "      <td>49.97</td>\n",
              "      <td>2.85</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.50</td>\n",
              "      <td>...</td>\n",
              "      <td>2.17</td>\n",
              "      <td>3.24</td>\n",
              "      <td>3.42</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.18</td>\n",
              "      <td>3.33</td>\n",
              "      <td>12.32</td>\n",
              "      <td>4.02</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\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>1.00</td>\n",
              "      <td>...</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>16.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\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>1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.02</td>\n",
              "      <td>1.59</td>\n",
              "      <td>2.93</td>\n",
              "      <td>3.70</td>\n",
              "      <td>3.72</td>\n",
              "      <td>41.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\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>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>2.59</td>\n",
              "      <td>4.43</td>\n",
              "      <td>4.83</td>\n",
              "      <td>6.23</td>\n",
              "      <td>6.41</td>\n",
              "      <td>6.86</td>\n",
              "      <td>49.51</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</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>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>5.00</td>\n",
              "      <td>7.27</td>\n",
              "      <td>7.65</td>\n",
              "      <td>9.25</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.45</td>\n",
              "      <td>58.17</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95%</th>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>71.19</td>\n",
              "      <td>11.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99%</th>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>79.86</td>\n",
              "      <td>18.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>...</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>89.53</td>\n",
              "      <td>42.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5a82608-9f00-4e42-9b0b-ca192fa41ac7')\"\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-d5a82608-9f00-4e42-9b0b-ca192fa41ac7 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-d5a82608-9f00-4e42-9b0b-ca192fa41ac7');\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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Avaliando frequências das variáveis qualitativas:\n",
        "\n"
      ],
      "metadata": {
        "id": "9t1SF-a7XRWF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3[[\n",
        "\"DPQ010\",\n",
        "\"DPQ020\",\n",
        "\"DPQ030\",\n",
        "\"DPQ040\",\n",
        "\"DPQ050\",\n",
        "\"DPQ060\",\n",
        "\"DPQ070\",\n",
        "\"DPQ080\",\n",
        "\"DPQ090\"]].agg(['value_counts'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "AcBDm14PXSmJ",
        "outputId": "4f78e85d-2090-44b9-f759-cdb9442db1e9"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          DPQ010       DPQ020       DPQ030       DPQ040       DPQ050  \\\n",
              "    value_counts value_counts value_counts value_counts value_counts   \n",
              "0.0         3850         3769         3152         2429         3813   \n",
              "1.0          673          769         1071         1706          691   \n",
              "2.0          180          179          286          370          160   \n",
              "3.0          124          114          320          326          168   \n",
              "9.0            9            5            7            5            4   \n",
              "\n",
              "          DPQ060       DPQ070       DPQ080       DPQ090  \n",
              "    value_counts value_counts value_counts value_counts  \n",
              "0.0         4134         4070         4362         4667  \n",
              "1.0          513          545          341          122  \n",
              "2.0           95          118           79           32  \n",
              "3.0           89           99           45           13  \n",
              "9.0            5            4            9            2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1ece34e-d8ad-47ff-8c84-61390e987e2d\">\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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>DPQ010</th>\n",
              "      <th>DPQ020</th>\n",
              "      <th>DPQ030</th>\n",
              "      <th>DPQ040</th>\n",
              "      <th>DPQ050</th>\n",
              "      <th>DPQ060</th>\n",
              "      <th>DPQ070</th>\n",
              "      <th>DPQ080</th>\n",
              "      <th>DPQ090</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "      <th>value_counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>3850</td>\n",
              "      <td>3769</td>\n",
              "      <td>3152</td>\n",
              "      <td>2429</td>\n",
              "      <td>3813</td>\n",
              "      <td>4134</td>\n",
              "      <td>4070</td>\n",
              "      <td>4362</td>\n",
              "      <td>4667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>673</td>\n",
              "      <td>769</td>\n",
              "      <td>1071</td>\n",
              "      <td>1706</td>\n",
              "      <td>691</td>\n",
              "      <td>513</td>\n",
              "      <td>545</td>\n",
              "      <td>341</td>\n",
              "      <td>122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>180</td>\n",
              "      <td>179</td>\n",
              "      <td>286</td>\n",
              "      <td>370</td>\n",
              "      <td>160</td>\n",
              "      <td>95</td>\n",
              "      <td>118</td>\n",
              "      <td>79</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>124</td>\n",
              "      <td>114</td>\n",
              "      <td>320</td>\n",
              "      <td>326</td>\n",
              "      <td>168</td>\n",
              "      <td>89</td>\n",
              "      <td>99</td>\n",
              "      <td>45</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9.0</th>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1ece34e-d8ad-47ff-8c84-61390e987e2d')\"\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-a1ece34e-d8ad-47ff-8c84-61390e987e2d 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-a1ece34e-d8ad-47ff-8c84-61390e987e2d');\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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribuição das Raças:"
      ],
      "metadata": {
        "id": "hGzfk00rid42"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3[[\"RIDRETH1\"]].value_counts(sort = False)\n",
        "\n",
        "# RIDRETH1 com códigos errados no desafio\n",
        "# 1\tMexican American\t\n",
        "# 2\tOther Hispanic\n",
        "# 3\tNon-Hispanic White\t\n",
        "# 4\tNon-Hispanic Black\t\n",
        "# 5\tOther Race - Including Multi-Racial"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FC1Q9wTeXd5S",
        "outputId": "687e1aee-00f6-4121-fd2d-0c46bfbb9cc0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RIDRETH1\n",
              "1           1133\n",
              "2            164\n",
              "3           2516\n",
              "4           1300\n",
              "5            221\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribuição das Escolaridades:"
      ],
      "metadata": {
        "id": "LTWIgMFkX7y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3[[\"DMDEDUC\"]].value_counts(sort = False)\n",
        "\n",
        "# 1 = Menor que 9o ano\n",
        "# 2 = 9-12o ano\n",
