O_Perfil_do_Profissional_de_Dados_no_Brasil.ipynb

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        "<a href=\"https://colab.research.google.com/github/ricardorocha86/Fundamentos-de-Python-para-ML/blob/main/O_Perfil_do_Profissional_de_Dados_no_Brasil.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **O Perfil do Profissional de Dados no Brasil**"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "Muito se especula sobre o profissional da área de dados no Brasil, principalmente nas profissões mais em alta, como o cientista de dados. Alguns chegam a falar em salários de 20mil, 30mil reais, mas será que o mercado está assim mesmo?\n",
        "\n",
        "Foi por isso que o pessoal do Data Hackers tomou essa excelente iniciativa e fez uma grande pesquisa em nível nacional para traçar o perfil desses profissionais. Foram entrevistados 1765 pessoas que responderam sobre diversas variáveis de suas carreirais, desde salário e tempo de experiência até plataformas de ensino e conceitos mais utilizados. Enfim, bastante informação para explorarmos. \n",
        "\n",
        "O objetivo dessa análise é entender, através dos dados, qual é o perfil do profissional da área de dados no Brasil. Após o carregamento dos dados, fizemos algumas considerações e criamos algumas variáveis novas para auxiliar na análise. Em seguida, respondemos sequencialmente diversas perguntas levantadas com o intuito de traçar o perfil desejado. "
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      "source": [
        "## **Sumário**\n",
        "[Importação dos dados](#imp)\n",
        "\n",
        "1. [Pergunta: Qual o perfil geral do profissional da área de dados no Brasil?](#p1)\n",
        "1. [Pergunta: Qual o perfil dos que se consideram atuar em Data Science?](#p2)\n",
        "3. [Pergunta: Qual o perfil do profissional empregado como Cientista de Dados?](#p3)\n",
        "3. [Pergunta: Vale a pena fazer pós-graduação?](#p4)\n",
        "3. [Pergunta: Há desigualdade nos salários em relação ao gênero na área de dados no Brasil?](#p5)\n",
        "3. [Pergunta: Quais são as habilidades mais predominantes nos Cientistas de Dados?](#p6)\n",
        "3. [Pergunta: Quais são os salários em relação às linguagens mais utilizadas?](#p7)\n",
        "3. [Pergunta: Quais são os salários em relação ao tipo de emprego?](#p8)\n",
        "3. [Pergunta: Quais são os salários em relação as plataformas de ensino preferidas?](#p9)\n",
        "3. [Pergunta: Quais são os salários em relação tempo de experiência?](#p10)\n",
        "3. [Pergunta: Como os cargos se distribuem em relação ao tempo de experiência?](#p11)\n",
        "3. [Pergunta: Como os profissionais estão distribuídos em seus empregos em relação às suas formações?](#p12)\n",
        "\n",
        "[Conclusões](#conc)\n",
        "\n",
        "[Exercício](#exer)\n",
        "\n",
        "[Referências](#refs)\n"
      ]
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      "source": [
        "## **Importação dos dados**  <a name=\"imp\"></a>"
      ]
    },
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        "id": "mDJsIcS4mYyi"
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      "source": [
        "Para maior conveniência, o conjunto de dados foi baixado do kaggle e colocado num repositório do github, em que é possível realizar a leitura através de seu link. "
      ]
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    {
      "cell_type": "code",
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      "source": [
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/ricardorocha86/Datasets/master/PesquisaDH/datahackers-survey-2019-anonymous-responses.csv'\n",
        "dados = pd.read_csv(url)"
      ],
      "execution_count": 2,
      "outputs": []
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      "source": [
        "dados.shape"
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        "n = dados.shape[0]"
      ],
      "execution_count": 4,
      "outputs": []
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      "source": [
        "dados.info()"
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      "execution_count": 5,
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1765 entries, 0 to 1764\n",
            "Columns: 170 entries, ('P0', 'id') to ('D6', 'anonymized_role')\n",
            "dtypes: float64(5), int64(146), object(19)\n",
            "memory usage: 2.3+ MB\n"
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          "name": "stdout"
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        "dados.head()"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Udemy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Outras Engenharias</td>\n",
              "      <td>Indústria (Manufatura)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Outras</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rmel8ewqpbffp2mnfbzermel8eqincov</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>1</td>\n",
              "      <td>São Paulo (SP)</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Estudante de Graduação</td>\n",
              "      <td>Estagiário</td>\n",
              "      <td>de 11 a 50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>de R$ 1.001/mês a R$ 2.000/mês</td>\n",
              "      <td>Menos de 1 ano</td>\n",
              "      <td>de 2 a 3 anos</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Ainda não conhecia o Data Hackers</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Alura</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>Computação / Engenharia de Software / Sistemas...</td>\n",
              "      <td>Tecnologia/Fábrica de Software</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Business Intelligence/Analista de BI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pj9mgud4d6mdct1l7vq0pj9mgu78h6ju</td>\n",
              "      <td>27.0</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Graduação/Bacharelado</td>\n",
              "      <td>Freelancer</td>\n",
              "      <td>de 6 a 10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>de R$ 6.001/mês a R$ 8.000/mês</td>\n",
              "      <td>de 4 a 5 anos</td>\n",
              "      <td>de 4 a 5 anos</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Blog/Medium do Data Hackers</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Udemy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Computação / Engenharia de Software / Sistemas...</td>\n",
              "      <td>Internet/Ecommerce</td>\n",
              "      <td>C-level (CDO, CIO, CTO)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 170 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                       ('P0', 'id')  ...             ('D6', 'anonymized_role')\n",
              "0  v9otv8j9wdvjrv9otvwnn9owhzq54ktv  ...                                Outras\n",
              "1  875ul998t0hqcv0871uptwf3oswcfv35  ...        Data Analyst/Analista de Dados\n",
              "2  puscuk079vw1pusbb900pzw2xvpxtgdk  ...                                Outras\n",
              "3  rmel8ewqpbffp2mnfbzermel8eqincov  ...  Business Intelligence/Analista de BI\n",
              "4  pj9mgud4d6mdct1l7vq0pj9mgu78h6ju  ...                                   NaN\n",
              "\n",
              "[5 rows x 170 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ruBnBuGZ9786",
        "cellView": "both"
      },
      "source": [
        "pd.set_option(\"display.max_columns\", 200)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RyabzWNG1mhm",
        "outputId": "fd317fc3-a995-447e-f981-03182a971f27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        }
      },
      "source": [
        "dados.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>('P0', 'id')</th>\n",
              "      <th>('P1', 'age')</th>\n",
              "      <th>('P2', 'gender')</th>\n",
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              "      <th>('P16', 'salary_range')</th>\n",
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              "      <th>('P20', 'survival_analysis')</th>\n",
              "      <th>('P20', 'longitudinal_data_analysis')</th>\n",
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              "      <th>('P21', 'sql_')</th>\n",
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              "      <th>('P21', 'c_c++_c#')</th>\n",
              "      <th>('P21', 'dotnet')</th>\n",
              "      <th>('P21', 'java')</th>\n",
              "      <th>('P21', 'julia')</th>\n",
              "      <th>('P21', 'sas_stata')</th>\n",
              "      <th>('P21', 'visual_basic_vba')</th>\n",
              "      <th>('P21', 'scala')</th>\n",
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              "      <th>('P21', 'php')</th>\n",
              "      <th>('P21', 'no_listed_languages')</th>\n",
              "      <th>('P22', 'most_used_proggraming_languages')</th>\n",
              "      <th>('P23', 'sql')</th>\n",
              "      <th>('P23', 'nosql')</th>\n",
              "      <th>('P23', 'images')</th>\n",
              "      <th>('P23', 'nlp')</th>\n",
              "      <th>('P23', 'videos')</th>\n",
              "      <th>('P23', 'sheets')</th>\n",
              "      <th>('P23', 'other')</th>\n",
              "      <th>('P24', 'sql')</th>\n",
              "      <th>('P24', 'nosql')</th>\n",
              "      <th>('P24', 'imagens')</th>\n",
              "      <th>('P24', 'nlp')</th>\n",
              "      <th>('P24', 'vídeos')</th>\n",
              "      <th>('P24', 'planilhas')</th>\n",
              "      <th>('P24', 'other')</th>\n",
              "      <th>('P25', 'aws')</th>\n",
              "      <th>('P25', 'gcp')</th>\n",
              "      <th>('P25', 'azure')</th>\n",
              "      <th>('P25', 'ibm')</th>\n",
              "      <th>('P25', 'on_premise_servers')</th>\n",
              "      <th>('P25', 'cloud_própria')</th>\n",
              "      <th>('P25', 'other')</th>\n",
              "      <th>('P26', 'mysql')</th>\n",
              "      <th>('P26', 'oracle')</th>\n",
              "      <th>('P26', 'sql_server')</th>\n",
              "      <th>('P26', 'aurora')</th>\n",
              "      <th>('P26', 'dynamodb')</th>\n",
              "      <th>('P26', 'coachdb')</th>\n",
              "      <th>('P26', 'cassandra')</th>\n",
              "      <th>('P26', 'mongodb')</th>\n",
              "      <th>('P26', 'mariadb')</th>\n",
              "      <th>('P26', 'datomic')</th>\n",
              "      <th>('P26', 's3')</th>\n",
              "      <th>('P26', 'postgresql')</th>\n",
              "      <th>('P26', 'elaticsearch')</th>\n",
              "      <th>('P26', 'db2')</th>\n",
              "      <th>('P26', 'ms_access')</th>\n",
              "      <th>('P26', 'sqlite')</th>\n",
              "      <th>('P26', 'sybase')</th>\n",
              "      <th>('P26', 'firebase')</th>\n",
              "      <th>('P26', 'vertica')</th>\n",
              "      <th>('P26', 'redis')</th>\n",
              "      <th>('P26', 'neo4j')</th>\n",
              "      <th>('P26', 'google_bigtable')</th>\n",
              "      <th>('P26', 'hbase')</th>\n",
              "      <th>('P26', 'other')</th>\n",
              "      <th>('P27', 'microsoft_powerbi')</th>\n",
              "      <th>('P27', 'qlik_view_qlik_sense')</th>\n",
              "      <th>('P27', 'tableau')</th>\n",
              "      <th>('P27', 'metabase')</th>\n",
              "      <th>('P27', 'superset')</th>\n",
              "      <th>('P27', 'redash')</th>\n",
              "      <th>('P27', 'microstrategy')</th>\n",
              "      <th>('P27', 'ibm_analytics_cognos')</th>\n",
              "      <th>('P27', 'sap_business_objects')</th>\n",
              "      <th>('P27', 'oracle_business_intelligence')</th>\n",
              "      <th>('P27', 'birst')</th>\n",
              "      <th>('P27', 'looker')</th>\n",
              "      <th>('P27', 'google_data_studio')</th>\n",
              "      <th>('P27', 'only_excel_gsheets')</th>\n",
              "      <th>('P27', 'no_bi_tool_at_work')</th>\n",
              "      <th>('P27', 'other')</th>\n",
              "      <th>('P28', 'sql_&amp;_stored_procedures')</th>\n",
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              "      <th>('P28', 'luigi')</th>\n",
              "      <th>('P28', 'aws_glue')</th>\n",
              "      <th>('P28', 'talend')</th>\n",
              "      <th>('P28', 'pentaho')</th>\n",
              "      <th>('P28', 'alteryx')</th>\n",
              "      <th>('P28', 'oracle_data_integrator')</th>\n",
              "      <th>('P28', 'ibm_data_stage')</th>\n",
              "      <th>('P28', 'sap_bw_etl')</th>\n",
              "      <th>('P28', 'siss_sql_server_integration_services')</th>\n",
              "      <th>('P28', 'other')</th>\n",
              "      <th>('P29', 'have_data_warehouse')</th>\n",
              "      <th>('P30', 'google_bigquery')</th>\n",
              "      <th>('P30', 'aws_redshift')</th>\n",
              "      <th>('P30', 'snowflake')</th>\n",
              "      <th>('P30', 'oracle')</th>\n",
              "      <th>('P30', 'postgres_mysql')</th>\n",
              "      <th>('P30', 'ibm')</th>\n",
              "      <th>('P30', 'teradata')</th>\n",
              "      <th>('P30', 'microsoft_azure')</th>\n",
              "      <th>('P30', 'do_not_know')</th>\n",
              "      <th>('P30', 'other')</th>\n",
              "      <th>('P31', 'data_hackers_blog')</th>\n",
              "      <th>('P31', 'data_hackers_podcast')</th>\n",
              "      <th>('P31', 'weekly_newsletter')</th>\n",
              "      <th>('P31', 'slack_channel')</th>\n",
              "      <th>('P31', 'data_hackers_bootcamp')</th>\n",
              "      <th>('P31', 'do_not_know_data_hackers')</th>\n",
              "      <th>('P32', 'prefered_data_hackers_initiative')</th>\n",
              "      <th>('P33', 'telegram_groups')</th>\n",
              "      <th>('P33', 'whatsapp_groups')</th>\n",
              "      <th>('P33', 'youtube_channels')</th>\n",
              "      <th>('P33', 'other_brasilian_blogs')</th>\n",
              "      <th>('P33', 'other_slack_channels')</th>\n",
              "      <th>('P33', 'twitter')</th>\n",
              "      <th>('P33', 'abroad_blogs')</th>\n",
              "      <th>('P33', 'abroad_podcasts')</th>\n",
              "      <th>('P33', 'meetups_and_events')</th>\n",
              "      <th>('P33', 'only_data_hackers')</th>\n",
              "      <th>('P33', 'other')</th>\n",
              "      <th>('P34', 'udacity')</th>\n",
              "      <th>('P34', 'coursera')</th>\n",
              "      <th>('P34', 'udemy')</th>\n",
              "      <th>('P34', 'height')</th>\n",
              "      <th>('P34', 'edx')</th>\n",
              "      <th>('P34', 'data_camp')</th>\n",
              "      <th>('P34', 'data_quest')</th>\n",
              "      <th>('P34', 'kaggle_learn')</th>\n",
              "      <th>('P34', 'online_courses')</th>\n",
              "      <th>('P34', 'other')</th>\n",
              "      <th>('P35', 'data_science_plataforms_preference')</th>\n",
              "      <th>('P35', 'other')</th>\n",
              "      <th>('P36', 'draw_participation')</th>\n",
              "      <th>('D1', 'living_macroregion')</th>\n",
              "      <th>('D2', 'origin_macroregion')</th>\n",
              "      <th>('D3', 'anonymized_degree_area')</th>\n",
              "      <th>('D4', 'anonymized_market_sector')</th>\n",
              "      <th>('D5', 'anonymized_manager_level')</th>\n",
              "      <th>('D6', 'anonymized_role')</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>v9otv8j9wdvjrv9otvwnn9owhzq54ktv</td>\n",
              "      <td>37.0</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>1</td>\n",
              "      <td>Minas Gerais (MG)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Estudante de Graduação</td>\n",
              "      <td>Empregado (CTL)</td>\n",
              "      <td>de 1 a 5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>de R$ 1.001/mês a R$ 2.000/mês</td>\n",
              "      <td>Não tenho experiência na área de dados</td>\n",
              "      <td>Não tive experiência na área de TI/Engenharia ...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Ainda não conhecia o Data Hackers</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Nunca fiz cursos online</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Computação / Engenharia de Software / Sistemas...</td>\n",
              "      <td>Outras</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Outras</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>875ul998t0hqcv0871uptwf3oswcfv35</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Feminino</td>\n",
              "      <td>1</td>\n",
              "      <td>São Paulo (SP)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Estudante de Graduação</td>\n",
              "      <td>Empregado (CTL)</td>\n",
              "      <td>Acima de 3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>de R$ 2.001/mês a R$ 3000/mês</td>\n",
              "      <td>Menos de 1 ano</td>\n",
              "      <td>Não tive experiência na área de TI/Engenharia ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Python</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Ainda não conhecia o Data Hackers</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Data Science Academy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Computação / Engenharia de Software / Sistemas...</td>\n",
