outliers.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bsl0I4HpQkrq"
      },
      "source": [
        "# Análise de *outliers*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thXRX95uSqWx"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import io\n",
        "#from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cb7ZSVRMRPE0"
      },
      "source": [
        "## Identificação *outliers*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAgC9lXrUiuL"
      },
      "source": [
        "#uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQS2HmIxUli4"
      },
      "source": [
        "# dados_credito = pd.read_csv(io.StringIO(uploaded['dados_credito_small.csv'].decode('utf-8')),\n",
        "#                             sep=',', decimal='.')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito = pd.read_csv('dados_credito_small.csv', sep=',', decimal='.')"
      ],
      "metadata": {
        "id": "PD7IgGyb4Yy9",
        "outputId": "bec159fa-77a1-4540-8c40-f6d7ab819276",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: 'dados_credito_small.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-35f059749749>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdados_credito\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dados_credito_small.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1444\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1736\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1737\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    854\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    857\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dados_credito_small.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBPgCFlrUm3L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "06504463-0d86-4ccb-9b67-4c17af245712"
      },
      "source": [
        "dados_credito"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  idade  educacao  tipo_emprego  renda  divida  outras_div    default  \\\n",
              "0      1     41         3            18    176    10.3        5.01  nao_pagou   \n",
              "1      2     27         1            10     31    17.3        4.00      pagou   \n",
              "2      3     40         1            15     55     5.5        2.17      pagou   \n",
              "3      4     41         1            15    120     2.9        0.82      pagou   \n",
              "4      5     24         2             2     28    17.3        3.06  nao_pagou   \n",
              "..   ...    ...       ...           ...    ...     ...         ...        ...   \n",
              "495  496     47         2             4     33     7.1        1.15  nao_pagou   \n",
              "496  497     30         2            12     98     7.2        4.12      pagou   \n",
              "497  498     44         2            12     86    13.0        9.70      pagou   \n",
              "498  499     25         1             0     18    18.9        2.89  nao_pagou   \n",
              "499  500     41         2             7     36    19.5        2.57  nao_pagou   \n",
              "\n",
              "     educacao_cat    idade_cat renda_cat   idade_z  outras_div_z  \\\n",
              "0       ensin_sup     acima_40  (54,446]  0.800922      0.597431   \n",
              "1    ensin_basico    abaixo_30   (24,34] -0.980602      0.293641   \n",
              "2    ensin_basico  entre_30_40  (54,446]  0.673671     -0.256789   \n",
              "3    ensin_basico     acima_40  (54,446]  0.800922     -0.662845   \n",
              "4     ensin_medio    abaixo_30   (24,34] -1.362357      0.010906   \n",
              "..            ...          ...       ...       ...           ...   \n",
              "495   ensin_medio     acima_40   (24,34]  1.564433     -0.563587   \n",
              "496   ensin_medio    abaixo_30  (54,446] -0.598847      0.329735   \n",
              "497   ensin_medio     acima_40  (54,446]  1.182678      2.008097   \n",
              "498  ensin_basico    abaixo_30   [14,24] -1.235105     -0.040227   \n",
              "499   ensin_medio     acima_40   (34,54]  0.800922     -0.136477   \n",
              "\n",
              "     outras_div_log  renda_log outras_div_cat  \n",
              "0          1.611436   5.170484           alta  \n",
              "1          1.386294   3.433987           alta  \n",
              "2          0.774727   4.007333     media-alta  \n",
              "3         -0.198451   4.787492          baixa  \n",
              "4          1.118415   3.332205     media-alta  \n",
              "..              ...        ...            ...  \n",
              "495        0.139762   3.496508    baixa-media  \n",
              "496        1.415853   4.584967           alta  \n",
              "497        2.272126   4.454347           alta  \n",
              "498        1.061257   2.890372     media-alta  \n",
              "499        0.943906   3.583519     media-alta  \n",
              "\n",
              "[500 rows x 16 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-78179ed3-97e8-462a-a251-4e6f496ac67c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>id</th>\n",
              "      <th>idade</th>\n",
              "      <th>educacao</th>\n",
              "      <th>tipo_emprego</th>\n",
              "      <th>renda</th>\n",
              "      <th>divida</th>\n",
              "      <th>outras_div</th>\n",
              "      <th>default</th>\n",
              "      <th>educacao_cat</th>\n",
              "      <th>idade_cat</th>\n",
              "      <th>renda_cat</th>\n",
              "      <th>idade_z</th>\n",
              "      <th>outras_div_z</th>\n",
              "      <th>outras_div_log</th>\n",
              "      <th>renda_log</th>\n",
              "      <th>outras_div_cat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>176</td>\n",
              "      <td>10.3</td>\n",
              "      <td>5.01</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.800922</td>\n",
              "      <td>0.597431</td>\n",
              "      <td>1.611436</td>\n",
              "      <td>5.170484</td>\n",
              "      <td>alta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>31</td>\n",
              "      <td>17.3</td>\n",
              "      <td>4.00</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>(24,34]</td>\n",
              "      <td>-0.980602</td>\n",
              "      <td>0.293641</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>3.433987</td>\n",
              "      <td>alta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>55</td>\n",
              "      <td>5.5</td>\n",
              "      <td>2.17</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>entre_30_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.673671</td>\n",
              "      <td>-0.256789</td>\n",
              "      <td>0.774727</td>\n",
              "      <td>4.007333</td>\n",
              "      <td>media-alta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>120</td>\n",
              "      <td>2.9</td>\n",
              "      <td>0.82</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.800922</td>\n",
              "      <td>-0.662845</td>\n",
              "      <td>-0.198451</td>\n",
              "      <td>4.787492</td>\n",
              "      <td>baixa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>28</td>\n",
              "      <td>17.3</td>\n",
              "      <td>3.06</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>(24,34]</td>\n",
              "      <td>-1.362357</td>\n",
              "      <td>0.010906</td>\n",
              "      <td>1.118415</td>\n",
              "      <td>3.332205</td>\n",
              "      <td>media-alta</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>496</td>\n",
              "      <td>47</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>33</td>\n",
              "      <td>7.1</td>\n",
              "      <td>1.15</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(24,34]</td>\n",
              "      <td>1.564433</td>\n",
              "      <td>-0.563587</td>\n",
              "      <td>0.139762</td>\n",
              "      <td>3.496508</td>\n",
              "      <td>baixa-media</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>497</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>98</td>\n",
              "      <td>7.2</td>\n",
              "      <td>4.12</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>-0.598847</td>\n",
              "      <td>0.329735</td>\n",
              "      <td>1.415853</td>\n",
              "      <td>4.584967</td>\n",
              "      <td>alta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>498</td>\n",
              "      <td>44</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>86</td>\n",
