bigdata_aula5.ipynb

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        "<a href=\"https://colab.research.google.com/github/Santiagoguii/Big-data/blob/main/untitled.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "<ipython-input-4-0255a7005364>:4: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use('seaborn-talk')\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-talk')"
      ]
    },
    {
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      "source": [
        "dados = pd.read_csv(\"/content/Dados-Pesquisa.csv\")\n"
      ],
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          "text": [
            "<ipython-input-6-7660a9ef244a>:1: DtypeWarning: Columns (21,57) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  dados = pd.read_csv(\"/content/Dados-Pesquisa.csv\")\n"
          ]
        }
      ]
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    {
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      "source": [
        "dados.head()"
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              "    Age  AttendedBootcamp  BootcampFinish  BootcampFullJobAfter  \\\n",
              "0  28.0               0.0             NaN                   NaN   \n",
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        "outputId": "d8c5297c-f5e2-4b79-b4d9-d8b2f849f320"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Age  AttendedBootcamp  BootcampFinish  BootcampFullJobAfter  \\\n",
              "0    28.0               0.0             NaN                   NaN   \n",
              "1    22.0               0.0             NaN                   NaN   \n",
              "2    19.0               0.0             NaN                   NaN   \n",
              "3    26.0               0.0             NaN                   NaN   \n",
              "4    20.0               0.0             NaN                   NaN   \n",
              "..    ...               ...             ...                   ...   \n",
              "108  15.0               0.0             NaN                   NaN   \n",
              "109  36.0               0.0             NaN                   NaN   \n",
              "110  26.0               0.0             NaN                   NaN   \n",
              "111  43.0               0.0             NaN                   NaN   \n",
              "112  24.0               0.0             NaN                   NaN   \n",
              "\n",
              "     BootcampLoanYesNo  BootcampMonthsAgo BootcampName  BootcampPostSalary  \\\n",
              "0                  NaN                NaN          NaN                 NaN   \n",
              "1                  NaN                NaN          NaN                 NaN   \n",
              "2                  NaN                NaN          NaN                 NaN   \n",
              "3                  NaN                NaN          NaN                 NaN   \n",
              "4                  NaN                NaN          NaN                 NaN   \n",
              "..                 ...                ...          ...                 ...   \n",
              "108                NaN                NaN          NaN                 NaN   \n",
              "109                NaN                NaN          NaN                 NaN   \n",
              "110                NaN                NaN          NaN                 NaN   \n",
              "111                NaN                NaN          NaN                 NaN   \n",
              "112                NaN                NaN          NaN                 NaN   \n",
              "\n",
              "     BootcampRecommend  ChildrenNumber  ... ResourceSoloLearn  \\\n",
              "0                  NaN             NaN  ...               NaN   \n",
              "1                  NaN             NaN  ...               NaN   \n",
              "2                  NaN             NaN  ...               NaN   \n",
              "3                  NaN             NaN  ...               NaN   \n",
              "4                  NaN             NaN  ...               NaN   \n",
              "..                 ...             ...  ...               ...   \n",
              "108                NaN             NaN  ...               NaN   \n",
              "109                NaN             2.0  ...               NaN   \n",
              "110                NaN             NaN  ...               NaN   \n",
              "111                NaN             1.0  ...               NaN   \n",
              "112                NaN             NaN  ...               NaN   \n",
              "\n",
              "     ResourceStackOverflow  ResourceTreehouse  ResourceUdacity  ResourceUdemy  \\\n",
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              "2                      NaN                NaN              NaN            NaN   \n",
              "3                      NaN                NaN              NaN            NaN   \n",
              "4                      NaN                NaN              NaN            NaN   \n",
              "..                     ...                ...              ...            ...   \n",
              "108                    NaN                NaN              NaN            NaN   \n",
              "109                    NaN                NaN              NaN            NaN   \n",
              "110                    NaN                NaN              NaN            NaN   \n",
              "111                    NaN                NaN              NaN            NaN   \n",
              "112                    NaN                NaN              NaN            NaN   \n",
              "\n",
              "     ResourceW3Schools  ResourceYouTube  \\\n",
              "0                  NaN              NaN   \n",
              "1                  NaN              NaN   \n",
              "2                  NaN              NaN   \n",
              "3                  NaN              NaN   \n",
              "4                  NaN              NaN   \n",
              "..                 ...              ...   \n",
              "108                NaN              NaN   \n",
              "109                NaN              NaN   \n",
              "110                NaN              NaN   \n",
              "111                NaN              NaN   \n",
              "112                NaN              NaN   \n",
              "\n",
              "                                SchoolDegree              SchoolMajor  \\\n",
              "0             some college credit, no degree                      NaN   \n",
              "1             some college credit, no degree                      NaN   \n",
              "2    high school diploma or equivalent (GED)                      NaN   \n",
              "3                          bachelor's degree  Cinematography And Film   \n",
              "4             some college credit, no degree                      NaN   \n",
              "..                                       ...                      ...   \n",
              "108                         some high school                      NaN   \n",
              "109                        bachelor's degree         Computer Science   \n",
              "110                        bachelor's degree                  Biology   \n",
              "111                                    Ph.D.   Electrical Engineering   \n",
              "112                        bachelor's degree   Information Technology   \n",
              "\n",
              "     StudentDebtOwe  \n",
              "0           20000.0  \n",
              "1               NaN  \n",
              "2               NaN  \n",
              "3            7000.0  \n",
              "4               NaN  \n",
              "..              ...  \n",
              "108             NaN  \n",
              "109             NaN  \n",
              "110         11400.0  \n",
              "111             NaN  \n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>43.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>24.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>Information Technology</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>113 rows × 113 columns</p>\n",
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              "  .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-6428bed6-3e18-449e-9886-6d3be09c16d7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dados"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.columns.to_list()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6p5WWD2Hkq6F",
        "outputId": "93fe17fd-0f33-40a4-ed82-b7f22a1fc2e2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Age',\n",
              " 'AttendedBootcamp',\n",
              " 'BootcampFinish',\n",
              " 'BootcampFullJobAfter',\n",
              " 'BootcampLoanYesNo',\n",
              " 'BootcampMonthsAgo',\n",
              " 'BootcampName',\n",
              " 'BootcampPostSalary',\n",
              " 'BootcampRecommend',\n",
              " 'ChildrenNumber',\n",
              " 'CityPopulation',\n",
              " 'CodeEventBootcamp',\n",
              " 'CodeEventCoffee',\n",
              " 'CodeEventConferences',\n",
              " 'CodeEventDjangoGirls',\n",
              " 'CodeEventGameJam',\n",
              " 'CodeEventGirlDev',\n",
              " 'CodeEventHackathons',\n",
              " 'CodeEventMeetup',\n",
              " 'CodeEventNodeSchool',\n",
              " 'CodeEventNone',\n",
              " 'CodeEventOther',\n",
              " 'CodeEventRailsBridge',\n",
              " 'CodeEventRailsGirls',\n",
              " 'CodeEventStartUpWknd',\n",
              " 'CodeEventWomenCode',\n",
              " 'CodeEventWorkshop',\n",
              " 'CommuteTime',\n",
              " 'CountryCitizen',\n",
              " 'CountryLive',\n",
              " 'EmploymentField',\n",
              " 'EmploymentFieldOther',\n",
              " 'EmploymentStatus',\n",
              " 'EmploymentStatusOther',\n",
              " 'ExpectedEarning',\n",
              " 'FinanciallySupporting',\n",
              " 'Gender',\n",
              " 'HasChildren',\n",
              " 'HasDebt',\n",