        "# 3 = Ensino médio\n",
        "# 4 = Ensino superior incompleto\n",
        "# 5 = Superior completo ou maior\n",
        "# 7 = Se recusou a responder\n",
        "# 9 = Não sabe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQrOj3q_X__b",
        "outputId": "098d8edb-fd48-4e42-ec72-bc3251175e87"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DMDEDUC\n",
              "1           618\n",
              "2           936\n",
              "3          1335\n",
              "4          1498\n",
              "5           939\n",
              "7             4\n",
              "9             4\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distribuição de renda:"
      ],
      "metadata": {
        "id": "-QELbjA9YEsF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3[[\"INDFMINC\"]].value_counts(sort = False)\n",
        "\n",
        "# 1 = 0-4999\n",
        "# 2 - 5000-9999\n",
        "# 3 = 10000-14999\n",
        "# 4 = 15000-19999\n",
        "# 5 = 20000-24999\n",
        "# 6 = 25000-34999\n",
        "# 7 = 35000-44999\n",
        "# 8 = 45000-54999\n",
        "# 9 = 55000-64999\n",
        "# 10 = 65000-74999\n",
        "# 11 = Maior ou igual a 75000\n",
        "# 12 = Maior que 20000\n",
        "# 13 = Menor que 20000\n",
        "# 77 = Se recusou a responder\n",
        "# 99 = Não sabe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfPVrH-OYG07",
        "outputId": "7934bbbf-102a-4394-ff04-92bcc4a44d93"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "INDFMINC\n",
              "1.0          190\n",
              "2.0          281\n",
              "3.0          440\n",
              "4.0          412\n",
              "5.0          426\n",
              "6.0          678\n",
              "7.0          535\n",
              "8.0          485\n",
              "9.0          297\n",
              "10.0         278\n",
              "11.0        1044\n",
              "12.0          78\n",
              "13.0          30\n",
              "77.0          36\n",
              "99.0          77\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agrupando/recategorizando variáveis qualitativas:\n",
        "\n",
        "1) Substituindo as categorias 7 e 9 por missing para as variáveis DPQ0X0 e DMDEDUC\n",
        "\n",
        "2) Agrupando as categorias 2 e 5 na variável RIDRETH1"
      ],
      "metadata": {
        "id": "cH74pGSFYOPv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "replace_map = {\n",
        "  \"DPQ010\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ020\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ030\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ040\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ050\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ060\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ070\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ080\": {7: np.nan, 9: np.nan},\n",
        "  \"DPQ090\": {7: np.nan, 9: np.nan},\n",
        "  \"RIDRETH1\": {5: 2}, # Other\n",
        "  \"DMDEDUC\": {7: np.nan, 9: np.nan},\n",
        "  \"INDFMINC\": {1: np.mean([0,4999]), 2: np.mean([5000,9999]), 3: np.mean([10000,14999]),4: np.mean([15000,19999]),\n",
        "               5: np.mean([20000,24999]),6: np.mean([25000,34999]), 7: np.mean([35000,44999]), 8: np.mean([45000,54999]),\n",
        "               9: np.mean([55000,64999]), 10: np.mean([65000,74999]), 11: 75000, 12: np.mean([20000, 90000]), 13: np.mean([0, 19999]),\n",
        "               77: np.nan, 99: np.nan}\n",
        "}\n",
        "df4 = df3.replace(replace_map)"
      ],
      "metadata": {
        "id": "8RbkoeREYStS"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA: Análise Univariada**"
      ],
      "metadata": {
        "id": "nX_c_3HZbleE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quantidade de linhas e de colunas:"
      ],
      "metadata": {
        "id": "EENxAqYucQX6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_NCEhRvbsbl",
        "outputId": "cc8a7387-fc50-47eb-aa1c-16c467bb6743"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5334, 34)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sumário estatístico de features numéricas do dataset com número máximo, mínimo, média, desvio padrão, quartis e contagem de valores não-nulos em cada coluna. "
      ],
      "metadata": {
        "id": "sQBw4VUkcUNb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCFcF7wzcbe1",
        "outputId": "236c5984-4283-4a9b-d630-d260c2207122"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SEQN', 'DPQ010', 'DPQ020', 'DPQ030', 'DPQ040', 'DPQ050', 'DPQ060',\n",
              "       'DPQ070', 'DPQ080', 'DPQ090', 'RIAGENDR', 'RIDAGEYR', 'RIDRETH1',\n",
              "       'DMDEDUC', 'INDFMINC', 'PAG_MINW', 'ADHERENCE', 'HEI2015C1_TOTALVEG',\n",
              "       'HEI2015C2_GREEN_AND_BEAN', 'HEI2015C3_TOTALFRUIT',\n",
              "       'HEI2015C4_WHOLEFRUIT', 'HEI2015C5_WHOLEGRAIN', 'HEI2015C6_TOTALDAIRY',\n",
              "       'HEI2015C7_TOTPROT', 'HEI2015C8_SEAPLANT_PROT', 'HEI2015C9_FATTYACID',\n",
              "       'HEI2015C10_SODIUM', 'HEI2015C11_REFINEDGRAIN', 'HEI2015C12_SFAT',\n",
              "       'HEI2015C13_ADDSUG', 'HEI2015_TOTAL_SCORE', 'phq9', 'phq_grp',\n",
              "       'phq_grp2'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Organizando os dados de acordo com o tipo de variavel "
      ],
      "metadata": {
        "id": "vOIi5ugpenoR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "var_quant = [\n",
        "    \"RIDAGEYR\", \n",
        "    \"INDFMINC\", \n",
        "    \"PAG_MINW\", \n",
        "    \"HEI2015C1_TOTALVEG\",\n",
        "    \"HEI2015C2_GREEN_AND_BEAN\",\n",
        "    \"HEI2015C3_TOTALFRUIT\",\n",
        "    \"HEI2015C4_WHOLEFRUIT\",\n",
        "    \"HEI2015C5_WHOLEGRAIN\",\n",
        "    \"HEI2015C6_TOTALDAIRY\",\n",
        "    \"HEI2015C7_TOTPROT\",\n",
        "    \"HEI2015C8_SEAPLANT_PROT\",\n",
        "    \"HEI2015C9_FATTYACID\",\n",
        "    \"HEI2015C10_SODIUM\",\n",
        "    \"HEI2015C11_REFINEDGRAIN\",\n",
        "    \"HEI2015C12_SFAT\",\n",
        "    \"HEI2015C13_ADDSUG\",\n",
        "    \"HEI2015_TOTAL_SCORE\",\n",
        "    \"phq9\"]\n",
        "\n",
        "var_quali = [\n",
        "    \"RIAGENDR\",\n",
        "    \"RIDRETH1\",\n",
        "    \"DMDEDUC\",\n",
        "    \"ADHERENCE\",\n",
        "    \"phq_grp2\"\n",
        "]\n",
        "\n",
        "label_quali = {\n",
        "  \"RIAGENDR\": {1: 'Masculino', 2: 'Feminino'},\n",
        "  \"RIDRETH1\": {1: 'Americano Mexicano', 2: 'Outro', 3: 'Branco \\n não hispânico', 4: 'Negro \\n não hispânico'},\n",
        "  \"DMDEDUC\": {1: \"< 9 ano\", 2: \"9-12 ano\", 3: \"Ensino \\n médio\", 4: \"Superior \\n incompleto\", 5: \"Superior \\n completo\"},\n",
        "  \"ADHERENCE\": {1: 'Baixo', 2: 'Adequado', 3: 'Acima'},\n",
        "  \"phq_grp2\": {0: \"Sem sintomas\", 1: \"Sintomas \\n leves\", 2: \"Sintomas \\n moderados-severos\"}\n",
        "}"
      ],
      "metadata": {
        "id": "piFK7o8vesmI"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4[var_quant].describe(percentiles = [.25, .5, .75, .95, .99]).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "D3-PA-RReu3M",
        "outputId": "b25bc015-4c90-451f-ee94-d841adf6da05"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       RIDAGEYR  INDFMINC  PAG_MINW  HEI2015C1_TOTALVEG  \\\n",
              "count   5334.00   5174.00   5334.00             5060.00   \n",
              "mean      45.09  40787.68    471.77                3.20   \n",
              "std       20.15  24247.24    780.35                1.47   \n",
              "min       18.00   2499.50      0.00                0.00   \n",
              "25%       27.00  17499.50     35.00                2.06   \n",
              "50%       43.00  39999.50    210.00                3.22   \n",
              "75%       62.00  69999.50    568.93                4.83   \n",