              "      <td>13.0</td>\n",
              "      <td>9.70</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>1.182678</td>\n",
              "      <td>2.008097</td>\n",
              "      <td>2.272126</td>\n",
              "      <td>4.454347</td>\n",
              "      <td>alta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>499</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>18.9</td>\n",
              "      <td>2.89</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-1.235105</td>\n",
              "      <td>-0.040227</td>\n",
              "      <td>1.061257</td>\n",
              "      <td>2.890372</td>\n",
              "      <td>media-alta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>500</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>36</td>\n",
              "      <td>19.5</td>\n",
              "      <td>2.57</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(34,54]</td>\n",
              "      <td>0.800922</td>\n",
              "      <td>-0.136477</td>\n",
              "      <td>0.943906</td>\n",
              "      <td>3.583519</td>\n",
              "      <td>media-alta</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78179ed3-97e8-462a-a251-4e6f496ac67c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-78179ed3-97e8-462a-a251-4e6f496ac67c 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-78179ed3-97e8-462a-a251-4e6f496ac67c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-08f86265-eede-4346-8c78-59cbe729b0a4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08f86265-eede-4346-8c78-59cbe729b0a4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-08f86265-eede-4346-8c78-59cbe729b0a4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQqK1BJhWEU8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ee8e5a4-104d-4aa0-c01a-2a46cde202e3"
      },
      "source": [
        "dados_credito.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGwulSVIRPJu"
      },
      "source": [
        "### Método *Score-z*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGJtq8s_RPPA"
      },
      "source": [
        "dados_credito['idade_z'] = (dados_credito['idade'] - dados_credito['idade'].mean()) / dados_credito['idade'].std()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito['outras_div_z'] = (dados_credito['outras_div'] - dados_credito['outras_div'].mean()) / dados_credito['outras_div'].std()"
      ],
      "metadata": {
        "id": "GiHR5lb6QshX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTY2P6LWRPTM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f4315f8f-15c9-4d73-e8ef-eb8ee2929674"
      },
      "source": [
        "dados_credito[['idade', 'idade_z']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   idade   idade_z\n",
              "0     41  0.800922\n",
              "1     27 -0.980602\n",
              "2     40  0.673671\n",
              "3     41  0.800922\n",
              "4     24 -1.362357"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: top;\n",
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              "      <th></th>\n",
              "      <th>idade</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>40</td>\n",
              "      <td>0.673671</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>41</td>\n",
              "      <td>0.800922</td>\n",
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              "      <th>4</th>\n",
              "      <td>24</td>\n",
              "      <td>-1.362357</td>\n",
              "    </tr>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3384d345-2b15-4bcf-aff1-1fc1794eabd0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3384d345-2b15-4bcf-aff1-1fc1794eabd0 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-3384d345-2b15-4bcf-aff1-1fc1794eabd0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f3dc6ef2-c90e-4f7c-b0ee-07ffbd7fc14b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3dc6ef2-c90e-4f7c-b0ee-07ffbd7fc14b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3dc6ef2-c90e-4f7c-b0ee-07ffbd7fc14b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[['outras_div_z', 'outras_div']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Pk40Ve-xQ0b6",
        "outputId": "5884b0e1-d8b4-4b77-88e9-201eb8589444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   outras_div_z  outras_div\n",
              "0      0.597431        5.01\n",
              "1      0.293641        4.00\n",
              "2     -0.256789        2.17\n",
              "3     -0.662845        0.82\n",
              "4      0.010906        3.06"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d4d29e70-18af-4292-ab8c-f69d9ea4255a\" 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>outras_div_z</th>\n",
              "      <th>outras_div</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.597431</td>\n",
              "      <td>5.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.293641</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.256789</td>\n",
              "      <td>2.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.662845</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.010906</td>\n",
              "      <td>3.06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4d29e70-18af-4292-ab8c-f69d9ea4255a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d4d29e70-18af-4292-ab8c-f69d9ea4255a 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-d4d29e70-18af-4292-ab8c-f69d9ea4255a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c7c97d49-45f7-4474-bd8f-50547e1b5ed1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c7c97d49-45f7-4474-bd8f-50547e1b5ed1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c7c97d49-45f7-4474-bd8f-50547e1b5ed1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P9DDLJ1KRPXk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "13e815ee-67c2-40b7-841e-c75ee24cfe46"
      },
      "source": [
        "dados_credito[['idade', 'idade_z']].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           idade       idade_z\n",
              "count  500.00000  5.000000e+02\n",
              "mean    34.70600 -3.872458e-16\n",
              "std      7.85844  1.000000e+00\n",
              "min     20.00000 -1.871364e+00\n",
              "25%     29.00000 -7.260983e-01\n",
              "50%     34.00000 -8.983972e-02\n",
              "75%     40.00000  6.736706e-01\n",
              "max     56.00000  2.709698e+00"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>idade</th>\n",
              "      <th>idade_z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>500.00000</td>\n",
              "      <td>5.000000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>34.70600</td>\n",
              "      <td>-3.872458e-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.85844</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>20.00000</td>\n",
              "      <td>-1.871364e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>29.00000</td>\n",
              "      <td>-7.260983e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>34.00000</td>\n",
              "      <td>-8.983972e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>40.00000</td>\n",
              "      <td>6.736706e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>2.709698e+00</td>\n",
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              "      buttonEl.style.display =\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bf77dee-da54-4688-91c2-297ff8b0fe2c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WvZQdl2kRPbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "d6eaa1f2-8a95-4edc-9ef2-ae25df083587"
      },
      "source": [
        "dados_credito[['idade', 'idade_z']].hist();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[['outras_div_z', 'outras_div']].hist();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "-3xzWCnbQ9IH",