              "      <td>Educação</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Data Analyst/Analista de Dados</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>puscuk079vw1pusbb900pzw2xvpxtgdk</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>1</td>\n",
              "      <td>São Paulo (SP)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Graduação/Bacharelado</td>\n",
              "      <td>Empregado (CTL)</td>\n",
              "      <td>Acima de 3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>de R$ 4.001/mês a R$ 6.000/mês</td>\n",
              "      <td>de 1 a 2 anos</td>\n",
              "      <td>de 6 a 10 anos</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Python</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Newsletter Semanal</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Udemy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Outras Engenharias</td>\n",
              "      <td>Indústria (Manufatura)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Outras</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>rmel8ewqpbffp2mnfbzermel8eqincov</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>1</td>\n",
              "      <td>São Paulo (SP)</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Estudante de Graduação</td>\n",
              "      <td>Estagiário</td>\n",
              "      <td>de 11 a 50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>de R$ 1.001/mês a R$ 2.000/mês</td>\n",
              "      <td>Menos de 1 ano</td>\n",
              "      <td>de 2 a 3 anos</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SQL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Ainda não conhecia o Data Hackers</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Alura</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>Região Sudeste</td>\n",
              "      <td>Computação / Engenharia de Software / Sistemas...</td>\n",
              "      <td>Tecnologia/Fábrica de Software</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Business Intelligence/Analista de BI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>pj9mgud4d6mdct1l7vq0pj9mgu78h6ju</td>\n",
              "      <td>27.0</td>\n",
              "      <td>Masculino</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Graduação/Bacharelado</td>\n",
              "      <td>Freelancer</td>\n",
              "      <td>de 6 a 10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>de R$ 6.001/mês a R$ 8.000/mês</td>\n",
              "      <td>de 4 a 5 anos</td>\n",
              "      <td>de 4 a 5 anos</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Python</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Blog/Medium do Data Hackers</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Udemy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Computação / Engenharia de Software / Sistemas...</td>\n",
              "      <td>Internet/Ecommerce</td>\n",
              "      <td>C-level (CDO, CIO, CTO)</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       ('P0', 'id')  ('P1', 'age') ('P2', 'gender')  \\\n",
              "0  v9otv8j9wdvjrv9otvwnn9owhzq54ktv           37.0        Masculino   \n",
              "1  875ul998t0hqcv0871uptwf3oswcfv35           24.0         Feminino   \n",
              "2  puscuk079vw1pusbb900pzw2xvpxtgdk           26.0        Masculino   \n",
              "3  rmel8ewqpbffp2mnfbzermel8eqincov           21.0        Masculino   \n",
              "4  pj9mgud4d6mdct1l7vq0pj9mgu78h6ju           27.0        Masculino   \n",
              "\n",
              "   ('P3', 'living_in_brasil') ('P5', 'living_state')  \\\n",
              "0                           1      Minas Gerais (MG)   \n",
              "1                           1         São Paulo (SP)   \n",
              "2                           1         São Paulo (SP)   \n",
              "3                           1         São Paulo (SP)   \n",
              "4                           1                    NaN   \n",
              "\n",
              "   ('P6', 'born_or_graduated') ('P8', 'degreee_level')  \\\n",
              "0                          1.0  Estudante de Graduação   \n",
              "1                          1.0  Estudante de Graduação   \n",
              "2                          1.0   Graduação/Bacharelado   \n",
              "3                          0.0  Estudante de Graduação   \n",
              "4                          1.0   Graduação/Bacharelado   \n",
              "\n",
              "  ('P10', 'job_situation') ('P12', 'workers_number')  ('P13', 'manager')  \\\n",
              "0          Empregado (CTL)                  de 1 a 5                 0.0   \n",
              "1          Empregado (CTL)             Acima de 3000                 0.0   \n",
              "2          Empregado (CTL)             Acima de 3000                 0.0   \n",
              "3               Estagiário                de 11 a 50                 0.0   \n",
              "4               Freelancer                 de 6 a 10                 1.0   \n",
              "\n",
              "          ('P16', 'salary_range') ('P17', 'time_experience_data_science')  \\\n",
              "0  de R$ 1.001/mês a R$ 2.000/mês  Não tenho experiência na área de dados   \n",
              "1   de R$ 2.001/mês a R$ 3000/mês                          Menos de 1 ano   \n",
              "2  de R$ 4.001/mês a R$ 6.000/mês                           de 1 a 2 anos   \n",
              "3  de R$ 1.001/mês a R$ 2.000/mês                          Menos de 1 ano   \n",
              "4  de R$ 6.001/mês a R$ 8.000/mês                           de 4 a 5 anos   \n",
              "\n",
              "                   ('P18', 'time_experience_before')  \\\n",
              "0  Não tive experiência na área de TI/Engenharia ...   \n",
              "1  Não tive experiência na área de TI/Engenharia ...   \n",
              "2                                     de 6 a 10 anos   \n",
              "3                                      de 2 a 3 anos   \n",
              "4                                      de 4 a 5 anos   \n",
              "\n",
              "   ('P19', 'is_data_science_professional')  ('P20', 'linear_regression')  \\\n",
              "0                                        0                             0   \n",
              "1                                        1                             0   \n",
              "2                                        1                             0   \n",
              "3                                        1                             0   \n",
              "4                                        1                             1   \n",
              "\n",
              "   ('P20', 'logistic_regression')  ('P20', 'glms')  ('P20', 'decision_tree')  \\\n",
              "0                               0                0                         0   \n",
              "1                               0                0                         0   \n",
              "2                               0                0                         0   \n",
              "3                               0                0                         0   \n",
              "4                               0                0                         1   \n",
              "\n",
              "   ('P20', 'random_forest')  ('P20', 'neural_networks')  \\\n",
              "0                         0                           0   \n",
              "1                         0                           0   \n",
              "2                         1                           1   \n",
              "3                         1                           0   \n",
              "4                         1                           0   \n",
              "\n",
              "   ('P20', 'bayesian_inference')  ('P20', 'ensemble')  ('P20', 'svms')  \\\n",
              "0                              0                    0                0   \n",
              "1                              0                    0                0   \n",
              "2                              0                    1                1   \n",
              "3                              0                    0                0   \n",
              "4                              0                    1                1   \n",
              "\n",
              "   ('P20', 'cnns')  ('P20', 'rnns')  ('P20', 'hmms')  ('P20', 'gans')  \\\n",
              "0                0                0                0                0   \n",
              "1                0                0                0                0   \n",
              "2                0                0                0                0   \n",
              "3                0                0                0                0   \n",
              "4                0                1                0                0   \n",
              "\n",
              "   ('P20', 'markov_chains')  ('P20', 'nlp')  \\\n",
              "0                         0               0   \n",
              "1                         0               0   \n",
              "2                         0               0   \n",
              "3                         0               0   \n",
              "4                         0               1   \n",
              "\n",
              "   ('P20', 'gradient_boosted_machines')  ('P20', 'cluster_analysis')  \\\n",
              "0                                     0                            0   \n",
              "1                                     0                            0   \n",
              "2                                     0                            1   \n",
              "3                                     0                            0   \n",
              "4                                     0                            0   \n",
              "\n",
              "   ('P20', 'survival_analysis')  ('P20', 'longitudinal_data_analysis')  \\\n",
              "0                             0                                      0   \n",
              "1                             0                                      0   \n",
              "2                             0                                      0   \n",
              "3                             0                                      0   \n",
              "4                             0                                      0   \n",
              "\n",
              "   ('P20', 'joint analysis')  ('P20', 'no_listed_methods')  ('P21', 'sql_')  \\\n",
              "0                          0                             0                0   \n",
              "1                          0                             1                1   \n",
              "2                          0                             0                0   \n",
              "3                          0                             0                1   \n",
              "4                          0                             0                1   \n",
              "\n",
              "   ('P21', 'r')  ('P21', 'python')  ('P21', 'c_c++_c#')  ('P21', 'dotnet')  \\\n",
              "0             0                  0                    0                  0   \n",
              "1             0                  1                    0                  0   \n",
              "2             0                  1                    0                  0   \n",
              "3             0                  1                    0                  0   \n",
              "4             0                  1                    1                  0   \n",
              "\n",
              "   ('P21', 'java')  ('P21', 'julia')  ('P21', 'sas_stata')  \\\n",
              "0                0                 0                     0   \n",
              "1                0                 0                     0   \n",
              "2                0                 0                     0   \n",
              "3                0                 0                     0   \n",
              "4                0                 0                     0   \n",
              "\n",
              "   ('P21', 'visual_basic_vba')  ('P21', 'scala')  ('P21', 'matlab')  \\\n",
              "0                            0                 0                  0   \n",
              "1                            0                 0                  0   \n",
              "2                            0                 0                  0   \n",
              "3                            0                 0                  0   \n",
              "4                            0                 0                  0   \n",
              "\n",
              "   ('P21', 'php')  ('P21', 'no_listed_languages')  \\\n",
              "0               0                               0   \n",
              "1               0                               0   \n",
              "2               0                               0   \n",
              "3               0                               0   \n",
              "4               0                               0   \n",
              "\n",
              "  ('P22', 'most_used_proggraming_languages')  ('P23', 'sql')  \\\n",
              "0                                        NaN               0   \n",
              "1                                     Python               1   \n",
              "2                                     Python               0   \n",
              "3                                        SQL               1   \n",
              "4                                     Python               1   \n",
              "\n",
              "   ('P23', 'nosql')  ('P23', 'images')  ('P23', 'nlp')  ('P23', 'videos')  \\\n",
              "0                 0                  0               0                  0   \n",
              "1                 0                  0               0                  0   \n",
              "2                 0                  0               0                  0   \n",
              "3                 0                  0               0                  0   \n",
              "4                 1                  0               1                  0   \n",
              "\n",
              "   ('P23', 'sheets')  ('P23', 'other')  ('P24', 'sql')  ('P24', 'nosql')  \\\n",
              "0                  0                 0               0                 0   \n",
              "1                  0                 0               1                 0   \n",
              "2                  1                 0               0                 0   \n",
              "3                  0                 0               1                 0   \n",
              "4                  1                 0               0                 0   \n",
              "\n",
              "   ('P24', 'imagens')  ('P24', 'nlp')  ('P24', 'vídeos')  \\\n",
              "0                   0               0                  0   \n",
              "1                   0               0                  0   \n",
              "2                   0               0                  0   \n",
              "3                   0               0                  0   \n",
              "4                   0               1                  0   \n",
              "\n",
              "   ('P24', 'planilhas')  ('P24', 'other')  ('P25', 'aws')  ('P25', 'gcp')  \\\n",
              "0                     0                 0               0               0   \n",
              "1                     0                 0               1               0   \n",
              "2                     1                 0               0               0   \n",
              "3                     0                 0               1               0   \n",
              "4                     0                 0               0               0   \n",
              "\n",
              "   ('P25', 'azure')  ('P25', 'ibm')  ('P25', 'on_premise_servers')  \\\n",
              "0                 0               0                              0   \n",
              "1                 0               0                              0   \n",
              "2                 0               0                              0   \n",
              "3                 0               0                              0   \n",
              "4                 0               0                              0   \n",
              "\n",
              "   ('P25', 'cloud_própria')  ('P25', 'other')  ('P26', 'mysql')  \\\n",
              "0                         0                 0                 0   \n",
              "1                         0                 0                 0   \n",
              "2                         1                 0                 0   \n",
              "3                         0                 0                 0   \n",
              "4                         0                 1                 1   \n",
              "\n",
              "   ('P26', 'oracle')  ('P26', 'sql_server')  ('P26', 'aurora')  \\\n",
              "0                  0                      0                  0   \n",
              "1                  1                      0                  0   \n",
              "2                  0                      0                  0   \n",
              "3                  0                      0                  0   \n",
              "4                  0                      0                  0   \n",
              "\n",
              "   ('P26', 'dynamodb')  ('P26', 'coachdb')  ('P26', 'cassandra')  \\\n",
              "0                    0                   0                     0   \n",
              "1                    0                   0                     0   \n",