              " 'HasFinancialDependents',\n",
              " 'HasHighSpdInternet',\n",
              " 'HasHomeMortgage',\n",
              " 'HasServedInMilitary',\n",
              " 'HasStudentDebt',\n",
              " 'HomeMortgageOwe',\n",
              " 'HoursLearning',\n",
              " 'ID.x',\n",
              " 'ID.y',\n",
              " 'Income',\n",
              " 'IsEthnicMinority',\n",
              " 'IsReceiveDiabilitiesBenefits',\n",
              " 'IsSoftwareDev',\n",
              " 'IsUnderEmployed',\n",
              " 'JobApplyWhen',\n",
              " 'JobPref',\n",
              " 'JobRelocateYesNo',\n",
              " 'JobRoleInterest',\n",
              " 'JobRoleInterestOther',\n",
              " 'JobWherePref',\n",
              " 'LanguageAtHome',\n",
              " 'MaritalStatus',\n",
              " 'MoneyForLearning',\n",
              " 'MonthsProgramming',\n",
              " 'NetworkID',\n",
              " 'Part1EndTime',\n",
              " 'Part1StartTime',\n",
              " 'Part2EndTime',\n",
              " 'Part2StartTime',\n",
              " 'PodcastChangeLog',\n",
              " 'PodcastCodeNewbie',\n",
              " 'PodcastCodingBlocks',\n",
              " 'PodcastDeveloperTea',\n",
              " 'PodcastDotNetRocks',\n",
              " 'PodcastHanselminutes',\n",
              " 'PodcastJSJabber',\n",
              " 'PodcastJsAir',\n",
              " 'PodcastNone',\n",
              " 'PodcastOther',\n",
              " 'PodcastProgrammingThrowDown',\n",
              " 'PodcastRubyRogues',\n",
              " 'PodcastSEDaily',\n",
              " 'PodcastShopTalk',\n",
              " 'PodcastTalkPython',\n",
              " 'PodcastWebAhead',\n",
              " 'ResourceBlogs',\n",
              " 'ResourceBooks',\n",
              " 'ResourceCodeWars',\n",
              " 'ResourceCodecademy',\n",
              " 'ResourceCoursera',\n",
              " 'ResourceDevTips',\n",
              " 'ResourceEdX',\n",
              " 'ResourceEggHead',\n",
              " 'ResourceFCC',\n",
              " 'ResourceGoogle',\n",
              " 'ResourceHackerRank',\n",
              " 'ResourceKhanAcademy',\n",
              " 'ResourceLynda',\n",
              " 'ResourceMDN',\n",
              " 'ResourceOdinProj',\n",
              " 'ResourceOther',\n",
              " 'ResourcePluralSight',\n",
              " 'ResourceReddit',\n",
              " 'ResourceSkillCrush',\n",
              " 'ResourceSoloLearn',\n",
              " 'ResourceStackOverflow',\n",
              " 'ResourceTreehouse',\n",
              " 'ResourceUdacity',\n",
              " 'ResourceUdemy',\n",
              " 'ResourceW3Schools',\n",
              " 'ResourceYouTube',\n",
              " 'SchoolDegree',\n",
              " 'SchoolMajor',\n",
              " 'StudentDebtOwe']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = dados.copy()"
      ],
      "metadata": {
        "id": "CuMt3_KulCb_"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7UifV50lG_8",
        "outputId": "697a4087-4490-41e7-9056-94a1a17f9a65"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                      2007\n",
              "AttendedBootcamp          240\n",
              "BootcampFinish          14687\n",
              "BootcampFullJobAfter    14985\n",
              "BootcampLoanYesNo       14686\n",
              "                        ...  \n",
              "ResourceW3Schools       15499\n",
              "ResourceYouTube         15499\n",
              "SchoolDegree             1941\n",
              "SchoolMajor              7170\n",
              "StudentDebtOwe          12106\n",
              "Length: 113, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.isna().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItmJ2NYDlU8u",
        "outputId": "2fa061c8-aaec-4462-c8d3-7944bdc93e97"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1183948"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.dropna(subset = ['Age'], inplace = True )"
      ],
      "metadata": {
        "id": "b7icycIdmNyH"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqSbXDEKmbyx",
        "outputId": "beff46f8-714c-4f69-d761-d760ec6f2a5b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                         0\n",
              "AttendedBootcamp          112\n",
              "BootcampFinish          12809\n",
              "BootcampFullJobAfter    13068\n",
              "BootcampLoanYesNo       12805\n",
              "                        ...  \n",
              "ResourceW3Schools       13508\n",
              "ResourceYouTube         13506\n",
              "SchoolDegree              113\n",
              "SchoolMajor              5265\n",
              "StudentDebtOwe          10121\n",
              "Length: 113, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.dtypes.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTXPmnU_mtER",
        "outputId": "593eddc4-c8ce-40a4-e1b2-e09ae2ea7306"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float64    85\n",
              "object     28\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando um novo Data Frame apenas com as colunas\n",
        "dados = dados[['Age','Gender','JobPref','JobRoleInterest','EmploymentField','JobRelocateYesNo','HoursLearning','MoneyForLearning']]\n",
        "dados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "B4cxdM1VnNY3",
        "outputId": "50fbc439-3e21-4cb5-f38f-422ed50cf126"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Age  Gender                          JobPref  \\\n",
              "0      28.0    male                       freelance    \n",
              "1      22.0    male               work for a startup   \n",
              "2      19.0    male          start your own business   \n",
              "3      26.0  female               work for a startup   \n",
              "4      20.0  female  work for a medium-sized company   \n",
              "...     ...     ...                              ...   \n",
              "15615  39.0    male          start your own business   \n",
              "15616  27.0    male               work for a startup   \n",
              "15617  37.0    male                       freelance    \n",
              "15618  26.0    male                              NaN   \n",
              "15619  22.0  female                       freelance    \n",
              "\n",
              "                 JobRoleInterest                        EmploymentField  \\\n",
              "0                            NaN      office and administrative support   \n",
              "1        Front-End Web Developer                      food and beverage   \n",
              "2                            NaN                                finance   \n",
              "3        Front-End Web Developer  arts, entertainment, sports, or media   \n",
              "4       Full-Stack Web Developer                              education   \n",
              "...                          ...                                    ...   \n",
              "15615                        NaN            software development and IT   \n",
              "15616    Front-End Web Developer            software development and IT   \n",
              "15617                        NaN                                    NaN   \n",
              "15618                        NaN                              education   \n",
              "15619                        NaN  arts, entertainment, sports, or media   \n",
              "\n",
              "       JobRelocateYesNo  HoursLearning  MoneyForLearning  \n",
              "0                   NaN           30.0               0.0  \n",
              "1                   1.0           30.0              20.0  \n",
              "2                   NaN           20.0              50.0  \n",
              "3                   NaN           20.0             100.0  \n",
              "4                   1.0           25.0            1300.0  \n",
              "...                 ...            ...               ...  \n",
              "15615               NaN           40.0               0.0  \n",
              "15616               1.0           15.0             100.0  \n",
              "15617               NaN           14.0               0.0  \n",
              "15618               NaN           10.0              30.0  \n",
              "15619               NaN            2.0               0.0  \n",
              "\n",
              "[13613 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b4e9bd5a-72f3-4931-b3f6-4d6d28c870ee\" 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>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobPref</th>\n",
              "      <th>JobRoleInterest</th>\n",
              "      <th>EmploymentField</th>\n",
              "      <th>JobRelocateYesNo</th>\n",
              "      <th>HoursLearning</th>\n",
              "      <th>MoneyForLearning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28.0</td>\n",
              "      <td>male</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>office and administrative support</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22.0</td>\n",
              "      <td>male</td>\n",
              "      <td>work for a startup</td>\n",
              "      <td>Front-End Web Developer</td>\n",
              "      <td>food and beverage</td>\n",
              "      <td>1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.0</td>\n",
              "      <td>male</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>finance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.0</td>\n",
              "      <td>female</td>\n",
              "      <td>work for a startup</td>\n",
              "      <td>Front-End Web Developer</td>\n",
              "      <td>arts, entertainment, sports, or media</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.0</td>\n",
              "      <td>female</td>\n",
              "      <td>work for a medium-sized company</td>\n",
              "      <td>Full-Stack Web Developer</td>\n",
              "      <td>education</td>\n",
              "      <td>1.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>1300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15615</th>\n",