        "outputId": "fbb4ddb9-4da3-4474-a1d4-b34c57e97d54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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xY3K73ZIkt9utjz/+WKdOnQqM8Xg8crlc6tWrV0NLAdAC0WOA1imkKyhz587V2LFjlZGRobNnz2rNmjXasWOHtm7dqri4OE2bNk05OTlKSEiQy+XSgw8+KLfbrSFDhkiSMjMz1atXL91zzz1aunSpiouLNW/ePGVnZ9f4CgZA60KPAVAppIBy6tQp3XvvvTp58qTi4uLUt29fbd26VaNHj5YkPf3004qMjNTkyZPl9XqVlZWl5557LvD4qKgobdq0STNnzpTb7Va7du00depULVq0KLxHBaBZoscAqBRSQHnxxRdrXd+mTRstX75cy5cvv+yYLl26tNhPqgNoGHoMgEr8Fg8AADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACME1JAycvL00033aQOHTooKSlJEydO1JEjR4LGDB8+XBEREUG3X/ziF0Fjjh07pvHjxys2NlZJSUmaM2eOLl261PCjAdBs0V8AVBUdyuCCggJlZ2frpptu0qVLl/TYY48pMzNThw8fVrt27QLjpk+frkWLFgXux8bGBv5dUVGh8ePHKyUlRbt27dLJkyd17733yuFw6De/+U0YDglAc0R/AVBVSAFly5YtQfdXr16tpKQkFRUVadiwYYHlsbGxSklJqXEb+fn5Onz4sN59910lJyfrxhtv1OLFi/Xoo48qNzdXMTEx9TgMAM0d/QVAVSEFlB86c+aMJCkhISFo+auvvqo///nPSklJ0YQJE/T4448HXuUUFhaqT58+Sk5ODozPysrSzJkzdejQIfXv37/afrxer7xeb+B+WVmZJMnn88nn89VaozPKqt/BXUbVfV5p36aifnuZXr8pdZneXyrXOSPD12NMmftwMf1ct1trnJ9QjrXeAcXv9+vhhx/WzTffrN69eweW/+xnP1OXLl2Ulpamjz76SI8++qiOHDmit99+W5JUXFwc1DwkBe4XFxfXuK+8vDwtXLiw2vL8/Pygy7s1WToopMO6os2bNwf+7fF4wrvxJkb99jK1/vLycrtLaDb9RZIWD/TX+biupGp/aUlMPddN0ZrmJ5T+Uu+Akp2drYMHD+r9998PWj5jxozAv/v06aPU1FSNHDlSR48e1TXXXFOvfc2dO1c5OTmB+2VlZUpPT1dmZqZcLletj+2du7Ve+7ycg7lZ8vl88ng8Gj16tBwOR1i33xSo316m1195BcFOzaG/VD6Pj++PlNcfUa99/9DB3KywbMcUpp/rdmuN8xNKf6lXQJk1a5Y2bdqknTt3qnPnzrWOHTx4sCTps88+0zXXXKOUlBTt3bs3aExJSYkkXfZ9ZafTKafTWW25w+G44pPqrQhP46i6z1D2bzLqt5ep9dtdU3PqL5Lk9UeErc/YPfeNxdRz3RStaX5COc6QvmZsWZZmzZqldevWafv27erWrdsVH3PgwAFJUmpqqiTJ7Xbr448/1qlTpwJjPB6PXC6XevXqFUo5AFoQ+guAqkK6gpKdna01a9Zow4YN6tChQ+A93bi4OLVt21ZHjx7VmjVrNG7cOCUmJuqjjz7S7NmzNWzYMPXt21eSlJmZqV69eumee+7R0qVLVVxcrHnz5ik7O7vGVzEAWgf6C4CqQrqCsmLFCp05c0bDhw9Xampq4PbGG29IkmJiYvTuu+8qMzNTPXr00L/8y79o8uTJ2rhxY2AbUVFR2rRpk6KiouR2u/Xzn/9c9957b9DfNQDQ+tBfAFQV0hUUy6r963Tp6ekqKCi44na6dOnSYj+tDqB+6C8AquK3eAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGCckAJKXl6ebrrpJnXo0EFJSUmaOHGijhw5EjTmwoULys7OVmJiotq3b6/JkyerpKQkaMyxY8c0fvx4xcbGKikpSXPmzNGlS5cafjQAmi36C4CqQgooBQUFys7O1u7du+XxeOTz+ZSZmanz588HxsyePVsbN27U2rVrVVBQoBMnTmjSpEmB9RUVFRo/frwuXryoXbt26eWXX9bq1as1f/788B0VgGaH/gKgquhQBm/ZsiXo/urVq5WUlKSioiINGzZMZ86c0Ysvvqg1a9ZoxIgRkqRVq1apZ8+e2r17t4YMGaL8/HwdPnxY7777rpKTk3XjjTdq8eLFevTRR5Wbm6uYmJjwHR2AZoP+AqCqkALKD505c0aSlJCQIEkqKiqSz+fTqFGjAmN69OihjIwMFRYWasiQISosLFSfPn2UnJwcGJOVlaWZM2fq0KFD6t+/f7X9eL1eeb3ewP2ysjJJks/nk8/nq7VGZ5RV/wOsQdV9XmnfpqJ+e5levyl1md5fKtc5I8PXY0yZ+3Ax/Vy3W2ucn1COtd4Bxe/36+GHH9bNN9+s3r17S5KKi4sVExOj+Pj4oLHJyckqLi4OjKnaPCrXV66rSV5enhYuXFhteX5+vmJjY2utc+mgOh1OnW3evDnwb4/HE96NNzHqt5ep9ZeXl9tdQrPpL5K0eKD/imPqqmp/aUlMPddN0ZrmJ5T+Uu+Akp2drYMHD+r999+v7ybqbO7cucrJyQncLysrU3p6ujIzM+VyuWp9bO/crWGt5WBulnw+nzwej0aPHi2HwxHW7TcF6reX6fVXXkGwU3PoL5XP4+P7I+X1R4SlloO5WWHZjilMP9ft1hrnJ5T+Uq+AMmvWLG3atEk7d+5U586dA8tTUlJ08eJFlZaWBr3KKSkpUUpKSmDM3r17g7ZX+Sn8yjE/5HQ65XQ6qy13OBxXfFK9FeFpHFX3Gcr+TUb99jK1frtrak79RZK8/oiw9Rm7576xmHqum6I1zU8oxxnSt3gsy9KsWbO0bt06bd++Xd26dQtaP2DAADkcDm3bti2w7MiRIzp27Jjcbrckye126+OPP9apU6cCYzwej1wul3r16hVKOQBaEPoLgKpCuoKSnZ2tNWvWaMOGDerQoUPgPd24uDi1bdtWcXFxmjZtmnJycpSQkCCXy6UHH3xQbrdbQ4YMkSRlZmaqV69euueee7R06VIVFxdr3rx5ys7OrvFVDIDWgf4CoKqQAsqKFSskScOHDw9avmrVKt13332SpKefflqRkZGaPHmyvF6vsrKy9NxzzwXGRkVFadOmTZo5c6bcbrfatWunqVOnatGiRQ07EgDNGv0FQFUhBRTLuvLX6dq0aaPly5dr+fLllx3TpUuXFvtpdQD1Q38BUBW/xQMAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGCTmg7Ny5UxMmTFBaWpoiIiK0fv36oPX33XefIiIigm5jxowJGnP69Gndfffdcrlcio+P17Rp03Tu3LkGHQiA5o/+AqBSyAHl/Pnz6tevn5YvX37ZMWPGjNHJkycDt9deey1o/d13361Dhw7J4/Fo06ZN2rlzp2bMmBF69QBaFPoLgErRoT5g7NixGjt2bK1jnE6nUlJSalz3ySefaMuWLdq3b58GDhwoSXr22Wc1btw4/e53v1NaWlqoJQFoIegvACqFHFDqYseOHUpKStJVV12lESNGaMmSJUpMTJQkFRYWKj4+PtA8JGnUqFGKjIzUnj17dPvtt1fbntfrldfrDdwvKyuTJPl8Pvl8vlprcUZZ4TikgKr7vNK+TUX99jK9flPrqmRKf6lc54wMX48xfe5DZfq5brfWOD+hHGvYA8qYMWM0adIkdevWTUePHtVjjz2msWPHqrCwUFFRUSouLlZSUlJwEdHRSkhIUHFxcY3bzMvL08KFC6stz8/PV2xsbK31LB1U/2OpyebNmwP/9ng84d14E6N+e5laf3l5ud0lXJZp/UWSFg/01+9galC1v7Qkpp7rpmhN8xNKfwl7QJkyZUrg33369FHfvn11zTXXaMeOHRo5cmS9tjl37lzl5OQE7peVlSk9PV2ZmZlyuVy1PrZ37tZ67fNyDuZmyefzyePxaPTo0XI4HGHdflOgfnuZXn/lFQQTmdRfKp/Hx/dHyuuPqNe+f+hgblZYtmMK0891u7XG+QmlvzTKWzxVXX311erYsaM+++wzjRw5UikpKTp16lTQmEuXLun06dOXfV/Z6XTK6XRWW+5wOK74pHorwtM4qu4zlP2bjPrtZWr9JtZ0OXb3F0ny+iPC1mea09yHwtRz3RStaX5COc5G/zsoX375pb755hulpqZKktxut0pLS1VUVBQYs337dvn9fg0ePLixywHQgtBfgJYr5Cso586d02effRa4//nnn+vAgQNKSEhQQkKCFi5cqMmTJyslJUVHjx7VI488ou7duysr6/tLlz179tSYMWM0ffp0rVy5Uj6fT7NmzdKUKVP4hD3QytFfAFQK+QrK/v371b9/f/Xv31+SlJOTo/79+2v+/PmKiorSRx99pJ/85Ce67rrrNG3aNA0YMED//d//HXQJ9dVXX1WPHj00cuRIjRs3TkOHDtULL7wQvqMC0CzRXwBUCvkKyvDhw2VZl/9a3datV/5QakJCgtasWRPqrgG0cPQXAJX4LR4AAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYJ+SAsnPnTk2YMEFpaWmKiIjQ+vXrg9ZblqX58+crNTVVbdu21ahRo/Tpp58GjTl9+rTuvvtuuVwuxcfHa9q0aTp37lyDDgRA80d/AVAp5IBy/vx59evXT8uXL69x/dKlS/XMM89o5cqV2rNnj9q1a6esrCxduHAhMObuu+/WoUOH5PF4tGnTJu3cuVMzZsyo/1EAaBHoLwAqRYf6gLFjx2rs2LE1rrMsS8uWLdO8efN02223SZL+9Kc/KTk5WevXr9eUKVP0ySefaMuWLdq3b58GDhwoSXr22Wc1btw4/e53v1NaWloDDgdAc0Z/AVAp5IBSm88//1zFxcUaNWpUYFlcXJwGDx6swsJCTZkyRYWFhYqPjw80D0kaNWqUIiMjtWfPHt1+++3Vtuv1euX1egP3y8rKJEk+n08+n6/WmpxRVkMPK8j1v94kZ6SlxQOlAYu2yOuPqPe2DuZmhbGyuqucsyvNnamov3GZWpdp/aVynTMyfD3m+l9vCtu27OovVZl+rtutNc5PKMca1oBSXFwsSUpOTg5anpycHFhXXFyspKSk4CKio5WQkBAY80N5eXlauHBhteX5+fmKjY2ttaalg+pcfsgWD/Q36PGbN28OUyX14/F4bN1/Q1F/4ygvL7e7hBqZ2F+khveBxmJ3f6nK1HPdFK1pfkLpL2ENKI1l7ty5ysnJCdwvKytTenq6MjMz5XK5an1s79ytYa/n+ysofj2+P7LZXkHxeDwaPXq0HA6HLTU0BPU3rsorCK1FfftL5fPY0D7QWEy5gmLyuW631jg/ofSXsAaUlJQUSVJJSYlSU1MDy0tKSnTjjTcGxpw6dSrocZcuXdLp06cDj/8hp9Mpp9NZbbnD4bjik+qtaLzG4fVHNGj7dp+QdZk/k1F/4zCxJsnM/iI1vA80FpOeR1PPdVO0pvkJ5TjD+ndQunXrppSUFG3bti2wrKysTHv27JHb7ZYkud1ulZaWqqioKDBm+/bt8vv9Gjx4cDjLAdCC0F+A1iXkKyjnzp3TZ599Frj/+eef68CBA0pISFBGRoYefvhhLVmyRNdee626deumxx9/XGlpaZo4caIkqWfPnhozZoymT5+ulStXyufzadasWZoyZQqfsAdaOfoLgEohB5T9+/fr1ltvDdyvfO926tSpWr16tR555BGdP39eM2bMUGlpqYYOHaotW7aoTZs2gce8+uqrmjVrlkaOHKnIyEhNnjxZzzzzTBgOB0BzRn8BUCnkgDJ8+HBZ1uW/VhcREaFFixZp0aJFlx2TkJCgNWvWhLprAC0c/QVAJX6LBwAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYJe0DJzc1VRERE0K1Hjx6B9RcuXFB2drYSExPVvn17TZ48WSUlJeEuA0ALRH8BWo9GuYJyww036OTJk4Hb+++/H1g3e/Zsbdy4UWvXrlVBQYFOnDihSZMmNUYZAFog+gvQOkQ3ykajo5WSklJt+ZkzZ/Tiiy9qzZo1GjFihCRp1apV6tmzp3bv3q0hQ4Y0RjkAWhD6C9A6NEpA+fTTT5WWlqY2bdrI7XYrLy9PGRkZKioqks/n06hRowJje/TooYyMDBUWFl62gXi9Xnm93sD9srIySZLP55PP56u1FmeUFYYj+sE2I62g/9bXlWpvLJX7tWv/DUX9jcvUuiqZ0l8q1zW0DzQWE55H0891u7XG+QnlWMMeUAYPHqzVq1fr+uuv18mTJ7Vw4UL9+Mc/1sGDB1VcXKyYmBjFx8cHPSY5OVnFxcWX3WZeXp4WLlxYbXl+fr5iY2NrrWfpoHodRp0sHuhv0OM3b94cpkrqx+Px2Lr/hqL+xlFeXm53CZdlWn+RGt4HGovd/aUqU891U7Sm+Qmlv0RYltWo8b+0tFRdunTRU089pbZt2+r+++8PerUiSYMGDdKtt96q3/72tzVuo6ZXOOnp6fr666/lcrlq3X/v3K0NP4gfcEZaWjzQr8f3R8rrj6j3dg7mZoWxqrrz+XzyeDwaPXq0HA6HLTU0BPU3rrKyMnXs2FFnzpy54v9fdrOzv1Q+jw3tA43Frv5Slennut1a4/yE0l8a5S2equLj43Xdddfps88+0+jRo3Xx4kWVlpYGvcopKSmp8T3lSk6nU06ns9pyh8NxxSfVW9F4jcPrj2jQ9u0+Iesyfyaj/sZhYk2XY3d/kRreBxqLSc+jqee6KVrT/IRynI3+d1DOnTuno0ePKjU1VQMGDJDD4dC2bdsC648cOaJjx47J7XY3dikAWhj6C9Byhf0Kyq9+9StNmDBBXbp00YkTJ7RgwQJFRUXprrvuUlxcnKZNm6acnBwlJCTI5XLpwQcflNvt5hP2AK6I/gK0HmEPKF9++aXuuusuffPNN+rUqZOGDh2q3bt3q1OnTpKkp59+WpGRkZo8ebK8Xq+ysrL03HPPhbsMAC0Q/QVoPcIeUF5//fVa17dp00bLly/X8uXLw71rAC0c/QVoPfgtHgAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwTtj/1D3qruu//ldYt/e3J8eHdXsAmi/6C5o7rqAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGCfa7gIQPnX9eXVnlKWlg6TeuVvlrYiocQw/rQ6gqrr2l6pq6zX0GFwJV1AAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4/Foga1eeHwWrDD4MBqCqcPYb+0jJxBQUAABiHgAIAAIxDQAEAAMaxNaAsX75cXbt2VZs2bTR48GDt3bvXznIAtCD0F6B5s+1Dsm+88YZycnK0cuVKDR48WMuWLVNWVpaOHDmipKQku8pCIwnnB+KcUZaWDpJ6526VtyKiwdvjA3YtD/2ldQn3h/rDif5Sf7ZdQXnqqac0ffp03X///erVq5dWrlyp2NhYvfTSS3aVBKCFoL8AzZ8tV1AuXryooqIizZ07N7AsMjJSo0aNUmFhYbXxXq9XXq83cP/MmTOSpNOnT8vn89W6r+hL58NUdZVt+i2Vl/sV7YtUhb/hr+CbGvUH6/6rN8NQVd05Iy3N6+/Xjb9+W94mnv89c0decczZs2clSZZlNXY5jaKp+ovP51N5eXmz/f+oKTT3XhMOtfUXO3tBYwh7f7Fs8Pe//92SZO3atSto+Zw5c6xBgwZVG79gwQJLEjdu3Jrwdvz48aZqCWFFf+HGzfxbXfpLs/hDbXPnzlVOTk7gvt/v1+nTp5WYmKiIiKZPnWVlZUpPT9fx48flcrmafP8NRf32Mr1+y7J09uxZpaWl2V1Kk6hvfzH9eTQBc1S71jg/ofQXWwJKx44dFRUVpZKSkqDlJSUlSklJqTbe6XTK6XQGLYuPj2/MEuvE5XI165OK+u1lcv1xcXF2l1BvTd1fTH4eTcEc1a61zU9d+4stH5KNiYnRgAEDtG3btsAyv9+vbdu2ye1221ESgBaC/gK0DLa9xZOTk6OpU6dq4MCBGjRokJYtW6bz58/r/vvvt6skAC0E/QVo/mwLKHfeeae++uorzZ8/X8XFxbrxxhu1ZcsWJScn21VSnTmdTi1YsKDaZeHmgvrt1dzrbw6aor/wPF4Zc1Q75qd2EZbVTL9LCAAAWix+iwcAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKCFavny5unbtqjZt2mjw4MHau3ev3SXVSV5enm666SZ16NBBSUlJmjhxoo4cOWJ3WfX25JNPKiIiQg8//LDdpYTk73//u37+858rMTFRbdu2VZ8+fbR//367y0KImmsfaAw7d+7UhAkTlJaWpoiICK1fvz5ovWVZmj9/vlJTU9W2bVuNGjVKn376qT3F2qAuvffChQvKzs5WYmKi2rdvr8mTJ1f7S8itEQElBG+88YZycnK0YMECffDBB+rXr5+ysrJ06tQpu0u7ooKCAmVnZ2v37t3yeDzy+XzKzMzU+fPh/7XnxrZv3z49//zz6tu3r92lhOTbb7/VzTffLIfDoXfeeUeHDx/W73//e1111VV2l4YQNOc+0BjOnz+vfv36afny5TWuX7p0qZ555hmtXLlSe/bsUbt27ZSVlaULFy40caX2qEvvnT17tjZu3Ki1a9eqoKBAJ06c0KRJk2ys2hDh+PXQ1mLQoEFWdnZ24H5FRYWVlpZm5eXl2VhV/Zw6dcqSZBUUFNhdSkjOnj1rXXvttZbH47FuueUW66GHHrK7pDp79NFHraFDh9pdBhqoJfWBcJNkrVu3LnDf7/dbKSkp1r/9278FlpWWllpOp9N67bXXbKjQfj/svaWlpZbD4bDWrl0bGPPJJ59YkqzCwkK7yjQCV1Dq6OLFiyoqKtKoUaMCyyIjIzVq1CgVFhbaWFn9nDlzRpKUkJBgcyWhyc7O1vjx44Oeh+biP//zPzVw4ED99Kc/VVJSkvr3768//OEPdpeFELS0PtDYPv/8cxUXFwfNV1xcnAYPHtxq5+uHvbeoqEg+ny9ojnr06KGMjIxWO0eVCCh19PXXX6uioqLan8pOTk5WcXGxTVXVj9/v18MPP6ybb75ZvXv3trucOnv99df1wQcfKC8vz+5S6uV///d/tWLFCl177bXaunWrZs6cqV/+8pd6+eWX7S4NddSS+kBTqJwT5ut7NfXe4uJixcTEVPsF7dY6R1XZ9ls8sE92drYOHjyo999/3+5S6uz48eN66KGH5PF41KZNG7vLqRe/36+BAwfqN7/5jSSpf//+OnjwoFauXKmpU6faXB2AxtYce6+duIJSRx07dlRUVFS1T1aXlJQoJSXFpqpCN2vWLG3atEnvvfeeOnfubHc5dVZUVKRTp07pRz/6kaKjoxUdHa2CggI988wzio6OVkVFhd0lXlFqaqp69eoVtKxnz546duyYTRUhVC2lDzSVyjlhvi7fe1NSUnTx4kWVlpYGjW+Nc/RDBJQ6iomJ0YABA7Rt27bAMr/fr23btsntdttYWd1YlqVZs2Zp3bp12r59u7p162Z3SSEZOXKkPv74Yx04cCBwGzhwoO6++24dOHBAUVFRdpd4RTfffHO1rxf+9a9/VZcuXWyqCKFq7n2gqXXr1k0pKSlB81VWVqY9e/a0mvm6Uu8dMGCAHA5H0BwdOXJEx44dazVzdFl2f0q3OXn99dctp9NprV692jp8+LA1Y8YMKz4+3iouLra7tCuaOXOmFRcXZ+3YscM6efJk4FZeXm53afXW3L7Fs3fvXis6Otp64oknrE8//dR69dVXrdjYWOvPf/6z3aUhBM25DzSGs2fPWh9++KH14YcfWpKsp556yvrwww+tL774wrIsy3ryySet+Ph4a8OGDdZHH31k3XbbbVa3bt2s7777zubKm0Zdeu8vfvELKyMjw9q+fbu1f/9+y+12W26328aqzUBACdGzzz5rZWRkWDExMdagQYOs3bt3211SnUiq8bZq1Sq7S6u35hZQLMuyNm7caPXu3dtyOp1Wjx49rBdeeMHuklAPzbUPNIb33nuvxt4ydepUy7K+/6rx448/biUnJ1tOp9MaOXKkdeTIEXuLbkJ16b3fffed9cADD1hXXXWVFRsba91+++3WyZMn7SvaEBGWZVlNfdUGAACgNnwGBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADG+X+P+MgDPtb81QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "y8SrT2nUo7z5",