              "95%       81.00  75000.00   1863.05                5.00   \n",
              "99%       85.00  75000.00   3672.06                5.00   \n",
              "max       85.00  75000.00  10777.83                5.00   \n",
              "\n",
              "       HEI2015C2_GREEN_AND_BEAN  HEI2015C3_TOTALFRUIT  HEI2015C4_WHOLEFRUIT  \\\n",
              "count                   5060.00               5060.00               5060.00   \n",
              "mean                       1.81                  2.49                  2.36   \n",
              "std                        2.14                  1.95                  2.17   \n",
              "min                        0.00                  0.00                  0.00   \n",
              "25%                        0.00                  0.45                  0.00   \n",
              "50%                        0.06                  2.32                  2.08   \n",
              "75%                        4.50                  4.94                  5.00   \n",
              "95%                        5.00                  5.00                  5.00   \n",
              "99%                        5.00                  5.00                  5.00   \n",
              "max                        5.00                  5.00                  5.00   \n",
              "\n",
              "       HEI2015C5_WHOLEGRAIN  HEI2015C6_TOTALDAIRY  HEI2015C7_TOTPROT  \\\n",
              "count               5060.00               5060.00            5060.00   \n",
              "mean                   1.76                  3.76               4.43   \n",
              "std                    1.92                  1.61               1.01   \n",
              "min                    0.00                  0.00               0.00   \n",
              "25%                    0.00                  2.59               4.17   \n",
              "50%                    1.03                  4.94               5.00   \n",
              "75%                    3.41                  5.00               5.00   \n",
              "95%                    5.00                  5.00               5.00   \n",
              "99%                    5.00                  5.00               5.00   \n",
              "max                    5.00                  5.00               5.00   \n",
              "\n",
              "       HEI2015C8_SEAPLANT_PROT  HEI2015C9_FATTYACID  HEI2015C10_SODIUM  \\\n",
              "count                  5060.00              5060.00            5060.00   \n",
              "mean                      2.59                 4.71               4.76   \n",
              "std                       2.17                 3.24               3.42   \n",
              "min                       0.00                 0.00               0.00   \n",
              "25%                       0.00                 2.02               1.59   \n",
              "50%                       2.59                 4.43               4.83   \n",
              "75%                       5.00                 7.27               7.65   \n",
              "95%                       5.00                10.00              10.00   \n",
              "99%                       5.00                10.00              10.00   \n",
              "max                       5.00                10.00              10.00   \n",
              "\n",
              "       HEI2015C11_REFINEDGRAIN  HEI2015C12_SFAT  HEI2015C13_ADDSUG  \\\n",
              "count                  5060.00          5060.00            5060.00   \n",
              "mean                      5.80             6.07               6.22   \n",
              "std                       3.50             3.18               3.33   \n",
              "min                       0.00             0.00               0.00   \n",
              "25%                       2.93             3.70               3.72   \n",
              "50%                       6.23             6.41               6.86   \n",
              "75%                       9.25             9.00               9.45   \n",
              "95%                      10.00            10.00              10.00   \n",
              "99%                      10.00            10.00              10.00   \n",
              "max                      10.00            10.00              10.00   \n",
              "\n",
              "       HEI2015_TOTAL_SCORE     phq9  \n",
              "count              5060.00  4836.00  \n",
              "mean                 49.97     2.85  \n",
              "std                  12.32     4.02  \n",
              "min                  16.06     0.00  \n",
              "25%                  41.03     0.00  \n",
              "50%                  49.51     1.00  \n",
              "75%                  58.17     4.00  \n",
              "95%                  71.19    11.00  \n",
              "99%                  79.86    18.00  \n",
              "max                  89.53    42.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8046452c-ea89-4d04-b09a-497f1a2be648\">\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>RIDAGEYR</th>\n",
              "      <th>INDFMINC</th>\n",
              "      <th>PAG_MINW</th>\n",
              "      <th>HEI2015C1_TOTALVEG</th>\n",
              "      <th>HEI2015C2_GREEN_AND_BEAN</th>\n",
              "      <th>HEI2015C3_TOTALFRUIT</th>\n",
              "      <th>HEI2015C4_WHOLEFRUIT</th>\n",
              "      <th>HEI2015C5_WHOLEGRAIN</th>\n",
              "      <th>HEI2015C6_TOTALDAIRY</th>\n",
              "      <th>HEI2015C7_TOTPROT</th>\n",
              "      <th>HEI2015C8_SEAPLANT_PROT</th>\n",
              "      <th>HEI2015C9_FATTYACID</th>\n",
              "      <th>HEI2015C10_SODIUM</th>\n",
              "      <th>HEI2015C11_REFINEDGRAIN</th>\n",
              "      <th>HEI2015C12_SFAT</th>\n",
              "      <th>HEI2015C13_ADDSUG</th>\n",
              "      <th>HEI2015_TOTAL_SCORE</th>\n",
              "      <th>phq9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5334.00</td>\n",
              "      <td>5174.00</td>\n",
              "      <td>5334.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>5060.00</td>\n",
              "      <td>4836.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>45.09</td>\n",
              "      <td>40787.68</td>\n",
              "      <td>471.77</td>\n",
              "      <td>3.20</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.49</td>\n",
              "      <td>2.36</td>\n",
              "      <td>1.76</td>\n",
              "      <td>3.76</td>\n",
              "      <td>4.43</td>\n",
              "      <td>2.59</td>\n",
              "      <td>4.71</td>\n",
              "      <td>4.76</td>\n",
              "      <td>5.80</td>\n",
              "      <td>6.07</td>\n",
              "      <td>6.22</td>\n",
              "      <td>49.97</td>\n",
              "      <td>2.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20.15</td>\n",
              "      <td>24247.24</td>\n",
              "      <td>780.35</td>\n",
              "      <td>1.47</td>\n",
              "      <td>2.14</td>\n",
              "      <td>1.95</td>\n",
              "      <td>2.17</td>\n",
              "      <td>1.92</td>\n",
              "      <td>1.61</td>\n",
              "      <td>1.01</td>\n",
              "      <td>2.17</td>\n",
              "      <td>3.24</td>\n",
              "      <td>3.42</td>\n",
              "      <td>3.50</td>\n",
              "      <td>3.18</td>\n",
              "      <td>3.33</td>\n",
              "      <td>12.32</td>\n",
              "      <td>4.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.00</td>\n",
              "      <td>2499.50</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>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>16.06</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.00</td>\n",