              "2                    0                   0                     0   \n",
              "3                    0                   0                     0   \n",
              "4                    0                   0                     0   \n",
              "\n",
              "   ('P26', 'mongodb')  ('P26', 'mariadb')  ('P26', 'datomic')  ('P26', 's3')  \\\n",
              "0                   0                   0                   0              0   \n",
              "1                   0                   0                   0              1   \n",
              "2                   0                   0                   0              0   \n",
              "3                   0                   0                   0              0   \n",
              "4                   1                   1                   0              0   \n",
              "\n",
              "   ('P26', 'postgresql')  ('P26', 'elaticsearch')  ('P26', 'db2')  \\\n",
              "0                      0                        0               0   \n",
              "1                      0                        0               0   \n",
              "2                      0                        0               0   \n",
              "3                      0                        0               0   \n",
              "4                      1                        1               0   \n",
              "\n",
              "   ('P26', 'ms_access')  ('P26', 'sqlite')  ('P26', 'sybase')  \\\n",
              "0                     0                  0                  0   \n",
              "1                     0                  0                  0   \n",
              "2                     0                  0                  0   \n",
              "3                     0                  0                  0   \n",
              "4                     0                  1                  0   \n",
              "\n",
              "   ('P26', 'firebase')  ('P26', 'vertica')  ('P26', 'redis')  \\\n",
              "0                    0                   0                 0   \n",
              "1                    0                   0                 0   \n",
              "2                    0                   0                 0   \n",
              "3                    0                   0                 0   \n",
              "4                    0                   0                 0   \n",
              "\n",
              "   ('P26', 'neo4j')  ('P26', 'google_bigtable')  ('P26', 'hbase')  \\\n",
              "0                 0                           0                 0   \n",
              "1                 0                           0                 0   \n",
              "2                 0                           0                 0   \n",
              "3                 0                           0                 0   \n",
              "4                 0                           0                 0   \n",
              "\n",
              "   ('P26', 'other')  ('P27', 'microsoft_powerbi')  \\\n",
              "0                 0                             0   \n",
              "1                 0                             1   \n",
              "2                 1                             0   \n",
              "3                 1                             0   \n",
              "4                 0                             0   \n",
              "\n",
              "   ('P27', 'qlik_view_qlik_sense')  ('P27', 'tableau')  ('P27', 'metabase')  \\\n",
              "0                                0                   0                    0   \n",
              "1                                0                   0                    0   \n",
              "2                                0                   1                    0   \n",
              "3                                0                   0                    0   \n",
              "4                                0                   0                    0   \n",
              "\n",
              "   ('P27', 'superset')  ('P27', 'redash')  ('P27', 'microstrategy')  \\\n",
              "0                    0                  0                         0   \n",
              "1                    0                  0                         0   \n",
              "2                    0                  0                         0   \n",
              "3                    0                  0                         0   \n",
              "4                    0                  0                         0   \n",
              "\n",
              "   ('P27', 'ibm_analytics_cognos')  ('P27', 'sap_business_objects')  \\\n",
              "0                                0                                0   \n",
              "1                                0                                0   \n",
              "2                                0                                0   \n",
              "3                                0                                0   \n",
              "4                                0                                0   \n",
              "\n",
              "   ('P27', 'oracle_business_intelligence')  ('P27', 'birst')  \\\n",
              "0                                        0                 0   \n",
              "1                                        0                 0   \n",
              "2                                        0                 0   \n",
              "3                                        0                 0   \n",
              "4                                        0                 0   \n",
              "\n",
              "   ('P27', 'looker')  ('P27', 'google_data_studio')  \\\n",
              "0                  0                              0   \n",
              "1                  0                              0   \n",
              "2                  0                              0   \n",
              "3                  0                              0   \n",
              "4                  0                              0   \n",
              "\n",
              "   ('P27', 'only_excel_gsheets')  ('P27', 'no_bi_tool_at_work')  \\\n",
              "0                              0                              0   \n",
              "1                              0                              0   \n",
              "2                              0                              0   \n",
              "3                              0                              0   \n",
              "4                              0                              1   \n",
              "\n",
              "   ('P27', 'other')  ('P28', 'sql_&_stored_procedures')  \\\n",
              "0                 0                                   0   \n",
              "1                 0                                   1   \n",
              "2                 0                                   0   \n",
              "3                 1                                   0   \n",
              "4                 0                                   0   \n",
              "\n",
              "   ('P28', 'apache_airflow')  ('P28', 'luigi')  ('P28', 'aws_glue')  \\\n",
              "0                          0                 0                    0   \n",
              "1                          0                 0                    0   \n",
              "2                          0                 0                    0   \n",
              "3                          0                 0                    0   \n",
              "4                          0                 0                    0   \n",
              "\n",
              "   ('P28', 'talend')  ('P28', 'pentaho')  ('P28', 'alteryx')  \\\n",
              "0                  0                   0                   0   \n",
              "1                  0                   0                   0   \n",
              "2                  0                   0                   0   \n",
              "3                  0                   1                   0   \n",
              "4                  0                   0                   0   \n",
              "\n",
              "   ('P28', 'oracle_data_integrator')  ('P28', 'ibm_data_stage')  \\\n",
              "0                                  0                          0   \n",
              "1                                  0                          0   \n",
              "2                                  0                          0   \n",
              "3                                  0                          0   \n",
              "4                                  0                          0   \n",
              "\n",
              "   ('P28', 'sap_bw_etl')  ('P28', 'siss_sql_server_integration_services')  \\\n",
              "0                      0                                                0   \n",
              "1                      0                                                0   \n",
              "2                      0                                                0   \n",
              "3                      0                                                0   \n",
              "4                      0                                                0   \n",
              "\n",
              "   ('P28', 'other')  ('P29', 'have_data_warehouse')  \\\n",
              "0                 0                             NaN   \n",
              "1                 0                             0.0   \n",
              "2                 1                             0.0   \n",
              "3                 0                             1.0   \n",
              "4                 1                             0.0   \n",
              "\n",
              "   ('P30', 'google_bigquery')  ('P30', 'aws_redshift')  ('P30', 'snowflake')  \\\n",
              "0                           0                        0                     0   \n",
              "1                           0                        0                     0   \n",
              "2                           0                        0                     0   \n",
              "3                           0                        1                     0   \n",
              "4                           0                        0                     0   \n",
              "\n",
              "   ('P30', 'oracle')  ('P30', 'postgres_mysql')  ('P30', 'ibm')  \\\n",
              "0                  0                          0               0   \n",
              "1                  0                          0               0   \n",
              "2                  0                          0               0   \n",
              "3                  0                          0               0   \n",
              "4                  0                          0               0   \n",
              "\n",
              "   ('P30', 'teradata')  ('P30', 'microsoft_azure')  ('P30', 'do_not_know')  \\\n",
              "0                    0                           0                       0   \n",
              "1                    0                           0                       0   \n",
              "2                    0                           0                       0   \n",
              "3                    0                           0                       0   \n",
              "4                    0                           0                       0   \n",
              "\n",
              "   ('P30', 'other')  ('P31', 'data_hackers_blog')  \\\n",
              "0                 0                             0   \n",
              "1                 0                             0   \n",
              "2                 0                             0   \n",
              "3                 0                             0   \n",
              "4                 0                             1   \n",
              "\n",
              "   ('P31', 'data_hackers_podcast')  ('P31', 'weekly_newsletter')  \\\n",
              "0                                0                             0   \n",
              "1                                0                             0   \n",
              "2                                1                             1   \n",
              "3                                0                             0   \n",
              "4                                0                             0   \n",
              "\n",
              "   ('P31', 'slack_channel')  ('P31', 'data_hackers_bootcamp')  \\\n",
              "0                         0                                 0   \n",
              "1                         0                                 0   \n",
              "2                         0                                 0   \n",
              "3                         0                                 0   \n",
              "4                         0                                 0   \n",
              "\n",
              "   ('P31', 'do_not_know_data_hackers')  \\\n",
              "0                                    1   \n",
              "1                                    1   \n",
              "2                                    0   \n",
              "3                                    1   \n",
              "4                                    0   \n",
              "\n",
              "  ('P32', 'prefered_data_hackers_initiative')  ('P33', 'telegram_groups')  \\\n",
              "0           Ainda não conhecia o Data Hackers                           0   \n",
              "1           Ainda não conhecia o Data Hackers                           0   \n",
              "2                          Newsletter Semanal                           0   \n",
              "3           Ainda não conhecia o Data Hackers                           0   \n",
              "4                 Blog/Medium do Data Hackers                           0   \n",
              "\n",
              "   ('P33', 'whatsapp_groups')  ('P33', 'youtube_channels')  \\\n",
              "0                           0                            1   \n",
              "1                           0                            1   \n",
              "2                           0                            0   \n",
              "3                           1                            0   \n",
              "4                           0                            0   \n",
              "\n",
              "   ('P33', 'other_brasilian_blogs')  ('P33', 'other_slack_channels')  \\\n",
              "0                                 0                                0   \n",
              "1                                 0                                0   \n",
              "2                                 1                                0   \n",
              "3                                 0                                0   \n",
              "4                                 0                                0   \n",
              "\n",
              "   ('P33', 'twitter')  ('P33', 'abroad_blogs')  ('P33', 'abroad_podcasts')  \\\n",
              "0                   0                        0                           0   \n",
              "1                   0                        0                           0   \n",
              "2                   0                        0                           0   \n",
              "3                   0                        0                           1   \n",
              "4                   0                        0                           0   \n",
              "\n",
              "   ('P33', 'meetups_and_events')  ('P33', 'only_data_hackers')  \\\n",
              "0                              0                             0   \n",
              "1                              0                             0   \n",
              "2                              0                             0   \n",
              "3                              0                             0   \n",
              "4                              0                             0   \n",
              "\n",
              "   ('P33', 'other')  ('P34', 'udacity')  ('P34', 'coursera')  \\\n",
              "0                 0                   0                    0   \n",
              "1                 0                   0                    0   \n",
              "2                 0                   0                    1   \n",
              "3                 0                   0                    1   \n",
              "4                 1                   1                    1   \n",
              "\n",
              "   ('P34', 'udemy')  ('P34', 'height')  ('P34', 'edx')  ('P34', 'data_camp')  \\\n",
              "0                 0                  0               0                     0   \n",
              "1                 1                  0               0                     0   \n",
              "2                 1                  0               0                     0   \n",
              "3                 1                  1               0                     0   \n",
              "4                 1                  0               0                     0   \n",
              "\n",
              "   ('P34', 'data_quest')  ('P34', 'kaggle_learn')  ('P34', 'online_courses')  \\\n",
              "0                      0                        0                          1   \n",
              "1                      0                        0                          0   \n",
              "2                      0                        1                          0   \n",
              "3                      0                        0                          0   \n",