              "      <td>39.0</td>\n",
              "      <td>male</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>software development and IT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15616</th>\n",
              "      <td>27.0</td>\n",
              "      <td>male</td>\n",
              "      <td>work for a startup</td>\n",
              "      <td>Front-End Web Developer</td>\n",
              "      <td>software development and IT</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15617</th>\n",
              "      <td>37.0</td>\n",
              "      <td>male</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15618</th>\n",
              "      <td>26.0</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>education</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15619</th>\n",
              "      <td>22.0</td>\n",
              "      <td>female</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>arts, entertainment, sports, or media</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13613 rows × 8 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-b4e9bd5a-72f3-4931-b3f6-4d6d28c870ee')\"\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-b4e9bd5a-72f3-4931-b3f6-4d6d28c870ee 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-b4e9bd5a-72f3-4931-b3f6-4d6d28c870ee');\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-f4d75d2f-6379-4318-a897-609176a9f49c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4d75d2f-6379-4318-a897-609176a9f49c')\"\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",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      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-f4d75d2f-6379-4318-a897-609176a9f49c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3122ef35-3764-40a3-9753-ce848952c0a8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dados')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_3122ef35-3764-40a3-9753-ce848952c0a8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dados');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dados",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 13613,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.017580419650095,\n        \"min\": 10.0,\n        \"max\": 86.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          51.0,\n          32.0,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"female\",\n          \"trans\",\n          \"genderqueer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobPref\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"work for a startup\",\n          \"work for a multinational corporation\",\n          \"start your own business\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRoleInterest\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"  User Experience Designer\",\n          \"Full-Stack Web Developer\",\n          \"Back-End Web Developer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EmploymentField\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"transportation\",\n          \"construction and extraction\",\n          \"office and administrative support\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRelocateYesNo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44022130268438,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoursLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.172324814852399,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          0.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MoneyForLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4752.7586514845025,\n        \"min\": 0.0,\n        \"max\": 180000.0,\n        \"num_unique_values\": 283,\n        \"samples\": [\n          600.0,\n          180000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados = dados.drop_duplicates()"
      ],
      "metadata": {
        "id": "On3GIXsko6U0"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qetFgG5pGGN",
        "outputId": "2965a680-5165-4896-e670-40e1fa75858c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12234, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Resumo estatistico\n",
        "dados.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sxKxokN4pM_2",
        "outputId": "d8a3a9e0-bf38-4961-ee21-6f84b9b6fb30"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age  JobRelocateYesNo  HoursLearning  MoneyForLearning\n",
              "count  12234.000000       4669.000000   11807.000000      11643.000000\n",
              "mean      29.598823          0.733348      15.713814       1193.690114\n",
              "std        9.215518          0.442256      14.548388       4999.765832\n",
              "min       10.000000          0.000000       0.000000          0.000000\n",
              "25%       23.000000          0.000000       5.000000          0.000000\n",
              "50%       28.000000          1.000000      10.000000         50.000000\n",
              "75%       34.000000          1.000000      20.000000        400.000000\n",
              "max       86.000000          1.000000     100.000000     180000.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ecedfddc-1397-4967-8bd1-96686b981aab\" 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>Age</th>\n",
              "      <th>JobRelocateYesNo</th>\n",
              "      <th>HoursLearning</th>\n",
              "      <th>MoneyForLearning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12234.000000</td>\n",
              "      <td>4669.000000</td>\n",
              "      <td>11807.000000</td>\n",
              "      <td>11643.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.598823</td>\n",
              "      <td>0.733348</td>\n",
              "      <td>15.713814</td>\n",
              "      <td>1193.690114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.215518</td>\n",
              "      <td>0.442256</td>\n",
              "      <td>14.548388</td>\n",
              "      <td>4999.765832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>86.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>180000.000000</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-ecedfddc-1397-4967-8bd1-96686b981aab')\"\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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ecedfddc-1397-4967-8bd1-96686b981aab 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-ecedfddc-1397-4967-8bd1-96686b981aab');\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-3080fe5a-6874-4948-91ad-6ee1137a8874\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3080fe5a-6874-4948-91ad-6ee1137a8874')\"\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-3080fe5a-6874-4948-91ad-6ee1137a8874 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4314.3364919514215,\n        \"min\": 9.215518239825427,\n        \"max\": 12234.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.59882295242766,\n          28.0,\n          12234.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRelocateYesNo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1650.5299345997373,\n        \"min\": 0.0,\n        \"max\": 4669.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7333476119083315,\n          1.0,\n          0.4422564678800309\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoursLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4166.179301410069,\n        \"min\": 0.0,\n        \"max\": 11807.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.713813839247903,\n          10.0,\n          11807.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MoneyForLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62846.13259724011,\n        \"min\": 0.0,\n        \"max\": 180000.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11643.0,\n          1193.6901142317272,\n          400.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#usando LOC\n",
        "dados.loc[dados['Age']>30]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "e3ioR4PtqMT0",
        "outputId": "15c1e128-9c4d-45fd-eef9-78d114ad3bb4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Age  Gender                  JobPref JobRoleInterest  \\\n",
              "5      34.0    male  start your own business             NaN   \n",
              "7      35.0    male               freelance              NaN   \n",
              "8      33.0    male                      NaN             NaN   \n",
              "9      33.0    male  start your own business             NaN   \n",
              "10     57.0    male               freelance              NaN   \n",
              "...     ...     ...                      ...             ...   \n",
              "15611  37.0    male  start your own business             NaN   \n",
              "15612  45.0  female  start your own business             NaN   \n",
              "15614  47.0    male               freelance              NaN   \n",
              "15615  39.0    male  start your own business             NaN   \n",
              "15617  37.0    male               freelance              NaN   \n",
              "\n",
              "                         EmploymentField  JobRelocateYesNo  HoursLearning  \\\n",
              "5                                finance               NaN            5.0   \n",
              "7      office and administrative support               NaN            NaN   \n",
              "8                   software development               NaN            5.0   \n",
              "9                                    NaN               NaN           20.0   \n",
              "10                                   NaN               NaN            8.0   \n",
              "...                                  ...               ...            ...   \n",
              "15611        software development and IT               NaN           40.0   \n",
              "15612                                NaN               NaN            2.0   \n",
              "15614                            finance               NaN            3.0   \n",
              "15615        software development and IT               NaN           40.0   \n",
              "15617                                NaN               NaN           14.0   \n",