              "      <td>17499.50</td>\n",
              "      <td>35.00</td>\n",
              "      <td>2.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.59</td>\n",
              "      <td>4.17</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.02</td>\n",
              "      <td>1.59</td>\n",
              "      <td>2.93</td>\n",
              "      <td>3.70</td>\n",
              "      <td>3.72</td>\n",
              "      <td>41.03</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43.00</td>\n",
              "      <td>39999.50</td>\n",
              "      <td>210.00</td>\n",
              "      <td>3.22</td>\n",
              "      <td>0.06</td>\n",
              "      <td>2.32</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.03</td>\n",
              "      <td>4.94</td>\n",
              "      <td>5.00</td>\n",
              "      <td>2.59</td>\n",
              "      <td>4.43</td>\n",
              "      <td>4.83</td>\n",
              "      <td>6.23</td>\n",
              "      <td>6.41</td>\n",
              "      <td>6.86</td>\n",
              "      <td>49.51</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>62.00</td>\n",
              "      <td>69999.50</td>\n",
              "      <td>568.93</td>\n",
              "      <td>4.83</td>\n",
              "      <td>4.50</td>\n",
              "      <td>4.94</td>\n",
              "      <td>5.00</td>\n",
              "      <td>3.41</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>7.27</td>\n",
              "      <td>7.65</td>\n",
              "      <td>9.25</td>\n",
              "      <td>9.00</td>\n",
              "      <td>9.45</td>\n",
              "      <td>58.17</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95%</th>\n",
              "      <td>81.00</td>\n",
              "      <td>75000.00</td>\n",
              "      <td>1863.05</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>71.19</td>\n",
              "      <td>11.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99%</th>\n",
              "      <td>85.00</td>\n",
              "      <td>75000.00</td>\n",
              "      <td>3672.06</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>79.86</td>\n",
              "      <td>18.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>85.00</td>\n",
              "      <td>75000.00</td>\n",
              "      <td>10777.83</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>89.53</td>\n",
              "      <td>42.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8046452c-ea89-4d04-b09a-497f1a2be648')\"\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-8046452c-ea89-4d04-b09a-497f1a2be648 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-8046452c-ea89-4d04-b09a-497f1a2be648');\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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df4['PAG_MINW'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgI1i05WpK6q",
        "outputId": "dbb1ba5e-7b71-44d5-c61f-74ed4d6250f9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0          0.000000\n",
            "1        264.711316\n",
            "2       1353.348730\n",
            "3       1011.478060\n",
            "4       1729.145497\n",
            "           ...     \n",
            "5329       9.237875\n",
            "5330     173.210162\n",
            "5331     184.711316\n",
            "5332      27.713626\n",
            "5333     685.912240\n",
            "Name: PAG_MINW, Length: 5334, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4['PAG_MINW_trunc'] = np.where(df4['PAG_MINW'] > 3600, 3600, df4['PAG_MINW'])\n",
        "df4[['PAG_MINW', 'PAG_MINW_trunc']].describe(percentiles = [.25, .5, .75, .95, .99]).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "bTxtEVOHL0Cf",
        "outputId": "08bb6ef6-e5a5-4104-cdad-04d400283506"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PAG_MINW  PAG_MINW_trunc\n",
              "count   5334.00         5334.00\n",
              "mean     471.77          454.86\n",
              "std      780.35          663.05\n",
              "min        0.00            0.00\n",
              "25%       35.00           35.00\n",
              "50%      210.00          210.00\n",
              "75%      568.93          568.93\n",
              "95%     1863.05         1863.05\n",
              "99%     3672.06         3600.00\n",
              "max    10777.83         3600.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-220a1a4a-940e-4dd9-bd4a-2d2a8ca49eed\">\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>PAG_MINW</th>\n",
              "      <th>PAG_MINW_trunc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5334.00</td>\n",
              "      <td>5334.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>471.77</td>\n",
              "      <td>454.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>780.35</td>\n",
              "      <td>663.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>35.00</td>\n",
              "      <td>35.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>210.00</td>\n",
              "      <td>210.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>568.93</td>\n",
              "      <td>568.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95%</th>\n",
              "      <td>1863.05</td>\n",
              "      <td>1863.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99%</th>\n",
              "      <td>3672.06</td>\n",
              "      <td>3600.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10777.83</td>\n",
              "      <td>3600.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-220a1a4a-940e-4dd9-bd4a-2d2a8ca49eed')\"\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-220a1a4a-940e-4dd9-bd4a-2d2a8ca49eed 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-220a1a4a-940e-4dd9-bd4a-2d2a8ca49eed');\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": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Variável PAG_MINW em horas:"
      ],
      "metadata": {
        "id": "J2h1EXgHxE7E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df4[\"PAG_HRW\"] = df4[\"PAG_MINW_trunc\"]/60"
      ],
      "metadata": {
        "id": "O6oTDgT6xjpt"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotando os graficos das variáveis:\n",
        "\n",
        "\n",
        "- RIDAGEYR   \n",
        "- INDFMINC          \n",
        "- PAG_MINW_trunc                     \n",
        "- PAG_HRW"
      ],
      "metadata": {
        "id": "Im4tPpsMxtlO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df4[['INDFMINC']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTprZL5Gz_ue",
        "outputId": "b26e749a-f263-4a4e-c6a4-bd225a67f3dc"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      INDFMINC\n",
            "0      17499.5\n",
            "1      75000.0\n",
            "2      75000.0\n",
            "3      55000.0\n",
            "4      75000.0\n",
            "...        ...\n",
            "5329   12499.5\n",
            "5330   49999.5\n",
            "5331    7499.5\n",
            "5332   39999.5\n",
            "5333   29999.5\n",
            "\n",
            "[5334 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df4[['RIDAGEYR']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViQ9sFyX02Kf",
        "outputId": "43f50010-33f0-4d84-c3a2-98444c3e8722"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      RIDAGEYR\n",
            "0           85\n",
            "1           44\n",
            "2           70\n",
            "3           73\n",
            "4           18\n",
            "...        ...\n",
            "5329        58\n",
            "5330        66\n",
            "5331        19\n",
            "5332        34\n",
            "5333        21\n",
            "\n",