              "4                      0                        0                          0   \n",
              "\n",
              "   ('P34', 'other') ('P35', 'data_science_plataforms_preference')  \\\n",
              "0                 0                       Nunca fiz cursos online   \n",
              "1                 1                                           NaN   \n",
              "2                 0                                         Udemy   \n",
              "3                 0                                         Alura   \n",
              "4                 0                                         Udemy   \n",
              "\n",
              "       ('P35', 'other')  ('P36', 'draw_participation')  \\\n",
              "0                   NaN                            1.0   \n",
              "1  Data Science Academy                            0.0   \n",
              "2                   NaN                            1.0   \n",
              "3                   NaN                            1.0   \n",
              "4                   NaN                            1.0   \n",
              "\n",
              "  ('D1', 'living_macroregion') ('D2', 'origin_macroregion')  \\\n",
              "0               Região Sudeste                          NaN   \n",
              "1               Região Sudeste                          NaN   \n",
              "2               Região Sudeste                          NaN   \n",
              "3               Região Sudeste               Região Sudeste   \n",
              "4                          NaN                          NaN   \n",
              "\n",
              "                    ('D3', 'anonymized_degree_area')  \\\n",
              "0  Computação / Engenharia de Software / Sistemas...   \n",
              "1  Computação / Engenharia de Software / Sistemas...   \n",
              "2                                 Outras Engenharias   \n",
              "3  Computação / Engenharia de Software / Sistemas...   \n",
              "4  Computação / Engenharia de Software / Sistemas...   \n",
              "\n",
              "  ('D4', 'anonymized_market_sector') ('D5', 'anonymized_manager_level')  \\\n",
              "0                             Outras                                NaN   \n",
              "1                           Educação                                NaN   \n",
              "2             Indústria (Manufatura)                                NaN   \n",
              "3     Tecnologia/Fábrica de Software                                NaN   \n",
              "4                 Internet/Ecommerce            C-level (CDO, CIO, CTO)   \n",
              "\n",
              "              ('D6', 'anonymized_role')  \n",
              "0                                Outras  \n",
              "1        Data Analyst/Analista de Dados  \n",
              "2                                Outras  \n",
              "3  Business Intelligence/Analista de BI  \n",
              "4                                   NaN  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzgfBVqenNeG",
        "outputId": "753ccc79-b004-4095-c727-a2780443c4b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "dados.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>('P1', 'age')</th>\n",
              "      <th>('P3', 'living_in_brasil')</th>\n",
              "      <th>('P6', 'born_or_graduated')</th>\n",
              "      <th>('P13', 'manager')</th>\n",
              "      <th>('P19', 'is_data_science_professional')</th>\n",
              "      <th>('P20', 'linear_regression')</th>\n",
              "      <th>('P20', 'logistic_regression')</th>\n",
              "      <th>('P20', 'glms')</th>\n",
              "      <th>('P20', 'decision_tree')</th>\n",
              "      <th>('P20', 'random_forest')</th>\n",
              "      <th>('P20', 'neural_networks')</th>\n",
              "      <th>('P20', 'bayesian_inference')</th>\n",
              "      <th>('P20', 'ensemble')</th>\n",
              "      <th>('P20', 'svms')</th>\n",
              "      <th>('P20', 'cnns')</th>\n",
              "      <th>('P20', 'rnns')</th>\n",
              "      <th>('P20', 'hmms')</th>\n",
              "      <th>('P20', 'gans')</th>\n",
              "      <th>('P20', 'markov_chains')</th>\n",
              "      <th>('P20', 'nlp')</th>\n",
              "      <th>('P20', 'gradient_boosted_machines')</th>\n",
              "      <th>('P20', 'cluster_analysis')</th>\n",
              "      <th>('P20', 'survival_analysis')</th>\n",
              "      <th>('P20', 'longitudinal_data_analysis')</th>\n",
              "      <th>('P20', 'joint analysis')</th>\n",
              "      <th>('P20', 'no_listed_methods')</th>\n",
              "      <th>('P21', 'sql_')</th>\n",
              "      <th>('P21', 'r')</th>\n",
              "      <th>('P21', 'python')</th>\n",
              "      <th>('P21', 'c_c++_c#')</th>\n",
              "      <th>('P21', 'dotnet')</th>\n",
              "      <th>('P21', 'java')</th>\n",
              "      <th>('P21', 'julia')</th>\n",
              "      <th>('P21', 'sas_stata')</th>\n",
              "      <th>('P21', 'visual_basic_vba')</th>\n",
              "      <th>('P21', 'scala')</th>\n",
              "      <th>('P21', 'matlab')</th>\n",
              "      <th>('P21', 'php')</th>\n",
              "      <th>('P21', 'no_listed_languages')</th>\n",
              "      <th>('P23', 'sql')</th>\n",
              "      <th>('P23', 'nosql')</th>\n",
              "      <th>('P23', 'images')</th>\n",
              "      <th>('P23', 'nlp')</th>\n",
              "      <th>('P23', 'videos')</th>\n",
              "      <th>('P23', 'sheets')</th>\n",
              "      <th>('P23', 'other')</th>\n",
              "      <th>('P24', 'sql')</th>\n",
              "      <th>('P24', 'nosql')</th>\n",
              "      <th>('P24', 'imagens')</th>\n",
              "      <th>('P24', 'nlp')</th>\n",
              "      <th>('P24', 'vídeos')</th>\n",
              "      <th>('P24', 'planilhas')</th>\n",
              "      <th>('P24', 'other')</th>\n",
              "      <th>('P25', 'aws')</th>\n",
              "      <th>('P25', 'gcp')</th>\n",
              "      <th>('P25', 'azure')</th>\n",
              "      <th>('P25', 'ibm')</th>\n",
              "      <th>('P25', 'on_premise_servers')</th>\n",
              "      <th>('P25', 'cloud_própria')</th>\n",
              "      <th>('P25', 'other')</th>\n",
              "      <th>('P26', 'mysql')</th>\n",
              "      <th>('P26', 'oracle')</th>\n",
              "      <th>('P26', 'sql_server')</th>\n",
              "      <th>('P26', 'aurora')</th>\n",
              "      <th>('P26', 'dynamodb')</th>\n",
              "      <th>('P26', 'coachdb')</th>\n",
              "      <th>('P26', 'cassandra')</th>\n",
              "      <th>('P26', 'mongodb')</th>\n",
              "      <th>('P26', 'mariadb')</th>\n",
              "      <th>('P26', 'datomic')</th>\n",
              "      <th>('P26', 's3')</th>\n",
              "      <th>('P26', 'postgresql')</th>\n",
              "      <th>('P26', 'elaticsearch')</th>\n",
              "      <th>('P26', 'db2')</th>\n",
              "      <th>('P26', 'ms_access')</th>\n",
              "      <th>('P26', 'sqlite')</th>\n",
              "      <th>('P26', 'sybase')</th>\n",
              "      <th>('P26', 'firebase')</th>\n",
              "      <th>('P26', 'vertica')</th>\n",
              "      <th>('P26', 'redis')</th>\n",
              "      <th>('P26', 'neo4j')</th>\n",
              "      <th>('P26', 'google_bigtable')</th>\n",
              "      <th>('P26', 'hbase')</th>\n",
              "      <th>('P26', 'other')</th>\n",
              "      <th>('P27', 'microsoft_powerbi')</th>\n",
              "      <th>('P27', 'qlik_view_qlik_sense')</th>\n",
              "      <th>('P27', 'tableau')</th>\n",
              "      <th>('P27', 'metabase')</th>\n",
              "      <th>('P27', 'superset')</th>\n",
              "      <th>('P27', 'redash')</th>\n",
              "      <th>('P27', 'microstrategy')</th>\n",
              "      <th>('P27', 'ibm_analytics_cognos')</th>\n",
              "      <th>('P27', 'sap_business_objects')</th>\n",
              "      <th>('P27', 'oracle_business_intelligence')</th>\n",
              "      <th>('P27', 'birst')</th>\n",
              "      <th>('P27', 'looker')</th>\n",
              "      <th>('P27', 'google_data_studio')</th>\n",
              "      <th>('P27', 'only_excel_gsheets')</th>\n",
              "      <th>('P27', 'no_bi_tool_at_work')</th>\n",
              "      <th>('P27', 'other')</th>\n",
              "      <th>('P28', 'sql_&amp;_stored_procedures')</th>\n",
              "      <th>('P28', 'apache_airflow')</th>\n",
              "      <th>('P28', 'luigi')</th>\n",
              "      <th>('P28', 'aws_glue')</th>\n",
              "      <th>('P28', 'talend')</th>\n",
              "      <th>('P28', 'pentaho')</th>\n",
              "      <th>('P28', 'alteryx')</th>\n",
              "      <th>('P28', 'oracle_data_integrator')</th>\n",
              "      <th>('P28', 'ibm_data_stage')</th>\n",
              "      <th>('P28', 'sap_bw_etl')</th>\n",
              "      <th>('P28', 'siss_sql_server_integration_services')</th>\n",
              "      <th>('P28', 'other')</th>\n",
              "      <th>('P29', 'have_data_warehouse')</th>\n",
              "      <th>('P30', 'google_bigquery')</th>\n",
              "      <th>('P30', 'aws_redshift')</th>\n",
              "      <th>('P30', 'snowflake')</th>\n",
              "      <th>('P30', 'oracle')</th>\n",
              "      <th>('P30', 'postgres_mysql')</th>\n",
              "      <th>('P30', 'ibm')</th>\n",
              "      <th>('P30', 'teradata')</th>\n",
              "      <th>('P30', 'microsoft_azure')</th>\n",
              "      <th>('P30', 'do_not_know')</th>\n",
              "      <th>('P30', 'other')</th>\n",
              "      <th>('P31', 'data_hackers_blog')</th>\n",
              "      <th>('P31', 'data_hackers_podcast')</th>\n",
              "      <th>('P31', 'weekly_newsletter')</th>\n",
              "      <th>('P31', 'slack_channel')</th>\n",
              "      <th>('P31', 'data_hackers_bootcamp')</th>\n",
              "      <th>('P31', 'do_not_know_data_hackers')</th>\n",
              "      <th>('P33', 'telegram_groups')</th>\n",
              "      <th>('P33', 'whatsapp_groups')</th>\n",
              "      <th>('P33', 'youtube_channels')</th>\n",
              "      <th>('P33', 'other_brasilian_blogs')</th>\n",
              "      <th>('P33', 'other_slack_channels')</th>\n",
              "      <th>('P33', 'twitter')</th>\n",
              "      <th>('P33', 'abroad_blogs')</th>\n",
              "      <th>('P33', 'abroad_podcasts')</th>\n",
              "      <th>('P33', 'meetups_and_events')</th>\n",
              "      <th>('P33', 'only_data_hackers')</th>\n",
              "      <th>('P33', 'other')</th>\n",
              "      <th>('P34', 'udacity')</th>\n",
              "      <th>('P34', 'coursera')</th>\n",
              "      <th>('P34', 'udemy')</th>\n",
              "      <th>('P34', 'height')</th>\n",
              "      <th>('P34', 'edx')</th>\n",
              "      <th>('P34', 'data_camp')</th>\n",
              "      <th>('P34', 'data_quest')</th>\n",
              "      <th>('P34', 'kaggle_learn')</th>\n",
              "      <th>('P34', 'online_courses')</th>\n",
              "      <th>('P34', 'other')</th>\n",
              "      <th>('P36', 'draw_participation')</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1741.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1731.000000</td>\n",
              "      <td>1527.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>793.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1765.000000</td>\n",
              "      <td>1761.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>29.800689</td>\n",
              "      <td>0.980737</td>\n",
              "      <td>0.810514</td>\n",
              "      <td>0.199738</td>\n",
              "      <td>0.518414</td>\n",
              "      <td>0.296884</td>\n",
              "      <td>0.230028</td>\n",
              "      <td>0.075921</td>\n",
              "      <td>0.249858</td>\n",
              "      <td>0.203399</td>\n",
              "      <td>0.151275</td>\n",
              "      <td>0.126912</td>\n",
              "      <td>0.095751</td>\n",
              "      <td>0.101983</td>\n",
              "      <td>0.057224</td>\n",
              "      <td>0.055524</td>\n",
              "      <td>0.010198</td>\n",
              "      <td>0.013598</td>\n",
              "      <td>0.044759</td>\n",
              "      <td>0.142776</td>\n",
              "      <td>0.100283</td>\n",
              "      <td>0.208499</td>\n",
              "      <td>0.040793</td>\n",
              "      <td>0.057224</td>\n",
              "      <td>0.044759</td>\n",
              "      <td>0.108782</td>\n",
              "      <td>0.404533</td>\n",
              "      <td>0.180170</td>\n",
              "      <td>0.444193</td>\n",
              "      <td>0.036827</td>\n",
              "      <td>0.019830</td>\n",
              "      <td>0.050425</td>\n",
              "      <td>0.007932</td>\n",
              "      <td>0.034561</td>\n",
              "      <td>0.047592</td>\n",
              "      <td>0.035127</td>\n",
              "      <td>0.016431</td>\n",
              "      <td>0.027195</td>\n",
              "      <td>0.004533</td>\n",
              "      <td>0.472521</td>\n",
              "      <td>0.213598</td>\n",
              "      <td>0.090652</td>\n",
              "      <td>0.186969</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.429462</td>\n",
              "      <td>0.019830</td>\n",
              "      <td>0.385836</td>\n",
              "      <td>0.077054</td>\n",
              "      <td>0.012465</td>\n",
              "      <td>0.047592</td>\n",
              "      <td>0.002833</td>\n",
              "      <td>0.217564</td>\n",
              "      <td>0.005666</td>\n",
              "      <td>0.233428</td>\n",
              "      <td>0.127479</td>\n",
              "      <td>0.108215</td>\n",
              "      <td>0.024929</td>\n",
              "      <td>0.086119</td>\n",
              "      <td>0.092351</td>\n",
              "      <td>0.039093</td>\n",
              "      <td>0.207365</td>\n",
              "      <td>0.125212</td>\n",
              "      <td>0.189235</td>\n",
              "      <td>0.013031</td>\n",
              "      <td>0.023229</td>\n",
              "      <td>0.002833</td>\n",
              "      <td>0.015297</td>\n",
              "      <td>0.117280</td>\n",
              "      <td>0.036261</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.107082</td>\n",
              "      <td>0.195467</td>\n",
              "      <td>0.065156</td>\n",
              "      <td>0.022096</td>\n",
              "      <td>0.019263</td>\n",
              "      <td>0.057790</td>\n",
              "      <td>0.010198</td>\n",
              "      <td>0.022663</td>\n",
              "      <td>0.002833</td>\n",
              "      <td>0.024929</td>\n",
              "      <td>0.011898</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.018697</td>\n",
              "      <td>0.053258</td>\n",
              "      <td>0.222663</td>\n",
              "      <td>0.053824</td>\n",
              "      <td>0.118414</td>\n",
              "      <td>0.081586</td>\n",
              "      <td>0.009632</td>\n",
              "      <td>0.014731</td>\n",
              "      <td>0.015297</td>\n",
              "      <td>0.011331</td>\n",
              "      <td>0.009632</td>\n",
              "      <td>0.005666</td>\n",
              "      <td>0.001133</td>\n",
              "      <td>0.008499</td>\n",
              "      <td>0.073088</td>\n",
              "      <td>0.059490</td>\n",
              "      <td>0.101416</td>\n",
              "      <td>0.047025</td>\n",
              "      <td>0.166006</td>\n",
              "      <td>0.088385</td>\n",
              "      <td>0.008499</td>\n",
              "      <td>0.058924</td>\n",
              "      <td>0.023796</td>\n",
              "      <td>0.096317</td>\n",
              "      <td>0.020963</td>\n",
              "      <td>0.020963</td>\n",
              "      <td>0.022096</td>\n",
              "      <td>0.013598</td>\n",
              "      <td>0.071388</td>\n",
              "      <td>0.116147</td>\n",
              "      <td>0.581337</td>\n",
              "      <td>0.030595</td>\n",
              "      <td>0.052125</td>\n",
              "      <td>0.007932</td>\n",
              "      <td>0.046459</td>\n",
              "      <td>0.060623</td>\n",
              "      <td>0.011331</td>\n",
              "      <td>0.015864</td>\n",
              "      <td>0.048159</td>\n",
              "      <td>0.033994</td>\n",
              "      <td>0.020397</td>\n",
              "      <td>0.677054</td>\n",
              "      <td>0.620963</td>\n",
              "      <td>0.497450</td>\n",
              "      <td>0.412465</td>\n",
              "      <td>0.038527</td>\n",
              "      <td>0.155807</td>\n",
              "      <td>0.269688</td>\n",
              "      <td>0.218130</td>\n",
              "      <td>0.574504</td>\n",
              "      <td>0.382436</td>\n",
              "      <td>0.052691</td>\n",
              "      <td>0.193201</td>\n",
              "      <td>0.441926</td>\n",
              "      <td>0.256091</td>\n",