        "outputId": "9533b2a2-82e6-49c8-e870-5173476d1023"
      },
      "source": [
        "dados_credito.sort_values('idade_z', ascending = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  idade  educacao  tipo_emprego  renda  divida  outras_div    default  \\\n",
              "445  446     56         1            11     59    15.0        4.18      pagou   \n",
              "189  190     55         1            20     78    15.6        7.76      pagou   \n",
              "231  232     55         1             3     40     8.0        2.64  nao_pagou   \n",
              "243  244     54         3            22    118    14.4        7.39      pagou   \n",
              "325  326     54         1            25    119    26.5       17.20  nao_pagou   \n",
              "..   ...    ...       ...           ...    ...     ...         ...        ...   \n",
              "112  113     21         3             0     24     7.7        1.01      pagou   \n",
              "359  360     21         3             0     26     7.7        0.47      pagou   \n",
              "45    46     21         2             0     16     6.8        0.94      pagou   \n",
              "24    25     21         2             1     16    18.0        2.64  nao_pagou   \n",
              "426  427     20         3             0     17     2.3        0.35      pagou   \n",
              "\n",
              "     educacao_cat  idade_cat renda_cat   idade_z  outras_div_z  \n",
              "445  ensin_basico   acima_40  (54,446]  2.709698      0.347782  \n",
              "189  ensin_basico   acima_40  (54,446]  2.582446      1.424581  \n",
              "231  ensin_basico   acima_40   (34,54]  2.582446     -0.115422  \n",
              "243     ensin_sup   acima_40  (54,446]  2.455195      1.313291  \n",
              "325  ensin_basico   acima_40  (54,446]  2.455195      4.263960  \n",
              "..            ...        ...       ...       ...           ...  \n",
              "112     ensin_sup  abaixo_30   [14,24] -1.744112     -0.605696  \n",
              "359     ensin_sup  abaixo_30   (24,34] -1.744112     -0.768118  \n",
              "45    ensin_medio  abaixo_30   [14,24] -1.744112     -0.626751  \n",
              "24    ensin_medio  abaixo_30   [14,24] -1.744112     -0.115422  \n",
              "426     ensin_sup  abaixo_30   [14,24] -1.871364     -0.804212  \n",
              "\n",
              "[500 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17b632d5-02d2-4ec9-9817-bb81b3af2d99\" 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>id</th>\n",
              "      <th>idade</th>\n",
              "      <th>educacao</th>\n",
              "      <th>tipo_emprego</th>\n",
              "      <th>renda</th>\n",
              "      <th>divida</th>\n",
              "      <th>outras_div</th>\n",
              "      <th>default</th>\n",
              "      <th>educacao_cat</th>\n",
              "      <th>idade_cat</th>\n",
              "      <th>renda_cat</th>\n",
              "      <th>idade_z</th>\n",
              "      <th>outras_div_z</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>446</td>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>59</td>\n",
              "      <td>15.0</td>\n",
              "      <td>4.18</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>2.709698</td>\n",
              "      <td>0.347782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>190</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>78</td>\n",
              "      <td>15.6</td>\n",
              "      <td>7.76</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>2.582446</td>\n",
              "      <td>1.424581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>232</td>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>40</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.64</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(34,54]</td>\n",
              "      <td>2.582446</td>\n",
              "      <td>-0.115422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>244</td>\n",
              "      <td>54</td>\n",
              "      <td>3</td>\n",
              "      <td>22</td>\n",
              "      <td>118</td>\n",
              "      <td>14.4</td>\n",
              "      <td>7.39</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>2.455195</td>\n",
              "      <td>1.313291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>325</th>\n",
              "      <td>326</td>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>119</td>\n",
              "      <td>26.5</td>\n",
              "      <td>17.20</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>2.455195</td>\n",
              "      <td>4.263960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>113</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>7.7</td>\n",
              "      <td>1.01</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-1.744112</td>\n",
              "      <td>-0.605696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359</th>\n",
              "      <td>360</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>7.7</td>\n",
              "      <td>0.47</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>(24,34]</td>\n",
              "      <td>-1.744112</td>\n",
              "      <td>-0.768118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>46</td>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>6.8</td>\n",
              "      <td>0.94</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-1.744112</td>\n",
              "      <td>-0.626751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>21</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.64</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-1.744112</td>\n",
              "      <td>-0.115422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>427</td>\n",
              "      <td>20</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.35</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-1.871364</td>\n",
              "      <td>-0.804212</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito.sort_values('outras_div_z', ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "kL0Whas3RG6B",
        "outputId": "f9e515c6-ae21-4b21-b751-ed17148eba8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  idade  educacao  tipo_emprego  renda  divida  outras_div    default  \\\n",
              "343  344     40         3             5    220    16.0       27.03  nao_pagou   \n",
              "466  467     47         3            16    221    17.6       23.10  nao_pagou   \n",
              "491  492     41         3            13     91    25.2       20.62  nao_pagou   \n",
              "478  479     51         2            27    166    11.5       18.27      pagou   \n",
              "325  326     54         1            25    119    26.5       17.20  nao_pagou   \n",
              "..   ...    ...       ...           ...    ...     ...         ...        ...   \n",
              "305  306     25         1             9     18     1.2        0.15      pagou   \n",
              "271  272     22         1             4     16     1.2        0.11      pagou   \n",
              "76    77     24         1             2     21     0.6        0.10      pagou   \n",
              "10    11     27         1             0     16     1.7        0.09      pagou   \n",
              "436  437     27         1             0     16     1.1        0.05      pagou   \n",
              "\n",
              "     educacao_cat    idade_cat renda_cat   idade_z  outras_div_z  \n",
              "343     ensin_sup  entre_30_40  (54,446]  0.673671      7.220645  \n",
              "466     ensin_sup     acima_40  (54,446]  1.564433      6.038572  \n",
              "491     ensin_sup     acima_40  (54,446]  0.800922      5.292634  \n",
              "478   ensin_medio     acima_40  (54,446]  2.073440      4.585797  \n",
              "325  ensin_basico     acima_40  (54,446]  2.455195      4.263960  \n",
              "..            ...          ...       ...       ...           ...  \n",