            "[5334 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df4, x=\"RIDAGEYR\", kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "W1M_nVjKysam",
        "outputId": "e0845df8-8134-471b-87ed-fd7d6f51d531"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Grafico da Variável \"INDFMINC\", que representa a Renda anual familiar (US$)"
      ],
      "metadata": {
        "id": "856r2Esao1iV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df4, x=\"INDFMINC\", kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "yMbdZdB6o1IT",
        "outputId": "6c36e981-0f60-46c7-df49-e905fc1d12af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gráfico da variável \"PAG_MINW\", só que truncado, ou seja, tempo de atividade na semana, considerando 60 horas semanais e maximo de 10.080 minutos. "
      ],
      "metadata": {
        "id": "RP6u1IpKwwEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df4, x=\"PAG_MINW_trunc\", kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "zorrvlM4wxAM",
        "outputId": "b0b3c16f-c046-463b-d620-f757b7b9151f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxc1X338c9vZqTR4kVe5E228QoOON4iwJBACSRgsjnpQxJ42kBSWp42JE1CkoY0fUo3Xk2apyVLU1IaKJCkAUIgmIQEHANhNXgB7zYIG9vyguR91f57/rhHZhCyLduaOZL9fb9e89K9556596eR/PXVmXvPmLsjIiKFl4pdgIjIqUoBLCISiQJYRCQSBbCISCQKYBGRSDKxC8iHWbNm+W9/+9vYZYiItLPOGk/KM+Bt27bFLkFE5KhOygAWEekNFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiyVsAm9kdZlZnZss7tH/ezFab2Qoz+5ec9q+bWY2ZrTGzy3LaZ4W2GjO7MV/1iogUWj5nQ7sT+Hfg7vYGM3svMBuY6u6NZjYktJ8JXAmcBYwAfmdmp4en/QB4P1ALLDCzOe6+sruLbWhoYMGCBW9pO/vssykpKenuQ4mIAHkMYHd/yszGdGj+C+Cb7t4Y+tSF9tnAPaF9nZnVAOeEbTXuvhbAzO4Jfbs9gBcsWMAt986latwkADatXc2XgAsuuKC7DyUiAhR+PuDTgQvM7GagAfiKuy8AqoD5Of1qQxvAxg7t53a2YzO7DrgOYPTo0cdVXNW4SYx7Z/VxPVdE5FgV+k24DDAQmAl8FbjPzDqdqPhYuftt7l7t7tWVlZXdsUsRkbwq9BlwLfCAuzvwopm1AYOBTcConH4jQxtHaBcR6dUKfQb8S+C9AOFNtmJgGzAHuNLMsmY2FpgIvAgsACaa2VgzKyZ5o25OgWsWEcmLvJ0Bm9nPgIuAwWZWC9wE3AHcES5NawKuCWfDK8zsPpI311qA6929Neznc8CjQBq4w91X5KtmEZFCyudVEFcdZtMfH6b/zcDNnbQ/AjzSjaWJiPQIuhNORCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSPIWwGZ2h5nVmdnyTrZ92czczAaHdTOz75lZjZktNbMZOX2vMbNXw+OafNUrIlJo+TwDvhOY1bHRzEYBlwIbcpovByaGx3XAraHvQOAm4FzgHOAmMxuQx5pFRAombwHs7k8BOzrZdAvwV4DntM0G7vbEfKDCzIYDlwFz3X2Hu+8E5tJJqIuI9EYFHQM2s9nAJndf0mFTFbAxZ702tB2uvbN9X2dmC81sYX19fTdWLSKSHwULYDMrA/4a+Nt87N/db3P3anevrqyszMchRES6VSHPgMcDY4ElZvY6MBJYbGbDgE3AqJy+I0Pb4dpFRHq9ggWwuy9z9yHuPsbdx5AMJ8xw963AHODqcDXETGC3u28BHgUuNbMB4c23S0ObiEivl8/L0H4GPA+cYWa1ZnbtEbo/AqwFaoD/Aj4L4O47gH8EFoTHP4Q2EZFeL5OvHbv7VUfZPiZn2YHrD9PvDuCObi1ORKQH0J1wIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJJG8BbGZ3mFmdmS3Pafu2ma02s6Vm9qCZVeRs+7qZ1ZjZGjO7LKd9VmirMbMb81WviEih5fMM+E5gVoe2ucBkd58CvAJ8HcDMzgSuBM4Kz/kPM0ubWRr4AXA5cCZwVegrItLr5S2A3f0pYEeHtsfcvSWszgdGhuXZwD3u3uju64Aa4JzwqHH3te7eBNwT+oqI9Hoxx4D/BPhNWK4CNuZsqw1th2t/GzO7zswWmtnC+vr6PJQrItK9ogSwmX0DaAF+2l37dPfb3L3a3asrKyu7a7ciInmTKfQBzezTwIeAS9zdQ/MmYFROt5GhjSO0i4j0agU9AzazWcBfAR9x9wM5m+YAV5pZ1szGAhOBF4EFwEQzG2tmxSRv1M0pZM0iIvmStzNgM/sZcBEw2MxqgZtIrnrIAnPNDGC+u/+5u68ws/uAlSRDE9e7e2vYz+eAR4E0cIe7r8hXzSIihZS3AHb3qzppvv0I/W8Gbu6k/RHgkW4sTUSkR9CdcCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESStwA2szvMrM7Mlue0DTSzuWb2avg6ILSbmX3PzGrMbKmZzch5zjWh/6tmdk2+6hURKbR8ngHfCczq0HYjMM/dJwLzwjrA5cDE8LgOuBWSwAZuAs4FzgFuag9tEZHeLm8B7O5PATs6NM8G7grLdwEfzWm/2xPzgQozGw5cBsx19x3uvhOYy9tDXUSkVyr0GPBQd98SlrcCQ8NyFbAxp19taDtc+9uY2XVmttDMFtbX13dv1SIieRDtTTh3d8C7cX+3uXu1u1dXVlZ2125FRPKm0AH8RhhaIHytC+2bgFE5/UaGtsO1i4j0eoUO4DlA+5UM1wAP5bRfHa6GmAnsDkMVjwKXmtmA8ObbpaFNRKTXy+Rrx2b2M+AiYLCZ1ZJczfBN4D4zuxZYD3widH8E+ABQAxwAPgPg7jvM7B+BBaHfP7h7xzf2RER6pbwFsLtfdZhNl3TS14HrD7OfO4A7urE0EZEeQXfCiYhEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiaRLAWxm7+5Km4iIdF1Xz4C/38U2ERHpoiNOxmNm5wHnA5VmdkPOpn5AOp+FiYic7I42G1ox0Cf065vTvge4Il9FiYicCo4YwO7+e+D3Znanu68vUE0iIqeErs4HnDWz24Axuc9x94vzUZSIyKmgqwH8c+CHwI+A1vyVIyJy6uhqALe4+615rURE5BTT1cvQHjazz5rZcDMb2P7Ia2UiIie5rp4Bt3+S8Vdz2hwY173liIicOroUwO4+Nt+FiIicaroUwGZ2dWft7n5395YjInLq6OoQxNk5yyUkHy2/GFAAi4gcp64OQXw+d93MKoB78lKRiMgp4nino9wPaFxYROQEdHUM+GGSqx4gmYTnHcB9+SpKRORU0NUx4P+Xs9wCrHf32jzUIyJyyujSEESYlGc1yYxoA4CmEzmomX3JzFaY2XIz+5mZlZjZWDN7wcxqzOxeMysOfbNhvSZsH3MixxYR6Sm6+okYnwBeBD4OfAJ4wcyOazpKM6sC/hKodvfJJEMaVwLfAm5x9wnATuDa8JRrgZ2h/ZbQT0Sk1+vqm3DfAM5292vc/WrgHOD/nsBxM0CpmWWAMmALcDFwf9h+F/DRsDw7rBO2X2JmdgLHFhHpEboawCl3r8tZ334Mz30Ld99EMqa8gSR4dwOLgF3u3hK61QJVYbkK2Bie2xL6D+q4XzO7zswWmtnC+vr64ylNRKSguhqivzWzR83s02b2aeDXwCPHc0AzG0ByVjsWGAGUA7OOZ1+53P02d6929+rKysoT3Z2ISN4d7TPhJgBD3f2rZvaHwHvCpueBnx7nMd8HrHP3+nCMB4B3AxVmlglnuSOBTaH/JmAUUBuGLPqTnIGLiPRqRzsD/g7J57/h7g+4+w3ufgPwYNh2PDYAM82sLIzlXgKsBJ7gzc+ZuwZ4KCzP4c3Z2K4AHnd3R0SklztaAA9192UdG0PbmOM5oLu/QPJm2mJgWajhNuBrwA1mVkMyxnt7eMrtwKDQfgNw4/EcV0SkpznajRgVR9hWerwHdfebgJs6NK8lubqiY98GksvfREROKkc7A15oZn/WsdHM/pTkygURETlORzsD/iLwoJn9EW8GbjVQDHwsn4WJiJzsjhjA7v4GcL6ZvReYHJp/7e6P570yEZGTXFfnA36C5CoFERHpJsc7H7CIiJwgBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISSZQANrMKM7vfzFab2SozO8/MBprZXDN7NXwdEPqamX3PzGrMbKmZzYhRs4hId4t1Bvxd4LfuPgmYCqwCbgTmuftEYF5YB7gcmBge1wG3Fr5cEZHuV/AANrP+wIXA7QDu3uTuu4DZwF2h213AR8PybOBuT8wHKsxseIHLFhHpdjHOgMcC9cB/m9lLZvYjMysHhrr7ltBnKzA0LFcBG3OeXxva3sLMrjOzhWa2sL6+Po/li4h0jxgBnAFmALe6+3RgP28ONwDg7g74sezU3W9z92p3r66srOy2YkVE8iVGANcCte7+Qli/nySQ32gfWghf68L2TcConOePDG0iIr1awQPY3bcCG83sjNB0CbASmANcE9quAR4Ky3OAq8PVEDOB3TlDFSIivVYm0nE/D/zUzIqBtcBnSP4zuM/MrgXWA58IfR8BPgDUAAdCXxGRXi9KALv7y0B1J5su6aSvA9fnvSgRkQLTnXAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiAj2DbwTY2bD8QuwwROUnFmguiR2tpbWPB7jJ+9fg+/PEnuHzyML798an0yerlEpHuozPgTjz16ja2Nhbx4fHF/OXFE3hs5RvccO/LtLUd0xTFIiJHpADuYP32/SzbtJux2QOc3riGs0vruGpSMY+tfIO/+fE8GhoaYpcoIicJ/U3dwcL1O+mTzTBg+3J+UbOdNU0DcYfK4jLuXdnChc+9yKyLL4xdpoicBHQGnGNPS4ranQeZOqo/hlM5chzj3lnN+CnVvH/aeFotw69ea4xdpoicJBTAOdYdyJJJGZNH9H/btsq+WUZkm/jd+ib2NjRHqE5ETjYK4KC1zdnSmGH8kD6UFKU77TOurImGFvjFotoCVyciJyMFcPDKzlaaPcX4yvLD9hlQ1Mr4ijR3P79eV0SIyAlTAAeL3mghhXPawMMHMMD7Tyti7bb9zF+3vUCVicjJSgEMuDuLtjYzuLiF4syRX5Lq4UWUF6f55UubClSdiJysFMDAzgPNlBUZw7JHf3MtmzZmTR7Ob5ZtpaG5tQDVicjJSgEMDCwv5uYL+jC6pGtXN3xsehV7G1uYt6ouz5WJyMlMAZzDrGv9zhs/iKH9sjz4kq6GEJHjpwA+DumUMXtaFU+uqWfH/qbY5YhIL6UAPk4fm15FS5vz66WbY5ciIr2UAvg4vWN4PyYN68sDuhpCRI6TAvgEfGTaCF7asIuNOzRpu4gcu2gBbGZpM3vJzH4V1sea2QtmVmNm95pZcWjPhvWasH1MrJo7+vCUEQA8rGEIETkOMc+AvwCsyln/FnCLu08AdgLXhvZrgZ2h/ZbQr0cYNbCMGaMrmPOyAlhEjl2UADazkcAHgR+FdQMuBu4PXe4CPhqWZ4d1wvZLQv+Ca2luYsmSJTz99NOHHrPOHMLqrXt59Y29MUoSkV4s1oTs3wH+Cugb1gcBu9y9JazXAlVhuQrYCODuLWa2O/TflrtDM7sOuA5g9OjReSm6buNaVu/cwZqmgQBsWruaz8y+hJTBw0s2c8OlZ+TluCJycir4GbCZfQioc/dF3blfd7/N3avdvbqysrI7d/0W7ZO0j3tnNVXjJlFRkmLmuEE8vHQL7pohTUS6LsYQxLuBj5jZ68A9JEMP3wUqzKz9jHwk0H591yZgFEDY3h/oUVORfWTqCNZt28/yTXtilyIivUjBA9jdv+7uI919DHAl8Li7/xHwBHBF6HYN8FBYnhPWCdsf9x52qjlr8jCK0qarIUTkmPSk64C/BtxgZjUkY7y3h/bbgUGh/Qbgxkj1HVZFWTEXTqzk4SWbNVG7iHRZ1E9FdvcngSfD8lrgnE76NAAfL2hhx+HDU0cwb3UdC9fv5JyxA2OXIyK9gD6W/gS0X5YGUN7iFKfgP3+7kCmfuZCSkpLI1YlIT6cAPgEdL0sbXFTKk68389z8F7n4ogsjVyciPZ0C+AS1X5YGkNq+n4de3sziuhYujlyXiPR8PelNuF5v9MAySlJt/H6j5ggWkaNTAHejlBmjS5pYXt/Kpl0HY5cjIj2cAribjSpNzn7vX6iPKxKRI1MAd7OytHPm4DQ/X7RR1wSLyBEpgPPgD0YVU7vzIM+91qPumBaRHkYBnAfvGpphQFkRdz3/euxSRKQHUwDnQXHa+OOZp/G7VW+wbtv+2OWISA+lAO5m7XfHnW5bSRv8033P0tDQELssEemBFMDdrG7jWn7x/GoeW7aJ4cVNPL6+gceffSF2WSLSAymA86D97rg/mDoBtzTz1jfHLklEeiAFcB4N6pOlsriZua830dDcGrscEelhFMB5NqGskT1Nzk9f2BC7FBHpYRTAeTa4uJWzBqX5wRM17GtsOfoTROSUodnQCuATk0q46dn9/OjptXzxfafT0NDAggUL3tLn7LPP1hzCIqcYBXABjKtIc/nkYfzXU2v51MzTWPnSAm65dy5V4yYBycfbfwm44IIL4hYqIgWlAC6QL196Oo+u2MoPnniN91ZA1bhJh+YRFpFTk8aAC2TCkL58onoUdz//Opv26ooIEVEAF9RXLzuD8myGO5c34JooTeSUpwAuoEF9stx4+SRW72iltqEodjkiEpkCuMA+WT2KCRVpVu4r0c0ZIqc4BXCBpVLGp99ZQrMbT71aH7scEYlIARzBaf3STChrZNWWvayt3xe7HBGJRAEcyenljVT2yfK7VXU0tlnsckQkAgVwJCmDS88aSlNLG0v3luK6LELklFPwADazUWb2hJmtNLMVZvaF0D7QzOaa2avh64DQbmb2PTOrMbOlZjaj0DXny+A+WWaOH8jWxiKe3KgpK0VONTHOgFuAL7v7mcBM4HozOxO4EZjn7hOBeWEd4HJgYnhcB9xa+JLzZ8boAQwuaubHKxpYuXlP7HJEpIAKfiuyu28BtoTlvWa2CqgCZgMXhW53AU8CXwvtd3vyN/p8M6sws+FhPz1e+0cU5VqyZAmtrf0BSJkxo/9BFuwt5vr/Wcycz72bviW6RljkVBB1LggzGwNMB14AhuaE6lZgaFiuAjbmPK02tPWKAK7buJbVO3ewpmngobYlzyxk2MQpTAzr2ZTz2eml/PML+/n6A8v4/lXTMdMbcyInu2gBbGZ9gF8AX3T3PbmB4+5uZsf0rpSZXUcyRMHo0aO7s9QT1v4RRe02r139tj6TBmX48qVn8O1H13Du2IF86rwxBaxQRGKIchWEmRWRhO9P3f2B0PyGmQ0P24cDdaF9EzAq5+kjQ9tbuPtt7l7t7tWVlZX5Kz4P2ocpJqc3M6Uyw98/vII7H35Sn6YscpKLcRWEAbcDq9z933I2zQGuCcvXAA/ltF8droaYCezuLeO/XdX+Scr3L6xlmO+giDZufnonjz2tT1MWOZnFGIJ4N/ApYJmZvRza/hr4JnCfmV0LrAc+EbY9AnwAqAEOAJ8pbLmFkTtMUbmvkXteXM93Fh7g0otaKSlKR65ORPIhxlUQzwCHe4fpkk76O3B9XovqYQb3yTK93wEW7i7nxl8s5ZZPTtObciInId0J10MNz7ZwxelZfvnyZr796JrY5YhIHugjiXqwj0wopqhiKP/x5GsM61/C1boyQuSkogDuwcyMf5x9FvV7G7lpzgoGlhfzoSkjYpclIt1EQxA9XCad4vtXTaf6tAF84Z6X+fXSk+oCEJFTms6Ae4HS4jS3XjWF//3DZ/j8zxazanUp5w5Pblc+++yzKSkpiVyhiBwPBXAvsWrpS5RunE/FgOn8YPEBnup7kMyWZXwJuOCCC2KXJyLHQUMQvcjocafzyfPP4LRB5SzdW8buIdNo0zzCIr2WAriXKc6k+PCUEUwZ2Z/XDmT57qKD7NzfFLssETkOGoLooTpOY/mWKSxTxkWnV9K2aytL6kq5/LtP82+fnMr54wfHKldEjoMCuIfqOI1lxykszYyxZU18ctpg/ns1/NGPXuDqmadxw/vPoH+Z5hMW6Q0UwD1Y7vwQnU1hCTCmf5pf/eV5fOs3q/nx/PU8sGgDHz8jy4Uji0inktuXdaWESM+kAD4JlBVn+PvZk5mY3sY//34Ldyzrxz0r9zOxrBHbspwvoyslRHoiBXAv1nGceNfry7mwsj/pkcN5cd0OluxNU9KvmgdeaWTilAaG9S+hoaGBBQsWvG1fOksWKTwFcC92uHHi86b3YdzgctZvP8Bzq9bz4KuNzPnW41wyaQhTyvcw74knGTlu0qH9bFq7WtcTi0SgAO7lDjdObGaMGVxOW8UBLjqrihofyn0LNvLY/iaK+56NZwcyaVhfhvTNxipd5JSnAD4FDClL8b8umMSX3nc6//Hgk9y7fA/Lanfz8sZdDCgrYohlqTvQFrtMkVOOAvgk13GcOPPGSqb36c/os6p5tW4fq7fuZc2uEr78xD7uWfccH51exQffOZyKsuKIVYucGhTAJ7nDXk9clGZyVX8mV/VnyYL5tGK8ti3FNx7cyd/+cjnvPaOSK6pHcdEZQ/SRSCJ5ogA+BRzteuK9m2vYsXMHM2acz4SiFKvf2MeCdcbvVtfTtyTDZWcN48NTR3D++EEUpXX3ukh3UQALkIT0+ClJSJcvfo53lK0lU3UWz29u5tdLarl/US0VpUV8YMpwPjRlOFOGlfHS4kVv248uZxPpOgWwvE37sMXkdw2kAnjvAFi5bjMlpaO4f2Ez//PCBkqsldKmHYwdVE5lcQvFKe/S5WydXYes0JaeqBC/qwpg6VTusAXAG+t+wo5XnuaSGedT15hh9cZ69vYbyeI9GQwY3DdLn8oSXtzSzBl7GxjSt/Nf0gULFnDLvXOpCtch6xpk6akK8buqAJYuqxw5jtOnVHM6wOafkKKRsdWXsH77ATbtOsj6vcV8f/FBvr94HoP7ZJk0rC9nDOvLGUP7MmpgGSMHlNLS5lSNm/SWcBfpqfL9u6oAluNmwPD+pQzvXwpAzdKFVE8cTuuA05LL27bu5Sfz19PY0vaW52RTfRm4aCN9shla9pbwm7WN7Oy3maF9swztV0L/Yli+ZPFbjqVhCjkZKYCl27S1NLF/wwqmDshwxjBgGMx410VsO9hG7c6D1O48wPNL1rCwdh+O8caeRvYeLGbtqkb+Z9VLb9lX2lsozRglqTbaDuzi/NUNVJ85gaH9ShjSr4Sh/bIM6VtCcUZXZUjvpQCWbtPxmuMNryzn/UuWMHXqVABGABMa1tBU3p+J084CYM2i55hQdoCqCWeys8HZ1djGqnWb2dBURqbvIPY3trKrNcUjrzXyq9dWvu2YfYuNASVGvyKoyMLgsgwVJSkGlBgV2RQXnjONwf3LKS/O0NTUqImIpEdRAEu36njN8S+eX30okOHtE8vX165lzc4dTG6pONRn/cJ5yaRC734nAM889BO279zB6dPfTUOr0dCWombNKtpK+9N/6GgaGlK8UreX1qIyWtNZkoGO4NlnATCD0jS0NDWSLcqQSTlF5jTt28VpT9YyathgSjNGNg3vmDCO/n1KKC/OUJ7NUFacpjybobw4TVk2Q6q1mUWLFr7te1eQy7FSAEtedbyaorMbQbrSZ8jIcbxj6rsOrbduWk66vI3z3j0ZSEI6na3g3Pd+kAPNrexvbOGVlcsZXNTMwGFVHGxxNmytpy5dQrZvP5pa22hqaeNgQyvL9qRYur8Rbw/ulauO+n2lvJWitJE2J2PQ0rCfIb/bSuWAfpRkjJI0jBk1gv5l2RDeGYqsjdr1r1GStqRPBs5913QG9iunrChNKmVHPa4UljscaGqhrQ0OtBr1B9pobfNDH3ZwonpNAJvZLOC7QBr4kbt/M3JJ0gOlUkafbIY+2QyvvrGaV3fuYHJx8hFN2xc/RtXEKZz3nsmH+j/z0E9Il1cw8+IP0urOMw/fx/Y9e5gwuZoWN1ocalYswUr6MGzM6bQ61K5fS9nA4QweNobmVqe5tY36rfvYvKeJbS2NtLjR3NoGG9bTerQPrX7ymUOLxWnIpqAoDdl0iuI0ZMwpSkE2k6Y4DcVpI00rRSmjtDhNUSr5zyBtUFJcRDoFGYN0yjhjwnhKS7IUZ4xMKkUmbRSnU2TSKYrSRltLM6tWLCeTMlIGaYMZ06dTVlpCOm2kzWhuauSlxYtIGaSs+z5hJea81K1tzp6Dzew40MTO/U3U722kbm8j9eFRt7eB+n2NbNq2l12N/fD6deGZ/Zj3xD4uvqC52+ZK6RUBbGZp4AfA+4FaYIGZzXH3tw8KiuToysc6QTJ9Z8aMDC2MGD6CqdNnHNrWXLuCdHkJ550zBYBnti4lTT3nvePcQ32eef1x0uUVnHfR2QC8svg5zirfx1nvnMLBFqexBZasXM0rB/sydPyZNLe2sfzFp9l7oIGho8fT0ma0uFG3pZamoiwlA4fQ0Gzs3rkDzxRRUt6fVoc2NxqaGiGVgVSGttzhFlrf+k29vPzYX7B5Tx9ho4M7Rb+ZR1EmTdqMVMpIG7S2tpA2QlBDWWkpmXSKtFlytujO/gP7MZLhoAMHDrKnoZls9s2wbWo4SP+5dZSXlYfDtWEGmVSKlCXPw9tImZFJp5LvvL1POkUKwwwam1tpaYNWM1raoKmljZY2aH64S3sAAApQSURBVGwz9jU5+5udzv5fTBkM7pOlsm+WIX2zDLSDbN6xl6qRo0injO2bXuecsQO7dW6UXhHAwDlAjbuvBTCze4DZQLcG8Kacf6D1mzeQLt3B2mULD9umPupzuD4rXniSZ7bXMXrx2kN9Xl/5MkPGn8mgkmSUum3DS/Qr7cP4slGH+rQsWUy6tA+Tx54PwJLlTyXr7zr/UJ8lzzx2qM0dXn5uHqnSvkyadi5tDg6sXPgsu3fvYtjocTgptrxeg2VLGFI1BsdwjLpNG+g3bDQjxkzEgY01qziwfx8DKoclwzFm7KjbSvmgoQwZMRp32LNrO6P7ljO4cghtDm3u1G3bziubt5It60MrRmPDQQaOGELf/hW4Q2sr7Nmzm611OyjKJpcsHty3h+KyPhSXZg+FYUPjfur2NVESAvjg/n1YKk22tBww3IzGgwcglaY4W4obNDU2YpYiU1yCG4DR0nAAw8kWF2HuNOzfjeGUl5ZS1NbMYG+mcVc975k8hrMmjKF/iVGRNfoW26GzfGhkyZJXeLyujn59GgHYtWU1F174/m4NYHM/2t9I8ZnZFcAsd//TsP4p4Fx3/1xOn+uA68LqGcCa4zjUYGDbCZbb3VTT0fW0ekA1ddWpUtM2d5/VsbG3nAEflbvfBtx2Ivsws4Xu3qNu0VJNR9fT6gHV1FWnek295Sr2TcConPWRoU1EpNfqLQG8AJhoZmPNrBi4EpgTuSYRkRPSK4Yg3L3FzD4HPEpyGdod7r4iD4c6oSGMPFFNR9fT6gHV1FWndE294k04EZGTUW8ZghAROekogEVEIlEAk9zmbGZrzKzGzG4s8LFfN7NlZvaymS0MbQPNbK6ZvRq+DgjtZmbfC3UuNbMZR957l2u4w8zqzGx5Ttsx12Bm14T+r5rZNXmo6e/MbFN4rV42sw/kbPt6qGmNmV2W094tP1szG2VmT5jZSjNbYWZfCO3RXqcj1BTzdSoxsxfNbEmo6e9D+1gzeyHs/97wZjpmlg3rNWH7mKPV2o013Wlm63Jep2mhvSC/4wC4+yn9IHlT7zVgHFAMLAHOLODxXwcGd2j7F+DGsHwj8K2w/AHgNyQ3Us0EXuimGi4EZgDLj7cGYCCwNnwdEJYHdHNNfwd8pZO+Z4afWxYYG36e6e782QLDgRlhuS/wSjhutNfpCDXFfJ0M6BOWi4AXwvd/H3BlaP8h8Bdh+bPAD8PylcC9R6q1m2u6E7iik/4F+R13d50Bk3Obs7s3Ae23Occ0G7grLN8FfDSn/W5PzAcqzGz4iR7M3Z8CdpxgDZcBc919h7vvBOYCb7vz5wRrOpzZwD3u3uju64Aakp9rt/1s3X2Luy8Oy3uBVUAVEV+nI9R0OIV4ndzd94XVovBw4GLg/tDe8XVqf/3uBy4xMztCrd1Z0+EU5HccNAQByS/sxpz1Wo78S9zdHHjMzBZZcjs1wFB33xKWtwJDw3Ihaz3WGgpV2+fCn4V3tP+5X+iawp/J00nOpHrE69ShJoj4OplZ2sxeBupIQuo1YJe7t3Sy/0PHDtt3A4PyXZO7t79ON4fX6RYzy3asqcOxu/1npwCO7z3uPgO4HLjezC7M3ejJ3z5RrxXsCTUEtwLjgWnAFuBfC12AmfUBfgF80d335G6L9Tp1UlPU18ndW919Gskdq+cAkwp5/M50rMnMJgNfJ6ntbJJhha8Vui4FcOTbnN19U/haBzxI8gv7RvvQQvhaF6HWY60h77W5+xvhH1Ib8F+8+SdpQWoysyKSoPupuz8QmqO+Tp3VFPt1aufuu4AngPNI/oxvv/Erd/+Hjh229we2F6CmWWEIx929EfhvIrxOCuCItzmbWbmZ9W1fBi4Flofjt7/Deg3wUFieA1wd3qWdCezO+fO3ux1rDY8Cl5rZgPAn76Whrdt0GO/+GMlr1V7TleEd9bHAROBFuvFnG8YlbwdWufu/5WyK9jodrqbIr1OlmVWE5VKSObxXkYTeFaFbx9ep/fW7Ang8/CVxuFq7q6bVOf9xGsmYdO7rVJjf8RN5B+9keZC86/kKyVjVNwp43HEk7/QuAVa0H5tkDGwe8CrwO2Cgv/lu7g9CncuA6m6q42ckf6o2k4xrXXs8NQB/QvJmSQ3wmTzU9ONwzKXhH8nwnP7fCDWtAS7v7p8t8B6S4YWlwMvh8YGYr9MRaor5Ok0BXgrHXg78bc7v+ovhe/45kA3tJWG9Jmwfd7Rau7Gmx8PrtBz4CW9eKVGQ33F3163IIiKxaAhCRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAlijMrDVMAbjczH5uZmWhPWNm9Wb2zQ79+5jZrWb2mpktDnNn/NkR9j/GzNzM/imnbbCZNZvZv4f1vzOzr4TlOy2ZwjGb0/f1sPygmX00Zz9rzOxvctZ/YWZ/eJg6plnOdJAiuRTAEstBd5/m7pOBJuDPQ/v7SW4I+Hi4Q6ndj4CdwERP5s6YRXL//pGsAz6Ys/5xkhteDqeV5EL7jp4Fzgcws0HAfpLba9udBzx3mH1OI7nJ4W1ybs2VU5QCWHqCp4EJYfkq4LvABkLImdl4kvv0/8aT+Q1w93p3/9ZR9nsAWGVm1WH9kyTz0h7Od4AvdRKMzxECOHx9GKgMt6qOJfnPZGvHnYXbev8B+GQ42/9kOOv+sZk9C/zYzD7dfkYenvMrM7soLO8zs5stmUh8vpkNDe1Dw1n5kvA4v+OxpXdQAEtUIewuB5aZWQnwPpKA+xlJGAOcBSxpD99jdA/JnAKjSM5wNx+h7wbgGeBTHdoXAZNDoJ4PPE9ye+w7wnqnZ7+ezK37tySTjE9z93vDpjOB97n7VZ09L0c5MN/dpwJPAe1DLt8Dfh/aZ3Dks3rpwRTAEkupJfOzLiQJvtuBDwFPuPtBkhm+Pmpm6Y5PNLNvhDPKI4Vpu9+SDGtcCdx7lL4A/wx8lZx/G57MlrWCJOxmksy5+zxJ+J5PMkRxLOaE7/FomoBfheVFwJiwfDHJlJN4MuvZ7mM8vvQQGoOSWA56Mj/rIWZ2FfCe9je/SCa6uRhYCUw1s5S7t7n7zSQTae/jKNy9ycwWAV8mOfP8yFH6vxr+Y/hEh03PknxMUl9332lm84HPkUyC/p9Hq6OD/TnLLbz1RKgkZ7nZ35yspRX9ez3p6AxYegQz6wdcAIx29zHuPga4HrjK3WtIzpT/qf2MOAxX2OH218G/Al9z965+xNHNwFc6tD0H/B+SmesgmVlrJjCaN6cx7Mxeks9rO5zXgWlmlgrDJF352J15wF/AoU966N+F50gPpACWnuJjJHPBNua0PQR8OFwa9qckZ8Q1lnx69Fzgr7qyY3df4e53Hb3nm/2BxR2anyOZUvH50KeFZPL1hUcZm34COLP9TbhOtj9LcrXGSpKx3Y7H7cwXgPea2TKSoYkzu/Ac6YE0HaWISCQ6AxYRiUSD+tKrmdk7ST4BIleju59b4DouAzpel7zO3T9WyDqkd9EQhIhIJBqCEBGJRAEsIhKJAlhEJBIFsIhIJP8f5vRseYodPoUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"PAG_MINW\" em horas = \"PAG_HRW\""
      ],
      "metadata": {
        "id": "8C9hE6NcxXhE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df4, x=\"PAG_HRW\", kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "rarGtbQRxXKM",