              "      <td>0.431728</td>\n",
              "      <td>0.078187</td>\n",
              "      <td>0.094051</td>\n",
              "      <td>0.298584</td>\n",
              "      <td>0.392635</td>\n",
              "      <td>0.637394</td>\n",
              "      <td>0.170538</td>\n",
              "      <td>0.166572</td>\n",
              "      <td>0.222096</td>\n",
              "      <td>0.036261</td>\n",
              "      <td>0.179037</td>\n",
              "      <td>0.095184</td>\n",
              "      <td>0.134844</td>\n",
              "      <td>0.813174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.595795</td>\n",
              "      <td>0.137488</td>\n",
              "      <td>0.392007</td>\n",
              "      <td>0.399934</td>\n",
              "      <td>0.499802</td>\n",
              "      <td>0.457014</td>\n",
              "      <td>0.420970</td>\n",
              "      <td>0.264946</td>\n",
              "      <td>0.433054</td>\n",
              "      <td>0.402641</td>\n",
              "      <td>0.358418</td>\n",
              "      <td>0.332969</td>\n",
              "      <td>0.294332</td>\n",
              "      <td>0.302712</td>\n",
              "      <td>0.232336</td>\n",
              "      <td>0.229065</td>\n",
              "      <td>0.100499</td>\n",
              "      <td>0.115847</td>\n",
              "      <td>0.206833</td>\n",
              "      <td>0.349944</td>\n",
              "      <td>0.300462</td>\n",
              "      <td>0.406350</td>\n",
              "      <td>0.197867</td>\n",
              "      <td>0.232336</td>\n",
              "      <td>0.206833</td>\n",
              "      <td>0.311454</td>\n",
              "      <td>0.490940</td>\n",
              "      <td>0.384438</td>\n",
              "      <td>0.497017</td>\n",
              "      <td>0.188391</td>\n",
              "      <td>0.139455</td>\n",
              "      <td>0.218882</td>\n",
              "      <td>0.088733</td>\n",
              "      <td>0.182717</td>\n",
              "      <td>0.212962</td>\n",
              "      <td>0.184154</td>\n",
              "      <td>0.127160</td>\n",
              "      <td>0.162699</td>\n",
              "      <td>0.067191</td>\n",
              "      <td>0.499386</td>\n",
              "      <td>0.409962</td>\n",
              "      <td>0.287194</td>\n",
              "      <td>0.389997</td>\n",
              "      <td>0.141393</td>\n",
              "      <td>0.495140</td>\n",
              "      <td>0.139455</td>\n",
              "      <td>0.486930</td>\n",
              "      <td>0.266752</td>\n",
              "      <td>0.110978</td>\n",
              "      <td>0.212962</td>\n",
              "      <td>0.053164</td>\n",
              "      <td>0.412706</td>\n",
              "      <td>0.075079</td>\n",
              "      <td>0.423132</td>\n",
              "      <td>0.333603</td>\n",
              "      <td>0.310740</td>\n",
              "      <td>0.155954</td>\n",
              "      <td>0.280619</td>\n",
              "      <td>0.289603</td>\n",
              "      <td>0.193872</td>\n",
              "      <td>0.405534</td>\n",
              "      <td>0.331053</td>\n",
              "      <td>0.391806</td>\n",
              "      <td>0.113440</td>\n",
              "      <td>0.150674</td>\n",
              "      <td>0.053164</td>\n",
              "      <td>0.122768</td>\n",
              "      <td>0.321845</td>\n",
              "      <td>0.186991</td>\n",
              "      <td>0.041204</td>\n",
              "      <td>0.309305</td>\n",
              "      <td>0.396672</td>\n",
              "      <td>0.246871</td>\n",
              "      <td>0.147038</td>\n",
              "      <td>0.137488</td>\n",
              "      <td>0.233413</td>\n",
              "      <td>0.100499</td>\n",
              "      <td>0.148869</td>\n",
              "      <td>0.053164</td>\n",
              "      <td>0.155954</td>\n",
              "      <td>0.108458</td>\n",
              "      <td>0.141393</td>\n",
              "      <td>0.135491</td>\n",
              "      <td>0.224611</td>\n",
              "      <td>0.416152</td>\n",
              "      <td>0.225735</td>\n",
              "      <td>0.323189</td>\n",
              "      <td>0.273811</td>\n",
              "      <td>0.097695</td>\n",
              "      <td>0.120508</td>\n",
              "      <td>0.122768</td>\n",
              "      <td>0.105874</td>\n",
              "      <td>0.097695</td>\n",
              "      <td>0.075079</td>\n",
              "      <td>0.033653</td>\n",
              "      <td>0.091821</td>\n",
              "      <td>0.260354</td>\n",
              "      <td>0.236607</td>\n",
              "      <td>0.301965</td>\n",
              "      <td>0.211753</td>\n",
              "      <td>0.372191</td>\n",
              "      <td>0.283935</td>\n",
              "      <td>0.091821</td>\n",
              "      <td>0.235548</td>\n",
              "      <td>0.152456</td>\n",
              "      <td>0.295109</td>\n",
              "      <td>0.143302</td>\n",
              "      <td>0.143302</td>\n",
              "      <td>0.147038</td>\n",
              "      <td>0.115847</td>\n",
              "      <td>0.257545</td>\n",
              "      <td>0.320492</td>\n",
              "      <td>0.493651</td>\n",
              "      <td>0.172266</td>\n",
              "      <td>0.222341</td>\n",
              "      <td>0.088733</td>\n",
              "      <td>0.210536</td>\n",
              "      <td>0.238706</td>\n",
              "      <td>0.105874</td>\n",
              "      <td>0.124985</td>\n",
              "      <td>0.214162</td>\n",
              "      <td>0.181266</td>\n",
              "      <td>0.141393</td>\n",
              "      <td>0.467735</td>\n",
              "      <td>0.485285</td>\n",
              "      <td>0.500135</td>\n",
              "      <td>0.492417</td>\n",
              "      <td>0.192519</td>\n",
              "      <td>0.362775</td>\n",
              "      <td>0.443924</td>\n",
              "      <td>0.413093</td>\n",
              "      <td>0.494558</td>\n",
              "      <td>0.486120</td>\n",
              "      <td>0.223480</td>\n",
              "      <td>0.394921</td>\n",
              "      <td>0.496757</td>\n",
              "      <td>0.436596</td>\n",
              "      <td>0.495457</td>\n",
              "      <td>0.268542</td>\n",
              "      <td>0.291982</td>\n",
              "      <td>0.457766</td>\n",
              "      <td>0.488475</td>\n",
              "      <td>0.480889</td>\n",
              "      <td>0.376212</td>\n",
              "      <td>0.372699</td>\n",
              "      <td>0.415773</td>\n",
              "      <td>0.186991</td>\n",
              "      <td>0.383492</td>\n",
              "      <td>0.293552</td>\n",
              "      <td>0.341654</td>\n",
              "      <td>0.389882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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>25.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       ('P1', 'age')  ('P3', 'living_in_brasil')  ('P6', 'born_or_graduated')  \\\n",
              "count    1741.000000                 1765.000000                  1731.000000   \n",
              "mean       29.800689                    0.980737                     0.810514   \n",
              "std         6.595795                    0.137488                     0.392007   \n",
              "min        18.000000                    0.000000                     0.000000   \n",
              "25%        25.000000                    1.000000                     1.000000   \n",
              "50%        29.000000                    1.000000                     1.000000   \n",
              "75%        34.000000                    1.000000                     1.000000   \n",
              "max        50.000000                    1.000000                     1.000000   \n",
              "\n",
              "       ('P13', 'manager')  ('P19', 'is_data_science_professional')  \\\n",
              "count         1527.000000                              1765.000000   \n",
              "mean             0.199738                                 0.518414   \n",
              "std              0.399934                                 0.499802   \n",
              "min              0.000000                                 0.000000   \n",
              "25%              0.000000                                 0.000000   \n",
              "50%              0.000000                                 1.000000   \n",
              "75%              0.000000                                 1.000000   \n",
              "max              1.000000                                 1.000000   \n",
              "\n",
              "       ('P20', 'linear_regression')  ('P20', 'logistic_regression')  \\\n",
              "count                   1765.000000                     1765.000000   \n",
              "mean                       0.296884                        0.230028   \n",
              "std                        0.457014                        0.420970   \n",
              "min                        0.000000                        0.000000   \n",
              "25%                        0.000000                        0.000000   \n",
              "50%                        0.000000                        0.000000   \n",
              "75%                        1.000000                        0.000000   \n",
              "max                        1.000000                        1.000000   \n",
              "\n",
              "       ('P20', 'glms')  ('P20', 'decision_tree')  ('P20', 'random_forest')  \\\n",
              "count      1765.000000               1765.000000               1765.000000   \n",
              "mean          0.075921                  0.249858                  0.203399   \n",
              "std           0.264946                  0.433054                  0.402641   \n",
              "min           0.000000                  0.000000                  0.000000   \n",
              "25%           0.000000                  0.000000                  0.000000   \n",
              "50%           0.000000                  0.000000                  0.000000   \n",
              "75%           0.000000                  0.000000                  0.000000   \n",
              "max           1.000000                  1.000000                  1.000000   \n",
              "\n",
              "       ('P20', 'neural_networks')  ('P20', 'bayesian_inference')  \\\n",
              "count                 1765.000000                    1765.000000   \n",
              "mean                     0.151275                       0.126912   \n",
              "std                      0.358418                       0.332969   \n",
              "min                      0.000000                       0.000000   \n",
              "25%                      0.000000                       0.000000   \n",
              "50%                      0.000000                       0.000000   \n",
              "75%                      0.000000                       0.000000   \n",
              "max                      1.000000                       1.000000   \n",
              "\n",
              "       ('P20', 'ensemble')  ('P20', 'svms')  ('P20', 'cnns')  ('P20', 'rnns')  \\\n",
              "count          1765.000000      1765.000000      1765.000000      1765.000000   \n",
              "mean              0.095751         0.101983         0.057224         0.055524   \n",
              "std               0.294332         0.302712         0.232336         0.229065   \n",
              "min               0.000000         0.000000         0.000000         0.000000   \n",
              "25%               0.000000         0.000000         0.000000         0.000000   \n",
              "50%               0.000000         0.000000         0.000000         0.000000   \n",
              "75%               0.000000         0.000000         0.000000         0.000000   \n",
              "max               1.000000         1.000000         1.000000         1.000000   \n",
              "\n",
              "       ('P20', 'hmms')  ('P20', 'gans')  ('P20', 'markov_chains')  \\\n",
              "count      1765.000000      1765.000000               1765.000000   \n",
              "mean          0.010198         0.013598                  0.044759   \n",
              "std           0.100499         0.115847                  0.206833   \n",
              "min           0.000000         0.000000                  0.000000   \n",
              "25%           0.000000         0.000000                  0.000000   \n",
              "50%           0.000000         0.000000                  0.000000   \n",
              "75%           0.000000         0.000000                  0.000000   \n",
              "max           1.000000         1.000000                  1.000000   \n",
              "\n",
              "       ('P20', 'nlp')  ('P20', 'gradient_boosted_machines')  \\\n",
              "count     1765.000000                           1765.000000   \n",
              "mean         0.142776                              0.100283   \n",
              "std          0.349944                              0.300462   \n",
              "min          0.000000                              0.000000   \n",
              "25%          0.000000                              0.000000   \n",
              "50%          0.000000                              0.000000   \n",
              "75%          0.000000                              0.000000   \n",
              "max          1.000000                              1.000000   \n",
              "\n",
              "       ('P20', 'cluster_analysis')  ('P20', 'survival_analysis')  \\\n",
              "count                  1765.000000                   1765.000000   \n",
              "mean                      0.208499                      0.040793   \n",
              "std                       0.406350                      0.197867   \n",
              "min                       0.000000                      0.000000   \n",
              "25%                       0.000000                      0.000000   \n",
              "50%                       0.000000                      0.000000   \n",
              "75%                       0.000000                      0.000000   \n",
              "max                       1.000000                      1.000000   \n",
              "\n",
              "       ('P20', 'longitudinal_data_analysis')  ('P20', 'joint analysis')  \\\n",
              "count                            1765.000000                1765.000000   \n",
              "mean                                0.057224                   0.044759   \n",
              "std                                 0.232336                   0.206833   \n",
              "min                                 0.000000                   0.000000   \n",
              "25%                                 0.000000                   0.000000   \n",
              "50%                                 0.000000                   0.000000   \n",
              "75%                                 0.000000                   0.000000   \n",
              "max                                 1.000000                   1.000000   \n",
              "\n",
              "       ('P20', 'no_listed_methods')  ('P21', 'sql_')  ('P21', 'r')  \\\n",
              "count                   1765.000000      1765.000000   1765.000000   \n",
              "mean                       0.108782         0.404533      0.180170   \n",
              "std                        0.311454         0.490940      0.384438   \n",
              "min                        0.000000         0.000000      0.000000   \n",
              "25%                        0.000000         0.000000      0.000000   \n",
              "50%                        0.000000         0.000000      0.000000   \n",
              "75%                        0.000000         1.000000      0.000000   \n",
              "max                        1.000000         1.000000      1.000000   \n",
              "\n",
              "       ('P21', 'python')  ('P21', 'c_c++_c#')  ('P21', 'dotnet')  \\\n",
              "count        1765.000000          1765.000000        1765.000000   \n",
              "mean            0.444193             0.036827           0.019830   \n",
              "std             0.497017             0.188391           0.139455   \n",
              "min             0.000000             0.000000           0.000000   \n",
              "25%             0.000000             0.000000           0.000000   \n",
              "50%             0.000000             0.000000           0.000000   \n",
              "75%             1.000000             0.000000           0.000000   \n",
              "max             1.000000             1.000000           1.000000   \n",
              "\n",
              "       ('P21', 'java')  ('P21', 'julia')  ('P21', 'sas_stata')  \\\n",
              "count      1765.000000       1765.000000           1765.000000   \n",
              "mean          0.050425          0.007932              0.034561   \n",
              "std           0.218882          0.088733              0.182717   \n",
              "min           0.000000          0.000000              0.000000   \n",
              "25%           0.000000          0.000000              0.000000   \n",
              "50%           0.000000          0.000000              0.000000   \n",
              "75%           0.000000          0.000000              0.000000   \n",