              "305  ensin_basico    abaixo_30   [14,24] -1.235105     -0.864369  \n",
              "271  ensin_basico    abaixo_30   [14,24] -1.616860     -0.876400  \n",
              "76   ensin_basico    abaixo_30   [14,24] -1.362357     -0.879408  \n",
              "10   ensin_basico    abaixo_30   [14,24] -0.980602     -0.882415  \n",
              "436  ensin_basico    abaixo_30   [14,24] -0.980602     -0.894447  \n",
              "\n",
              "[500 rows x 13 columns]"
            ],
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              "      <th>343</th>\n",
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              "      <td>ensin_basico</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-0.980602</td>\n",
              "      <td>-0.882415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>436</th>\n",
              "      <td>437</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.05</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>abaixo_30</td>\n",
              "      <td>[14,24]</td>\n",
              "      <td>-0.980602</td>\n",
              "      <td>-0.894447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb137578-95f8-4cc5-ad07-01c21c1b80b7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bb137578-95f8-4cc5-ad07-01c21c1b80b7 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-bb137578-95f8-4cc5-ad07-01c21c1b80b7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3a183fd3-fc17-40d8-8f10-38258a084269\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a183fd3-fc17-40d8-8f10-38258a084269')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3a183fd3-fc17-40d8-8f10-38258a084269 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wW-lB-kRPgH"
      },
      "source": [
        "### Método *Box-Plot*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3a8mONWjRPkj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "5eecd664-2acd-42a7-c5f2-545056556ba0"
      },
      "source": [
        "dados_credito[['idade', 'outras_div']].boxplot();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3yR8UEtzw9Jsnvi271KzHAwCs4VH4WL9+/XX3BwUFKSMjQxkZGc0qCq3f8f/9ov9d5mETRvPXm98eMGGcH4XYmnwFOQDAAnxKo0km3vbjJOKbe3ZWcIBfk8cpLCnXgi2HtWrGYMVGhDW7rhCbv6K7hzR7HABAyyF8oEnCQwJ139CoZo9TvzDYzT1CNKhv88MHAKD14662AADAUoQPAABgKcIHAACwFOEDAABYivABADCd0+lUTk6O9u7dq5ycHDmdTm+XhFaE8AEAMFVmZqZiYmKUlJSk1atXKykpSTExMcrMzPR2aWglCB8AANNkZmZqxowZGjx4sHJzc/X2228rNzdXgwcP1owZMwggkET4AACYxOl0asGCBUpOTlZWVpYSEhIUHByshIQEZWVlKTk5WQsXLuQUDAgfAABz5Obm6sSJE1q8eLF8fd2/Xnx9fZWenq6ioiLl5uZ6qUK0FoQPAIApSkpKJEmDBg266v769vp+6LgIHwAAU0REREiSCgoKrrq/vr2+HzouwgcAwBSjRo3SgAEDtHz5ctXV1bntq6ur04oVKxQdHa1Ro0Z5qUK0FoQPAIAp/Pz8tGrVKm3btk3Tp09XXl6eqqurlZeXp+nTp2vbtm1auXKl/PyafidstA/c1RYAYJqUlBRt2bJFCxYs0OjRo13t0dHR2rJli1JSUrxYHVoLwgcAwFQpKSmaNm2aPv30U3388ceaPHmyxo0bxxEPuBA+AACm8/Pz05gxY1RZWakxY8YQPOCGOR8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAwndPpVE5Ojvbu3aucnBw5nU5vl4RWhPABADBVZmamYmJilJSUpNWrVyspKUkxMTHKzMz0dmloJQgfAADTZGZmasaMGRo8eLByc3P19ttvKzc3V4MHD9aMGTMIIJBE+AAAmMTpdGrBggVKTk5WVlaWEhISFBwcrISEBGVlZSk5OVkLFy7kFAwIHwAAc+Tm5urEiRNavHixfH3dv158fX2Vnp6uoqIi5ebmeqlCtBaEDwCAKUpKSiRJgwYNuur++vb6fui4CB8AAFNERERIkgoKCq66v769vh86LsIHAMAUo0aN0oABA7R8+XLV1dW57aurq9OKFSsUHR2tUaNGealCtBaEDwCAKfz8/LRq1Spt27ZN06dPV15enqqrq5WXl6fp06dr27ZtWrlypfz8/LxdKrzM39sFAADaj5SUFG3ZskULFizQ6NGjXe3R0dHasmWLUlJSvFgdWgvCBwDAVCkpKZo2bZo+/fRTffzxx5o8ebLGjRvHEQ+4ED4AAKbz8/PTmDFjVFlZqTFjxhA84IY5HwAAwFKEDwAAYCnCBwAAsBThAwBgOqfTqZycHO3du1c5OTnczwVuCB8AAFNlZmYqJiZGSUlJWr16tZKSkhQTE8MdbeFC+AAAmCYzM1MzZszQ4MGDlZubq7ffflu5ubkaPHiwZsyYQQCBJMIHAMAkTqdTCxYsUHJysrKyspSQkKDg4GAlJCQoKytLycnJWrhwIadg4Fn4WLFihX7xi1+oS5cu6tmzp6ZPn67CwkK3PjU1NZozZ466deumzp07KzU1VWfPnjW1aABA65Obm6sTJ05o8eLF8vV1/3rx9fVVenq6ioqKlJub66UK0Vp4FD5ycnI0Z84c5eXlKTs7Ww6HQxMnTlRlZaWrz/z58/Xhhx9q8+bNysnJUXFxMcvpAkAHUFJSIkkaNGjQVffXt9f3Q8fl0QqnO3bscNveuHGjevbsqfz8fI0ePVrl5eVav369Nm3apHvuuUeStGHDBg0cOFB5eXkaNmyYeZUDAFqViIgISVJBQcFVP+8LCgrc+qHjatacj/LycklSeHi4JCk/P18Oh0MTJkxw9YmLi1NUVJT27dvXnKcCALRyo0aN0oABA7R8+XLV1dW57aurq9OKFSsUHR2tUaNGealCtBZNvrdLXV2d5s2bpxEjRrgOpZWWliowMFBdu3Z169urVy+VlpZedRy73S673e7arqiokCQ5HA45HI6mlodWoKqqqsGcoJ/6e0m57KXfquBQoC6fDbvhmLGxserUqZNZJQIw2X/8x3/ovvvu07333qsFCxaourpan332mVatWqXt27frnXfeUV1dXYNwgrbPk+/sJoePOXPmqKCgQJ999llTh5D04yTWZcuWNWjftWsXXzJt3PHjx7VgwYJG9f3HPzduzFWrVunmm29uRlUAWpLNZtOiRYu0YcMG1+l36cf/CV20aJFsNpu2b9/uxQrRUqqqqhrd18cwDMPTJ3jiiSf0wQcfaO/evYqOjna1f/LJJxo/frx++OEHt6Mf/fv317x58zR//vwGY13tyEdkZKS+++47hYaGeloaWpHGHPm4VG3XztwDmjTqF+ocbLvhmBz5ANoGp9OpPXv2KDs7W0lJSRo7dix3tm3nKioq1L17d5WXl9/w+9ujIx+GYWju3Ll6//33tWfPHrfgIUnx8fEKCAjQ7t27lZqaKkkqLCzUqVOnlJiYeNUxbTabbLaGXzoBAQEKCAjwpDy0MmFhYRo6dOh1+zgcDl288L1GDR/G6w20IwEBARo/frzsdrvGjx/P+7sD8OQ19ih8zJkzR5s2bdIHH3ygLl26uOZxhIWFKTg4WGFhYZo9e7bS0tIUHh6u0NBQzZ07V4mJiVzpAgAAJHkYPtatWydJGjt2rFv7hg0bNGvWLEnSmjVr5Ovrq9TUVNntdk2aNElr1641pVgAAND2eXza5UaCgoKUkZGhjIyMJhcFAGjbrryrbUhIiMaNG8ecD7hwbxcAgKm4qy1uhPABADANd7VFYxA+AACm4K62aCzCBwDAFNzVFo1F+AAAmIK72qKxCB8AAFNceVfbK692ycnJkdPp5K62cGnS8uotqaKiQmFhYY1anhVtn8Ph0Pbt2zVlyhRWQATaOKfTqZiYGHXv3l3nzp3TyZMnXfv69++vHj166Pz58zp27BiX3bZDnnx/c+QDAGAKPz8//frXv9bBgwdVU1OjdevW6U9/+pPWrVunmpoaHTx4UDNmzCB4gCMf8C6OfADtx5VHPr777judOHHCtS86OlrdunXjyEc7xpEPAIDl6q92eeWVV1RYWKiVK1dqypQpWrlypY4ePaqXX36Zq10gifABADBJ/VUsx48fV2xsrBYuXKjt27dr4cKFio2N1X//93+79UPHRfgAAJii/iqWBx988KornD744INu/dBxeXRjOQAArmX48OHy9/dXt27dlJmZKcMwdP78eSUkJCgzM1P9+vXT+fPnNXz4cG+XCi/jyAcAwBRffPGFamtrVVZWppSUFOXl5am6ulp5eXlKSUlRWVmZamtr9cUXX3i7VHgZ4QMAYIr6uRxvvvmmDh8+rNGjR+v+++/X6NGjVVBQoDfffNOtHzouwgcAwBT1czluvvlmffvtt8rOzlZaWpqys7N17Ngx3XTTTW790HERPgAAphg1apQGDBig5cuXq66uzm1fXV2dVqxYoejoaI0aNcpLFaK1YMIpAMAUfn5+WrVqlVJTUxUWFqbq6mpJ0urVqxUcHKzq6mpt3bqVBcbAkQ8AgLl8fHyu2na1dnRMhA8AgCmcTqcWLFig5ORklZeXu835uHDhgpKTk7Vw4UI5nU5vlwovI3wAAExRv7z64sWL5evr/vXi6+ur9PR0lleHJMIHAMAkVy6vHhMTo6SkJK1evVpJSUmKiYlheXW4MOEUAGCKK5dXT05O1vz583Xs2DHdcsstys7OZnl1uBA+AACmqF9ePSQkRH/961+1bds2176oqCiFhYWpsrKS5dVB+AAAmKN+efXy8nIFBQVp3bp1stlsstvtWrp0qcrLy139xo4d691i4VWEDwCAKc6cOSNJuuuuu/TDDz/osccec+2Ljo7WXXfdpa+++srVDx0XE04BAKY4d+6cJOnxxx+/6vLq//zP/+zWDx0X4QMAYIoePXpIkjIzM+Xj46MxY8Zo9OjRGjNmjHx8fJSVleXWDx0X4QMAYIq+fftKknbs2KHp06crLy9P1dXVysvL0/Tp07Vjxw63fui4mPMBADBF/Y3lunfvrsOHD2v06NGufdHR0YqPj9f58+e5sRwIHwAAc9TfWG7GjBmaMmWKkpOT9fe//1233nqrioqKtH37dm3ZsoUby4HwAQAwT0pKihYuXKjVq1e77uGya9cu+fn5aeHChUpJSfFyhWgNmPMBADBNZmamXnjhBQUGBrq1BwYG6oUXXlBmZqaXKkNrQvgAAJjC6XS6Lqe9lscee4y72oLwAQAwx549e1xreBiG4bavfrusrEx79uyxujS0MoQPAIApPvnkE1P7of1iwikAwBQnT550/Xv8+PGaNGmS62qXnTt36qOPPmrQDx0T4QMAYIr6Uys2m00FBQWusCFJ/fv3d91k7qenZNDxED4AAKay2+06deqUW9upU6cIHXBhzgcAwBRRUVGuf/v4+Ljtu3L7yn7omAgfAABThIeHu/5ts9nc9l25fWU/dEyEDwCAKS5cuOD69/VOsVzZDx0T4QMAYApf3///lXKtdT5+2g8dE78BAABTjB07VpLUt29f1dbWuu1zOBzq27evWz90XFztAgAwxdixYxUaGqozZ86oZ8+eGj16tL7//nuFh4dr7969OnPmjEJDQwkfIHwAAMwTFBSkiooKlZWVacuWLVfdD3DaBQBgitzcXJWVlUmSgoOD3fZ16tRJ0o/3dsnNzbW8NrQuhA8AgCnOnDkjSZo8ebLKy8uVnZ2ttLQ0ZWdn68KFC5o8ebJbP3RchA8AgCnq72ibkpLS4IoWX19fTZ8+3a0fOi7mfAAATNGjRw9J0tq1a/Xv//7vriXWV69eraioKHXr1s2tHzouj4987N27V1OnTlWfPn3k4+OjrKwst/2GYWjJkiWKiIhQcHCwJkyYoGPHjplVLwCglaq/lParr77S6dOn3fadPn1aX331lVs/dFweh4/KykrdcccdysjIuOr+559/Xi+//LJeffVV7d+/XyEhIZo0aZJqamqaXSwAoPUaPny463TLtRYZ8/X11fDhwy2vDa2Lx6ddJk+e7Jo09FOGYejFF1/U73//e02bNk2S9MYbb6hXr17KysrSfffd17xqAQCtVm5ururq6q7bp66uTrm5uRo/frxFVaE1MnXOR1FRkUpLSzVhwgRXW1hYmBISErRv376rhg+73S673e7arqiokPTjangOh8PM8tAK1b/GvNZA27djx45G9xs9enQLVwOrefI5bmr4KC0tlST16tXLrb1Xr16ufT+1YsUKLVu2rEH7rl27XNeFo/3Lzs72dgkAmikzM7PR/UaOHNnC1cBqVVVVje7r9atd0tPTlZaW5tquqKhQZGSkJk6cqNDQUC9WBis4HA5lZ2crKSlJAQEB3i4HQDMsWLCgUf18fHw0ZcqUFq4GVqs/c9EYpoaP3r17S5LOnj2riIgIV/vZs2d15513XvUxNptNNputQXtAQABfRh0IrzfQvnTr1k3PPPOMgoKCVFNTo9///vc6f/68az/v9/bHk9fU1PARHR2t3r17a/fu3a6wUVFRof379+uxxx4z86kAAK3Mlef8Kysr3T73r7ynC3O84HH4uHTpkr799lvXdlFRkQ4dOqTw8HBFRUVp3rx5euaZZ3TLLbcoOjpa//Zv/6Y+ffq4VrYDALRPTqfT9e+fLq9w5faV/dAxeRw+Dh48qHHjxrm26+drPPzww9q4caMWLVqkyspK/fa3v9WFCxc0cuRI7dixgzsZAkA7Fxsb26j7tsTGxlpQDVozH+OnK8F4WUVFhcLCwlReXs6E0w7A4XBo+/btmjJlCueAgTZu27Ztmjp16g37ffjhh0pOTragIljJk+9vbiwHADCFn5+fqf3QfhE+AACmWLVqlan90H4RPgAApjhy5Iip/dB+ET4AAKaora01tR/aL6+vcAoAaB9+9rOfqaysTJLUo0cP/eEPf5DNZpPdbteSJUt07tw5Vz90bIQPAIApvvvuO9e/z507d83FJa/sh46J8AEA8FhVVZWOHj3q1lZdXd2ox1ZXV+vLL79s0B4XF8cNRTsIwgcAwGNHjx5VfHx8kx5bVVV11cfm5+dryJAhzS0NbQDhAwDgsbi4OOXn57u17d27V/Pnz7/hY9esWaPRo0dfdUx0DKxwCq9ihVOg/XA6nQoKCrru1Sz+/v6qqalhobF2iBVOAQCW8/Pz07vvvnvdPu+++y7BA4QPAIB5UlJStHXrVvXr18+tPTIyUlu3blVKSoqXKkNrQvgAAJgqJSVFJ06c0B/fzlL3qf+iP76dpaKiIoIHXJhwCgAwnZ+fn+5OHKmQQ/66O3EYp1rghiMfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKX8vV0AAKD1KfquUpX22maNcfxcpetvf//mf92E2PwV3T2k2ePA+wgfAAA3Rd9VatzKPaaNt2DLYdPG+nThWAJIO0D4AAC4qT/i8eLMOxXTs3PTx6m2a9uefUoem6iQYFuzavq27JLmvXuo2Udj0DoQPgAAVxXTs7MG9Q1r8uMdDodKe0hD+v9MAQEBJlaGto4JpwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAluJSWwCAG7uzRr5BZ1RUUSjfoKav81FbW6vi2mId+f5Is1c4Laq4JN+gM7I7ayQ1/fJftA6EDwCAm+LKkwqJfkWL/2LOeGt3rDVlnJBoqbjyTsWrlynjwXsIHwAAN31C+quyaK5emnmnbm7GCqe1tbX6/LPPNWLkiGYf+ThedklPvXtIfcb1b9Y4aB0IHwAANza/INXV9FV0aKx+3q15K5wW+RdpYPjAZq9wWldTrrqac7L5BTVrHLQOTDgFAACWInwAAABLcdoFAOCm2uGUJBWcKW/WOJXVdh08J/U++YMpd7VF+0H4AAC4Of6/X/S/yzxswmj+evPbAyaM86MQG19b7QGvIgDAzcTbekuSbu7ZWcEBfk0ep7CkXAu2HNaqGYMVG9H8tTlCbP6K7h7S7HHgfS0WPjIyMvTCCy+otLRUd9xxh1555RUNHTq0pZ4OAGCS8JBA3Tc0qtnj1NbWSpJu7hGiQX1ZGAz/X4tMOH333XeVlpamp59+Wl9++aXuuOMOTZo0SWVlZS3xdAAAoA1pkSMfq1ev1m9+8xs98sgjkqRXX31VH330kf70pz/pd7/7XUs8JQDAQlVVVTp69Oh1+xSWXJC99FsdKQhW3fmuNxwzLi5OnTp1MqlCtGamh4/Lly8rPz9f6enprjZfX19NmDBB+/bta9DfbrfLbre7tisqKiT9uDiNw+Ewuzy0MvWvMa810LYUFBQoISGhUX3/z58bN+b+/ft11113NaMqeJMnn+Omh4/vvvtOTqdTvXq5r73fq1evq6bkFStWaNmyZQ3ad+3aRQLuQLKzs71dAgAP2O12rVq16rp9HHXS9zVSeJAU0IiT/CdOnFBJSYlJFcJqVVVVje7r9atd0tPTlZaW5tquqKhQZGSkJk6cqNDQUC9WBis4HA5lZ2crKSmp2csvA2hdeH93LPVnLhrD9PDRvXt3+fn56ezZs27tZ8+eVe/evRv0t9lsstkaLj4TEBDAL2sHwusNtF+8vzsGT15j0692CQwMVHx8vHbv3u1qq6ur0+7du5WYmGj20wEAgDamRU67pKWl6eGHH9bdd9+toUOH6sUXX1RlZaXr6hcAANBxtUj4mDlzps6dO6clS5aotLRUd955p3bs2NFgEioAAOh4WmzC6RNPPKEnnniipYYHAABtVIuscAoAAHAthA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKW8flfbnzIMQ5Jnd8dD2+VwOFRVVaWKigpuPAW0M7y/O5b67+367/HraXXh4+LFi5KkyMhIL1cCAAA8dfHiRYWFhV23j4/RmIhiobq6OhUXF6tLly7y8fHxdjloYRUVFYqMjNTp06cVGhrq7XIAmIj3d8diGIYuXryoPn36yNf3+rM6Wt2RD19fX/Xr18/bZcBioaGhfDgB7RTv747jRkc86jHhFAAAWIrwAQAALEX4gFfZbDY9/fTTstls3i4FgMl4f+NaWt2EUwAA0L5x5AMAAFiK8AEAACxF+AAAAJYifKDJxo4dq3nz5l1z/4ABA/Tiiy826zk2btyorl27NmsMAO3L0qVLdeedd7q2Z82apenTp3utHniu1S0yhrYjMzOT+zUA7dTSpUuVlZWlQ4cOebuUG3rppZcadT8RtB6EDzRZeHi4t0sA4GUOh8Pr/xPS2FU10Xpw2gVNduVpl7KyMk2dOlXBwcGKjo7WW2+91aD/6tWrNXjwYIWEhCgyMlKPP/64Ll265NZn48aNioqKUqdOnfSrX/1K58+fbzDOBx98oCFDhigoKEg33XSTli1bptra2hb5GYG2ym6368knn1TPnj0VFBSkkSNH6sCBA5KufjozKyvLdT+tjRs3atmyZfr666/l4+MjHx8fbdy4UZLk4+OjdevW6d5771VISIieffZZOZ1OzZ49W9HR0QoODlZsbKxeeuklt/H37NmjoUOHKiQkRF27dtWIESN08uTJRv0szz33nHr16qUuXbpo9uzZqqmpcdt/5WmX119/XX369FFdXZ1bn2nTpunRRx9t1POh5RE+YIpZs2bp9OnT+vTTT7VlyxatXbtWZWVlbn18fX318ssv65tvvtGf//xnffLJJ1q0aJFr//79+zV79mw98cQTOnTokMaNG6dnnnnGbYzc3Fw99NBDeuqpp/S3v/1Nr732mjZu3Khnn33Wkp8TaCsWLVqkrVu36s9//rO+/PJLxcTEaNKkSfr+++9v+NiZM2dqwYIFuu2221RSUqKSkhLNnDnTtX/p0qX61a9+pcOHD+vRRx9VXV2d+vXrp82bN+tvf/ublixZosWLF+u9996TJNXW1mr69OkaM2aM/vrXv2rfvn367W9/26ibh7733ntaunSpli9froMHDyoiIkJr1669Zv9f//rXOn/+vD799FNX2/fff68dO3bogQceuOHzwSIG0ERjxowxnnrqKaOwsNCQZPzlL39x7Tty5IghyVizZs01H79582ajW7duru3777/fmDJlilufmTNnGmFhYa7t8ePHG8uXL3fr8+abbxoRERHN+2GAduTSpUtGQECA8dZbb7naLl++bPTp08d4/vnnjQ0bNri9rwzDMN5//33jyq+Ep59+2rjjjjsajC3JmDdv3g1rmDNnjpGammoYhmGcP3/ekGTs2bPH458lMTHRePzxx93aEhIS3Gp7+OGHjWnTprm2p02bZjz66KOu7ddee83o06eP4XQ6PX5+tAyOfKDZjhw5In9/f8XHx7va4uLiGhzW/a//+i+NHz9effv2VZcuXfSP//iPOn/+vKqqqlzjJCQkuD0mMTHRbfvrr7/WH/7wB3Xu3Nn15ze/+Y1KSkpc4wAd3fHjx+VwODRixAhXW0BAgIYOHaojR440e/y77767QVtGRobi4+PVo0cPde7cWa+//rpOnTol6cf5YbNmzdKkSZM0depUvfTSSyopKWnUczXmc+GnHnjgAW3dulV2u12S9NZbb+m+++674W3eYR1eCVjixIkTSk5O1u23366tW7cqPz9fGRkZkqTLly83epxLly5p2bJlOnTokOvP4cOHdezYMQUFBbVU+UC74uvr2+DqEIfD0ejHh4SEuG2/8847WrhwoWbPnq1du3bp0KFDeuSRR9ze2xs2bNC+ffs0fPhwvfvuu7r11luVl5fXvB/kGqZOnSrDMPTRRx/p9OnTys3N5ZRLK0P4QLPFxcWptrZW+fn5rrbCwkJduHDBtZ2fn6+6ujqtWrVKw4YN06233qri4mK3cQYOHKj9+/e7tf30w2nIkCEqLCxUTExMgz/8Xw3wo5tvvlmBgYH6/PPPXW0Oh0MHDhzQz3/+c/Xo0UMXL15UZWWla/9PL6kNDAyU0+ls1PN9/vnnGj58uB5//HHdddddiomJ0fHjxxv0u+uuu5Senq4vvvhCgwYN0qZNm244dmM+F34qKChIKSkpeuutt/T2228rNjZWQ4YMadTPAmtwqS2aLTY2Vv/wD/+gf/qnf9K6devk7++vefPmKTg42NUnJiZGDodDr7zyiqZOnarPP/9cr776qts4Tz75pEaMGKGVK1dq2rRp2rlzp3bs2OHWZ8mSJUpOTlZUVJRmzJghX19fff311yooKGgwORXoqEJCQvTYY4/pX/7lXxQeHq6oqCg9//zzqqqq0uzZs2UYhjp16qTFixfrySef1P79+11Xs9QbMGCAioqKdOjQIfXr109dunS55t1pb7nlFr3xxhvauXOnoqOj9eabb+rAgQOKjo6WJBUVFen111/Xvffeqz59+qiwsFDHjh3TQw89dMOf5amnntKsWbN09913a8SIEXrrrbf0zTff6Kabbrru4x544AElJyfrm2++0YMPPti4/3CwjpfnnKANq59wahiGUVJSYvzyl780bDabERUVZbzxxhtG//793Sacrl692oiIiDCCg4ONSZMmGW+88YYhyfjhhx9cfdavX2/069fPCA4ONqZOnWqsXLmywcS4HTt2GMOHDzeCg4ON0NBQY+jQocbrr7/e8j8w0IZUV1cbc+fONbp3727YbDZjxIgRbpPC33//fSMmJsYIDg42kpOTjddff91twmlNTY2RmppqdO3a1ZBkbNiwwTCMHyecvv/++27PVVNTY8yaNcsICwszunbtajz22GPG7373O9ek0NLSUmP69OlGRESEERgYaPTv399YsmRJoyeAPvvss0b37t2Nzp07Gw8//LCxaNGi6044NQzDcDqdRkREhCHJOH78eKP/u8EaPobBsnAAAMA6nCQHAACWInwAALzmtttuc7t0/so/V1spGe0Dp10AAF5z8uTJa17mW7+kOtofwgcAALAUp10AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEv9P1ptyq9+Yc5CAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[['idade', 'outras_div', 'renda']].boxplot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "_Zz5npVyRnfc",
        "outputId": "7fb6941b-0dc0-4b54-d99a-586fa9b7caf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito['outras_div_log'] = np.log(dados_credito['outras_div'])\n",
        "dados_credito['renda_log'] = np.log(dados_credito['renda'])"
      ],
      "metadata": {
        "id": "gtjZGPPkRzkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[['outras_div', 'outras_div_log']].boxplot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "4HDCsw-3R2EG",
        "outputId": "0335b30d-bf99-45ba-b77e-66da213398ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[['renda', 'renda_log']].boxplot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "lrRKlOK6R2IY",
        "outputId": "cc81e41b-eaa8-42ec-de58-f9b33e556755"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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SAOAiIKDAWMXFxUpMTFR6eroWLlyo9PR0JSYmqri42NelAQC8jIACIxUXFys3N1dJSUmy2Wxavny5bDabkpKSlJubS0gBAD9HQIFxHA6HCgsLNXXqVJWUlCglJUVhYWFKSUlRSUmJpk6dqpkzZ3K6BwD8GAEFxrHZbDp48KAeffRRWSyeX1GLxaKioiIdOHBANpvNRxUCALyNgALjHDt2TJI0fPjws/a3tbeNAwD4HwIKjNOvXz9J0u7du8/a39beNg4A4H8IKDDOuHHjNGjQID399NNyOp0efU6nU/PmzVNCQoLGjRvnowoBAN5GQIFxrFarFixYoNLSUk2bNk3V1dU6ffq0qqurNW3aNJWWlmr+/PmyWq2+LhUA4CWBvi4AOJvs7GytWLFChYWFuuGGG9ztCQkJWrFihbKzs31YHQDA2wgoMFZ2draysrK0ceNGrV27VpMnT9aECRM4cgIAPQABBUazWq1KS0tTU1OT0tLSCCcA0EMwBwUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUGA0h8OhyspKbd68WZWVlTwgEAB6CAIKjFVcXKzExESlp6dr4cKFSk9PV2JiooqLi31dGgDAywgoMFJxcbFyc3NVV1fn0V5XV6fc3FxCCgD4OQIKjONwOJSXlyeXy6WJEyfKZrNp+fLlstlsmjhxolwul/Ly8jjdAwB+jIAC42zatEn19fUaO3asVq9erZSUFIWFhSklJUWrV6/WmDFjVF9fr02bNvm6VACAlxBQYJy24DF37lxZLJ5fUYvFojlz5niMAwD4HwIKAAAwDgEFxhk/frwk6YknnpDT6fToczqdmjt3rsc4AID/IaDAOOPHj9ell16qDz74QFlZWaqurtbp06dVXV2trKwsffDBB4qJiSGgAIAf42nGMI7VatXSpUuVk5OjDRs2qLS01N0XHh4uSVqyZAlPNgYAP8YRFBgpOztbK1euVExMjEd7TEyMVq5cqezsbB9VBgC4GDiCAmNlZ2crKytLGzdu1Nq1azV58mRNmDCBIycA0AMQUGA0q9WqtLQ0NTU1KS0tjXACAD0Ep3gAAIBxCCgAAMA4BBQAAGAcAgoAADBOhwLKvHnzdP3116tXr16KiYnRtGnTtGfPHo8xzc3Nys/PV9++fRUZGamcnBzV1dV5jDl8+LCmTJmi8PBwxcTE6OGHH9aZM2c6vzUAAMAvdCigVFZWKj8/X9XV1aqoqJDdbldGRoaamprcYx566CGtWbNG7777riorK3X06FGPe1Y4HA5NmTJFra2t2rp1q9544w0tW7ZMs2fP7rqtAgAA3VqHLjNet26dx/KyZcsUExOjmpoa3XDDDWpoaNBrr72mt956SzfeeKMk6fXXX9fQoUNVXV2t0aNHq7y8XJ988on++te/KjY2VsnJyXrqqac0a9YszZkzR8HBwV23dQAAoFvq1H1QGhoaJEl9+vSRJNXU1Mhut2vSpEnuMUOGDNGAAQNUVVWl0aNHq6qqSklJSYqNjXWPyczMVF5enj7++GNde+217d6npaVFLS0t7uXGxkZJkt1ul91u78wmoBto+4z5rAH/w/7ds3Tkc77ggOJ0OvXggw9qzJgxGj58uCSptrZWwcHB6t27t8fY2NhY1dbWusd8M5y09bf1nc28efPcT7D9pvLycvezWeD/KioqfF0CAC9h/+4ZTp06dd5jLzig5Ofna/fu3frggw8udBXnraioSAUFBe7lxsZGxcfHKyMjQ1FRUV5/f/iW3W5XRUWF0tPTFRQU5OtyAHQh9u+epe0MyPm4oIAyY8YMlZaWavPmzbr88svd7XFxcWptbdWJEyc8jqLU1dUpLi7OPWbbtm0e62u7yqdtzLeFhIQoJCSkXXtQUBBf6B6EzxvwX+zfPUNHPuMOXcXjcrk0Y8YMrVq1Su+//74SEhI8+keMGKGgoCBt2LDB3bZnzx4dPnxYqampkqTU1FTt2rVL9fX17jEVFRWKiorSsGHDOlIOAADwUx06gpKfn6+33npLq1evVq9evdxzRqKjoxUWFqbo6Gjdc889KigoUJ8+fRQVFaUHHnhAqampGj16tCQpIyNDw4YN01133aVnn31WtbW1euyxx5Sfn3/WoyQAAKDn6VBAWbJkiSRp/PjxHu2vv/66/u3f/k2StGjRIlksFuXk5KilpUWZmZl6+eWX3WOtVqtKS0uVl5en1NRURUREaPr06XryySc7tyUAAMBvdCiguFyu7x0TGhqqxYsXa/Hixd85ZuDAgSorK+vIWwMAgB6EZ/EAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAKjORwOVVZWavPmzaqsrJTD4fB1SQCAi4CAAmMVFxcrMTFR6enpWrhwodLT05WYmKji4mJflwYA8DICCoxUXFys3NxcJSUlyWazafny5bLZbEpKSlJubi4hBQD8HAEFxnE4HCosLNTUqVNVUlKilJQUhYWFKSUlRSUlJZo6dapmzpzJ6R4A8GMEFBjHZrPp4MGDevTRR2WxeH5FLRaLioqKdODAAdlsNh9VCADwNgIKjHPs2DFJ0vDhw8/a39beNg4A4H8IKDBOv379JEm7d+8+a39be9s4AID/IaDAOOPGjdOgQYP09NNPy+l0evQ5nU7NmzdPCQkJGjdunI8qBAB4W6CvCwC+zWq1asGCBcrNzVVWVpbS09O1d+9eHTp0SBUVFXrvvfe0YsUKWa1WX5cKAPASAgqMlJ2drZkzZ2rRokUqLS11twcGBmrmzJnKzs72YXUAAG8joMBIxcXFmj9/vqZMmeI+gjJ48GBVVFRo/vz5Gj16NCEFAPxYgMvlcvm6iI5qbGxUdHS0GhoaFBUV5ety0MUcDocSExOVlJSkkpISORwOlZWV6eabb5bVatW0adO0e/du7d27l9M8QDdnt9vd+3dQUJCvy4GXdeTvN5NkYRzugwIAIKDAON+8D8rZHhbIfVAAwP8xBwXGabu/yR/+8ActXbpUhw4dkiQtXLhQAwcO1C9+8QuPcQAA/8McFBjH4XCof//+qq+vV2hoqJqbm919bcsxMTE6evQoc1CAbo45KD1LR/5+cwQFRmoLJdHR0Vq0aJFCQkLU0tKiOXPmqLm52SO0AAD8DwEFxtm0aZMaGxs1ZMgQNTc3Ky8vz92XkJCgIUOG6LPPPtOmTZs0ceJEH1YKAPAWJsnCOJs2bZIkLV68WPv27VNFRYUKCgpUUVGhvXv36qWXXvIYBwDwPxxBgdGsVqvS0tLU1NSktLQ05pwAQA/BERQYZ/z48ZKkJ5544qwPC5wzZ47HOACA/yGgwDjjx49XTEyMPvjgA2VlZam6ulqnT59WdXW1srKytGXLFsXExBBQAMCPcYoHxrFarVqyZIlyc3O1YcMGj4cFhoeHKyAgQEuWLOF0DwD4MY6gwEjZ2dlasWKFYmJiPNpjYmK0YsUKHhQIAH6OgAKjffs+gt+ekwIA8E8EFBipuLhYOTk5On78uEf78ePHlZOTo+LiYh9VBgC4GAgoMI7D4dB9990nSZo4caJsNpuWL18um83mvjFbXl6eHA6HL8sEAHgRAQXG2bRpk44fP66xY8dq9erVSklJUVhYmFJSUrR69WqNHTtW9fX13KgNAPwYAQXGaQsec+fOlcXi+RW1WCx64oknPMYBAPwPAQUAABiHgALjcCdZAAA3aoNxvnkn2VtvvVUZGRnau3evDh06pPLycu4kCwA9AAEFxmm7k2xOTo7Kysr03nvvufsCAgIkiTvJAoCf4xQPjBYSEuKxHBoa6qNKAAAXEwEFxnE4HCosLNTIkSMVFxfn0RcbG6uRI0dq5syZ3AcFAPwYAQXGsdlsOnjwoGpqapSUlORxo7akpCTV1NTowIEDstlsvi4VAOAlBBQY5/PPP5ck3XTTTVq5cqWam5u1fft2NTc3a+XKlbrppps8xgEA/A+TZGGctufvDBo0SFdeeaUOHjwoSVq4cKEGDRrkDijffk4PAMB/EFBgnEsvvVTS11fqTJkyRQUFBfrnP/+pK6+8UuvXr9fSpUs9xgEA/A8BBcb55sTY999/3+My47CwsLOOAwD4F+agwGgul8vXJQAAfIAjKDBObW2t++eJEye67yQ7ePBglZeXu4+ofHMcAMC/EFBgnLbJr3l5ee3uJDto0CD94he/0CuvvMIkWQDwYwQUGKdt8uvKlStVX1/v0Xfw4EGtWrXKYxwAwP8wBwXGueyyyySpXThp09beNg4A4H8IKDDOyJEju3QcAKD7IaDAOI888kiXjgMAdD8EFBhn27Zt7p8tFs+v6DeXvzkOAOBfCCgwTtu9T4KDg3XixAndd999Sk5O1n333acTJ04oODjYYxwAwP9wFQ+MEx0dLUlqbW1V79695XQ6JUk7duzQH//4R/dy2zgAgP/hCAqMExUV5f7Z6XQqIyNDTz/9tDIyMtzh5NvjAAD+hSMoME5qaqpWr17tXi4vL1d5eflZxwEA/BNHUGCcb06E/ebDAb+9/O0JtAAA/8FveBjn8OHD7p+bm5s9+lpaWs46DgDgXwgoMM4VV1whScrMzFRAQEC7/vT0dI9xAAD/Q0CBce6//34FBgaqurpa8fHxHn3x8fHatm2bAgMDdf/99/uoQgCAtzFJFsYJDg7WlClTtHr1ap0+fVq33XabwsPDderUKa1atUqtra3Kyspy3w8FAOB/CCgwjsPh0M6dOxUXF6fa2lq98847Hv1xcXH6xz/+IYfDIavV6qMqAQDexCkeGMdms+ngwYOqra1VaGioR19oaKhqa2t14MAB2Ww2H1UIAPC2DgeUzZs365ZbblH//v0VEBCgkpISj36Xy6XZs2erX79+CgsL06RJk7R3716PMV9++aXuuOMORUVFqXfv3rrnnnt08uTJTm0I/Mfnn3/u/vnbV/F8c/mb4wAA/qXDAaWpqUnXXHONFi9efNb+Z599Vi+++KKWLl2qDz/8UBEREcrMzPT4w3LHHXfo448/VkVFhUpLS7V582bde++9F74V8Ct1dXXunwMCAnTnnXdq0aJFuvPOOz2u6vnmOACAf+nwHJTJkydr8uTJZ+1zuVx6/vnn9dhjjykrK0uS9Oc//1mxsbEqKSnR7bffrk8//VTr1q3T9u3bNXLkSEnSSy+9pJtvvlnz589X//79O7E58AfHjh1z/3zy5EkFBQWprKxM+fn5euWVVxQREdFuHADAv3TpJNkDBw6otrZWkyZNcrdFR0crJSVFVVVVuv3221VVVaXevXu7w4kkTZo0SRaLRR9++KH+5V/+pd16W1paPG7Q1djYKEmy2+2y2+1duQm4yE6dOqU9e/Z4tH1zUuxNN92kH9/+U+0/9j86/PlRvfv2co9xP/7xjz1ee9VVVyk8PNy7RQPoMm2/w/ld3jN05HPu0oBSW1srSYqNjfVoj42NdffV1tYqJibGs4jAQPXp08c95tvmzZunuXPntmsvLy/nj1E3t3//fhUWFn5nv81m+87JsEeOHFFKSopH24IFC7iBG9ANVVRU+LoEXASnTp0677Hd4jLjoqIiFRQUuJcbGxsVHx+vjIwMnmjbzZ06dUpjx471aJs5c6Y7lIwZM0ZXDEtWycf/rWk/6qv9n+zQli1bJEnjxo3T/PnzPV7LERSge7Hb7aqoqFB6erqCgoJ8XQ68rO0MyPno0oASFxcn6evJi/369XO319XVKTk52T2mvr7e43VnzpzRl19+6X79t4WEhCgkJKRde1BQEF/obi46OlqjRo3yaCsrK1OvXr0kSTU1Ne5A8uet8rjsuKysTJGRkRevWABew+/znqEjn3GX3gclISFBcXFx2rBhg7utsbFRH374oVJTUyVJqampOnHihGpqatxj3n//fTmdznaH69EzRUZG6vrrr5f03ZcZX3/99YQTAPBjHT6CcvLkSe3bt8+9fODAAe3YsUN9+vTRgAED9OCDD+q3v/2tBg8erISEBD3++OPq37+/pk2bJkkaOnSobrrpJv385z/X0qVLZbfbNWPGDN1+++1cwQO3bdu2adSoUdq+fXu7vuuvv17btm3zQVUAgIulwwHlo48+0oQJE9zLbXNDpk+frmXLlumRRx5RU1OT7r33Xp04cUJjx47VunXrPA7Nv/nmm5oxY4YmTpwoi8WinJwcvfjii12wOfAn27Zt08mTJ3VL9k+05e+faMy1w7Sm+P9y5AQAeoAAl8vl8nURHdXY2Kjo6Gg1NDQwSbYH2HHovzVtSbVK8kYreWBfX5cDoAvZ7XaVlZXp5ptvZg5KD9CRv988iwcAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIwT6OsC4L8OfNGkppYznV7P/uNN7v8GBnb+KxsREqiESyI6vR4AgPcQUOAVB75o0oT5m7p0nYUrdnXZujbOHE9IAQCDEVDgFW1HTp6/LVmJMZGdW9fpFpVuqtLU8amKCAvp1Lr21Z/Ug+/s6JIjOwAA7yGgwKsSYyI1/LLoTq3Dbrer9lLpuoE/UFBQUBdVBgAwGZNkAQCAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAODwuEV7Q4mmUJ/VwHGvfIEtq5pxmfOXNGR88c1adffqrAwM59ZQ80npQl9HO1OJolde4hhgAA7yGgwCuONh1SRMJLenRb163z5XUvd8l6IhKko03JGqHYLlkfAKDrEVDgFf0jBqrpwAN64bZkXRHT+SMoWz7YojFjx3T6CMr++pP61Ts71H/CwE6tBwDgXQQUeEWINVTO5suUEHWVhvXt3KkUu92uA4EHNLTPUAUFBXVqXc7mBjmbjyvEGtqp9QAAvItJsgAAwDgEFAAAYBwCCgAAMA5zUOAVp+0OSdLuzxs6va6m0y366LgUd+h/FBEW0ql17as/2el6AADeR0CBV+z/3yDw6+JdXbTGQP2ffdu7aF1SRAhffQAwGb+l4RUZP4qTJF0RE6mwIGun1rXnWIMKV+zSgtwkXdWv8zdXiwgJVMIlEZ1eDwDAewgo8Io+EcG6fdSALlnXmTNnJElXXBqh4Zdx91cA6AmYJAsAAIxDQAEAAMYhoAAAAOMQUAAAgHGYJAufOnXqlD777LNzjtlz7IRaavfp091hcv5373OOHTJkiMLDw7uwQgCALxBQ4FOfffaZRowYcV5jf/bG94+pqanRdddd18mqAAC+RkCBTw0ZMkQ1NTXnHHPydIve21ilKRNSFfk9d5IdMmRIV5YHwIsaGho0efJk7d27V4MHD9batWsVHc2tBPA1Agp8Kjw8/HuPeNjtdv3PF/VKHTVSQUFBF6kyAN6UmJio/fv3u5e/+OIL9e7dW1dccYX27dvnw8pgCibJAgAuqm+Gk8zMTD3zzDPKzMyUJO3fv1+JiYm+LA+GIKAAAC6ahoYGdzhpamrSmjVrNGTIEK1Zs0ZNTU2Svg4pDQ2df9AoujdO8QAAvOJsV+n9+7//uyQpNTVVn332mU6ebtHWXfv1g0s+UmRYiEaPHq3q6mqlpaXpT3/6k8druUqvZ/FpQFm8eLGee+451dbW6pprrtFLL72kUaNG+bIkAMB5OPBFk5pazpxzzCe7dui2yePP2ldVVeVxBd+z3+rfuXNnuyv83lm7ScOSks/5njwM1H/4LKC88847Kigo0NKlS5WSkqLnn39emZmZ2rNnj2JiYnxVFgDge/z9SL1yXiv53nGO0ycVe+fPPdpO7lgnx1fHZf1Bf0UOn9juNSd3/VWOE8dk7XWpIpNv8uh7eGONrNV7vvd9y/JyNCS27/eOg9l8FlAWLlyon//857r77rslSUuXLtV7772nP/3pT/r1r3/tq7IAAN9j0//brYiEl85v8DDPxUsnRUtqu5S4qt3wSydFSEr8jv7248/mv06OIKD4AZ8ElNbWVtXU1KioqMjdZrFYNGnSJFVVtf8CtrS0qKWlxb3c2Ngo6evLT+12u/cLhk+1fcZ81oAZsn90jZzORbrsB6EKDbR+57jmlmYd+6/D7doXPPW4/vuL45KkxCuH6oqhw7X/093a989PJUl9L7lUhY8/1e51/S4foNCQ0HPWFhps0aj+g/l9YaiOfC4+CShffPGFHA6HYmNjPdpjY2PPetvzefPmae7cue3ay8vLmTDVg1RUVPi6BAD/K1GSGpvOOSZM0g/Vu1374sdf0n333afa2lrtPvR37a74u7svLi5Oix//jqMz/9UoqfGc79kiaeOnteccA985derUeY/tFlfxFBUVqaCgwL3c2Nio+Ph4ZWRkKCoqyoeV4WKw2+2qqKhQeno6N2oD/MThw4fV0NCgW265Rfv27VNiYqLWrFnDnWT9XNsZkPPhk4ByySWXyGq1qq6uzqO9rq5OcXFx7caHhIQoJKT9Lc6DgoL4g9WD8HkD/uWSSy7R5s2bVVZWpptvvpn9uwfoyGfskxu1BQcHa8SIEdqwYYO7zel0asOGDUpNTfVFSQAAwCA+O8VTUFCg6dOna+TIkRo1apSef/55NTU1ua/qAQAAPZfPAsptt92m48ePa/bs2aqtrVVycrLWrVvXbuIsAADoeXw6SXbGjBmaMWOGL0sAAAAG4mGBAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjdIuHBX6by+WS1LGHDqH7stvtOnXqlBobG3lWB+Bn2L97lra/221/x8+lWwaUr776SpIUHx/v40oAAEBHffXVV9/75OoA1/nEGMM4nU4dPXpUvXr1UkBAgK/LgZc1NjYqPj5eR44cUVRUlK/LAdCF2L97FpfLpa+++kr9+/eXxXLuWSbd8giKxWLR5Zdf7usycJFFRUXxCwzwU+zfPcf3HTlpwyRZAABgHAIKAAAwDgEFxgsJCdETTzyhkJAQX5cCoIuxf+O7dMtJsgAAwL9xBAUAABiHgAIAAIxDQAEAAMYhoMAvBAQEqKSkxNdlADiHrtxP58yZo+Tk5C5ZF8xEQAEAAMYhoOCiaW1t9XUJAL4H+ylMQUCB14wfP14zZszQgw8+qEsuuUSZmZnavXu3Jk+erMjISMXGxuquu+7SF1984fGaX/7yl3rkkUfUp08fxcXFac6cOR7r3bt3r2644QaFhoZq2LBhqqioaPfes2bN0pVXXqnw8HD98Ic/1OOPPy673e7tTQa6HX/ZT51Op5588kldfvnlCgkJUXJystatW+cxZuvWrUpOTlZoaKhGjhypkpISBQQEaMeOHRf0nvAuAgq86o033lBwcLC2bNmiZ555RjfeeKOuvfZaffTRR1q3bp3q6ur0k5/8pN1rIiIi9OGHH+rZZ5/Vk08+6f7l5nQ6lZ2dreDgYH344YdaunSpZs2a1e59e/XqpWXLlumTTz7RCy+8oFdffVWLFi26KNsMdDf+sJ++8MILWrBggebPn69//OMfyszM1K233qq9e/dK+vqhhLfccouSkpL0t7/9TU899dRZa4JBXICXpKWlua699lr38lNPPeXKyMjwGHPkyBGXJNeePXvcrxk7dqzHmOuvv941a9Ysl8vlcq1fv94VGBjo+vzzz939a9eudUlyrVq16jtree6551wjRozo7CYBfqe77qdPPPGE65prrnEv9+/f3/W73/2uXU3333+/y+VyuZYsWeLq27ev6/Tp0+7+V1991SXJ9fe///283hMXV7d8mjG6jxEjRrh/3rlzpzZu3KjIyMh24/bv368rr7xSknT11Vd79PXr10/19fWSpE8//VTx8fHq37+/uz81NbXd+t555x29+OKL2r9/v06ePKkzZ87wpFTgO3T3/bSxsVFHjx7VmDFjPNrHjBmjnTt3SpL27Nmjq6++WqGhoe7+UaNGdfi9cPEQUOBVERER7p9PnjypW265Rb///e/bjevXr5/756CgII++gIAAOZ3O837Pqqoq3XHHHZo7d64yMzMVHR2tt99+WwsWLLiALQD8H/spTERAwUVz3XXXaeXKlRo0aJACAy/sqzd06FAdOXJEx44dc/+yrK6u9hizdetWDRw4UL/5zW/cbYcOHbrwwoEepDvup1FRUerfv7+2bNmitLQ0d/uWLVvcR0muuuoq/eUvf1FLS4v7wYTbt2+/oPfDxcEkWVw0+fn5+vLLL/XTn/5U27dv1/79+7V+/Xrdfffdcjgc57WOSZMm6corr9T06dO1c+dO2Ww2j19wkjR48GAdPnxYb7/9tvbv368XX3xRq1at8sYmAX6nu+6nDz/8sH7/+9/rnXfe0Z49e/TrX/9aO3bs0K9+9StJ0s9+9jM5nU7de++9+vTTT7V+/XrNnz9f0tdHf2AeAgoumrb/w3E4HMrIyFBSUpIefPBB9e7dWxbL+X0VLRaLVq1apdOnT2vUqFH6j//4D/3ud7/zGHPrrbfqoYce0owZM5ScnKytW7fq8ccf98YmAX6nu+6nv/zlL1VQUKDCwkIlJSVp3bp1+s///E8NHjxY0tdHWdasWaMdO3YoOTlZv/nNbzR79mxJ8piXAnMEuFwul6+LAADgYnvzzTd19913q6GhQWFhYb4uB9/CHBQAQI/w5z//WT/84Q912WWXaefOnZo1a5Z+8pOfEE4MxSkeAIBxfvSjHykyMvKs/958880LWmdtba3uvPNODR06VA899JB+/OMf649//GMXV46uwikeAIBxDh069J23vY+NjVWvXr0uckW42AgoAADAOJziAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACM8/8BolnC29szoioAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[['renda_log']].boxplot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "i4G8RtUKSWh0",
        "outputId": "a2dd6189-7e77-4bfa-9655-2e943eb3f67d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito[dados_credito['renda_log'] > 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "65NU2tKaShjf",
        "outputId": "9c4d39a0-d032-4a4b-8e68-707cc4f69c36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  idade  educacao  tipo_emprego  renda  divida  outras_div    default  \\\n",
              "0      1     41         3            18    176    10.3        5.01  nao_pagou   \n",
              "292  293     51         2            19    159     2.8        3.38      pagou   \n",
              "343  344     40         3             5    220    16.0       27.03  nao_pagou   \n",
              "437  438     40         3            18    157     6.6        7.04      pagou   \n",
              "444  445     43         4            18    446     6.5       12.96  nao_pagou   \n",
              "450  451     43         1            25    242     2.6        4.66      pagou   \n",
              "456  457     37         5             9    277     5.9        9.56      pagou   \n",
              "466  467     47         3            16    221    17.6       23.10  nao_pagou   \n",
              "478  479     51         2            27    166    11.5       18.27      pagou   \n",
              "\n",
              "     educacao_cat    idade_cat renda_cat   idade_z  outras_div_z  \\\n",
              "0       ensin_sup     acima_40  (54,446]  0.800922      0.597431   \n",
              "292   ensin_medio     acima_40  (54,446]  2.073440      0.107157   \n",
              "343     ensin_sup  entre_30_40  (54,446]  0.673671      7.220645   \n",