              "\n",
              "       MoneyForLearning  \n",
              "5                   0.0  \n",
              "7                   0.0  \n",
              "8                3000.0  \n",
              "9                  30.0  \n",
              "10                400.0  \n",
              "...                 ...  \n",
              "15611             200.0  \n",
              "15612             300.0  \n",
              "15614               0.0  \n",
              "15615               0.0  \n",
              "15617               0.0  \n",
              "\n",
              "[4510 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d6d86245-48c3-4d8f-a98d-775d95b4617f\" 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>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>JobPref</th>\n",
              "      <th>JobRoleInterest</th>\n",
              "      <th>EmploymentField</th>\n",
              "      <th>JobRelocateYesNo</th>\n",
              "      <th>HoursLearning</th>\n",
              "      <th>MoneyForLearning</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>34.0</td>\n",
              "      <td>male</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>finance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>35.0</td>\n",
              "      <td>male</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>office and administrative support</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>33.0</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>software development</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>33.0</td>\n",
              "      <td>male</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>57.0</td>\n",
              "      <td>male</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15611</th>\n",
              "      <td>37.0</td>\n",
              "      <td>male</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>software development and IT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15612</th>\n",
              "      <td>45.0</td>\n",
              "      <td>female</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15614</th>\n",
              "      <td>47.0</td>\n",
              "      <td>male</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>finance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15615</th>\n",
              "      <td>39.0</td>\n",
              "      <td>male</td>\n",
              "      <td>start your own business</td>\n",
              "      <td>NaN</td>\n",
              "      <td>software development and IT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15617</th>\n",
              "      <td>37.0</td>\n",
              "      <td>male</td>\n",
              "      <td>freelance</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4510 rows × 8 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-d6d86245-48c3-4d8f-a98d-775d95b4617f')\"\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-d6d86245-48c3-4d8f-a98d-775d95b4617f 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-d6d86245-48c3-4d8f-a98d-775d95b4617f');\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-a7aa78b4-ce82-479e-aaaa-d8ec890adb5d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7aa78b4-ce82-479e-aaaa-d8ec890adb5d')\"\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-a7aa78b4-ce82-479e-aaaa-d8ec890adb5d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 4510,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.7246684715264955,\n        \"min\": 31.0,\n        \"max\": 86.0,\n        \"num_unique_values\": 46,\n        \"samples\": [\n          73.0,\n          42.0,\n          54.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"female\",\n          \"trans\",\n          \"genderqueer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobPref\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"freelance \",\n          \"work for a multinational corporation\",\n          \"work for a medium-sized company\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRoleInterest\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"  Data Scientist / Data Engineer\",\n          \"  Front-End Web Developer\",\n          \"  User Experience Designer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EmploymentField\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"construction and extraction\",\n          \"architecture or physical engineering\",\n          \"finance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRelocateYesNo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.485389704346022,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoursLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.76495411646453,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 56,\n        \"samples\": [\n          5.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MoneyForLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5527.169410396651,\n        \"min\": 0.0,\n        \"max\": 120000.0,\n        \"num_unique_values\": 172,\n        \"samples\": [\n          60000.0,\n          1300.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados['Age'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jufdcYmSq1X4",
        "outputId": "883e8999-bac8-443d-d97d-d6528c825286"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([28., 22., 19., 26., 20., 34., 23., 35., 33., 57., 27., 47., 37.,\n",
              "       31., 29., 30., 32., 25., 44., 21., 24., 18., 39., 41., 43., 36.,\n",
              "       40., 38., 16., 46., 15., 14., 50., 49., 45., 55., 51., 17., 53.,\n",
              "       52., 48., 56., 42., 54., 58., 65., 59., 75., 61., 80., 62., 60.,\n",
              "       66., 12., 11., 13., 63., 64., 70., 73., 69., 71., 68., 86., 10.,\n",
              "       67., 72.])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.Age.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a9yimyV5q87t",
        "outputId": "4b3f9e25-b724-4959-e777-ca5a32a56f09"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([28., 22., 19., 26., 20., 34., 23., 35., 33., 57., 27., 47., 37.,\n",
              "       31., 29., 30., 32., 25., 44., 21., 24., 18., 39., 41., 43., 36.,\n",
              "       40., 38., 16., 46., 15., 14., 50., 49., 45., 55., 51., 17., 53.,\n",
              "       52., 48., 56., 42., 54., 58., 65., 59., 75., 61., 80., 62., 60.,\n",
              "       66., 12., 11., 13., 63., 64., 70., 73., 69., 71., 68., 86., 10.,\n",
              "       67., 72.])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#variavel age\n",
        "idade = dados.Age #Dados ['Age]\n"
      ],
      "metadata": {
        "id": "fl3bJlWnrXHs"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Histograma\n",
        "plt.hist(idade, label =\"Idades\", color = 'lightseagreen')\n",
        "plt.title(\"Distribuição de idade\")\n",
        "plt.xlabel(\"Idades\")\n",
        "plt.ylabel(\"Quantidade\")\n",
        "plt.legend()\n",
        "#grid\n",
        "#plt.grid (True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "Z2-CzKPgri23",
        "outputId": "1d0942c0-b06c-45a6-cce7-a66c4c453fbd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1040x715 with 1 Axes>"
            ],
            "image/png": 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A+yRjY2MlSWPHjrW7fzY9PV2PP/54vsHexcVF48aNU05Ojrp06aLdu3c71GRmZmrhwoX5HicAKHVK8MmwAIBSwvqKjw4dOhj9+/c3+vfvb0RFRRktW7a0vT7BycnJGDZsmMM7Cw0j/1d87Nq1y5BkuLm5Gc2aNTP+85//GD179rS9t8/Ly8vhlQzdunWzvRqjT58+xsMPP2w888wztuUFvb7j7/7tFR+PPvqo4erqatSuXdvo3bu30aZNG8PJycmQZEyaNMmhv3379tmOQc2aNY2ePXsaTZo0MSwWizFmzJgresWHYRjG9u3bDT8/P0OS4e/vb3Tr1s3o2bOnERYWZjg5ORkvvPCCrXbz5s2Gi4uLIcmoU6eOER0dbdx9991GmTJljN69e9vO2z/fz7h//37b+yCDg4ON6Oho4/777zfc3NwMSUbv3r3zfcdmQY4dO2ZUrVrVkGQEBgYavXr1svXXpUuXAsdx+vRpo0WLFoYkw9PT04iIiDB69+5tdOvWzahfv75hsVgMf39/0+Mo6Nymp6cbt99+u+3VJj169DC6dOlieHl5GU2bNjWaN2/u8IqPnJwc2zo+Pj5G586dje7duxt+fn5GpUqVjIEDBxqS7M6HYRjGhQsXjHbt2hmSjLJlyxqdO3c2evbsadxyyy1GaGio0blz5wK/R5944glDkmGxWIw77rjD6NGjh9GrVy/jzjvvtJ2bhIQE08cDAEoKIRIAYAsB1i+LxWJ4e3sbISEhRseOHY0pU6bYvVj9n/ILkenp6cbrr79udO7c2ahevbpRtmxZw8fHx7j11luNYcOGOQQOwzCM1NRU4+GHHzaqVKliODs7OwSxogiR8+bNM+Lj443777/fKFeunOHh4WE0bdrUWLRoUYF97ty50+jQoYPh7e1teHp6Gs2bNzdWrVp1xe+JtEpOTjZGjhxp1KlTx3bM69atazz++OPGL7/84rDt++67z6hYsaLh6elpNGzY0Jg2bZqRm5tbYHgzDMM4ceKEMXz4cKNmzZqGq6ur4e3tbbRo0cKYP3/+FQVIq5SUFGPw4MGGv7+/4ebmZtSqVcuYOHGikZOTc9lxXLhwwZg3b55x7733Grfccovh7Oxs+Pv7G40bNzZGjBhhbN682fQYLnf+09LSjJEjRxrBwcGGq6urUbVqVWP48OHG2bNnjdatW+f7nsi0tDTbMXJzczOCgoKMQYMGGcePHzdeeOGFfEOkYRhGVlaW8eKLL9qObUBAgDFo0CDjjz/++Nfv0W+//daIiooygoKCDFdXV6N8+fLGrbfeavTv399Yvny5kZOTY/p4AEBJsRhGKXykGwAAN4nU1FTVrl1bH3zwgcPDcwAAKI0IkQAAlLAuXbro1KlT+u6770p6KAAA/Cte8QEAQAmYPn261q9fL0lau3atGjduXMIjAgDAHEIkAAAlwMfHR5s3b1Z6errq16+v119/vaSHBACAKUxnBQAAAACYxnsiAQAAAACmMZ21FMjLy1NycrK8vb1lsVhKejgAAAAAbkKGYejs2bMKDAyUk1PB1xsJkaVAcnKygoODS3oYAAAAAKBjx46pSpUqBS4nRJYC3t7eki6dLB8fnxIeDQAAAICbUXp6uoKDg235pCCEyFLAOoXVx8eHEAkAAACgRP3bLXY8WAcAAAAAYBohEgAAAABgGiESAAAAAGAaIRIAAAAAYBohEgAAAABgGiESAAAAAGAar/gAAAAAblJ5eXlKT09Xdna2pH9/tQOuL4ZhSJLc3Nzk4+MjJ6eiuYZIiAQAAABuQnl5eTpx4oTKlSsnX19fAuQNyjAMZWVl6cSJE/L39y+SIEmIBAAAAG5C6enpKleunDw8PEp6KChGFovFdo6t57ywuCcSAAAAuAllZ2fL3d29pIeBa8Td3d02bbmwCJEAAADATYoprDePojzXhEgAAADgJkSAvPkU1TknRAIAAAAATCNEAgAAAABMI0QCAAAAAEzjFR8AAAAA7FRbvbSkh2DnSGTUNdvWgAED9P7778swjCLtNzExUdWqVdMLL7ygCRMmFGnf1xpXIgEAAADc0DZs2CCLxaL//e9/JT2UGwIhEgAAAABgGiESAAAAAGAaIRIAAADATScrK0ujRo1SYGCgPDw8FB4erq+++irf2h07dmjAgAGqXbu2PD095e3trbvuuksrVqzIt/7777/XXXfdJQ8PD/n7++uJJ55QRkZGvrWGYejtt99W48aN5enpKS8vL919991av369Q+0HH3yg8PBwlStXTmXLllX16tXVt29f/fnnn1d/IK4CD9YBAAAAcNPp3bu3Pv30U0VGRqpDhw46fPiwunfvrmrVqjnUrlixQvv27VOvXr0UEhKiv/76S++//766d++ujz76SH369LHVbt++XW3btpW3t7eeeeYZlStXTosXL9ZDDz2U7zj69eunRYsWqWfPnho4cKCys7P10UcfqV27dlq+fLk6d+4sSfrwww/Vv39/tWzZUpMmTZKHh4eOHTumtWvX6uTJk6pYsWLxHKh8ECIBAAAA3FS++uorffrpp+rfv7/mz59va2/VqpW6devmUD9u3DhNnTrVru3JJ5/UHXfcocmTJ9uFyOHDhysvL0+bN29W7dq1JUmPP/64WrRo4dDvihUr9NFHH+ndd9/VI488Ymt/6qmn1KxZMz311FOKjIyUxWLRihUr5O3trW+//VbOzv8f4yZNmnTVx+FqMZ0VAAAAwE3l008/lSSNGjXKrr1r166qU6eOQ33ZsmVtf87MzNRff/2lzMxM3XPPPUpISFB6erok6eTJk9q6dau6dOliC5CS5OrqquHDhzv0u2DBAnl7e6tr165KTU21fZ05c0aRkZFKTEzUwYMHJUm+vr7KzMzUmjVrivz1I1eKK5EAAAAAbiq//fabnJyc7IKeVb169bR//367tpMnT2rcuHFauXKlTp486bDOmTNn5OPjo99++02SVLduXYeaW2+91aEtISFBZ8+elb+/f4FjPXHihGrXrq1nn31WmzZtUteuXXXLLbeodevWuv/++xUdHS1vb+9/3eeiRIgErlJpewlvaXUtXw4MAABQ1AzDUPv27ZWQkKCnnnpKTZo0ka+vr8qUKaN58+Zp4cKFysvLu+q+K1asqIULFxZYc9ttt0mSatWqpV9//VXffPONvvnmG23cuFExMTF64YUXtGnTJtWoUeOqxnA1CJEAAAAAbirVq1dXXl6eDhw4oPr169stS0hIsPu8d+9e7dmzR+PHj9fEiRPtls2ZM8fus/WhPPv27XPY5q+//urQVqtWLR04cEDNmjWTl5fXv47bzc1NHTt2VMeOHSVJa9euVadOnfT666/rzTff/Nf1iwr3RAIAAAC4qXTp0kWS9Oqrr9q1f/rppw5TWcuUKSNJDvch/vzzzw6v+PD391ezZs20cuVKHThwwNaek5OjadOmOYzjoYceUl5ensaOHZvvOE+cOGH7c2pqqsPysLAwSdKpU6fyXb+4cCUSAAAAwE2lQ4cOioyM1Pvvv69Tp07pvvvu0+HDh/Xuu+/qtttu088//2yrrVevnurXr6///ve/yszMVJ06dXTgwAG9++67atCggXbu3GnX9+uvv642bdrorrvu0tChQ22v+Lh48aLDOKyv9Zg5c6Z+/PFHPfDAA/Lz89Px48e1detWHTp0yHafZfv27VWuXDm1bNlSwcHBOnPmjObPny+LxaJ+/foV7wH7B0IkAAAAADs3wzMNlixZonHjxumjjz7S119/rQYNGmj58uVauHChXYgsU6aM1qxZo6efflrvv/++zp07p9tuu03vv/++9uzZ4xAimzdvrq+//lpjxozRyy+/LF9fX/Xs2VOPPfaYGjRo4DCOuXPn6u6779asWbM0depU5eTkqHLlygoLC7N7rchjjz2mjz/+WO+++65OnTqlW265RXfccYdmzJihu+++u/gOVD4sRkk/H9aEzMxM3XbbbTpy5IiGDh2qmTNn2i3fv3+/nnnmGW3cuFE5OTkKCwvTxIkTdc899zj0lZaWpnHjxmn58uX666+/VKNGDT3xxBMaMmSILBaLXW1eXp7eeOMNvfvuu0pMTFTFihXVq1cvTZo0ye4xv4WVnp4uX19fpaWlycfHp8j6RfHiwTrm3Az/CAEAcD06efKkKlWqVNLDwDX0b+fcbC65Lu6JHD9+vP788898lx0+fFgRERHaunWrRo8erVdffVUZGRnq0KGD1q1bZ1ebk5Ojdu3a6Z133lF0dLRmzJihOnXq6PHHH3e4SVa69KLQESNG6NZbb9WMGTMUFRWl6dOnKzIy8qqfwAQAAAAA17NSP531xx9/VFxcnP773/9q5MiRDsvHjh2rM2fOaOfOnWrUqJGkSzeo1q9fX0OHDtW+fftsVxjnzJmj+Ph4TZ8+XbGxsZKkmJgY9ejRQy+99JIGDhyokJAQSdIvv/yiGTNmqHv37lq2bJlte9WqVdOTTz6pxYsXq0+fPsW89wAAAABQupTqK5G5ubmKiYnRfffdp+7duzssP3funFatWqU2bdrYAqQkeXl5afDgwTpw4IDi4+Nt7QsXLpSnp6diYmLs+hk2bJguXLigJUuW2NoWLVokwzA0bNgwu9qYmBh5enpqwYIFRbOTAAAAAHAdKdVXIqdNm6Z9+/bZXQn8u7179yo7O1vNmzd3WNasWTNJUnx8vMLDw5WXl6cff/xRYWFhcnd3t6sNDw+XxWKxC5zx8fFycnJSeHi4Xa27u7saNWpkV3ulAgIC7D4zNRY3Mu4d/XfcNwoAAK4npfZK5JEjR/TCCy9o/PjxCg0NzbcmOTlZkhQUFOSwzNqWlJQkSTp9+rTOnz+fb62bm5v8/Pxstda+/fz85Obmlm/fqampysnJueL9AgAAAIDrWam9EjlkyBBVr15dI0aMKLAmMzNTkvINetarjdaay9Va66011vrL1VprXF1d/21XHKSkpNh9tj4FCQAAALhWroOXNKCIFdU5L5UhcsGCBfr666+1adMmubi4FFjn6ekpScrOznZYlpWVZVdzuVprvbXGWn/y5MkCa//eJwAAAHA9MgzD4TV3uDEV5S8NSt101uzsbI0YMUIdO3ZU5cqVdejQIR06dEhHjx6VdOk9j4cOHdKZM2cUGBgoSXbTUK2sbdbpq+XLl5eHh0e+tdnZ2UpNTbWb6hoYGKjU1NR8Q2dSUpL8/Pyu6iokAAAAUBq4ubnZLo7gxpeVlVXgTMsrVepC5Pnz5/Xnn39qzZo1qlWrlu2rTZs2ki5dpaxVq5bmzJmjBg0ayM3NTVu3bnXoZ9u2bZKkJk2aSJKcnJwUFhamXbt2OQTDHTt2yDAMW60kNW3aVHl5edqxY4ddbVZWlnbv3m1XCwAAAFxvfHx8dObMGZ0/f56prTcwwzB0/vx5nTlzRj4+PkXSZ6mbzlq2bFktXer4NMc///xTjz/+uO677z49/PDDatiwoby8vBQZGanly5drz549uv322yVJGRkZmjNnjmrVqmX3dNXevXtr8+bNmjVrlu09kZIUFxcnZ2dnRUdH29qio6P10ksvKS4uTi1btrS1z549W5mZmerbt29x7D4AAABwTTg5Ocnf31/p6elKT0+XJKa23mCsvxxwc3OTv7+/nJyK5hpiqQuRLi4u6tmzp0N7YmKiJKlGjRp2y6dOnapvvvlG7du31/Dhw+Xj46PZs2crKSlJa9assftBiImJ0bx58zRixAglJiaqXr16Wrt2rVasWKFx48bZPQW2QYMGGjp0qGbOnKnu3burY8eOSkhI0PTp09W6dWv16dOn2I4BAAAAcC04OTmpXLlyJT0MXGdKXYi8UjVr1tTmzZs1ZswYvfzyy8rJyVFYWJi++OILtW3b1q7W1dVV69at07hx47Ro0SL99ddfqlGjhmbMmKGhQ4c69B0XF6fQ0FDNmjVLa9askZ+fn2JjYzVp0qQiS/EAAAAAcD2xGEyALnHWV3ykpaUV2TxlFL9qqx2nXQNX40hkVEkPAQAAwHQu4XIaAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwDRCJAAAAADANEIkAAAAAMA0QiQAAAAAwLRSFyL379+vvn37ql69evL19ZWnp6fq1q2rESNGKCUlxa52woQJslgs+X7973//c+g7Ly9P06ZNU926deXu7q7g4GCNHDlS586dy3csa9euVUREhMqWLasKFSooKipKR44cKZb9BgAAAIDrgXNJD+Cfjh8/rpSUFHXr1k1VqlSRs7OzfvrpJ82aNUuLFy/W7t27ValSJbt1pk2bJj8/P7u2xo0bO/Q9fPhwTZ8+Xd26ddPIkSOVkJCg6dOna9euXVq3bp2cnP4/Uy9fvlw9e/bU7bffrldffVVpaWmKi4vTXXfdpR9++EGBgYHFcwAAAAAAoBQrdSHy3nvv1b333uvQ3qpVK/Xq1Uvz58/X6NGj7ZZ17dpVoaGhl+33l19+0YwZM9S9e3ctW7bM1l6tWjU9+eSTWrx4sfr06SNJunDhgmJjYxUcHKzvvvtOXl5ekqT7779fjRs31oQJEzRr1qxC7ikAAAAAXH9K3XTWgoSEhEiSTp8+ne/y9PR0Xbx4scD1Fy1aJMMwNGzYMLv2mJgYeXp6asGCBba2jRs3Kjk5WYMHD7YFSElq1KiR2rRpoyVLlujChQuF2BsAAAAAuD6V2hCZlZWl1NRUHT9+XF999ZUeffRRSVLHjh0dahs2bChfX1+5u7srIiJCn3/+uUNNfHy8nJycFB4ebtfu7u6uRo0aKT4+3q5Wkpo3b+7QT7NmzZSenq4DBw5c9b4FBATYfdWqVeuq+wIAAACAa6nUhsg5c+aoYsWKCg4OVocOHXTmzBktWLBALVu2tNWUK1dOjzzyiGbMmKGVK1dq6tSpOnr0qDp16qT58+fb9ZecnCw/Pz+5ubk5bCsoKEipqanKycmx1Vrb86uVpKSkpKLaVQAAAAC4bpS6eyKtunbtqrp16yojI0O7du3SqlWrlJqaalfzz6mpkjRo0CDddtttGj58uHr27GmbjpqZmZlvgJQuXY201ri6uiozM1OS8q3/e+3V+udTZtPT0+Xr63vV/QEAAADAtVJqQ2SVKlVUpUoVSZcCZY8ePdS0aVNlZmZq7NixBa53yy23aMiQIZowYYK2bNmi9u3bS5I8PT118uTJfNfJysqy1fz9v9nZ2f9aCwAAAAA3k1I7nfWfGjZsqDvuuENvvfXWv9Zan9T69yuXgYGBSk1NzTcYJiUlyc/PT66urrZaa3t+tVL+U10BAAAA4EZ33YRISTp//rxOnTr1r3UHDx6UJPn7+9vamjZtqry8PO3YscOuNisrS7t371aTJk3saiVp69atDn1v27ZNPj4+ql279lXtAwAAAABcz0pdiPzjjz/ybV+/fr1+/vlnNWvWTJJ08eJFpaWlOdQdO3ZMb7/9tm655RZFRETY2qOjo2WxWBQXF2dXP3v2bGVmZqpv3762ttatWysgIEBz5sxRRkaGrX3Pnj3asGGDoqKi5OLiUpjdBAAAAIDrUqm7J/Kxxx5TSkqK7rnnHoWEhCgrK0s7d+7U4sWL5e3trddee02SlJGRoWrVqqlr166qV6+eypcvr/3799uC36JFi+Th4WHrt0GDBho6dKhmzpyp7t27q2PHjkpISND06dPVunVr9enTx1br4uKiN954Q9HR0WrZsqViYmKUnp6uadOmqWLFipo4ceI1Py4AAAAAUBpYDMMwSnoQf/fxxx/rgw8+0J49e/Tnn3/KYrEoJCRE7dq106hRo1S1alVJlx56M3ToUG3fvl3Hjx9XRkaG/Pz8dNddd2n06NEO74OUpNzcXMXFxWnWrFlKTEyUn5+foqOjNWnSJNtTXP/us88+0+TJk7V37165ubnp3nvv1SuvvKIaNWoU6T5bn86alpYmHx+fIu0bxafa6qUlPQTcII5ERpX0EAAAAEznklIXIm9GhMjrEyESRYUQCQAASgOzuaTU3RMJAAAAACi9CJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANNKXYjcv3+/+vbtq3r16snX11eenp6qW7euRowYoZSUlHzru3btqvLly6ts2bJq2bKlvv3223z7TktLU2xsrIKCguTu7q769evr7bfflmEYDrV5eXmaNm2a6tatK3d3dwUHB2vkyJE6d+5cke8zAAAAAFwvnEt6AP90/PhxpaSkqFu3bqpSpYqcnZ31008/adasWVq8eLF2796tSpUqSZIOHz6siIgIOTs7a/To0fL19dXs2bPVoUMHff7552rbtq2t35ycHLVr1067du1SbGys6tWrp88//1yPP/64Tpw4oQkTJtiNY/jw4Zo+fbq6deumkSNHKiEhQdOnT9euXbu0bt06OTmVuvwNAAAAAMXOYuR3Ga4UWrp0qXr16qVXXnlFo0ePliT16tVLy5Yt086dO9WoUSNJUkZGhurXry93d3ft27dPFotFkvTWW29p6NChmj59umJjY2399ujRQ6tXr9bBgwcVEhIiSfrll1/UoEEDdevWTcuWLbPVzpgxQ08++aQ++ugj9enTp8j2LT09Xb6+vkpLS5OPj0+R9YviVW310pIeAm4QRyKjSnoIAAAApnPJdXM5zRrwTp8+LUk6d+6cVq1apTZt2tgCpCR5eXlp8ODBOnDggOLj423tCxculKenp2JiYuz6HTZsmC5cuKAlS5bY2hYtWiTDMDRs2DC72piYGHl6emrBggVFvHcAAAAAcH0oddNZrbKyspSRkaGsrCz9+uuveuaZZyRJHTt2lCTt3btX2dnZat68ucO6zZo1kyTFx8crPDxceXl5+vHHHxUWFiZ3d3e72vDwcFksFrvAGR8fLycnJ4WHh9vVuru7q1GjRna1VyMgIMDuc15eXqH6AwAAAIBrpdReiZwzZ44qVqyo4OBgdejQQWfOnNGCBQvUsmVLSVJycrIkKSgoyGFda1tSUpKkS1cvz58/n2+tm5ub/Pz8bLXWvv38/OTm5pZv36mpqcrJySn8TgIAAADAdabUXons2rWr6tatq4yMDO3atUurVq1SamqqbXlmZqYk5Rv0rFcbrTWXq7XWW2us9Zertda4urpe6W5JksNTZq1zjwEAAACgtCu1IbJKlSqqUqWKpEuBskePHmratKkyMzM1duxYeXp6SpKys7Md1s3KypIkW83laq311hpr/cmTJwus/XufAAAAAHAzKbXTWf+pYcOGuuOOO/TWW29JkgIDAyXJbhqqlbXNOn21fPny8vDwyLc2OztbqampdlNdAwMDlZqamm/oTEpKkp+f31VfhQQAAACA69l1EyIl6fz58zp16pQkqUGDBnJzc9PWrVsd6rZt2yZJatKkiSTJyclJYWFh2rVrl0Mw3LFjhwzDsNVKUtOmTZWXl6cdO3bY1WZlZWn37t12tQAAAABwMyl1IfKPP/7It339+vX6+eefbU9e9fLyUmRkpDZs2KA9e/bY6jIyMjRnzhzVqlXL7umqvXv3VmZmpmbNmmXXb1xcnJydnRUdHW1ri46OlsViUVxcnF3t7NmzlZmZqb59+xZ2NwEAAADgulTq7ol87LHHlJKSonvuuUchISHKysrSzp07tXjxYnl7e+u1116z1U6dOlXffPON2rdvr+HDh8vHx0ezZ89WUlKS1qxZI4vFYquNiYnRvHnzNGLECCUmJqpevXpau3atVqxYoXHjxik0NNRW26BBAw0dOlQzZ85U9+7d1bFjRyUkJGj69Olq3bq1+vTpcy0PCQAAAACUGhbDMIySHsTfffzxx/rggw+0Z88e/fnnn7JYLAoJCVG7du00atQoVa1a1a4+ISFBY8aM0caNG5WTk6OwsDBNmDBBbdu2dej7zJkzGjdunJYvX66//vpLNWrU0OOPP66hQ4faBU5Jys3NVVxcnGbNmqXExET5+fkpOjpakyZNkpeXV5Hus/XprGlpafLx8SnSvlF8qq1eWtJDwA3iSGRUSQ8BAADAdC4pdSHyZkSIvD4RIlFUCJEAAKA0MJtLSt09kQAAAACA0osQCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMK1IQuTKlSsVHR2t22+/XTVr1rS1JyQk6L///a+SkpKKYjMAAAAAgBLmXJiVDcPQgw8+qMWLF0uSPDw8dP78edvyChUq6LnnnlNubq7Gjh1buJECAAAAAEpcoa5ETp8+XYsWLdLgwYN16tQpPf3003bL/f391aJFC61Zs8Z0nwcOHND48ePVrFkzVaxYUd7e3mrUqJGmTJmic+fO2dVOmDBBFosl36///e9/Dn3n5eVp2rRpqlu3rtzd3RUcHKyRI0c69Gu1du1aRUREqGzZsqpQoYKioqJ05MgR0/sCAAAAADeaQl2JnDt3rsLCwvTuu+9KkiwWi0NNzZo19fnnn19Rn2+++aY6d+6svn37ysXFRevXr9e4ceP08ccfa9u2bfLw8LBbZ9q0afLz87Nra9y4sUPfw4cP1/Tp09WtWzeNHDlSCQkJmj59unbt2qV169bJyen/M/Xy5cvVs2dP3X777Xr11VeVlpamuLg43XXXXfrhhx8UGBhoep8AAAAA4EZRqBB58OBBPfbYY5et8fPzU2pqquk+e/bsqbFjx8rX19fWNmTIENWqVUtTpkzRe++9pyeeeMJuna5duyo0NPSy/f7yyy+aMWOGunfvrmXLltnaq1WrpieffFKLFy9Wnz59JEkXLlxQbGysgoOD9d1338nLy0uSdP/996tx48aaMGGCZs2aZXqfAAAAAOBGUajprK6ursrIyLhszbFjx+Tj42O6zyZNmtgFSKvo6GhJ0s8//5zveunp6bp48WKB/S5atEiGYWjYsGF27TExMfL09NSCBQtsbRs3blRycrIGDx5sC5CS1KhRI7Vp00ZLlizRhQsXTO8TAAAAANwoChUib7/9dq1bt67AQHX27Fl9/fXXatKkSWE2I0k6fvy4pEv3Wf5Tw4YN5evrK3d3d0VEROQ7fTY+Pl5OTk4KDw+3a3d3d1ejRo0UHx9vVytJzZs3d+inWbNmSk9P14EDB656XwICAuy+atWqddV9AQAAAMC1VKgQOXjwYB05ckQDBgxwuCKZmpqqPn36KDU1VY8++mihBpmbm6sXX3xRzs7OtimnklSuXDk98sgjmjFjhlauXKmpU6fq6NGj6tSpk+bPn2/XR3Jysvz8/OTm5ubQf1BQkFJTU5WTk2OrtbbnVyuJ15YAAAAAuCkV6p7Ifv36ad26dfrwww+1bNkylStXTpJ066236vDhw7pw4YIGDx6sLl26FGqQw4YN09atW/XSSy+pTp06du3/NGjQIN12220aPny4evbsaZuOmpmZmW+AlC5djbTWuLq6KjMzU5Lyrf977dVKSUmx+5yenp7vFF4AAAAAKG0KdSVSkt5//33Nnj1bdevW1Z9//inDMLRv3z7VqlVL7777bqEfQPP8889r5syZeuSRR0y9a/KWW27RkCFDdObMGW3ZssXW7unpqezs7HzXycrKstX8/b/51f+zFgAAAABuJoW6Emn18MMP6+GHH9b58+d1+vRp+fr6qmzZsoXud8KECZo8ebIGDhyod955x/R61ie1/v2psIGBgfr111+VnZ3tcIUxKSlJfn5+cnV1tdVa2+vVq+dQK+U/1RUAAAAAbnSFvhL5dx4eHgoMDCyyADlx4kT1799fc+bMyfcdlAU5ePCgJPuH8DRt2lR5eXnasWOHXW1WVpZ2795t9/Cfpk2bSpK2bt3q0Pe2bdvk4+Oj2rVrX9H+AAAAAMCNoEhDZFGZNGmSJk6cqH79+mnu3LlycnIc5sWLF5WWlubQfuzYMb399tu65ZZbFBERYWuPjo6WxWJRXFycXf3s2bOVmZmpvn372tpat26tgIAAzZkzx+6BQXv27NGGDRsUFRUlFxeXIthTAAAAALi+XNF0Vicnpyu6ImhlsVgu+w7Hv3vzzTf1wgsvqGrVqmrbtq0WLlxot9zf31/t2rVTRkaGqlWrpq5du6pevXoqX7689u/fbwt+ixYtkoeHh229Bg0aaOjQoZo5c6a6d++ujh07KiEhQdOnT1fr1q3tnvrq4uKiN954Q9HR0WrZsqViYmKUnp6uadOmqWLFipo4ceIVHwMAAAAAuBFcUYhs1aqVQ4g8ffq09u7dKycnJwUHB6ty5cr6448/dOzYMeXl5alhw4YqX7686W1Y39H4+++/q3///g7LW7durXbt2snDw0M9evTQ9u3b9emnnyojI0N+fn5q27atRo8e7fA+SEmKi4tTaGioZs2apTVr1sjPz0+xsbGaNGmSw9XOqKgoeXh4aPLkyXr66afl5uame++9V6+88gr3QwIAAAC4aVkMwzCuduWUlBRFRESocePGevXVV1WtWjXbsiNHjujpp5/Wrl27tGXLFlWuXLlIBnwjsr7iIy0tTT4+PiU9HJhUbfXSkh4CbhBHIqNKeggAAACmc0mh7ol85plnVL58eX3yySd2AVKSqlWrpk8++US+vr565plnCrMZAAAAAEApUagQ+eWXX6pDhw4FLrdYLOrQoYO++OKLwmwGAAAAAFBKFCpEnj17Nt8npP5dWlqazp49W5jNAAAAAABKiUKFyHr16mnJkiU6duxYvsuPHj2qJUuW6NZbby3MZgAAAAAApcQVPZ31n0aNGqU+ffrojjvu0JNPPqlWrVrJ399fJ06c0MaNGzVjxgylpaVp1KhRRTVeAAAAAEAJKlSI/M9//qOUlBSNGTPG4d2JhmHIxcVF//vf/xQdHV2oQQIAAAAASodChUhJGj58uLp3764FCxZo165dSktLk6+vr8LCwtS3b1+FhIQUxTgBAAAAAKVAoUOkJIWEhOi5554riq4AAAAAAKVYoR6sAwAAAAC4uRTJlcjs7GzFx8crKSlJ2dnZ+dY89NBDRbEpAAAAAEAJKnSIfOedd/Tcc8/pzJkz+S43DEMWi4UQCQAAAAA3gEJNZ125cqUef/xxBQUF6X//+58Mw1CXLl00ZcoUdejQQYZhqGfPnpo7d25RjRcAAAAAUIIKFSLj4uLk5+enLVu2aPjw4ZKkRo0aacyYMVq7dq3mz5+vFStWqFq1akUyWAAAAABAySpUiNy9e7c6d+4sLy8vW1teXp7tzw899JBat26tF198sTCbAQAAAACUEoUKkdnZ2fL397d9dnd3V1paml1NWFiYfvjhh8JsBgAAAABQShQqRFauXFknTpywfa5SpYp+/vlnu5oTJ07IYrEUZjMAAAAAgFKiUCGyUaNGdqHxnnvu0YYNG7Rw4UKdO3dOn3/+uZYuXarbb7+90AMFAAAAAJS8QoXIyMhI7d27V4mJiZKksWPHysfHR/369ZOPj48eeOAB5eXlafLkyUUxVgAAAABACSvUeyIHDhyogQMH2j6HhIQoPj5er7/+ug4fPqzQ0FA99thjatiwYaEHCgAAAAAoeYUKkfmpXr26Zs6cWdTdAgAAAABKgUJNZwUAAAAA3Fyu6Erkpk2brnpDrVq1uup1AQAAAAClwxWFyDZt2lz16zpyc3Ovaj0AAAAAQOlxRSFy/PjxDiFy+/bt+uKLL1SjRg21aNFClStX1h9//KHvv/9ehw8f1v3336/w8PAiHTQAAAAAoGRcUYicMGGC3edt27Zp6tSpeuONNzR06FA5Of3/LZZ5eXmaMWOGxowZo/HjxxfJYAEAAAAAJctiGIZxtSu3a9dObm5u+uyzzwqs6dSpky5evKgvv/zyajdzw0tPT5evr6/S0tLk4+NT0sOBSdVWLy3pIeAGcSQyqqSHAAAAYDqXFOrprDt27FCjRo0uW9OoUSNt27atMJsBAAAAAJQShQqRhmHo8OHDl605dOhQYTYBAAAAAChFChUiIyIitGzZsgKns65atUrLly/XXXfdVZjNAAAAAABKiSt6sM4/TZkyRa1atVKXLl3UunVrtWrVSv7+/jpx4oQ2btyoTZs2ycPDQ1OmTCmq8QIAAAAASlChQmTjxo319ddfa9CgQdqwYYM2bNggi8Ui67N66tSpo/fee0933HFHkQwWAAAAAFCyChUipUtTWvft26ctW7boxx9/VFpamnx9fRUWFqaIiIiiGCMAAAAAoJQodIi0ioiIIDQCAAAAwA2uUA/WAQAAAADcXK7oSuSkSZNksVg0dOhQVahQQZMmTTK1nsVi0fPPP39VAwQAAAAAlB4Ww/oUHBOcnJxksViUkJCg2rVry8nJ3IVMi8Wi3Nzcqx7kjS49PV2+vr5KS0uTj49PSQ8HJlVbvbSkh4AbxJHIqJIeAgAAgOlcckVXItevXy9Jqlq1qt1nAAAAAMDN4YpCZOvWrS/7GQAAAABwYyvU01k/+OADNWrUSA0bNiyw5ueff9aPP/6ohx56qDCbAoAbFlOj/x1TfgEAKD0K9XTWAQMG6NNPP71szcqVKzVw4MDCbAYAAAAAUEoU+ys+cnNzTT+ABwAAAABQuhV7utu1a5cqVKhQ3JsBAAAAAFwDV3xP5D333GP3ef78+dqwYYNDXW5uro4fP67ExET16tXrqgcIAAAAACg9rjhE/j0wWiwWJSYmKjEx0aHOyclJFSpUUFRUlOLi4goxRAAAAABAaXHFITIvL8/2ZycnJ02YMEHjx48v0kEBAAAAAEqnQr3iY968ebrjjjuKaiwAAAAAgFKuUCGyf//+RTUOAAAAAMB1oFAh0urkyZP64YcfdPr0aeXm5uZb89BDDxXFpgAAAAAAJahQIfLChQsaMmSIPvjgA7t7Jf/OMAxZLBZCJAAAAADcAAoVIp9//nnNmzdPNWrUUN++fRUcHCxn5yK5uAkAAAAAKIUKlfgWLlyo2rVra9euXfLw8CiSAR04cEALFizQV199pcOHDysrK0s1atRQVFSUhg0bprJly9rV79+/X88884w2btyonJwchYWFaeLEiQ7vs5SktLQ0jRs3TsuXL9dff/2lGjVq6IknntCQIUNksVjsavPy8vTGG2/o3XffVWJioipWrKhevXpp0qRJDmMAAAAAgJuFU2FWPnnypDp27FhkAVKS5s6dq2nTpqlGjRoaP368Xn31VdWpU0fjxo1TRESEzp8/b6s9fPiwIiIitHXrVo0ePVqvvvqqMjIy1KFDB61bt86u35ycHLVr107vvPOOoqOjNWPGDNWpU0ePP/64Jk6c6DCO4cOHa8SIEbr11ls1Y8YMRUVFafr06YqMjCxw6i4AAAAA3OgshmEYV7ty7dq11apVK82ZM6fIBvTDDz+oVq1a8vX1tWsfN26cpkyZohkzZuiJJ56QJPXq1UvLli3Tzp071ahRI0lSRkaG6tevL3d3d+3bt892hfGtt97S0KFDNX36dMXGxtr67dGjh1avXq2DBw8qJCREkvTLL7+oQYMG6tatm5YtW2arnTFjhp588kl99NFH6tOnT5Htc3p6unx9fZWWliYfH58i6xfFq9rqpSU9BOCmcSQyqqSHAADADc9sLinUlcgBAwbo888/V1paWmG6sdOkSROHAClJ0dHRkqSff/5ZknTu3DmtWrVKbdq0sQVISfLy8tLgwYN14MABxcfH29oXLlwoT09PxcTE2PU7bNgwXbhwQUuWLLG1LVq0SIZhaNiwYXa1MTEx8vT01IIFCwq7mwAAAABwXSpUiBwzZoxatGihtm3bav369UpPTy+qcTk4fvy4JMnf31+StHfvXmVnZ6t58+YOtc2aNZMkW4jMy8vTjz/+qDvuuEPu7u52teHh4bJYLHaBMz4+Xk5OTgoPD7erdXd3V6NGjexqr0ZAQIDdV61atQrVHwAAAABcK4V6sI6Li4ukS6/xaNu2bYF1FotFFy9evOrt5Obm6sUXX5Szs7NtGmlycrIkKSgoyKHe2paUlCRJOn36tM6fP59vrZubm/z8/Gy11r79/Pzk5uaWb99btmxRTk6OXF1dr3qfAAAAAOB6VKgQ2bJlS4enmhaHYcOGaevWrXrppZdUp04dSVJmZqYk5Rv0rFcbrTWXq7XWW2us9ZertdZcbYhMSUmx+2ydewwAAAAApV2hQuSGDRuKaBgFe/755zVz5kw98sgjGjt2rK3d09NTkpSdne2wTlZWll3N5Wqt9dYaa/3JkycLrP17nwAAAABwMynUPZHFbcKECZo8ebIGDhyod955x25ZYGCgJNlNQ7Wytlmnr5YvX14eHh751mZnZys1NdVuqmtgYKBSU1PzDZ1JSUny8/NjKisAAACAm1KpDZETJkzQxIkT1b9/f82ZM8dh2myDBg3k5uamrVu3Oqy7bds2SZee9CpJTk5OCgsL065duxyC4Y4dO2QYhq1Wkpo2baq8vDzt2LHDrjYrK0u7d++2qwUAAACAm0mhQ2ReXp6WLFmiwYMH6/7779c999zj8HXvvfdeUZ+TJk3SxIkT1a9fP82dO1dOTo7D9PLyUmRkpDZs2KA9e/bY2jMyMjRnzhzVqlXL7umqvXv3VmZmpmbNmmXXT1xcnJydnW2vEJEuvU7EYrEoLi7Ornb27NnKzMxU3759r2h/AAAAAOBGUah7Is+fP6/27dtry5YtMgxDFotFhmHYlls/X8nDd95880298MILqlq1qtq2bauFCxfaLff391e7du0kSVOnTtU333yj9u3ba/jw4fLx8dHs2bOVlJSkNWvW2G03JiZG8+bN04gRI5SYmKh69epp7dq1WrFihcaNG6fQ0FBbbYMGDTR06FDNnDlT3bt3V8eOHZWQkKDp06erdevWtifEAgAAAMDNplAhcsqUKdq8ebNeeOEFxcbGys/PTxMmTNAjjzyiTZs2aezYsWratKk++ugj031a38H4+++/q3///g7LW7dubQuRNWvW1ObNmzVmzBi9/PLLysnJUVhYmL744guHV464urpq3bp1GjdunBYtWqS//vpLNWrU0IwZMzR06FCH7cTFxSk0NFSzZs3SmjVr5Ofnp9jYWE2aNCnfK6MAAAAAcDOwGH+/dHiF6tWrJx8fH23fvl3SpXsPJ0yYoPHjx0uSjh49qkaNGmnMmDF65plnimbENyDrKz7S0tLk4+NT0sOBSdVWLy3pIQA3jSORUSU9BAAAbnhmc0mhLqklJiaqefPmts8Wi0U5OTm2zyEhIerUqZPef//9wmwGAAAAAFBKFCpEenh42L3qwsfHRydOnLCrCQoKUmJiYmE2AwAAAAAoJQoVIoODg3Xs2DHb53r16um7776ze7jOli1bVLFixcJsBgAAAABQShQqRLZq1UqbNm2yhcb//Oc/OnDggDp16qQ333xT0dHR2rJlizp27FgkgwUAAAAAlKxCPZ114MCBunjxoo4fP67g4GA9/vjj+vbbb7Vq1Sp98cUXkqTmzZtrypQpRTJYAAAAAEDJKlSIDAsL09tvv/3/nTk769NPP9XOnTt16NAhhYaGqmnTprwSAwAAAABuEIUKkQVp3LixGjduXBxdAwAAAABKEJcIAQAAAACmFepK5KBBg0zVWSwWvffee4XZFAAAAACgFChUiJw/f/5ll1ssFhmGQYgEAAAAgBtEoULkkSNH8m0/c+aM4uPj9eKLLyoiIkIvv/xyYTYDAAAAACglChUiQ0JCCmy//fbb1aFDBzVs2FBt27bVww8/XJhNAQAAAABKgWJ9sE5wcLAiIyP1xhtvFOdmAAAAAADXSLE/ndXf318HDx4s7s0AAAAAAK6BYg2Rubm5+vbbb+Xr61ucmwEAAAAAXCOFuidy06ZN+bZfvHhRx44d07x587R7924NHjy4MJsBAAAAAJQShQqRbdq0kcViKXC5YRhq1aqVXn311cJsBgAAAABQShQqRI4fPz7fEOnk5KTy5csrPDxc4eHhhdkEAAAAAKAUKVSInDBhQhENAwAAAABwPShUiJSkHTt2aNOmTTp+/LiysrJUsWJFNW7cWPfdd5/c3d2LYowAAAAAgFLiqkNkfHy8hgwZot27d0u6dP+jJNv01goVKuiVV17RoEGD7Na7ePGinJ0LnV0BAAAAACXgqtLcmjVr1KNHD+Xk5CggIEB33323goODJUnHjh3Thg0blJycrJiYGB08eFBTp06VJH355Zf66aef9PTTTxfdHgAAAAAArpkrDpEnTpxQ79695eTkpLfffluDBw9WmTJl7Gry8vL03nvvadiwYfrvf/+rdu3aKT09Xb1799bYsWOLbPAAAAAAgGvrikPktGnTlJGRoWXLlqlbt2751jg5OSkmJkYVK1ZU9+7d9eCDDyo1NVUVK1YscB0AAAAAQOnndKUrfPHFF7rzzjtNhcGuXbvqzjvv1B9//KFbb71V27dvV4MGDa5qoAAAAACAknfFIfLIkSOKiIgwXR8RESGLxaLNmzerSpUqV7o5AAAAAEApcsUh8sKFC3J1dTVd7+LiIjc3N5UtW/ZKNwUAAAAAKGWuOEQGBATop59+Ml3/yy+/KCAg4Eo3AwAAAAAoha44RLZq1Upff/219u3b96+1CQkJ+vLLL9W6deurGhwAAAAAoHS54hD5xBNP6MKFC3rggQf066+/FliXkJCgyMhI5ebmaujQoYUaJAAAAACgdLjiV3w0btxYo0aN0quvvqqwsDB1795d9957r4KDgyVJx44d07p167RixQrl5ORo9OjRaty4cZEPHAAAAABw7V1xiJSkV155RWXLltXkyZO1ePFiLVmyxG65YRhydnbWhAkTNH78+CIZKAAAAACg5F1ViJSk8ePHq3///po7d642b96slJQUSVLlypXVokULDRw4UKGhoUU1TgAAAABAKXDVIVKSQkJCNHHixKIaCwAAAACglLviB+sAAAAAAG5ehEgAAAAAgGmESAAAAACAaYRIAAAAAIBphEgAAAAAgGmESAAAAACAaYRIAAAAAIBphEgAAAAAgGmESAAAAACAaYRIAAAAAIBphEgAAAAAgGmESAAAAACAaYRIAAAAAIBphEgAAAAAgGmESAAAAACAaYRIAAAAAIBppTJETp06VVFRUapevbosFotCQ0MLrB0wYIAsFku+X5988olDfXZ2tsaPH69q1arJzc1NNWrU0OTJk3XhwoV8+//ggw90xx13yMPDQ/7+/ho8eLD+/PPPotpVAAAAALiuOJf0APLz7LPPqkKFCgoLC9OZM2dMrfPhhx86tIWHhzu0RUdHa+XKlRo0aJCaN2+urVu36vnnn9ehQ4c0f/58u9pp06ZpxIgRat26td544w0dP35cr7/+urZu3aodO3aobNmyV7N7AAAAAHDdKpUh8vDhw6pevbok6bbbblNGRsa/rvPggw/+a83atWu1cuVKjRgxQq+99pokafDgwSpXrpxef/11PfLII4qIiJAkpaamaty4cWratKm++eYblSlTRpLUtGlTde7cWW+88YaeffbZq91FAAAAALgulcrprNYAeSUMw1B6erry8vIKrFm4cKEkadiwYXbt1s8LFiywtX366afKzMxUbGysLUBKUmRkpKpXr25XCwAAAAA3i1IZIq+Gr6+vfH195eHhoXbt2mn79u0ONfHx8QoKClJwcLBde3BwsAIDAxUfH29XK0nNmzd36KdZs2bat2+fqSuk+QkICLD7qlWr1lX1AwAAAADXWqmcznolKleurOHDh6tx48YqW7as9uzZo7i4OLVs2VJr165V27ZtbbXJycm69dZb8+0nKChIx48ft6u1tudXaxiGkpOTVbt27SLeIwAAAAAova77EPnyyy/bfe7atav69OmjRo0a6bHHHtPBgwdtyzIzM+Xm5pZvP+7u7srMzLSrlZRvvbu7u13NlUpJSbH7nJ6eLl9f36vqCwAAAACupRtmOuvf1apVS7169dKhQ4d04MABW7unp6eys7PzXScrK0uenp52tZLyrc/KyrKrAQAAAICbxQ0ZIiXZ3i2ZmppqawsMDFRSUlK+9UlJSXZTVwMDA23t+dVaLBZbDQAAAADcLG7YEGmdxurv729ra9q0qZKSknTs2DG72mPHjik5OVlNmjSxq5WkrVu3OvS9bds21alTR15eXsUxdAAAAAAota7rEHnu3Dnb1NK/27Vrl5YuXap69eqpRo0atvbevXtLkuLi4uzqrZ/79u1ra+vSpYs8PDw0c+ZM5ebm2tpXr16t3377za4WAAAAAG4WpfLBOh9++KGOHj0qSfrzzz+Vk5OjyZMnS5JCQkLUr18/SZeuNt5///3q2rWratWqZXs669y5c1WmTBnNmjXLrt9OnTrpgQce0Ouvv660tDQ1b95cW7du1XvvvacHH3xQLVq0sNVWrFhRL774op5++mm1bdtWvXv3VlJSkl577TXVrVvX4V2TAAAAAHAzsBiGYZT0IP6pTZs22rhxY77LWrdurQ0bNkiS/vjjD40aNUrx8fFKTk7W+fPnFRAQoLvvvltjx45V3bp1HdbPysrS5MmTtWDBAqWkpCgoKEgDBw7UmDFj5OLi4lA/f/58TZs2Tfv375ePj48eeOABvfzyy6pUqVKR7a/16axpaWny8fEpsn5RvKqtXlrSQwBuGkcio0p6CAAA3PDM5pJSGSJvNoTI6xMhErh2CJEAABQ/s7nkur4nEgAAAABwbREiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmESIBAAAAAKYRIgEAAAAAphEiAQAAAACmlcoQOXXqVEVFRal69eqyWCwKDQ29bP327dvVtm1beXt7y8fHR/fdd592796db21ycrIeeughVaxYUR4eHmrSpImWLl2ab212drbGjx+vatWqyc3NTTVq1NDkyZN14cKFQu4hAAAAAFyfnEt6APl59tlnVaFCBYWFhenMmTOXrd22bZvatGmjoKAgTZo0SZI0c+ZMtWzZUlu2bFGDBg1stadOnVKLFi108uRJjRgxQlWqVNHChQvVq1cvzZ07VwMHDrTrOzo6WitXrtSgQYPUvHlzbd26Vc8//7wOHTqk+fPnF/VuAwAAAECpZzEMwyjpQfzTb7/9purVq0uSbrvtNmVkZCgxMTHf2vDwcO3bt08JCQkKCgqSJCUlJalevXpq1qyZvvrqK1vt6NGj9eqrr2rVqlWKjIyUJOXm5qp58+Y6fPiwjh49Ki8vL0nS2rVr1alTJ40YMUKvvfaarY+RI0fq9ddf1+bNmxUREVEk+5ueni5fX1+lpaXJx8enSPpE8au2Ov8r2ACK3pHIqJIeAgAANzyzuaRUTme1Bsh/c+jQIcXHxysqKsoWICUpKChIUVFRWrdunf744w9b+8KFC1WjRg1bgJSkMmXKKDY2VqdOndLatWvtaiVp2LBhdtu0fl6wYMGV7hYAAAAAXPdKZYg0Kz4+XpLUvHlzh2XNmjWTYRjauXOnJCklJUVJSUlq1qxZvrV/78/656CgIAUHB9vVBgcHKzAw0K72SgUEBNh91apV66r7AgAAAIBr6boOkcnJyZJkdxXS6u9TW6+01lqfX621/u+1AAAAAHCzKJUP1jErMzNTkuTm5uawzN3d3a7mSmqtf86v1lr/99orlZKSYvfZOvcYAAAAAEq76/pKpKenp6RLr+L4p6ysLLuaK6m1/jm/Wmv932sBAAAA4GZxXYfIwMBAScp3aqm1zTol9UpqrfUFTVlNSkoqcKorAAAAANzIrusQ2bRpU0nS1q1bHZZt27ZNFotFjRs3lnTpYTZBQUHatm1bvrWS1KRJE7u+k5KSdOzYMbvaY8eOKTk52a4WAAAAAG4W13WIrFmzppo0aaKlS5faHpwjXXooztKlS3XPPfeocuXKtvbevXvr8OHDWr16ta0tNzdXM2bMULly5dSxY0e7WkmKi4uz26b1c9++fYthjwAAAACgdCuVD9b58MMPdfToUUnSn3/+qZycHE2ePFmSFBISon79+tlq33jjDd19991q2bKlYmNjJUkzZsxQXl6eXnvtNbt+x4wZo6VLl6pPnz4aMWKEgoKCtGjRIsXHx2vOnDny9va21Xbq1EkPPPCAXn/9daWlpal58+baunWr3nvvPT344INq0aJFcR8GAAAAACh1LIZhGCU9iH9q06aNNm7cmO+y1q1ba8OGDXZtW7du1bhx47R9+3ZZLBZFRERo6tSpCgsLc1g/KSlJY8aM0eeff66MjAzdeuuteuaZZxQdHe1Qm5WVpcmTJ2vBggVKSUlRUFCQBg4cqDFjxsjFxaVI9lX6/6ezpqWlycfHp8j6RfGqtnppSQ8BuGkciYwq6SEAAHDDM5tLSmWIvNkQIq9PhEjg2iFEAgBQ/Mzmkuv6nkgAAAAAwLVFiAQAAAAAmEaIBAAAAACYRogEAAAAAJhGiAQAAAAAmEaIBAAAAACYRogEAAAAAJhGiAQAAAAAmEaIBAAAAACYRogEAAAAAJhGiAQAAAAAmEaIBAAAAACY5lzSAwAA4N9UW720pIdwXTgSGVXSQwAA3AS4EgkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAw7boPkRaLJd8vLy8vh9r9+/era9euKl++vMqWLauWLVvq22+/zbfftLQ0xcbGKigoSO7u7qpfv77efvttGYZR3LsEAAAAAKWWc0kPoCi0bNlSjzzyiF2bi4uL3efDhw8rIiJCzs7OGj16tHx9fTV79mx16NBBn3/+udq2bWurzcnJUbt27bRr1y7FxsaqXr16+vzzz/X444/rxIkTmjBhwrXYrRJVbfXSkh4CAAAAgFLohgiR1atX14MPPnjZmrFjx+rMmTPauXOnGjVqJEl66KGHVL9+fQ0dOlT79u2TxWKRJM2ZM0fx8fGaPn26YmNjJUkxMTHq0aOHXnrpJQ0cOFAhISHFuk8AAAAAUBpd99NZrXJycpSRkZHvsnPnzmnVqlVq06aNLUBKkpeXlwYPHqwDBw4oPj7e1r5w4UJ5enoqJibGrp9hw4bpwoULWrJkSbHsAwAAAACUdjdEiPzkk0/k6ekpb29vVapUSbGxsUpLS7Mt37t3r7Kzs9W8eXOHdZs1ayZJthCZl5enH3/8UXfccYfc3d3tasPDw2WxWOwC59UICAiw+6pVq1ah+gMAAACAa+W6n84aHh6uqKgo1axZU+np6Vq7dq1mzpypjRs3asuWLfLy8lJycrIkKSgoyGF9a1tSUpIk6fTp0zp//ny+tW5ubvLz87PVAgAAAMDN5roPkdu3b7f7/NBDD6lhw4Z67rnn9MYbb+i5555TZmampEsh8J+sVxutNZertdZba65WSkqK3ef09HT5+voWqk8AAAAAuBZuiOms/zRq1Ci5urpqzZo1kiRPT09JUnZ2tkNtVlaWXc3laq311hoAAAAAuNnckCHSxcVFgYGBSk1NlSQFBgZKUr7TUK1t1umr5cuXl4eHR7612dnZSk1NzXeqKwAAAADcDG7IEJmVlaXjx4/L399fktSgQQO5ublp69atDrXbtm2TJDVp0kSS5OTkpLCwMO3atcvhauSOHTtkGIatFgAAAABuNtf1PZF//fWXbrnlFof2559/XhcvXlRkZKSkS6/yiIyM1PLly7Vnzx7dfvvtkqSMjAzNmTNHtWrVUnh4uG393r17a/PmzZo1a5btPZGSFBcXJ2dnZ0VHRxfzngEAcOWqrV5a0kMo9Y5ERpX0EADgunddh8jJkydr27Ztuvvuu1W1alVlZGRo7dq1Wr9+ve688067ADh16lR98803at++vYYPHy4fHx/Nnj1bSUlJWrNmjSwWi602JiZG8+bN04gRI5SYmKh69epp7dq1WrFihcaNG6fQ0NAS2FsAAAAAKHnXdYhs06aNfv31V73//vv666+/VKZMGdWqVUtTpkzRiBEj7N7zWLNmTW3evFljxozRyy+/rJycHIWFhemLL75Q27Zt7fp1dXXVunXrNG7cOC1atEh//fWXatSooRkzZmjo0KHXejcBAAAAoNSwGIZhlPQgbnbWV3ykpaXJx8enpIcjiSlRAIAbE9NZAaBgZnPJDflgHQAAAABA8SBEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMcy7pAQAAAFwr1VYvLekhlHpHIqNKeggASjmuRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0QCAAAAAEwjRAIAAAAATCNEAgAAAABMI0ReRl5enqZNm6a6devK3d1dwcHBGjlypM6dO1fSQwMAAACAEuFc0gMozYYPH67p06erW7duGjlypBISEjR9+nTt2rVL69atk5MTGRwAANxYqq1eWtJDKPWOREaV9BCAEkWILMAvv/yiGTNmqHv37lq2bJmtvVq1anryySe1ePFi9enTpwRHCAAAAADXHpfSCrBo0SIZhqFhw4bZtcfExMjT01MLFiwomYEBAAAAQAniSmQB4uPj5eTkpPDwcLt2d3d3NWrUSPHx8Vfdd0BAgN3n3NxcSVJ6evpV91nU8jIzS3oIAAAApVLIkvdLegjXhZ/u71bSQ8AVsuYRwzAuW0eILEBycrL8/Pzk5ubmsCwoKEhbtmxRTk6OXF1dC70t60kKDg4udF8AAABAaeCrASU9BFyls2fPytfXt8DlhMgCZGZm5hsgpUtXI601VxMiU1JS7D7n5eUpOTlZ3t7eslgsVz7YIlarVi1J0sGDB0t4JDcvzkHJ4viXLI5/yeMclCyOf8njHJQsjn/JMQxDZ8+eVWBg4GXrCJEF8PT01MmTJ/NdlpWVZaspCk5OTqpSpUqR9FUUrE+d9fHxKeGR3Lw4ByWL41+yOP4lj3NQsjj+JY9zULI4/iXrclcgrXiwTgECAwOVmpqq7Oxsh2VJSUny8/MrkqmsAAAAAHA9IUQWoGnTpsrLy9OOHTvs2rOysrR79241adKkhEYGAAAAACXHYvzbo3duUj/99JNuv/12devWze49kTNmzNCTTz6pDz/8UA8++GAJjhAAAAAArj1C5GXExsZq5syZ6tatmzp27KiEhARNnz5dd911l7799lvbfG0AAAAAuFkQIi8jNzdXcXFxmjVrlhITE+Xn56fo6GhNmjRJXl5eJT08AAAAALjmCJEAAAAAANOYjwkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QCQAAAAAwjRAJAAAAADCNEAkAAAAAMI0QeROaOnWqoqKiVL16dVksFoWGhl62fvv27Wrbtq28vb3l4+Oj++67T7t3774mY70RHThwQOPHj1ezZs1UsWJFeXt7q1GjRpoyZYrOnTvnUL9//3517dpV5cuXV9myZdWyZUt9++23JTDyG8P+/fvVt29f1atXT76+vvL09FTdunU1YsQIpaSk5FvP8S9emZmZtr+PnnjiCYflnIOiZ7FY8v3y8vJyqOX4F49Tp07p6aefVs2aNeXu7q6KFSvq7rvv1nfffWdXx7/BRW/ChAkF/gxYLBa5uLjY1fMzUPQyMjL00ksvqUGDBvL29pafn58iIiI0f/58GYZhV8vPQOnkXNIDwLX37LPPqkKFCgoLC9OZM2cuW7tt2za1adNGQUFBmjRpkiRp5syZatmypbZs2aIGDRpcgxHfWObOnas333xTnTt3Vt++feXi4qL169dr3Lhx+vjjj7Vt2zZ5eHhIkg4fPqyIiAg5Oztr9OjR8vX11ezZs9WhQwd9/vnnatu2bQnvzfXn+PHjSklJUbdu3VSlShU5Ozvrp59+0qxZs7R48WLt3r1blSpVksTxv1bGjx+vP//8M99lnIPi07JlSz3yyCN2bf/8n2eOf/E4evSo2rRpo4yMDD388MOqXbu20tLStHfvXiUlJdnq+De4eHTv3l01a9Z0aN+7d69effVVRUZG2tr4GSh6eXl5uv/++7Vlyxb1799fsbGxyszM1KJFizRw4EAlJCTolVdekcTPQKlm4KZz+PBh25/r169vhISEFFjbtGlTw9vb2zh+/Lit7fjx44a3t7fRrl274hzmDSs+Pt44c+aMQ/tzzz1nSDJmzJhha4uKijKcnJyMXbt22drOnj1rVK1a1ahdu7aRl5d3LYZ8U/j4448NScYrr7xia+P4F7+dO3caZcqUMV577TVDkjF06FC75ZyD4iHJ6N+//7/WcfyLR4sWLYwqVaoYycnJl63j3+Br65FHHjEkGZ999pmtjZ+BordlyxZDkjFs2DC79uzsbKNatWqGr6+vrY2fgdKL6aw3oerVq5uqO3TokOLj4xUVFaWgoCBbe1BQkKKiorRu3Tr98ccfxTXMG1aTJk3k6+vr0B4dHS1J+vnnnyVJ586d06pVq9SmTRs1atTIVufl5aXBgwfrwIEDio+PvyZjvhmEhIRIkk6fPi2J438t5ObmKiYmRvfdd5+6d+/usJxzUPxycnKUkZGR7zKOf/HYtGmTvv/+e40ePVoBAQG6cOGCMjMzHer4N/jaOnfunBYvXqwqVarovvvus7XxM1D00tPTJUmBgYF27a6urvLz81PZsmUl8TNQ2hEiUSDrX4zNmzd3WNasWTMZhqGdO3de62HdsI4fPy5J8vf3l3RpWk12dnaBx18S/3gVQlZWllJTU3X8+HF99dVXevTRRyVJHTt2lMTxvxamTZumffv2aebMmfku5xwUr08++USenp7y9vZWpUqVFBsbq7S0NNtyjn/xWLt2rSSpatWqioyMlIeHh8qWLavatWtrwYIFtjr+Db62li5dqvT0dA0YMEBlypSRxM9AcQkPD1e5cuX03//+V0uXLtXvv/+uffv2aezYsdq5c6cmTJggiZ+B0o57IlGg5ORkSbL77Y+Vte3v927g6uXm5urFF1+Us7Oz+vTpI4njX9zmzJmj2NhY2+fQ0FAtWLBALVu2lMTxL25HjhzRCy+8oPHjxys0NFSJiYkONZyD4hMeHq6oqCjVrFlT6enpWrt2rWbOnKmNGzdqy5Yt8vLy4vgXk/3790uSYmJiVKtWLb3//vvKycnRa6+9pn79+unChQsaOHAgx/8ae++992SxWDRo0CBbG+egeJQvX16rVq3S4MGD1atXL1u7t7e3li1bpq5du0ri+Jd2hEgUyDq9xs3NzWGZu7u7XQ0KZ9iwYdq6dateeukl1alTRxLHv7h17dpVdevWVUZGhnbt2qVVq1YpNTXVtpzjX7yGDBmi6tWra8SIEQXWcA6Kz/bt2+0+P/TQQ2rYsKGee+45vfHGG3ruuec4/sXk7Nmzki79D/P69evl6uoq6dLfSdWrV9ezzz6r/v37c/yvof379+v777/Xvffeq2rVqtnaOQfFx8vLS7fddps6d+6siIgInTp1Sm+++ab69OmjlStXql27dhz/Uo4QiQJ5enpKkrKzsx2WZWVl2dXg6j3//POaOXOmHnnkEY0dO9bWzvEvXlWqVFGVKlUkXfqftx49eqhp06bKzMzU2LFjOf7FaMGCBfr666+1adMmh6eB/h3n4NoaNWqUJk6cqDVr1ui5557j+BcT69O3e/fubQuQ0qWrM507d9YHH3yg/fv3c/yvoffee0+SNHjwYLt2zkHx+OmnnxQREaFp06ZpyJAhtvbevXvrtttuU0xMjA4fPszxL+W4JxIFst7wnN9UAWtbflMMYN6ECRM0efJkDRw4UO+8847dMo7/tdWwYUPdcccdeuuttyRx/ItLdna2RowYoY4dO6py5co6dOiQDh06pKNHj0qS0tLSdOjQIZ05c4ZzcI25uLgoMDDQdkWe4188rL+8qly5ssOygIAASZce8MXxvzYuXryoDz74QLfccou6detmt4xzUDymTZumrKwsRUVF2bV7enqqU6dOOnr0qBITEzn+pRwhEgVq2rSpJGnr1q0Oy7Zt2yaLxaLGjRtf62HdMCZMmKCJEyeqf//+mjNnjiwWi93yBg0ayM3NrcDjL1160iuKzvnz53Xq1ClJHP/icv78ef35559as2aNatWqZftq06aNpEtXKWvVqqU5c+ZwDq6xrKwsHT9+3PZwL45/8QgPD5f0/w9T+ztrW6VKlfg3+BpZvXq1Tpw4oQcffNBh2iQ/A8XDGgBzc3Mdll28eNH2X34GSrkSfsUISti/vSeySZMmhre3t5GUlGRrS0pKMry9vY177733GozwxjRx4kRDktGvXz8jNze3wLqePXsaTk5Oxu7du21t1vdT1apVi/dTXYWUlJR827/99lvDycnJuOeee2xtHP+il5OTYyxdutTh66233jIkGffdd5+xdOlSY//+/YZhcA6KQ2pqar7tTz/9tMO7Ujn+Re/UqVOGt7e3ERQUZJw9e9bWnpycbJQtW9aoXbu2rY1/g4tfp06dDEnG3r17813Oz0DRGzZsmMPfNYZhGKdPnzYCAgKM8uXLGxcvXjQMg5+B0sxiGIZRoikW19yHH35omzo2Y8YM5eTkaOTIkZIuvSuvX79+ttotW7bo7rvvVpUqVWxPspwxY4ZOnDihzZs36/bbb7/2O3Cde/PNN/XEE0+oatWqevHFF+XkZD8hwN/fX+3atZN06R1J4eHhcnFx0fDhw+Xj46PZs2frp59+0po1a9ShQ4eS2IXrWrdu3ZSSkqJ77rlHISEhysrK0s6dO7V48WJ5enpqw4YNtveBcfyvncTERFWrVk1Dhw61e+UH56DoDR8+XNu2bdPdd9+tqlWrKiMjQ2vXrtX69et15513av369bb79jj+xWPWrFl69NFHVb9+fQ0aNEg5OTl6++23lZKSos8++0zt27eXxL/BxS05OVlVq1ZV48aNHR42ZcXPQNE7evSowsLCdPr0afXt21d33XWXTp06pdmzZysxMVFvvvmmHn/8cUn8DJRqJZ1ice21bt3akJTvV+vWrR3qt2zZYtxzzz1G2bJlDS8vL6N9+/bGzp07r/3AbxD9+/cv8Pjndw5+/fVXo3Pnzoavr6/h4eFh3HXXXcbXX39dMoO/ASxZssTo1KmTUaVKFcPNzc1wd3c36tSpYzzxxBPG0aNHHeo5/tfGkSNHDEnG0KFDHZZxDorWp59+arRv394IDAw03NzcDE9PT+P22283pkyZYpw/f96hnuNfPJYtW2bceeedhqenp+Hl5WW0a9fO+P777x3q+De4+EyZMsWQZMyaNeuydfwMFL1Dhw4ZDz30kBEUFGQ4Ozsb3t7eRsuWLY1ly5Y51PIzUDpxJRIAAAAAYBoP1gEAAAAAmEaIBAAAAACYRogEAAAAAJhGiAQAAAAAmEaIBAAAAACYRogEAAAAAJhGiAQAAAAAmEaIBAAAAACYRogEAAAAAJhGiAQAoIRZLBa1adOm2PrfsGGDLBaLJkyYUGzbAADcPAiRAAAUIYvFoipVqpT0MAAAKDaESAAAAACAaYRIAAAAAIBphEgAAK6BnJwcvfjii6pRo4bc3NxUrVo1jRs3TtnZ2fnWHzhwQM8884waN24sPz8/ubm5qXr16oqNjVVqamq+65w4cUIPP/yw/P395eHhoUaNGun999+/7Lh+//13DRkyRKGhoXJzc1OlSpXUq1cvJSQkONQePnxYgwcPVo0aNeTu7q4KFSqoQYMGeuyxx3T69OkrPygAgOuSc0kPAACAG51hGOrVq5dWrlypGjVq6IknnlBOTo7mzp2rn376Kd91li9frlmzZumee+5Ry5YtVaZMGf3444+aOXOmvvzyS/3www/y8fGx1aempioiIkK//fabWrRooRYtWiglJUVDhgxR+/bt893GDz/8oPbt2ystLU3333+/oqKilJKSouXLl+vzzz/X+vXr1aRJE0lSSkqKmjZtqrNnz6pjx47q2bOnsrKy9Ntvv+mDDz7QU089pfLlyxf9wQMAlDqESAAAitmiRYu0cuVKNWvWTOvXr5e7u7skaeLEiWratGm+6/Tr10/Dhw+Xm5ubXfu8efM0aNAgvfnmmxo7dqyt/dlnn9Vvv/2mYcOGadq0abb2J554Qs2bN3fo/8KFC+rVq5eysrL0/fff29UkJCQoPDxcgwcP1u7duyVJn3zyiU6fPq24uDg99dRTdn1lZGTI2Zn/pQCAmwXTWQEAKGbz5s2TJL300ku2AClJFSpU0PPPP5/vOkFBQQ4BUpIGDBggHx8fff3117a2Cxcu6KOPPpK3t7fDazyaNGmivn37OvSzZs0aHTlyRE8++aRDyKxXr55iYmK0Z88e/fLLL3bLPDw8HPry8vKy2y8AwI2NXxsCAFDMfvzxRzk5OalFixYOywp6P6RhGPrggw80f/587d27V2fOnFFeXp5teVJSku3P+/btU2Zmplq2bClfX998t/HPeyO3bt0qSfrtt9/yfX/k/v37bX3Xr19fnTt31rPPPquhQ4fqyy+/VIcOHXTXXXfp1ltvlcVi+ddjAAC4cRAiAQAoZmlpaapQoYJcXFwcllWuXDnfdYYNG6bp06erSpUq6tSpk92Vybi4OLsH8qSlpUmS/P398+0rv22cOnVKkrR06dLLjj0jI0OSFBISoh07dmjChAn64osvtHz5cklScHCwRo8erSeeeOKy/QAAbhyESAAAipmvr69OnTqlCxcuOATJP/74w6H+5MmTmjlzpho2bKgtW7aobNmytmV5eXn673//69C/dOnprPnJbxvWdRYtWqT//Oc/pvajXr16WrJkiS5evKg9e/boq6++0owZMxQbGytvb2/179/fVD8AgOsb90QCAFDMwsLClJeXp++//95h2YYNGxzafvvtN+Xl5alt27Z2AVK69ETV8+fP27XVrVtXnp6e2r17t+2q5L9t484775T0/9Nar4Szs7MaN26ssWPHavHixZKkTz/99Ir7AQBcnwiRAAAUs4EDB0qSnnvuOWVlZdnaT506pcmTJzvUh4aGSpI2b96s3NxcW/tff/2V77RRFxcX9e3bV2fPnnW4v/GHH37QRx995LBOly5dFBoaqrfeektffvmlw/K8vDy78Llz5858A6r1Kqenp6fDMgDAjYnprAAAFLPevXtryZIlWrVqlW677TZ16dJFFy5c0CeffKKmTZvq8OHDdvWVK1dWVFSUli5dqrCwMLVr106nTp3S559/rurVqyswMNBhGy+99JK++eYbxcXF6YcffrC9J3LJkiXq2LGjVq1aZVfv6uqqZcuWqUOHDrrvvvvUqlUrNWzYUK6urvr999+1detWpaam2kLvhx9+qHfffVctWrRQjRo1VL58eR06dEirV6+Wu7u7w2s/AAA3LkIkAADFzGKxaOnSpXr55Zc1f/58zZw5UwEBARo4cKDGjx+f7+sx5s2bp+rVq+vjjz+21ffr108vvPCC6tev71Dv5+enzZs369lnn9Xq1av1ww8/qE6dOnr77bcVGhrqECKlS9Ns9+7dq9dee01r1qzRnDlz5OzsrMDAQLVp00Y9evSw1fbu3VvZ2dnavHmzdu7cqfPnzysoKEi9e/fWqFGjdOuttxbtQQMAlFoWwzCMkh4EAAAAAOD6wD2RAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0wiRAAAAAADTCJEAAAAAANMIkQAAAAAA0/4PhASmym8JVBkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Análise de gênero"
      ],
      "metadata": {
        "id": "p7pMgRhctKid"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.Gender.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QG7vi-jetRXv",
        "outputId": "0cbee04e-4f4f-4fad-d69e-6c4aa1701143"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male           9360\n",
              "female         2697\n",
              "genderqueer      65\n",
              "agender          36\n",
              "trans            36\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.Gender.value_counts().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcsME-5XteG-",
        "outputId": "58f4af4c-3eae-4fc1-b6b7-65e2da703835"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12194"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_genero = pd.DataFrame(dados.Gender.value_counts())\n"
      ],
      "metadata": {
        "id": "zYTOvlAOtfxv"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_genero.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "749yLj5nuWOn",
        "outputId": "5d8f58fd-1e76-4a19-b9a7-f17773e6b6d4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Gender\n",
              "male           9360\n",
              "female         2697\n",
              "genderqueer      65\n",
              "agender          36\n",
              "trans            36"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0c5d35c4-8f50-4165-82d8-2eaa35a70fa6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>9360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>2697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>genderqueer</th>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agender</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trans</th>\n",
              "      <td>36</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-0c5d35c4-8f50-4165-82d8-2eaa35a70fa6')\"\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-0c5d35c4-8f50-4165-82d8-2eaa35a70fa6 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-0c5d35c4-8f50-4165-82d8-2eaa35a70fa6');\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-9a57470e-e7df-47c5-bcc1-47851a57d2c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a57470e-e7df-47c5-bcc1-47851a57d2c2')\"\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-9a57470e-e7df-47c5-bcc1-47851a57d2c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tipos_de_genero",