              "max           1.000000          1.000000              1.000000   \n",
              "\n",
              "       ('P21', 'visual_basic_vba')  ('P21', 'scala')  ('P21', 'matlab')  \\\n",
              "count                  1765.000000       1765.000000        1765.000000   \n",
              "mean                      0.047592          0.035127           0.016431   \n",
              "std                       0.212962          0.184154           0.127160   \n",
              "min                       0.000000          0.000000           0.000000   \n",
              "25%                       0.000000          0.000000           0.000000   \n",
              "50%                       0.000000          0.000000           0.000000   \n",
              "75%                       0.000000          0.000000           0.000000   \n",
              "max                       1.000000          1.000000           1.000000   \n",
              "\n",
              "       ('P21', 'php')  ('P21', 'no_listed_languages')  ('P23', 'sql')  \\\n",
              "count     1765.000000                     1765.000000     1765.000000   \n",
              "mean         0.027195                        0.004533        0.472521   \n",
              "std          0.162699                        0.067191        0.499386   \n",
              "min          0.000000                        0.000000        0.000000   \n",
              "25%          0.000000                        0.000000        0.000000   \n",
              "50%          0.000000                        0.000000        0.000000   \n",
              "75%          0.000000                        0.000000        1.000000   \n",
              "max          1.000000                        1.000000        1.000000   \n",
              "\n",
              "       ('P23', 'nosql')  ('P23', 'images')  ('P23', 'nlp')  ('P23', 'videos')  \\\n",
              "count       1765.000000        1765.000000     1765.000000        1765.000000   \n",
              "mean           0.213598           0.090652        0.186969           0.020397   \n",
              "std            0.409962           0.287194        0.389997           0.141393   \n",
              "min            0.000000           0.000000        0.000000           0.000000   \n",
              "25%            0.000000           0.000000        0.000000           0.000000   \n",
              "50%            0.000000           0.000000        0.000000           0.000000   \n",
              "75%            0.000000           0.000000        0.000000           0.000000   \n",
              "max            1.000000           1.000000        1.000000           1.000000   \n",
              "\n",
              "       ('P23', 'sheets')  ('P23', 'other')  ('P24', 'sql')  ('P24', 'nosql')  \\\n",
              "count        1765.000000       1765.000000     1765.000000       1765.000000   \n",
              "mean            0.429462          0.019830        0.385836          0.077054   \n",
              "std             0.495140          0.139455        0.486930          0.266752   \n",
              "min             0.000000          0.000000        0.000000          0.000000   \n",
              "25%             0.000000          0.000000        0.000000          0.000000   \n",
              "50%             0.000000          0.000000        0.000000          0.000000   \n",
              "75%             1.000000          0.000000        1.000000          0.000000   \n",
              "max             1.000000          1.000000        1.000000          1.000000   \n",
              "\n",
              "       ('P24', 'imagens')  ('P24', 'nlp')  ('P24', 'vídeos')  \\\n",
              "count         1765.000000     1765.000000        1765.000000   \n",
              "mean             0.012465        0.047592           0.002833   \n",
              "std              0.110978        0.212962           0.053164   \n",
              "min              0.000000        0.000000           0.000000   \n",
              "25%              0.000000        0.000000           0.000000   \n",
              "50%              0.000000        0.000000           0.000000   \n",
              "75%              0.000000        0.000000           0.000000   \n",
              "max              1.000000        1.000000           1.000000   \n",
              "\n",
              "       ('P24', 'planilhas')  ('P24', 'other')  ('P25', 'aws')  ('P25', 'gcp')  \\\n",
              "count           1765.000000       1765.000000     1765.000000     1765.000000   \n",
              "mean               0.217564          0.005666        0.233428        0.127479   \n",
              "std                0.412706          0.075079        0.423132        0.333603   \n",
              "min                0.000000          0.000000        0.000000        0.000000   \n",
              "25%                0.000000          0.000000        0.000000        0.000000   \n",
              "50%                0.000000          0.000000        0.000000        0.000000   \n",
              "75%                0.000000          0.000000        0.000000        0.000000   \n",
              "max                1.000000          1.000000        1.000000        1.000000   \n",
              "\n",
              "       ('P25', 'azure')  ('P25', 'ibm')  ('P25', 'on_premise_servers')  \\\n",
              "count       1765.000000     1765.000000                    1765.000000   \n",
              "mean           0.108215        0.024929                       0.086119   \n",
              "std            0.310740        0.155954                       0.280619   \n",
              "min            0.000000        0.000000                       0.000000   \n",
              "25%            0.000000        0.000000                       0.000000   \n",
              "50%            0.000000        0.000000                       0.000000   \n",
              "75%            0.000000        0.000000                       0.000000   \n",
              "max            1.000000        1.000000                       1.000000   \n",
              "\n",
              "       ('P25', 'cloud_própria')  ('P25', 'other')  ('P26', 'mysql')  \\\n",
              "count               1765.000000       1765.000000       1765.000000   \n",
              "mean                   0.092351          0.039093          0.207365   \n",
              "std                    0.289603          0.193872          0.405534   \n",
              "min                    0.000000          0.000000          0.000000   \n",
              "25%                    0.000000          0.000000          0.000000   \n",
              "50%                    0.000000          0.000000          0.000000   \n",
              "75%                    0.000000          0.000000          0.000000   \n",
              "max                    1.000000          1.000000          1.000000   \n",
              "\n",
              "       ('P26', 'oracle')  ('P26', 'sql_server')  ('P26', 'aurora')  \\\n",
              "count        1765.000000            1765.000000        1765.000000   \n",
              "mean            0.125212               0.189235           0.013031   \n",
              "std             0.331053               0.391806           0.113440   \n",
              "min             0.000000               0.000000           0.000000   \n",
              "25%             0.000000               0.000000           0.000000   \n",
              "50%             0.000000               0.000000           0.000000   \n",
              "75%             0.000000               0.000000           0.000000   \n",
              "max             1.000000               1.000000           1.000000   \n",
              "\n",
              "       ('P26', 'dynamodb')  ('P26', 'coachdb')  ('P26', 'cassandra')  \\\n",
              "count          1765.000000         1765.000000           1765.000000   \n",
              "mean              0.023229            0.002833              0.015297   \n",
              "std               0.150674            0.053164              0.122768   \n",
              "min               0.000000            0.000000              0.000000   \n",
              "25%               0.000000            0.000000              0.000000   \n",
              "50%               0.000000            0.000000              0.000000   \n",
              "75%               0.000000            0.000000              0.000000   \n",
              "max               1.000000            1.000000              1.000000   \n",
              "\n",
              "       ('P26', 'mongodb')  ('P26', 'mariadb')  ('P26', 'datomic')  \\\n",
              "count         1765.000000         1765.000000         1765.000000   \n",
              "mean             0.117280            0.036261            0.001700   \n",
              "std              0.321845            0.186991            0.041204   \n",
              "min              0.000000            0.000000            0.000000   \n",
              "25%              0.000000            0.000000            0.000000   \n",
              "50%              0.000000            0.000000            0.000000   \n",
              "75%              0.000000            0.000000            0.000000   \n",
              "max              1.000000            1.000000            1.000000   \n",
              "\n",
              "       ('P26', 's3')  ('P26', 'postgresql')  ('P26', 'elaticsearch')  \\\n",
              "count    1765.000000            1765.000000              1765.000000   \n",
              "mean        0.107082               0.195467                 0.065156   \n",
              "std         0.309305               0.396672                 0.246871   \n",
              "min         0.000000               0.000000                 0.000000   \n",
              "25%         0.000000               0.000000                 0.000000   \n",
              "50%         0.000000               0.000000                 0.000000   \n",
              "75%         0.000000               0.000000                 0.000000   \n",
              "max         1.000000               1.000000                 1.000000   \n",
              "\n",
              "       ('P26', 'db2')  ('P26', 'ms_access')  ('P26', 'sqlite')  \\\n",
              "count     1765.000000           1765.000000        1765.000000   \n",
              "mean         0.022096              0.019263           0.057790   \n",
              "std          0.147038              0.137488           0.233413   \n",
              "min          0.000000              0.000000           0.000000   \n",
              "25%          0.000000              0.000000           0.000000   \n",
              "50%          0.000000              0.000000           0.000000   \n",
              "75%          0.000000              0.000000           0.000000   \n",
              "max          1.000000              1.000000           1.000000   \n",
              "\n",
              "       ('P26', 'sybase')  ('P26', 'firebase')  ('P26', 'vertica')  \\\n",
              "count        1765.000000          1765.000000         1765.000000   \n",
              "mean            0.010198             0.022663            0.002833   \n",
              "std             0.100499             0.148869            0.053164   \n",
              "min             0.000000             0.000000            0.000000   \n",
              "25%             0.000000             0.000000            0.000000   \n",
              "50%             0.000000             0.000000            0.000000   \n",
              "75%             0.000000             0.000000            0.000000   \n",
              "max             1.000000             1.000000            1.000000   \n",
              "\n",
              "       ('P26', 'redis')  ('P26', 'neo4j')  ('P26', 'google_bigtable')  \\\n",
              "count       1765.000000       1765.000000                 1765.000000   \n",
              "mean           0.024929          0.011898                    0.020397   \n",
              "std            0.155954          0.108458                    0.141393   \n",
              "min            0.000000          0.000000                    0.000000   \n",
              "25%            0.000000          0.000000                    0.000000   \n",
              "50%            0.000000          0.000000                    0.000000   \n",
              "75%            0.000000          0.000000                    0.000000   \n",
              "max            1.000000          1.000000                    1.000000   \n",
              "\n",
              "       ('P26', 'hbase')  ('P26', 'other')  ('P27', 'microsoft_powerbi')  \\\n",
              "count       1765.000000       1765.000000                   1765.000000   \n",
              "mean           0.018697          0.053258                      0.222663   \n",
              "std            0.135491          0.224611                      0.416152   \n",
              "min            0.000000          0.000000                      0.000000   \n",
              "25%            0.000000          0.000000                      0.000000   \n",
              "50%            0.000000          0.000000                      0.000000   \n",
              "75%            0.000000          0.000000                      0.000000   \n",
              "max            1.000000          1.000000                      1.000000   \n",
              "\n",
              "       ('P27', 'qlik_view_qlik_sense')  ('P27', 'tableau')  \\\n",
              "count                      1765.000000         1765.000000   \n",
              "mean                          0.053824            0.118414   \n",
              "std                           0.225735            0.323189   \n",
              "min                           0.000000            0.000000   \n",
              "25%                           0.000000            0.000000   \n",
              "50%                           0.000000            0.000000   \n",
              "75%                           0.000000            0.000000   \n",
              "max                           1.000000            1.000000   \n",
              "\n",
              "       ('P27', 'metabase')  ('P27', 'superset')  ('P27', 'redash')  \\\n",
              "count          1765.000000          1765.000000        1765.000000   \n",
              "mean              0.081586             0.009632           0.014731   \n",
              "std               0.273811             0.097695           0.120508   \n",
              "min               0.000000             0.000000           0.000000   \n",
              "25%               0.000000             0.000000           0.000000   \n",
              "50%               0.000000             0.000000           0.000000   \n",
              "75%               0.000000             0.000000           0.000000   \n",
              "max               1.000000             1.000000           1.000000   \n",
              "\n",
              "       ('P27', 'microstrategy')  ('P27', 'ibm_analytics_cognos')  \\\n",
              "count               1765.000000                      1765.000000   \n",
              "mean                   0.015297                         0.011331   \n",
              "std                    0.122768                         0.105874   \n",
              "min                    0.000000                         0.000000   \n",
              "25%                    0.000000                         0.000000   \n",
              "50%                    0.000000                         0.000000   \n",
              "75%                    0.000000                         0.000000   \n",
              "max                    1.000000                         1.000000   \n",
              "\n",
              "       ('P27', 'sap_business_objects')  \\\n",
              "count                      1765.000000   \n",
              "mean                          0.009632   \n",
              "std                           0.097695   \n",
              "min                           0.000000   \n",
              "25%                           0.000000   \n",
              "50%                           0.000000   \n",
              "75%                           0.000000   \n",
              "max                           1.000000   \n",
              "\n",
              "       ('P27', 'oracle_business_intelligence')  ('P27', 'birst')  \\\n",
              "count                              1765.000000       1765.000000   \n",
              "mean                                  0.005666          0.001133   \n",
              "std                                   0.075079          0.033653   \n",
              "min                                   0.000000          0.000000   \n",
              "25%                                   0.000000          0.000000   \n",
              "50%                                   0.000000          0.000000   \n",
              "75%                                   0.000000          0.000000   \n",
              "max                                   1.000000          1.000000   \n",
              "\n",
              "       ('P27', 'looker')  ('P27', 'google_data_studio')  \\\n",
              "count        1765.000000                    1765.000000   \n",
              "mean            0.008499                       0.073088   \n",
              "std             0.091821                       0.260354   \n",
              "min             0.000000                       0.000000   \n",
              "25%             0.000000                       0.000000   \n",
              "50%             0.000000                       0.000000   \n",