              "437     ensin_sup  entre_30_40  (54,446]  0.673671      1.208018   \n",
              "444     ensin_sup     acima_40  (54,446]  1.055426      2.988646   \n",
              "450  ensin_basico     acima_40  (54,446]  1.055426      0.492157   \n",
              "456     ensin_sup  entre_30_40  (54,446]  0.291915      1.965988   \n",
              "466     ensin_sup     acima_40  (54,446]  1.564433      6.038572   \n",
              "478   ensin_medio     acima_40  (54,446]  2.073440      4.585797   \n",
              "\n",
              "     outras_div_log  renda_log  \n",
              "0          1.611436   5.170484  \n",
              "292        1.217876   5.068904  \n",
              "343        3.296947   5.393628  \n",
              "437        1.951608   5.056246  \n",
              "444        2.561868   6.100319  \n",
              "450        1.539015   5.488938  \n",
              "456        2.257588   5.624018  \n",
              "466        3.139833   5.398163  \n",
              "478        2.905260   5.111988  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a6742da0-46cd-45ec-9c06-0a5e7af225f9\" 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>id</th>\n",
              "      <th>idade</th>\n",
              "      <th>educacao</th>\n",
              "      <th>tipo_emprego</th>\n",
              "      <th>renda</th>\n",
              "      <th>divida</th>\n",
              "      <th>outras_div</th>\n",
              "      <th>default</th>\n",
              "      <th>educacao_cat</th>\n",
              "      <th>idade_cat</th>\n",
              "      <th>renda_cat</th>\n",
              "      <th>idade_z</th>\n",
              "      <th>outras_div_z</th>\n",
              "      <th>outras_div_log</th>\n",
              "      <th>renda_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>176</td>\n",
              "      <td>10.3</td>\n",
              "      <td>5.01</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.800922</td>\n",
              "      <td>0.597431</td>\n",
              "      <td>1.611436</td>\n",
              "      <td>5.170484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>293</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>159</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.38</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>2.073440</td>\n",
              "      <td>0.107157</td>\n",
              "      <td>1.217876</td>\n",
              "      <td>5.068904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>344</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>220</td>\n",
              "      <td>16.0</td>\n",
              "      <td>27.03</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>entre_30_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.673671</td>\n",
              "      <td>7.220645</td>\n",
              "      <td>3.296947</td>\n",
              "      <td>5.393628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>438</td>\n",
              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "      <td>157</td>\n",
              "      <td>6.6</td>\n",
              "      <td>7.04</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>entre_30_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.673671</td>\n",
              "      <td>1.208018</td>\n",
              "      <td>1.951608</td>\n",
              "      <td>5.056246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>444</th>\n",
              "      <td>445</td>\n",
              "      <td>43</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>446</td>\n",
              "      <td>6.5</td>\n",
              "      <td>12.96</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>1.055426</td>\n",
              "      <td>2.988646</td>\n",
              "      <td>2.561868</td>\n",
              "      <td>6.100319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>450</th>\n",
              "      <td>451</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>242</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.66</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_basico</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>1.055426</td>\n",
              "      <td>0.492157</td>\n",
              "      <td>1.539015</td>\n",
              "      <td>5.488938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>457</td>\n",
              "      <td>37</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>277</td>\n",
              "      <td>5.9</td>\n",
              "      <td>9.56</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>entre_30_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>0.291915</td>\n",
              "      <td>1.965988</td>\n",
              "      <td>2.257588</td>\n",
              "      <td>5.624018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>467</td>\n",
              "      <td>47</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>221</td>\n",
              "      <td>17.6</td>\n",
              "      <td>23.10</td>\n",
              "      <td>nao_pagou</td>\n",
              "      <td>ensin_sup</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>1.564433</td>\n",
              "      <td>6.038572</td>\n",
              "      <td>3.139833</td>\n",
              "      <td>5.398163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>479</td>\n",
              "      <td>51</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>166</td>\n",
              "      <td>11.5</td>\n",
              "      <td>18.27</td>\n",
              "      <td>pagou</td>\n",
              "      <td>ensin_medio</td>\n",
              "      <td>acima_40</td>\n",
              "      <td>(54,446]</td>\n",
              "      <td>2.073440</td>\n",
              "      <td>4.585797</td>\n",
              "      <td>2.905260</td>\n",
              "      <td>5.111988</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      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",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-a6742da0-46cd-45ec-9c06-0a5e7af225f9 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-a6742da0-46cd-45ec-9c06-0a5e7af225f9');\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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7b6e28c-56e6-42eb-9010-91d48cc35f42')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f7b6e28c-56e6-42eb-9010-91d48cc35f42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SUUs8mVvRPpO"
      },
      "source": [
        "### Gráfico de dispersão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-lKOAxo7RPt3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "be3642f8-38bc-49b7-ba27-515a00133d08"
      },
      "source": [
        "dados_credito.plot.scatter(x = 'divida', y = 'outras_div');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito.plot.scatter(x = 'renda', y = 'outras_div');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "JxfRpZpZS0qV",
        "outputId": "35416036-2259-4163-ed02-33aaa4cfb73d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_credito.plot.scatter(x = 'renda_log', y = 'outras_div_log');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "0GVbG1jXS0vU",
        "outputId": "630dc8b7-c7cb-4117-8a8a-fcf58bff4614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1MdccklRP4Z"
      },
      "source": [
        "## Tratamento *outliers*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbRwOvCJe_4R"
      },
      "source": [
        "### Categorizar variável"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQ_LmuCDfADm"
      },
      "source": [
        "dados_credito['outras_div_cat'] = pd.qcut(dados_credito['outras_div'],\n",
        "                                          q = 4,\n",
        "                                          labels = ['baixa', 'baixa-media', 'media-alta', 'alta'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ae31jwVqfATU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b957d599-80e4-4a3d-c73d-9faa4a724db1"
      },
      "source": [
        "dados_credito[['id', 'idade', 'outras_div', 'outras_div_cat']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  idade  outras_div outras_div_cat\n",
              "0   1     41        5.01           alta\n",
              "1   2     27        4.00           alta\n",
              "2   3     40        2.17     media-alta\n",
              "3   4     41        0.82          baixa\n",
              "4   5     24        3.06     media-alta"
            ],
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              "                                                    [key], {});\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ea627ee4-9f24-4d37-88d3-4887808936a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gorXBs5m04c"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}