              "summary": "{\n  \"name\": \"tipos_de_genero\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4035,\n        \"min\": 36,\n        \"max\": 9360,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2697,\n          36,\n          9360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_genero.columns.name = 'Tipo'"
      ],
      "metadata": {
        "id": "fCN2r3efuqa-"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_genero.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XpMgSh6Yu3YP",
        "outputId": "8cc2da0d-20fe-4e73-d8c0-4db4a7150b02"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tipo         Gender\n",
              "male           9360\n",
              "female         2697\n",
              "genderqueer      65\n",
              "agender          36\n",
              "trans            36"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-57edb5e1-d344-45dc-92a3-8323048149f1\" 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>Tipo</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>9360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>2697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>genderqueer</th>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agender</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trans</th>\n",
              "      <td>36</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-57edb5e1-d344-45dc-92a3-8323048149f1')\"\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-57edb5e1-d344-45dc-92a3-8323048149f1 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-57edb5e1-d344-45dc-92a3-8323048149f1');\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-8fa74261-5554-4ce5-9de1-f5ee156d9a84\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fa74261-5554-4ce5-9de1-f5ee156d9a84')\"\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-8fa74261-5554-4ce5-9de1-f5ee156d9a84 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tipos_de_genero",
              "summary": "{\n  \"name\": \"tipos_de_genero\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4035,\n        \"min\": 36,\n        \"max\": 9360,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2697,\n          36,\n          9360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tipos_de_genero.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mI5KNDvZvCyj",
        "outputId": "3ae6b154-674b-4d98-fb2f-f632496cb804"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tipo\n",
              "Gender    9360\n",
              "Name: male, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Selecionado a linha famele tirando porcentagem"
      ],
      "metadata": {
        "id": "o2FNwKFBvS6N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feminino = tipos_de_genero.iloc[1]"
      ],
      "metadata": {
        "id": "SgIrCCOKvVKE"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feminino.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5mjGF9fJvhjf",
        "outputId": "87e0d2d1-f447-4b76-f10f-952651e06907"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tipo\n",
              "Gender    2697\n",
              "Name: female, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_genero = dados.Gender.value_counts().sum()\n",
        "total_genero"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDQa8z1bv9yc",
        "outputId": "84dcb509-2057-41a9-9853-319e5e2facb8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12194"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fem = (feminino / total_genero * 100)\n",
        "fem"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoJr2mj6wkcP",
        "outputId": "13905dfc-603b-4158-83e5-53848ae32e11"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Tipo\n",
              "Gender    22.117435\n",
              "Name: female, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.groupby('Gender').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "N5eCeYqawuH0",
        "outputId": "7cc42d4c-9e87-4851-e2ef-e2fcc0e6e648"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-6f95caccf843>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  dados.groupby('Gender').mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Age  JobRelocateYesNo  HoursLearning  MoneyForLearning\n",
              "Gender                                                                   \n",
              "agender      25.277778          0.642857      13.685714       1023.088235\n",
              "female       30.797553          0.653043      14.537481       1558.847478\n",
              "genderqueer  28.215385          0.684211      14.311475       1600.316667\n",
              "male         29.300000          0.758493      16.066402       1083.079552\n",
              "trans        30.000000          0.714286      17.828571       3019.588235"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"female\",\n          \"trans\",\n          \"genderqueer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1444427633155496,\n        \"min\": 25.27777777777778,\n        \"max\": 30.797552836484982,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          30.797552836484982,\n          30.0,\n          28.215384615384615\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRelocateYesNo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04717305777660951,\n        \"min\": 0.6428571428571429,\n        \"max\": 0.7584927205252641,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.6530426884650318,\n          0.7142857142857143,\n          0.6842105263157895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoursLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6691756697317917,\n        \"min\": 13.685714285714285,\n        \"max\": 17.82857142857143,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          14.537480680061824,\n          17.82857142857143,\n          14.311475409836065\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MoneyForLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 806.3377738123274,\n        \"min\": 1023.0882352941177,\n        \"max\": 3019.5882352941176,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1558.8474775127102,\n          3019.5882352941176,\n          1600.3166666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.groupby('Gender').median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "Puzp-HwvxSHl",
        "outputId": "0e0f55eb-6d30-4812-ce6d-e076bacf0138"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-57-c8bdc22ceb4c>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.median is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  dados.groupby('Gender').median()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age  JobRelocateYesNo  HoursLearning  MoneyForLearning\n",
              "Gender                                                              \n",
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              "      <td>28.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trans</th>\n",
              "      <td>27.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>32.5</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-2a107a6e-881b-4b25-858c-d69cc7fd79c4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </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-2a107a6e-881b-4b25-858c-d69cc7fd79c4 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-2a107a6e-881b-4b25-858c-d69cc7fd79c4');\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-458a14be-48ee-46c0-8ba9-b9cadada7e13\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-458a14be-48ee-46c0-8ba9-b9cadada7e13')\"\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-458a14be-48ee-46c0-8ba9-b9cadada7e13 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"female\",\n          \"trans\",\n          \"genderqueer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3038404810405297,\n        \"min\": 25.5,\n        \"max\": 29.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          29.0,\n          28.0,\n          25.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"JobRelocateYesNo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HoursLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8944271909999159,\n        \"min\": 10.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          12.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MoneyForLearning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.64227876437735,\n        \"min\": 10.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genero = dados.Gender.value_counts()"
      ],
      "metadata": {
        "id": "XBda3qKCx51X"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(genero)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761
        },
        "id": "iexzP4HFyDNT",
        "outputId": "54d930b3-07f9-4ddb-b934-e4d61026c81a"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<matplotlib.patches.Wedge at 0x7876e64b7520>,\n",
              "  <matplotlib.patches.Wedge at 0x7876e64b61a0>,\n",
              "  <matplotlib.patches.Wedge at 0x7876e64b7430>,\n",
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              "  <matplotlib.patches.Wedge at 0x7876e64b6b30>],\n",
              " [Text(-0.8195924148082909, 0.7336676860736846, ''),\n",
              "  Text(0.7931919072339293, -0.7621329269219392, ''),\n",
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              "  Text(1.099952689214474, -0.010202033613305905, '')])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1040x715 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genero = dados.Gender.value_counts()\n",
        "plt.pie(genero)\n",
        "plt.legend (dados.Gender.value_counts().index)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "id": "vXNjPr6QzTE9",
        "outputId": "23da5434-b4b9-462a-b2a1-7128a461b795"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1040x715 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genero = dados.Gender.value_counts()\n",
        "plt.pie(genero)\n",
        "plt.legend (dados.Gender.value_counts().index, bbox_to_anchor = (1,1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "id": "ERA8xgLE0e_c",
        "outputId": "f067171c-59ce-4ae0-d6ca-d94b391a9935"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1040x715 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}