        "outputId": "527a3eeb-2ce1-425e-d5c7-0df21573a15a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXhc1Znn8e9bVVJpsSxZtrzJNt7tgLGxI8CGQLMEYrI56cnGpIOTodvT0ySdhHQS6PQ00wtPJ5N+miydJkODG8gGhCUYQgDHQNiNZYOMVxA2tuVN8r7I2t/5o66dQngRskqnSvp9nqce3XvuqVuv/ZR/uj517ilzd0REpPfFQhcgItJfKYBFRAJRAIuIBKIAFhEJRAEsIhJIInQBmTB37lx//PHHQ5chInKUHa+xT14B79q1K3QJIiKn1CcDWEQkFyiARUQCUQCLiASiABYRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBJKxADazhWZWb2arOrV/xczWmdlqM/u/ae03mlmtma03sw+ltc+N2mrN7IZM1Ssi0tsyuRrancC/A3cfbTCzS4F5wAx3bzazoVH7mcDngLOAkcDvzWxy9LSfAFcAdcAyM1vk7mt6utimpiaWLVv2jrZzzz2XgoKCnn4pEREggwHs7s+a2dhOzf8L+K67N0d96qP2ecA9UftGM6sFzouO1br7BgAzuyfq2+MBvGzZMm65dzGV46cCsHXDOr4OXHTRRT39UiIiQO+vBzwZuMjMbgaagL9x92VAJfByWr+6qA1gS6f28493YjNbACwAGDNmTLeKqxw/lfFnV3XruSIi71VvfwiXAMqB2cA3gfvM7LgLFb9X7n6bu1e5e1VFRUVPnFJEJKN6+wq4DnjQ3R14xcw6gCHAVmB0Wr9RURsnaRcRyWm9fQX8G+BSgOhDtnxgF7AI+JyZJc1sHDAJeAVYBkwys3Fmlk/qg7pFvVyziEhGZOwK2Mx+BVwCDDGzOuAmYCGwMJqa1gLMj66GV5vZfaQ+XGsDrnP39ug8XwaeAOLAQndfnamaRUR6UyZnQVx9gkN/doL+NwM3H6f9MeCxHixNRCQr6E44EZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhKIAlhEJBAFsIhIIApgEZFAFMAiIoEogEVEAslYAJvZQjOrN7NVxzn2DTNzMxsS7ZuZ/cjMas1spZnNSus738zejB7zM1WviEhvy+QV8J3A3M6NZjYauBLYnNZ8FTApeiwAbo36lgM3AecD5wE3mdmgDNYsItJrMhbA7v4ssOc4h24BvgV4Wts84G5PeRkoM7MRwIeAxe6+x933Aos5TqiLiOSiXh0DNrN5wFZ3r+l0qBLYkrZfF7WdqP14515gZtVmVt3Q0NCDVYuIZEavBbCZFQF/C/x9Js7v7re5e5W7V1VUVGTiJUREelRvXgFPAMYBNWb2NjAKWGFmw4GtwOi0vqOithO1i4jkvF4LYHd/3d2HuvtYdx9LajhhlrvvABYB10SzIWYD+919O/AEcKWZDYo+fLsyahMRyXmZnIb2K+AlYIqZ1ZnZtSfp/hiwAagF/hP4KwB33wP8E7Asevxj1CYikvMSmTqxu199iuNj07YduO4E/RYCC3u0OBGRLKA74UREAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhKIAlhEJBAFsIhIIApgEZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEkjGAtjMFppZvZmtSmv7vpmtM7OVZvaQmZWlHbvRzGrNbL2ZfSitfW7UVmtmN2SqXhGR3pbJK+A7gbmd2hYD09x9OvAGcCOAmZ0JfA44K3rOf5hZ3MziwE+Aq4AzgaujviIiOS9jAezuzwJ7OrU96e5t0e7LwKhoex5wj7s3u/tGoBY4L3rUuvsGd28B7on6iojkvJBjwP8D+F20XQlsSTtWF7WdqP1dzGyBmVWbWXVDQ0MGyhUR6VlBAtjMvgO0Ab/oqXO6+23uXuXuVRUVFT11WhGRjEn09gua2ReBjwKXu7tHzVuB0WndRkVtnKRdRCSn9eoVsJnNBb4FfNzdG9MOLQI+Z2ZJMxsHTAJeAZYBk8xsnJnlk/qgblFv1iwikikZuwI2s18BlwBDzKwOuInUrIcksNjMAF52979099Vmdh+whtTQxHXu3h6d58vAE0AcWOjuqzNVs4hIb8pYALv71cdpvuMk/W8Gbj5O+2PAYz1YmohIVtCdcCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhKIAlhEJBAFsIhIIApgEZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCIigSiARUQCUQCLiASSsQA2s4VmVm9mq9Lays1ssZm9Gf0cFLWbmf3IzGrNbKWZzUp7zvyo/5tmNj9T9YqI9LZMXgHfCczt1HYDsMTdJwFLon2Aq4BJ0WMBcCukAhu4CTgfOA+46Whoi4jkuowFsLs/C+zp1DwPuCvavgv4RFr73Z7yMlBmZiOADwGL3X2Pu+8FFvPuUBcRyUm9PQY8zN23R9s7gGHRdiWwJa1fXdR2ovZ3MbMFZlZtZtUNDQ09W7WISAYE+xDO3R3wHjzfbe5e5e5VFRUVPXVaEZGM6e0A3hkNLRD9rI/atwKj0/qNitpO1C4ikvN6O4AXAUdnMswHHk5rvyaaDTEb2B8NVTwBXGlmg6IP366M2kREcl4iUyc2s18BlwBDzKyO1GyG7wL3mdm1wCbgM1H3x4APA7VAI/AlAHffY2b/BCyL+v2ju3f+YE9EJCdlLIDd/eoTHLr8OH0duO4E51kILOzB0kREsoLuhBMRCUQBLCISiAJYRCQQBbCISCAKYBGRQBTAIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhJIlwLYzC7sSpuIiHRdV6+Af9zFNhER6aKTLsZjZnOAC4AKM7s+7dBAIJ7JwkRE+rpTrYaWDwyI+pWktR8APpWpokRE+oOTBrC7/wH4g5nd6e6beqkmEZF+oavrASfN7DZgbPpz3P2yTBQlItIfdDWAfw38FLgdaM9cOSIi/UdXA7jN3W/NaCUiIv1MV6ehPWJmf2VmI8ys/Ogjo5WJiPRxXb0CPvpNxt9Ma3NgfM+WIyLSf3QpgN19XKYLERHpb7oUwGZ2zfHa3f3uni1HRKT/6OoQxLlp2wWkvlp+BaAAFhHppq4OQXwlfd/MyoB7MlKRiEg/0d3lKA8DGhcWETkNXR0DfoTUrAdILcLzPuC+TBUlItIfdHUM+F/TttuATe5el4F6RET6jS4NQUSL8qwjtSLaIKDldF7UzL5uZqvNbJWZ/crMCsxsnJktNbNaM7vXzPKjvslovzY6PvZ0XltEJFt09RsxPgO8Anwa+Ayw1My6tRylmVUCfw1Uufs0UkManwO+B9zi7hOBvcC10VOuBfZG7bdE/UREcl5XP4T7DnCuu89392uA84D/fRqvmwAKzSwBFAHbgcuA+6PjdwGfiLbnRftExy83MzuN1xYRyQpdDeCYu9en7e9+D899B3ffSmpMeTOp4N0PLAf2uXtb1K0OqIy2K4Et0XPbov6DO5/XzBaYWbWZVTc0NHSnNBGRXtXVEH3czJ4wsy+a2ReB3wKPdecFzWwQqavaccBIoBiY251zpXP329y9yt2rKioqTvd0IiIZd6rvhJsIDHP3b5rZnwIfiA69BPyim6/5QWCjuzdEr/EgcCFQZmaJ6Cp3FLA16r8VGA3URUMWpaSuwEVEctqproB/QOr733D3B939ene/HngoOtYdm4HZZlYUjeVeDqwBnuaP3zM3H3g42l7EH1dj+xTwlLs7IiI57lQBPMzdX+/cGLWN7c4LuvtSUh+mrQBej2q4Dfg2cL2Z1ZIa470jesodwOCo/Xrghu68rohItjnVjRhlJzlW2N0XdfebgJs6NW8gNbuic98mUtPfRET6lFNdAVeb2V90bjSzPyc1c0FERLrpVFfAXwMeMrPP88fArQLygU9msjARkb7upAHs7juBC8zsUmBa1Pxbd38q45WJiPRxXV0P+GlSsxRERKSHdHc9YBEROU0KYBGRQBTAIiKBKIBFRAJRAIuIBKIAFhEJRAEsIhKIAlhEJBAFsIhIIApgEZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYRCSQIAFsZmVmdr+ZrTOztWY2x8zKzWyxmb0Z/RwU9TUz+5GZ1ZrZSjObFaJmEZGeFuoK+IfA4+4+FZgBrAVuAJa4+yRgSbQPcBUwKXosAG7t/XJFRHperwewmZUCFwN3ALh7i7vvA+YBd0Xd7gI+EW3PA+72lJeBMjMb0ctli4j0uBBXwOOABuC/zOxVM7vdzIqBYe6+PeqzAxgWbVcCW9KeXxe1vYOZLTCzajOrbmhoyGD5IiI9I0QAJ4BZwK3uPhM4zB+HGwBwdwf8vZzU3W9z9yp3r6qoqOixYkVEMiVEANcBde6+NNq/n1Qg7zw6tBD9rI+ObwVGpz1/VNQmIpLTej2A3X0HsMXMpkRNlwNrgEXA/KhtPvBwtL0IuCaaDTEb2J82VCEikrMSgV73K8AvzCwf2AB8idQvg/vM7FpgE/CZqO9jwIeBWqAx6isikvOCBLC7vwZUHefQ5cfp68B1GS9KRKSX6U44EZFAFMAiIoEogEVEAlEAi4gEogAWEQlEASwiEogCWEQkEAWwiEggCmARkUAUwCex60gHm3c3hi5DRPqoUGtBZLW29g6W7S/i0acO4U89zVXThvP9T89gQFJ/XSLSc3QFfBzPvrmLHc15fGxCPn992USeXLOT6+99jY6O97REsYjISSmAO9m0+zCvb93PuGQjk5vXc25hPVdPzefJNTv5u58toampKXSJItJH6P/UnVRv2suAZIJBu1fxQO1u1reU4w4V+UXcu6aNi198hbmXXRy6TBHpA3QFnOZAW4y6vUeYMboUw6kYNZ7xZ1cxYXoVV5wzgXZL8OhbzaHLFJE+QgGcZmNjkkTMmDay9F3HKkqSjEy28PtNLRxsag1QnYj0NQrgSHuHs705wYShAyjIix+3z/iiFpra4IHldb1cnYj0RQrgyBt722n1GBMqik/YZ1BeOxPK4tz90ibNiBCR06YAjizf2UYM54zyEwcwwBVn5LFh12Fe3ri7lyoTkb5KAQy4O8t3tDIkv438xMn/SqpG5FGcH+c3r27tpepEpK9SAAN7G1spyjOGJ0/94VoybsydNoLfvb6Dptb2XqhORPoqBTBQXpzPzRcNYExB12Y3fHJmJQeb21iytj7DlYlIX6YATmPWtX5zJgxm2MAkD72q2RAi0n0K4G6Ix4x551TyzPoG9hxuCV2OiOQoBXA3fXJmJW0dzm9XbgtdiojkKAVwN71vxECmDi/hQc2GEJFuUgCfho+fM5JXN+9jyx4t2i4i712wADazuJm9amaPRvvjzGypmdWa2b1mlh+1J6P92uj42FA1d/ax6SMBeETDECLSDSGvgL8KrE3b/x5wi7tPBPYC10bt1wJ7o/Zbon5ZYXR5EbPGlLHoNQWwiLx3QQLYzEYBHwFuj/YNuAy4P+pyF/CJaHtetE90/PKof69ra22hpqaG55577thj7plDWbfjIG/uPBiiJBHJYaEWZP8B8C2gJNofDOxz97Zovw6ojLYrgS0A7t5mZvuj/rvST2hmC4AFAGPGjMlI0fVbNrBu7x7Wt5QDsHXDOr4073JiBo/UbOP6K6dk5HVFpG/q9StgM/soUO/uy3vyvO5+m7tXuXtVRUVFT576HY4u0j7+7Coqx0+lrCDG7PGDeWTldty1QpqIdF2IIYgLgY+b2dvAPaSGHn4IlJnZ0SvyUcDR+V1bgdEA0fFSIKuWIvv4jJFs3HWYVVsPhC5FRHJIrwewu9/o7qPcfSzwOeApd/888DTwqajbfODhaHtRtE90/CnPskvNudOGkxc3zYYQkfckm+YBfxu43sxqSY3x3hG13wEMjtqvB24IVN8JlRXlc/GkCh6p2aaF2kWky4J+K7K7PwM8E21vAM47Tp8m4NO9Wlg3fGzGSJasq6d6017OG1ceuhwRyQH6WvrTcHRaGkBxm5Mfg//3eDXTv3QxBQUFgasTkWynAD4NnaelDckr5Jm3W3nx5Ve47JKLA1cnItlOAXyajk5LA4jtPszDr21jRX0blwWuS0SyXzZ9CJfzxpQXURDr4A9btEawiJyaArgHxcwYU9DCqoZ2tu47ErocEclyCuAeNrowdfV7f7W+rkhETk4B3MOK4s6ZQ+L8evkWzQkWkZNSAGfAn4zOp27vEV58K6vumBaRLKMAzoD3D0swqCiPu156O3QpIpLFFMAZkB83/mz2Gfx+7U427jocuhwRyVIK4B529O64ybaDuME/3/cCTU1NocsSkSykAO5h9Vs28MBL63jy9a2MyG/hqU1NPPXC0tBliUgWUgBnwNG74/5kxkTc4izZ1Bq6JBHJQgrgDBo8IElFfiuL326hqbU9dDkikmUUwBk2saiZAy3OL5ZuDl2KiGQZBXCGDclv56zBcX7ydC2HmttO/QQR6Te0Glov+MzUAm564TC3P7eBr31wMk1NTSxbtuwdfc4991ytISzSzyiAe8H4sjhXTRvOfz67gS/MPoM1ry7jlnsXUzl+KpD6evuvAxdddFHYQkWkVymAe8k3rpzME6t38JOn3+LSMqgcP/XYOsIi0j9pDLiXTBxawmeqRnP3S2+z9aBmRIiIArhXffNDUyhOJrhzVROuhdJE+j0FcC8aPCDJDVdNZd2eduqa8kKXIyKBKYB72WerRjOxLM6aQwW6OUOkn1MA97JYzPji2QW0uvHsmw2hyxGRgBTAAZwxMM7EombWbj/IhoZDocsRkUAUwIFMLm6mYkCS36+tp7nDQpcjIgEogAOJGVx51jBa2jpYebAQ17QIkX6n1wPYzEab2dNmtsbMVpvZV6P2cjNbbGZvRj8HRe1mZj8ys1ozW2lms3q75kwZMiDJ7Anl7GjO45ktWrJSpL8JcQXcBnzD3c8EZgPXmdmZwA3AEnefBCyJ9gGuAiZFjwXArb1fcubMGjOIIXmt/Gx1E2u2HQhdjoj0ol6/FdndtwPbo+2DZrYWqATmAZdE3e4CngG+HbXf7an/o79sZmVmNiI6T9Y7+hVF6WpqamhvLwUgZsas0iMsO5jPdb9cwaIvX0hJgeYIi/QHQdeCMLOxwExgKTAsLVR3AMOi7UpgS9rT6qK2nAjg+i0bWLd3D+tbyo+11TxfzfBJ05kU7Sdjzl/NLORflh7mxgdf58dXz8RMH8yJ9HXBAtjMBgAPAF9z9wPpgePubmbv6VMpM1tAaoiCMWPG9GSpp+3oVxQdtW3Dunf1mTo4wTeunML3n1jP+ePK+cKcsb1YoYiEEGQWhJnlkQrfX7j7g1HzTjMbER0fAdRH7VuB0WlPHxW1vYO73+buVe5eVVFRkbniM+DoMMW0+DamVyT4h0dWc+cjz+jblEX6uBCzIAy4A1jr7v+WdmgRMD/ang88nNZ+TTQbYjawP1fGf7vq6Dcp319dx3DfQx4d3PzcXp58Tt+mLNKXhRiCuBD4AvC6mb0Wtf0t8F3gPjO7FtgEfCY69hjwYaAWaAS+1Lvl9o70YYqKQ83c88omflDdyJWXtFOQFw9cnYhkQohZEM8DJ/qE6fLj9HfguowWlWWGDEgyc2Aj1fuLueGBldzy2XP0oZxIH6Q74bLUiGQbn5qc5DevbeP7T6wPXY6IZIC+kiiLfXxiPnllw/iPZ95ieGkB12hmhEifogDOYmbGP807i4aDzdy0aDXlxfl8dPrI0GWJSA/REESWS8Rj/PjqmVSdMYiv3vMav13ZpyaAiPRrugLOAYX5cW69ejr//afP85VfrWDtukLOH5G6Xfncc8+loKAgcIUi0h0K4ByxduWrFG55mbJBM/nJikaeLTlCYvvrfB246KKLQpcnIt2gIYgcMmb8ZD57wRTOGFzMyoNF7B96Dh1aR1gkZymAc0x+IsbHpo9k+qhS3mpM8sPlR9h7uCV0WSLSDRqCyFKdl7F8xxKWMeOSyRV07NtBTX0hV/3wOf7tszO4YMKQUOWKSDcogLNU52UsOy9haWaMK2rhs+cM4b/WwedvX8o1s8/g+iumUFqk9YRFcoECOIulrw9xvCUsAcaWxnn0r+fwvd+t42cvb+LB5Zv59JQkF4/KIx5L3b6smRIi2UkB3AcU5Sf4h3nTmBTfxb/8YTsLXx/IPWsOM6moGdu+im+gmRIi2UgBnMM6jxPve3sVF1eUEh81glc27qHmYJyCgVU8+EYzk6Y3Mby0gKamJpYtW/auc+kqWaT3KYBz2InGiefMHMD4IcVs2t3Ii2s38dCbzSz63lNcPnUo04sPsOTpZxg1fuqx82zdsE7ziUUCUADnuBONE5sZY4cU01HWyCVnVVLrw7hv2RaePNxCfsm5eLKcqcNLGFqSDFW6SL+nAO4HhhbF+G8XTeXrH5zMfzz0DPeuOsDrdft5bcs+BhXlMdSS1Dd2hC5TpN9RAPdxnceJEzvXMHNAKWPOquLN+kOs23GQ9fsK+MbTh7hn44t8YmYlHzl7BGVF+QGrFukfFMB93AnnE+fFmVZZyrTKUmqWvUw7xlu7Ynznob38/W9WcemUCj5VNZpLpgzVVyKJZIgCuB841Xzig9tq2bN3D7NmXcDEvBjrdh5i2Ubj9+saKClI8KGzhvOxGSO5YMJg8uK6e12kpyiABUiF9ITpqZAuXvEi7yvaQKLyLF7a1spva+q4f3kdZYV5fHj6CD46fQTThxfx6orl7zqPprOJdJ0CWN7l6LDFtPeXUwZcOgjWbNxGQeFo7q9u5ZdLN1Ng7RS27GHc4GIq8tvIj3mXprMdbx6yQluyUW+8VxXAclzpwxYAOzf+nD1vPMflsy6gvjnBui0NHBw4ihUHEhgwpCTJgIoCXtneypSDTQwtOf6bdNmyZdxy72Iqo3nImoMs2ao33qsKYOmyilHjmTy9iskA235OjGbGVV3Opt2NbN13hE0H8/nxiiP8eMUShgxIMnV4CVOGlzBlWAmjy4sYNaiQtg6ncvzUd4S7SLbK9HtVASzdZsCI0kJGlBYCULuymqpJI2gfdEZqetuOg/z85U00t3W84znJWAnly7cwIJmg7WABv9vQzN6B2xhWkmTYwAJK82FVzYp3vJaGKaQvUgBLj+loa+Hw5tXMGJRgynBgOMx6/yXsOtJB3d4j1O1t5KWa9VTXHcIxdh5o5uCRfDasbeaXa199x7ni3kZhwiiIddDRuI8L1jVRdeZEhg0sYOjAAoYNTDK0pID8hGZlSO5SAEuP6TznePMbq7iipoYZM2YAMBKY2LSeluJSJp1zFgDrl7/IxKJGKieeyd4mZ19zB2s3bmNzSxGJksEcbm5nX3uMx95q5tG31rzrNUvyjUEFxsA8KEvCkKIEZQUxBhUYZckYF593DkNKiynOT9DS0qyFiCSrKIClR3Wec/zAS+uOBTK8e2H5hroNrN+7h2ltZcf6bKpeklpU6MKzAXj+4Z+ze+8eJs+8kKZ2o6kjRu36tXQUllI6bAxNTTHeqD9Ie14R7fEkqYGOyAsvAGAGhXFoa2kmmZcgEXPyzGk5tI8znqlj9PAhFCaMZBzeN3E8pQMKKM5PUJxMUJQfpziZoDg/TlEyQay9leXLq9/1Z1eQy3ulAJaM6jyb4ng3gnSlz9BR43nfjPcf22/fuop4cQdzLpwGpEI6nizj/Es/QmNrO4eb23hjzSqG5LVSPrySI23O5h0N1McLSJYMpKW9g5a2Do40tfP6gRgrDzfjR4N7zdpT/rli3k5e3IibkzBoazrM0N/voGLQQAoSRkEcxo4eSWlRMgrvBHnWQd2mtyiIW6pPAs5//0zKBxZTlBcnFrNTvq70LndobGmjowMa242Gxg7aO/zYlx2crpwJYDObC/wQiAO3u/t3A5ckWSgWMwYkEwxIJnhz5zre3LuHafmpr2javeJJKidNZ84Hph3r//zDPydeXMbsyz5CuzvPP3Ifuw8cYOK0KtrcaHOoXV2DFQxg+NjJtDvUbdpAUfkIhgwfS2u709reQcOOQ2w70MKutmba3Ght74DNm2g/1ZdWP/P8sc38OCRjkBeHZDxGfhwS5uTFIJmIkx+H/LgRp528mFGYHycvlvplEDcoyM8jHoOEQTxmTJk4gcKCJPkJIxGLkYgb+fEYiXiMvLjR0dbK2tWrSMSMmEHcYNbMmRQVFhCPG3EzWluaeXXFcmIGMeu5b1gJuS51e4dz4Egrexpb2Hu4hYaDzdQfbKYhetQfbKLhUDNbdx1kX/NAvGFj9MyBLHn6EJdd1Npja6XkRACbWRz4CXAFUAcsM7NF7v7uQUGRNF35WidILd+ZMCNBGyNHjGTGzFnHjrXWrSZeXMCc86YD8PyOlcRpYM77zj/W5/m3nyJeXMacS84F4I0VL3JW8SHOOns6R9qc5jaoWbOON46UMGzCmbS2d7Dqlec42NjEsDETaOsw2tyo315HS16SgvKhNLUa+/fuwRN5FBSX0u7Q4UZTSzPEEhBL0JE+3EL7O/9Qr616739hS547yUEHd/J+t4S8RJy4GbGYETdob28jbkRBDUWFhSTiMeJmqatFdw43HsZIDQc1Nh7hQFMryeQfw7al6Qili+spLiqOXq4DM0jEYsQs9Ty8g5gZiXgs9Sc/2iceI4ZhBs2t7bR1QLsZbR3Q0tZBWwc0dxiHWpzDrc7xfi/GDLem8F8AAAgbSURBVIYMSFJRkmRoSZJyO8K2PQepHDWaeMzYvfVtzhtX3qNro+REAAPnAbXuvgHAzO4B5gE9GsBb0/6BNmzbTLxwDxterz5hm/qoz4n6rF76DM/vrmfMig3H+ry95jWGTjiTwQWpUeqOza8ysHAAE4pGH+vTVrOCeOEApo27AICaVc+m9t9/wbE+Nc8/eazNHV57cQmxwhKmnnM+HQ4OrKl+gf379zF8zHicGNvfrsWSBQytHItjOEb91s0MHD6GkWMn4cCW2rU0Hj7EoIrhqeEYM/bU76B48DCGjhyDOxzYt5sxJcUMqRhKh0OHO/W7dvPGth0kiwbQjtHcdITykUMpKS3DHdrb4cCB/eyo30NeMjVl8cihA+QXDSC/MHksDJuaD1N/qIWCKICPHD6ExeIkC4sBw81oPtIIsTj5yULcoKW5GbMYifwC3ACMtqZGDCeZn4e503R4P4ZTXFhIXkcrQ7yV5n0NfGDaWM6aOJbSAqMsaZTk27GrfGimpuYNnqqvZ+CAZgD2bV/HxRdf0aMBbO6n+j9SeGb2KWCuu/95tP8F4Hx3/3JanwXAgmh3CrC+Gy81BNh1muX2JtWbeblWs+rNrO7Wu8vd53ZuzJUr4FNy99uA207nHGZW7e45c4uW6s28XKtZ9WZWT9ebK7PYtwKj0/ZHRW0iIjkrVwJ4GTDJzMaZWT7wOWBR4JpERE5LTgxBuHubmX0ZeILUNLSF7r46Ay91WkMYAajezMu1mlVvZvVovTnxIZyISF+UK0MQIiJ9jgJYRCQQBTCp25zNbL2Z1ZrZDaHrOR4zW2hm9Wa2Kq2t3MwWm9mb0c9BIWtMZ2ajzexpM1tjZqvN7KtRe1bWbGYFZvaKmdVE9f5D1D7OzJZG7417ow+Bs4aZxc3sVTN7NNrP9nrfNrPXzew1M6uO2rLyPQFgZmVmdr+ZrTOztWY2pyfr7fcBnHab81XAmcDVZnZm2KqO606g80TuG4Al7j4JWBLtZ4s24BvufiYwG7gu+nvN1pqbgcvcfQZwDjDXzGYD3wNucfeJwF7g2oA1Hs9XgfTVg7K9XoBL3f2ctPm02fqegNT6M4+7+1RgBqm/656r19379QOYAzyRtn8jcGPouk5Q61hgVdr+emBEtD0CWB+6xpPU/jCptTyyvmagCFgBnE/qrqfE8d4roR+k5sMvAS4DHiV1h3PW1hvV9DYwpFNbVr4ngFJgI9FkhUzU2++vgIFKYEvafl3UlguGufv2aHsHMCxkMSdiZmOBmcBSsrjm6L/zrwH1wGLgLWCfu7dFXbLtvfED4FvA0e98Gkx21wuppSqeNLPl0fIBkL3viXFAA/Bf0TDP7WZWTA/WqwDuIzz16zjr5hSa2QDgAeBr7n4g/Vi21ezu7e5+Dqkry/OAqYFLOiEz+yhQ7+7LQ9fyHn3A3WeRGvK7zswuTj+YZe+JBDALuNXdZwKH6TTccLr1KoBz+zbnnWY2AiD6WR+4nncwszxS4fsLd38was7qmgHcfR/wNKn/wpeZ2dEblrLpvXEh8HEzexu4h9QwxA/J3noBcPet0c964CFSv+iy9T1RB9S5+9Jo/35Sgdxj9SqAc/s250XA/Gh7Pqlx1qxgZgbcAax1939LO5SVNZtZhZmVRduFpMar15IK4k9F3bKmXne/0d1HuftYUu/Zp9z982RpvQBmVmxmJUe3gSuBVWTpe8LddwBbzGxK1HQ5qSVwe67e0APd2fAAPgy8QWrM7zuh6zlBjb8CtgOtpH4zX0tqzG8J8Cbwe6A8dJ1p9X6A1H/NVgKvRY8PZ2vNwHTg1ajeVcDfR+3jgVeAWuDXQDJ0rcep/RLg0WyvN6qtJnqsPvpvLVvfE1Ft5wDV0fviN8CgnqxXtyKLiASiIQgRkUAUwCIigSiARUQCUQCLiASiABYRCUQBLCISiAJYcpqZtUdLG64ys1+bWVHUnjCzBjP7bqf+A8zsVjN7y8xWRGsS/MVJzj82fQnQqO3/mNnfRNt3mtnGqIYaM7s8an/IzD6R9pz1ZvZ3afsPmNmf9szfguQqBbDkuiOeWtpwGtAC/GXUfgWpm2s+Hd2Vd9TtpJZpnOSpNQnmAuWnWcM3PbWGxNeAn0ZtLwAXAJjZYFLrCMxJe84c4MXTfF3JcQpg6UueAyZG21eTWhthM1HwmdkEUmsP/J27dwC4e4O7f6+HXv8l/rj62ItEARz9fASosJRxpH5x7Oih15UclRPfiixyKtECNFcBj5tZAfBB4H8CZaTC+EXgLKDmaPi+BxOiZSqPGg7863H6zSV1uyrAcmBatL7IBcAfSN2K+z5SS3Pq6ld0BSw5rzAKx2pSV7t3AB8Fnnb3I6RWY/tE9M0n72Bm34nGbred4jXeioY5zomGGn7a6fj3zewN4JekvpECd28mtd7BLFLfCLKU1BXyBdHjhe79caUvUQBLrjuSFo5fcfcWUle8H4yWalxOavGUy0itZDXDzGIA7n5zFKgDT7OGb7r7ZODbwMK09heAi4ESd98LvMwfA1hXwKIAlr7FzAYCFwFj3H2sp5ZrvA642t1rSV0p//PRK+JouMJOdL736N+BmJl9KNp/kdQwSE20v5LU1fAYUiuuST+nAJa+5pOk1sZtTmt7GPiYmSWBPyd1RVwbfSvvYlJf63PaPLW04D+nne9FUuO+L0XH20gt3l3djXFo6YO0HKWISCC6AhYRCUTT0EQAMzsb+Fmn5mZ3Pz9EPdI/aAhCRCQQDUGIiASiABYRCUQBLCISiAJYRCSQ/w9RGm9BgWdlvwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gráfico de barras:"
      ],
      "metadata": {
        "id": "2xEGSxCV0GEc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def grafico_barras_prop(data, variable):\n",
        "    (data[[variable]]\n",
        "     .value_counts(normalize=True, sort = False)\n",
        "     .rename(\"Proportion\")\n",
        "     .reset_index()\n",
        "     .pipe((sns.barplot, \"data\"), x=variable, y=\"Proportion\"))\n",
        "    plt.ylim(0,1)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "G8nDdpUM0JUz"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grafico_barras_prop(df4.replace(label_quali), variable = \"RIAGENDR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "1MoYkQbe0eXt",
        "outputId": "42343782-ac6c-4ad8-bd51-c309ede13f61"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grafico_barras_prop(df4.replace(label_quali), \n",
        "                    variable = \"RIDRETH1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "WZWX_9OT0rwb",
        "outputId": "fd741fa4-7771-49a4-cf98-ee49776dcccf"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grafico_barras_prop(df4.replace(label_quali), \n",
        "                    variable = \"DMDEDUC\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "msWEp4Gg0rqf",
        "outputId": "5a178ae1-a440-4925-a97a-02267239966c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grafico_barras_prop(df4.replace(label_quali), \n",
        "                    variable = \"ADHERENCE\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "KC2Ge_FG01ki",
        "outputId": "9c38ded4-190e-4465-e617-cf5305f75a2e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grafico_barras_prop(df4.replace(label_quali), \n",
        "                    variable = \"phq_grp2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "iULy6j7T01Xs",
        "outputId": "3e6f7673-b3b8-411a-c61c-2aec761fd4d9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA: Análise Bivariada**"
      ],
      "metadata": {
        "id": "JrQWqPnA_-Qu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Variável quantitativa versus grupo de depressão"
      ],
      "metadata": {
        "id": "AwmyXHbQzekJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Função para construir boxplot\n",
        "\n",
        "def grafico_boxplot_grp(data, variable, label):\n",
        "    \n",
        "    if label == \"\": label = variable\n",
        "    sns.boxplot(x=\"phq_grp2\", y=variable, data=data)\n",
        "    plt.ylabel(label)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "e91g2GkiAAze"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perfil com maiores incidências de sintomas de depressão"
      ],
      "metadata": {
        "id": "qtgCLSq71ofj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grafico_boxplot_grp(df4.replace(label_quali), \"RIDAGEYR\", \"Idade\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "J3DYfRZ21p8I",
        "outputId": "32e960b6-a574-41b2-b95e-b004a66c7e78"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}