              "75%             0.000000                       0.000000   \n",
              "max             1.000000                       1.000000   \n",
              "\n",
              "       ('P27', 'only_excel_gsheets')  ('P27', 'no_bi_tool_at_work')  \\\n",
              "count                    1765.000000                    1765.000000   \n",
              "mean                        0.059490                       0.101416   \n",
              "std                         0.236607                       0.301965   \n",
              "min                         0.000000                       0.000000   \n",
              "25%                         0.000000                       0.000000   \n",
              "50%                         0.000000                       0.000000   \n",
              "75%                         0.000000                       0.000000   \n",
              "max                         1.000000                       1.000000   \n",
              "\n",
              "       ('P27', 'other')  ('P28', 'sql_&_stored_procedures')  \\\n",
              "count       1765.000000                         1765.000000   \n",
              "mean           0.047025                            0.166006   \n",
              "std            0.211753                            0.372191   \n",
              "min            0.000000                            0.000000   \n",
              "25%            0.000000                            0.000000   \n",
              "50%            0.000000                            0.000000   \n",
              "75%            0.000000                            0.000000   \n",
              "max            1.000000                            1.000000   \n",
              "\n",
              "       ('P28', 'apache_airflow')  ('P28', 'luigi')  ('P28', 'aws_glue')  \\\n",
              "count                1765.000000       1765.000000          1765.000000   \n",
              "mean                    0.088385          0.008499             0.058924   \n",
              "std                     0.283935          0.091821             0.235548   \n",
              "min                     0.000000          0.000000             0.000000   \n",
              "25%                     0.000000          0.000000             0.000000   \n",
              "50%                     0.000000          0.000000             0.000000   \n",
              "75%                     0.000000          0.000000             0.000000   \n",
              "max                     1.000000          1.000000             1.000000   \n",
              "\n",
              "       ('P28', 'talend')  ('P28', 'pentaho')  ('P28', 'alteryx')  \\\n",
              "count        1765.000000         1765.000000         1765.000000   \n",
              "mean            0.023796            0.096317            0.020963   \n",
              "std             0.152456            0.295109            0.143302   \n",
              "min             0.000000            0.000000            0.000000   \n",
              "25%             0.000000            0.000000            0.000000   \n",
              "50%             0.000000            0.000000            0.000000   \n",
              "75%             0.000000            0.000000            0.000000   \n",
              "max             1.000000            1.000000            1.000000   \n",
              "\n",
              "       ('P28', 'oracle_data_integrator')  ('P28', 'ibm_data_stage')  \\\n",
              "count                        1765.000000                1765.000000   \n",
              "mean                            0.020963                   0.022096   \n",
              "std                             0.143302                   0.147038   \n",
              "min                             0.000000                   0.000000   \n",
              "25%                             0.000000                   0.000000   \n",
              "50%                             0.000000                   0.000000   \n",
              "75%                             0.000000                   0.000000   \n",
              "max                             1.000000                   1.000000   \n",
              "\n",
              "       ('P28', 'sap_bw_etl')  ('P28', 'siss_sql_server_integration_services')  \\\n",
              "count            1765.000000                                      1765.000000   \n",
              "mean                0.013598                                         0.071388   \n",
              "std                 0.115847                                         0.257545   \n",
              "min                 0.000000                                         0.000000   \n",
              "25%                 0.000000                                         0.000000   \n",
              "50%                 0.000000                                         0.000000   \n",
              "75%                 0.000000                                         0.000000   \n",
              "max                 1.000000                                         1.000000   \n",
              "\n",
              "       ('P28', 'other')  ('P29', 'have_data_warehouse')  \\\n",
              "count       1765.000000                      793.000000   \n",
              "mean           0.116147                        0.581337   \n",
              "std            0.320492                        0.493651   \n",
              "min            0.000000                        0.000000   \n",
              "25%            0.000000                        0.000000   \n",
              "50%            0.000000                        1.000000   \n",
              "75%            0.000000                        1.000000   \n",
              "max            1.000000                        1.000000   \n",
              "\n",
              "       ('P30', 'google_bigquery')  ('P30', 'aws_redshift')  \\\n",
              "count                 1765.000000              1765.000000   \n",
              "mean                     0.030595                 0.052125   \n",
              "std                      0.172266                 0.222341   \n",
              "min                      0.000000                 0.000000   \n",
              "25%                      0.000000                 0.000000   \n",
              "50%                      0.000000                 0.000000   \n",
              "75%                      0.000000                 0.000000   \n",
              "max                      1.000000                 1.000000   \n",
              "\n",
              "       ('P30', 'snowflake')  ('P30', 'oracle')  ('P30', 'postgres_mysql')  \\\n",
              "count           1765.000000        1765.000000                1765.000000   \n",
              "mean               0.007932           0.046459                   0.060623   \n",
              "std                0.088733           0.210536                   0.238706   \n",
              "min                0.000000           0.000000                   0.000000   \n",
              "25%                0.000000           0.000000                   0.000000   \n",
              "50%                0.000000           0.000000                   0.000000   \n",
              "75%                0.000000           0.000000                   0.000000   \n",
              "max                1.000000           1.000000                   1.000000   \n",
              "\n",
              "       ('P30', 'ibm')  ('P30', 'teradata')  ('P30', 'microsoft_azure')  \\\n",
              "count     1765.000000          1765.000000                 1765.000000   \n",
              "mean         0.011331             0.015864                    0.048159   \n",
              "std          0.105874             0.124985                    0.214162   \n",
              "min          0.000000             0.000000                    0.000000   \n",
              "25%          0.000000             0.000000                    0.000000   \n",
              "50%          0.000000             0.000000                    0.000000   \n",
              "75%          0.000000             0.000000                    0.000000   \n",
              "max          1.000000             1.000000                    1.000000   \n",
              "\n",
              "       ('P30', 'do_not_know')  ('P30', 'other')  ('P31', 'data_hackers_blog')  \\\n",
              "count             1765.000000       1765.000000                   1765.000000   \n",
              "mean                 0.033994          0.020397                      0.677054   \n",
              "std                  0.181266          0.141393                      0.467735   \n",
              "min                  0.000000          0.000000                      0.000000   \n",
              "25%                  0.000000          0.000000                      0.000000   \n",
              "50%                  0.000000          0.000000                      1.000000   \n",
              "75%                  0.000000          0.000000                      1.000000   \n",
              "max                  1.000000          1.000000                      1.000000   \n",
              "\n",
              "       ('P31', 'data_hackers_podcast')  ('P31', 'weekly_newsletter')  \\\n",
              "count                      1765.000000                   1765.000000   \n",
              "mean                          0.620963                      0.497450   \n",
              "std                           0.485285                      0.500135   \n",
              "min                           0.000000                      0.000000   \n",
              "25%                           0.000000                      0.000000   \n",
              "50%                           1.000000                      0.000000   \n",
              "75%                           1.000000                      1.000000   \n",
              "max                           1.000000                      1.000000   \n",
              "\n",
              "       ('P31', 'slack_channel')  ('P31', 'data_hackers_bootcamp')  \\\n",
              "count               1765.000000                       1765.000000   \n",
              "mean                   0.412465                          0.038527   \n",
              "std                    0.492417                          0.192519   \n",
              "min                    0.000000                          0.000000   \n",
              "25%                    0.000000                          0.000000   \n",
              "50%                    0.000000                          0.000000   \n",
              "75%                    1.000000                          0.000000   \n",
              "max                    1.000000                          1.000000   \n",
              "\n",
              "       ('P31', 'do_not_know_data_hackers')  ('P33', 'telegram_groups')  \\\n",
              "count                          1765.000000                 1765.000000   \n",
              "mean                              0.155807                    0.269688   \n",
              "std                               0.362775                    0.443924   \n",
              "min                               0.000000                    0.000000   \n",
              "25%                               0.000000                    0.000000   \n",
              "50%                               0.000000                    0.000000   \n",
              "75%                               0.000000                    1.000000   \n",
              "max                               1.000000                    1.000000   \n",
              "\n",
              "       ('P33', 'whatsapp_groups')  ('P33', 'youtube_channels')  \\\n",
              "count                 1765.000000                  1765.000000   \n",
              "mean                     0.218130                     0.574504   \n",
              "std                      0.413093                     0.494558   \n",
              "min                      0.000000                     0.000000   \n",
              "25%                      0.000000                     0.000000   \n",
              "50%                      0.000000                     1.000000   \n",
              "75%                      0.000000                     1.000000   \n",
              "max                      1.000000                     1.000000   \n",
              "\n",
              "       ('P33', 'other_brasilian_blogs')  ('P33', 'other_slack_channels')  \\\n",
              "count                       1765.000000                      1765.000000   \n",
              "mean                           0.382436                         0.052691   \n",
              "std                            0.486120                         0.223480   \n",
              "min                            0.000000                         0.000000   \n",
              "25%                            0.000000                         0.000000   \n",
              "50%                            0.000000                         0.000000   \n",
              "75%                            1.000000                         0.000000   \n",
              "max                            1.000000                         1.000000   \n",
              "\n",
              "       ('P33', 'twitter')  ('P33', 'abroad_blogs')  \\\n",
              "count         1765.000000              1765.000000   \n",
              "mean             0.193201                 0.441926   \n",
              "std              0.394921                 0.496757   \n",
              "min              0.000000                 0.000000   \n",
              "25%              0.000000                 0.000000   \n",
              "50%              0.000000                 0.000000   \n",
              "75%              0.000000                 1.000000   \n",
              "max              1.000000                 1.000000   \n",
              "\n",
              "       ('P33', 'abroad_podcasts')  ('P33', 'meetups_and_events')  \\\n",
              "count                 1765.000000                    1765.000000   \n",
              "mean                     0.256091                       0.431728   \n",
              "std                      0.436596                       0.495457   \n",
              "min                      0.000000                       0.000000   \n",
              "25%                      0.000000                       0.000000   \n",
              "50%                      0.000000                       0.000000   \n",
              "75%                      1.000000                       1.000000   \n",
              "max                      1.000000                       1.000000   \n",
              "\n",
              "       ('P33', 'only_data_hackers')  ('P33', 'other')  ('P34', 'udacity')  \\\n",
              "count                   1765.000000       1765.000000         1765.000000   \n",
              "mean                       0.078187          0.094051            0.298584   \n",
              "std                        0.268542          0.291982            0.457766   \n",
              "min                        0.000000          0.000000            0.000000   \n",
              "25%                        0.000000          0.000000            0.000000   \n",
              "50%                        0.000000          0.000000            0.000000   \n",
              "75%                        0.000000          0.000000            1.000000   \n",
              "max                        1.000000          1.000000            1.000000   \n",
              "\n",
              "       ('P34', 'coursera')  ('P34', 'udemy')  ('P34', 'height')  \\\n",
              "count          1765.000000       1765.000000        1765.000000   \n",
              "mean              0.392635          0.637394           0.170538   \n",
              "std               0.488475          0.480889           0.376212   \n",
              "min               0.000000          0.000000           0.000000   \n",
              "25%               0.000000          0.000000           0.000000   \n",
              "50%               0.000000          1.000000           0.000000   \n",
              "75%               1.000000          1.000000           0.000000   \n",
              "max               1.000000          1.000000           1.000000   \n",
              "\n",
              "       ('P34', 'edx')  ('P34', 'data_camp')  ('P34', 'data_quest')  \\\n",
              "count     1765.000000           1765.000000            1765.000000   \n",
              "mean         0.166572              0.222096               0.036261   \n",
              "std          0.372699              0.415773               0.186991   \n",
              "min          0.000000              0.000000               0.000000   \n",
              "25%          0.000000              0.000000               0.000000   \n",
              "50%          0.000000              0.000000               0.000000   \n",
              "75%          0.000000              0.000000               0.000000   \n",
              "max          1.000000              1.000000               1.000000   \n",
              "\n",
              "       ('P34', 'kaggle_learn')  ('P34', 'online_courses')  ('P34', 'other')  \\\n",
              "count              1765.000000                1765.000000       1765.000000   \n",
              "mean                  0.179037                   0.095184          0.134844   \n",
              "std                   0.383492                   0.293552          0.341654   \n",
              "min                   0.000000                   0.000000          0.000000   \n",
              "25%                   0.000000                   0.000000          0.000000   \n",
              "50%                   0.000000                   0.000000          0.000000   \n",
              "75%                   0.000000                   0.000000          0.000000   \n",
              "max                   1.000000                   1.000000          1.000000   \n",
              "\n",
              "       ('P36', 'draw_participation')  \n",
              "count                    1761.000000  \n",
              "mean                        0.813174  \n",
              "std                         0.389882  \n",
              "min                         0.000000  \n",
              "25%                         1.000000  \n",
              "50%                         1.000000  \n",
              "75%                         1.000000  \n",
              "max                         1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkHHrg2-rXe5"
      },
      "source": [
        "Checando por valores faltantes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ki9rKl4RrXCi",
        "outputId": "51c7e9f0-c652-438c-fe6e-7fa1852cc372",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "dados.isnull().sum() "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P0', 'id')                             0\n",
              "('P1', 'age')                           24\n",
              "('P2', 'gender')                         3\n",
              "('P3', 'living_in_brasil')               0\n",
              "('P5', 'living_state')                 337\n",
              "                                      ... \n",
              "('D2', 'origin_macroregion')          1440\n",
              "('D3', 'anonymized_degree_area')        35\n",
              "('D4', 'anonymized_market_sector')     243\n",
              "('D5', 'anonymized_manager_level')    1460\n",
              "('D6', 'anonymized_role')              514\n",
              "Length: 170, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Br3nDWnGtMd3",
        "outputId": "a2da78c2-4109-498a-c7a0-49a963beaf02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "(dados.isnull().sum() != 0).sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDYbaCG0tdNm",
        "outputId": "2066069d-7cdf-43df-8a38-7cd81b598e64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "dados.isnull().mean()[dados.isnull().sum() != 0]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P1', 'age')                                    0.013598\n",
              "('P2', 'gender')                                 0.001700\n",
              "('P5', 'living_state')                           0.190935\n",
              "('P6', 'born_or_graduated')                      0.019263\n",
              "('P12', 'workers_number')                        0.134844\n",
              "('P13', 'manager')                               0.134844\n",
              "('P16', 'salary_range')                          0.134844\n",
              "('P22', 'most_used_proggraming_languages')       0.486686\n",
              "('P29', 'have_data_warehouse')                   0.550708\n",
              "('P35', 'data_science_plataforms_preference')    0.079320\n",
              "('P35', 'other')                                 0.920680\n",
              "('P36', 'draw_participation')                    0.002266\n",
              "('D1', 'living_macroregion')                     0.190935\n",
              "('D2', 'origin_macroregion')                     0.815864\n",
              "('D3', 'anonymized_degree_area')                 0.019830\n",
              "('D4', 'anonymized_market_sector')               0.137677\n",
              "('D5', 'anonymized_manager_level')               0.827195\n",
              "('D6', 'anonymized_role')                        0.291218\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPzVJgbnjqOg"
      },
      "source": [
        "Criando novas variáveis\n",
        "\n",
        "Frequentemente, é necessário fazer transformações em algumas variáveis de forma a deixá-las mais convenientes para a análise. \n",
        "\n",
        "Inicialmente, faremos duas transformações: tornaremos uma variável contínua em categórica e uma variável categórica em contínua. \n",
        "\n",
        "A variável *('P16', 'salary_range')* será trasnformada em contínua, assumindo que o valor do salário de cada pessoa se dá pelo valor central da faixa salarial em que se encontra. O dicionário de conversão abaixo ilustra os detalhes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnonLiRkkiqF"
      },
      "source": [
        "dicionario_de_salarios = {\n",
        "    'Menos de R$ 1.000/mês': 1000,\n",
        "    'de R$ 1.001/mês a R$ 2.000/mês': 1500, \n",
        "    'de R$ 2.001/mês a R$ 3000/mês': 2500,\n",
        "    'de R$ 3.001/mês a R$ 4.000/mês': 3500,\n",
        "    'de R$ 4.001/mês a R$ 6.000/mês': 5000, \n",
        "    'de R$ 6.001/mês a R$ 8.000/mês': 7000,\n",
        "    'de R$ 8.001/mês a R$ 12.000/mês': 10000,\n",
        "    'de R$ 12.001/mês a R$ 16.000/mês': 14000,\n",
        "    'de R$ 16.001/mês a R$ 20.000/mês': 18000,\n",
        "    'de R$ 20.001/mês a R$ 25.000/mês': 22500, \n",
        "    'Acima de R$ 25.001/mês': 25000}\n",
        "\n",
        "dados['salario'] = dados[\"('P16', 'salary_range')\"].map(dicionario_de_salarios) "
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMdvh5_3mVHF",
        "outputId": "c23545e3-b428-4235-b5eb-c7278a879986",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "dados['salario'].value_counts().sort_index().plot(kind = 'bar');"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yu9ixqH4nkT4"
      },
      "source": [
        "Considere também essa segunda transformação dos salários em categorias mais amplas. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mik-MMtAFCAN"
      },
      "source": [
        "dicionario_de_salarios2 = {\n",
        "    'Menos de R$ 1.000/mês': 'Até 4mil',\n",
        "    'de R$ 1.001/mês a R$ 2.000/mês': 'Até 4mil', \n",
        "    'de R$ 2.001/mês a R$ 3000/mês': 'Até 4mil',\n",
        "    'de R$ 3.001/mês a R$ 4.000/mês': 'Até 4mil',\n",
        "    'de R$ 4.001/mês a R$ 6.000/mês': 'De 4mil a 8mil', \n",
        "    'de R$ 6.001/mês a R$ 8.000/mês': 'De 4mil a 8mil',\n",
        "    'de R$ 8.001/mês a R$ 12.000/mês': 'De 8mil a 12mil',\n",
        "    'de R$ 12.001/mês a R$ 16.000/mês': 'De 12mil a 16mil',\n",
        "    'de R$ 16.001/mês a R$ 20.000/mês': 'Mais de 16mil',\n",
        "    'de R$ 20.001/mês a R$ 25.000/mês': 'Mais de 16mil', \n",
        "    'Acima de R$ 25.001/mês': 'Mais de 16mil'\n",
        "}\n",
        "        \n",
        "dados['salario2'] = dados[\"('P16', 'salary_range')\"].map(dicionario_de_salarios2) "
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qxBpXnlGias",
        "outputId": "532b6039-be0e-4455-dde8-c62d352bc8b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        }
      },
      "source": [
        "dados['salario2'].value_counts().plot(kind = 'bar');"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWNOkP1mmlAv"
      },
      "source": [
        "E, finalmente, a discretização das idades em faixas de 18 a 24, 25 a 30, 31 a 40 e 41 a 50."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghAPDoFb3V5f"
      },
      "source": [
        "def idades(x):\n",
        "    if x <= 24:\n",
        "        return '[18,24]'\n",
        "    elif x <= 30:\n",
        "        return '[25,30]'\n",
        "    elif x <= 40:\n",
        "        return '[31,40]'\n",
        "    else:\n",
        "        return '[41,50]'\n",
        "\n",
        "dados['idade'] = dados[\"('P1', 'age')\"].apply(idades) "
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hud4TshU4L73",
        "outputId": "0fef511c-6a68-4dcd-98d1-6692ee36e9aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "dados['idade'].value_counts().plot(kind = 'bar');"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eszMwddNrMKG"
      },
      "source": [
        "## **Pergunta: Qual o perfil geral do profissional da área de dados no Brasil?**  <a name=\"p1\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c2v6lDz11saY",
        "outputId": "09355993-1cfe-4661-c06c-c65c0c8152ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "dados[\"('P1', 'age')\"].plot(kind = 'hist', edgecolor = 'black');"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1DAVJUstQkCS1Dum7AEnzxKLFJOll169ftoLtW7/by77nG0NB0sGx8yWOvfjWXna95coze9nvfNRLKCR5HHgO2Am8XFVrkhwFfBFYCTwOnFNVP+yjPklaqPq8pvD2qlpdVWua+UuAO6pqFXBHMy9JGqPZdKH5LGB9M70eOLvHWiRpQeorFAr4uyT3JlnbtC2pqu3N9JPAkqk2TLI2ycYkGycnJ8dRqyQtGH1daP7VqtqW5GeB25N8a3hhVVWSmmrDqloHrANYs2bNlOtIkvZPL2cKVbWt+bsDuAk4BXgqyVKA5u+OPmqTpIVs7KGQ5DVJXrdrGvgN4EHgFuD8ZrXzgZvHXZskLXR9dB8tAW5qHnI5BLi+qv42yT3AhiQXAFuAc3qoTZIWtLGHQlV9G3jzFO3fB94x7nokSa+YTbekSpJ65jAXkua+nsZdmo9jLhkKkua+nsZdmo9jLhkKkrS/5uHIsIaCJO2veTgyrBeaJUktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEmtWRcKSU5P8miSzUku6bseSVpIZlUoJFkE/C/gXcAJwHlJTui3KklaOGZVKACnAJur6ttV9SJwA3BWzzVJ0oKRquq7hlaS9wGnV9V/buY/ALylqi4aWmctsLaZfSPw6NgL3dPRwD/1XcQBsP7+zOXaYW7XP5drhwOr/9iqmphqwSH7X08/qmodsK7vOoYl2VhVa/quY39Zf3/mcu0wt+ufy7VDd/XPtu6jbcCKofnlTZskaQxmWyjcA6xKclySnwLOBW7puSZJWjBmVfdRVb2c5CLg/wKLgGur6qGeyxrFrOrO2g/W35+5XDvM7frncu3QUf2z6kKzJKlfs637SJLUI0NBktQyFGYoyYokdyZ5OMlDST7StB+V5PYkjzV/j+y71t1NU/vlSbYl2dS8zui71qkkOSzJ3Un+oan/D5v245Lc1QyN8sXmJoVZZ5r6P5fkO0PHf3Xfte5NkkVJvpnk1mZ+Thx7mLL2uXTcH0/yQFPnxqatk+8cQ2HmXgY+VlUnAKcCFzZDcVwC3FFVq4A7mvnZZm+1A1xVVaub1239lTitF4DTqurNwGrg9CSnAlcyqP8XgB8CF/RY43T2Vj/A7w8d/039lbhPHwEeGZqfK8ce9qwd5s5xB3h7U+euZxM6+c4xFGaoqrZX1X3N9HMM/iNbxmA4jvXNauuBs/upcO+mqX1OqIHnm9nFzauA04Abm/ZZeexh2vrnhCTLgXcDf9bMhzly7HevfZ7o5DvHUDgASVYCJwJ3AUuqanuz6ElgSU9ljWS32gEuSnJ/kmtnY9fXLk0XwCZgB3A78I/A01X1crPKVmZx0O1ef1XtOv5XNMf/qiSH9ljidP4Y+G/Aj5v5n2HuHPvda99lLhx3GPzj4e+S3NsM9QMdfecYCvspyWuBLwEfrapnh5fV4D7fWfsvwClqvxp4A4Muje3AJ3ssb1pVtbOqVjN42v0U4PieS5qR3etP8kvApQw+x8nAUcDFPZY4pSRnAjuq6t6+a5mpaWqf9cd9yK9W1UkMRpC+MMm/G154ML9zDIX9kGQxgy/VL1TVl5vmp5IsbZYvZfAvwVlnqtqr6qnmy+rHwGcZfNnOalX1NHAn8CvAEUl2PYg5J4ZGGar/9KZbr6rqBeDPmZ3H/63Ae5I8zmD04tOATzM3jv0etSf5izly3AGoqm3N3x3ATQxq7eQ7x1CYoaYf9Rrgkar61NCiW4Dzm+nzgZvHXdu+7K32Xf9hNd4LPDju2kaRZCLJEc304cA7GVwXuRN4X7ParDz2sNf6vzX0P3YY9AvPuuNfVZdW1fKqWslg+JmvVtXvMAeO/V5qf/9cOO4ASV6T5HW7poHfYFBrJ985s2qYiznircAHgAeavmGAy4BPABuSXABsAc7pqb7p7K3285rb8Qp4HPhwP+Xt01JgfQY/xvQqYENV3ZrkYeCGJH8EfJNB8M1Ge6v/q0kmgACbgN/rs8gZupi5ceyn8oU5ctyXADcNsotDgOur6m+T3EMH3zkOcyFJatl9JElqGQqSpJahIElqGQqSpJahIElqGQqSpJahIElq/X/Hh3GlM6BL5wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nURCeiVdBlz0",
        "outputId": "078317fb-2d1f-40eb-9627-da8bbc0a65bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "dados[\"('P1', 'age')\"].plot(kind = 'box');"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KovSfBoUDuBQ",
        "outputId": "ed4af2e7-7951-4d5b-ef05-6c7233d67f95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "dados[\"('P2', 'gender')\"].value_counts()/n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Masculino    0.813598\n",
              "Feminino     0.184703\n",
              "Name: ('P2', 'gender'), dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Io2mK2NEBfuD",
        "outputId": "6cd3692a-ce5c-4add-c8f7-a0e92da4778e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "dados[\"('P2', 'gender')\"].value_counts().plot(kind = 'bar');"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJAGZhxRDL0x",
        "outputId": "9df283f3-5336-4139-92c9-24f7b09a9050",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "dados[\"('P3', 'living_in_brasil')\"].value_counts().plot(kind = 'pie', autopct='%1.1f%%');"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIIba6xMEEst",
        "outputId": "304f1550-6e61-4eed-837b-413211a62514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "dados[\"('P6', 'born_or_graduated')\"].value_counts().plot(kind = 'pie', autopct='%1.1f%%');"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAPhJAONEU2m",
        "outputId": "bbcb5563-3fb4-4eee-98a1-96a8eaf07e2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "dados[\"('P8', 'degreee_level')\"].value_counts()/n"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graduação/Bacharelado         0.327479\n",
              "Pós-graduação                 0.298584\n",
              "Estudante de Graduação        0.211898\n",
              "Mestrado                      0.113881\n",
              "Doutorado ou Phd              0.028329\n",
              "Não tenho graduação formal    0.019263\n",
              "Prefiro não informar          0.000567\n",
              "Name: ('P8', 'degreee_level'), dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoKqSwjdEpzZ",
        "outputId": "206e7dde-47eb-4fde-ab01-76bd4d1aaa9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "dados[\"('P8', 'degreee_level')\"].value_counts().plot(kind = 'bar');"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k7Wz7Aw2GNRRJGwHHArtQ/rgvAA63vaDRwAbzKeBFtq8HkPQ04OvADo1GNaA2/u5tbw4g6YvAqXXGGpL2APZpMrZB9OLvgBuBn0k6Hfhjr9H2v810IJ2cLSPpSmBb24/U85WBy2xv3Wxkg5N0NvA14JTatD+wn+0XNhfVYCRdOfZ3PV7bqGr57/4q28+aqG2USVoP2AKY1Wtry1WfpA+N1277qBmPpcPJfdfeOF0dzzuvLckFQNLltredqG0USfoS8Ajwldq0H7Cy7Tc0F9XgWv67/yHlSqP/d/9823/TXFSDk/RG4HBK2ZLLgZ2AX9h+QaOBtVBXZ8t8FLhM0r9LmksZz/uXhmMa1l2S9pe0cv3YH7ir6aAG9BbgGuCw+nFNbWuLNv/u9wVmA6fWjyfUtrY4HNgRuNn2bsB2wD3NhjQ4SbMlfULSmZJ+0vtoJJYu9twBJD2R8kcCcLHt25uMZ1iSNqWM++5MGff9OXCY7f9qNLAVQH73zZF0ie0dJV1OqSb7oKSrbT+j6dgGIelHwDeA9wCHAAcAd9p+/4zH0qXkLmn7ZT1v+9KZimVFJOkx9T/jFpSrp61YfNx05Gc8tF29ef0eYDP6Jky0ZVhD0qnAgcA7gBdQZv2sanvPRgMbkKT5tnfov8fUe8Oa6Vi6NlvmU/VxFjAHuIIytWprYB6lJ9YKkmYBBwHPYPEEOcrj1l8EXg98GfgQcAywG+U/68gPAUo6ltJTH5ftw2YwnMn6FnA8cCLwcMOxDM3239bDIyWdC6wDnNVgSMPqree4TdJewH8Dy5zDP11G/j/cMGzvVsfpbgO2tz3H9g6UcbslasqPIkm9u+qnAH8B/A3wU8oNpj80FdeAenX8V7d9DuXK8GbbRwJTt8XM9JlHuT8zC9ge+HX92BZYrcG4hrHQ9nG2L7Y9v/fRdFDDkLSepK0pf+8LgGc2HNIw/lnSOsC7KVdQJwLvbCKQrvXce7a0fVXvxPavJP1lkwENoXf59lTbr5K0t+25kr5GmQUxynpj0g9KWgn4dS3/fCuwZnNhDcb2XABJbwF2sb2wnh/PiP/u+1Z4niHprZSbqQ/2nm/DCk8ASR8B/p4yX/yR2mzKEM3Is/29engv5aq1MV1N7ldKOpHFp4Nd2WA8w+gNwfQu7+6R9EzgdsrMh5Fl+4B6eDjwWMpMmY9Q/sgPWNrnjaD1gLWBXkJcs7aNsrG98/f2HbdihWf1auApo7DCczIkbQ68nSXvebxspmPpanI/kDL17vB6fj5wXHPhDOU19fGEupjjf1N2sloT+KfGohrO5bWWzH3AgZJEu/7WjqZMpT2Xcs/m+cCRjUY0gQ6t8PwVpWTCHU0HMkn/CZwEnMGiK49GdGq2TIwGSb+glB/4Qz1fG/gB5WbrqravazK+QUj6C+A59fSiUZ9KW2cofQJ4KnAV8B7brbjP1E/SHOA0SpLvH1aa8Z7vZEi6yPZzJn7l9Otkcu/CVDxJJwDvs31PPV8P+KjtQyTJI/wPt5QVnrcCnwHeZHuLZiIbXNuWwEu6ADiZcpX6MmBn2y9vNqrhSboa+ALlDerRnq/tnzYW1BAkvZbyd/MjFn9zmvFp2G26VB5GK6fijbFjL7ED2L5b0kskfYFy03WZc/ob9kdJ29i+AkDSdsAttj8uaaTvG8DSl8Az2jf11rL9xXr8CUltXdNxv+3PNh3EcngW8DrK30qjN4S7mtxXt31O7eHeTJkzO5/2jFkDrCRp3TE997ttv1nSqPdi3gF8R9J/U8as/4J6L8H2e5oMbEC9JfC/tL2bpKcD/9pwTBOZVd9EeyVzV+8/b9ECvgtqVdfTabjnO0mvAp48CjeEu5rcWzkVb4xPAT+X9G3Kf9BXUIaasP3XTQY2EduX1KmnW9amtm3W8YDtByT1Vt1eJ2nLiT+tUbcB/WVlb+87b81UQsqaFChXSz1tin9kbgh3dcx9R+Bayi/5I5RVbh+3/ctGAxuSpGewaK7sT2xf02Q8w6jTN8fe8zi5uYgG1/Yl8G1VS3MfZvuYpmOZLEnnUVbEX0LDN4Q7mdy7pI5R9yfIkS9eVWta70pJ7mcCewAX2n5lk3FNhqS/pi6BH4VL7a6TdLHtZzcdx2TVv5clNHFDuFPJXdIZLLs2SCumU8Gj2wR+CngS5RJvU+DaNlTHk3QVsA1lg5RtJG0AfKUlm12sDFxt++lNx7IiknQMsCqlsmL/TkYjP+Y+an87XRtz/2TTAUyhj1DGHX9seztJu1F2BGqDP9l+RNLCOsf9DhbVnRlpLvtgXi9pkzZcJXVQbwrth/vaWjHmPmp/O51K7v2XPpJWBzZx3cezhR6yfZeklSStZPtcSZ9uOqgBzZO0LqVK5HzKStVfNBvSUNYDrpZ0MYv3Hkf+yk/S88drH+U5+v1q4b82G5m/nU4Ny/RIeimlF7+a7c0lbQt8uA3/OXsk/ZiysfFHgcdTer872n5uo4ENSdJmwNq221LbZ6TGTYdVhyZ7ZlE2ip/fonru61DWqPTepH5K+b97b3NRDW6U/na6mtznUy7jzrO9XW1r2ybBawAPUKZB7ke5qfdV2yO/3Vvbe4+SPjZ255zx2tpA0sbAp22/oulYBiHpO5TphHNr0+uAbdq02rbeY+rfBa6RaZFdTe6/tL2TpMv6kvujO6PE9OpA7/FS29uPaWvl308t2na17a2ajmUQSyld0YrNyQEkvZpS4+c8Ssfsr4D32v72TMfSqTH3PlfXGg8r1zozh1H2wWwNSX9g0cyf1SgzCP5oe+3mohqM7Zf2n/d6jw2FM7Bax/2twFMk9Q8jrQX8rJmohqPFd5NaiXKDcuRnmvT5k6RdbF8IIOl5wJ8ajmkYH6QMn94BZcNs4MdAkvsUeTvll/wg8DXgh8A/NxrRkGyv1Tuuva+9WXzVXpssANqwWcrXKNUrPwoc0df+h7ZsdkHZTapnIfB12614Y6oOAU6uY+9QFpC1aS+AlcYMw9xFQ3WtOjcsU+ea/rgDd92X0D/MNMqW0nu8yXYrpnJKegqwwGWz710pKw5P7i/kNsokrQY8rZ62ovSDpMNtf0bS82z/rE6hxfbvm45tGJI+Qfl7+Xpt+jvgyibu13QuuQNIOgd4eVvusI9HUv8NpJUoG37/te2R3+RbUn9PayElsbem9yjpcsrvezPKCtvTgGe0ofxAfTOaC9xEGfPdGDhg1G9m98bVx7vf0Qa1BtGD9fjlwC71qQtsn9pETF0dlrkPuErS2Sw+17QNu9f39I9bL6T8Z927mVCG47oXaYs9Ynth/U96rO1jJV3WdFAD+hRlo5TrASQ9jdKL3KHRqCZ2raRfA08ac79DgFtwM/sXwPaSTrH9OuC7TQfU1eT+XUbgl7s8bB/YdAzDqmUHllX+YdT/g/Y8JGlfys5RvTfZVRuMZxir9i/cs/1/JY187Lb3Vdn96oeUzUbaZrU6ieO5Y666AbA94/mok8m9zT3HMePVSxjxq4+X1MdD6+Mp9XF/lvEzjaADKTf2/sX2b1Q2PT5lgs8ZFfO05Obw85bx+pHhspXhNk3HMUmHUH7X67L4VTeUv/0ZT+5dHXNv7TZ7fePVz6PE/416/irgGtuHNBLYEMa78du2sdS2lq+Q9BjKm+ujY77A53vjwTG9JB1k+6Sm44DuJvcLWbTN3kup2+zZbs1OTJJ+Cexie2E9X5Vyc2bkp0PWG5KH9m6iSnouJcG0ZSFK68tXRHPq3/tm9I2MNLGXQSeHZejGNnvrAWsDvfnVa9a2NjgI+FKdqyzKXOU3NBvSUI6krKo9D8D25ZJG/qqvSyStCWD7vqZjGYakU4CnUPbefbg2m7J5+YzqanLvwjZ7RwOXSTqXkiCfT0k6I8/2fGCb3kKUFk5Jfcj2vWXt2KMeWdqLY+pIehYlEa5fTnUnZSrnr5qNbGBzgK08AkMiXU3uhwOPpZQd+AiliFibVrlh+8uSfgA8pza9v95wagVJewHPoGzcDIDtDy/zk0ZHF8pXtLLnC3wBeJftc+HRefsnAG2phvoryobwtzUdSCfH3LtC0nrAFix+U3ikF6MASDqe8ua6G3Ai8EpKdbyDGg1sQJIeSylf8SLKVdMPgY/YfqDRwAYwtucLtKrnK+kK29tM1Daq6pX2tsDFZA/VqVcXbryXsjVd/02NVlQlBJD0RsoVyEaU8budgF+04WfoVVDse1wT+IHtv2o6tq6T9HPgg2N6vv/aln0AVDYnv5TFp9HuYPtvm4tqcKNUz72rwzLfAo6n7AT08ASvHVWHU2pC/9L2bpKeDvxrwzENqtfDvV/SkyjFk57YYDwDkXT6sp5vyWyZNXqJHcD2eXVvgLZ4A3AUi+aFX0CLbsY3kcSXpqvJfaHt45oOYjk9YPsBSb26FddJ2rLpoAZ0hso2e5+g9MJMeaMddTsDt1CW619EGdZomxsl/SOL93xvbDCeodi+m3KPo1UkXWh7lzGlumFR+YQZL9XdqWEZSevXw8Mo29KdyuLjXm0p29q7PD0QeAflhvDdlKXlI128qs5S2sn2z+v5Y4BZbZgxUyuKvhDYl1LZ7/uUkrlXNxrYEOp9mqNYfBHTkTVpjrxa//x91JvxvfY2DEeOmq4l999Q3jXH63G5DStUx1PH8dYBzrL956bjmUhbShMvS31T2pdy9XGU7c81HFKnSfq87bdK+hFlVfZ7KEv6DwDubKJkbtt1Krl3Re1BXm376U3HMhmSPkmpkvfdUZjvO4ya1PeiJPbNgNOBL9m+tcm4JqKyteGyahKN9P0CSWfbfqGk+bZ3UN+2hpIusb3jRF8jFtfJMXdJh1I2k76nnq8H7Gv7881GNhjbD0u6XtImtv+r6Xgm4c3Au4CFknqbfDcy7jgMSScDz6TUcD+qLdMHq0/Wx5dT5ln3CoftC/y2kYiG07si7W0scltdK/HflGmdMaRO9tw1/ia7rRoqkHQ+sB1lvmx/TfqR7oG1maRHWPS7HombYsOSNM/2nInaRo2kVW0/JOkllPsEGwPHUkpwHGV7mTOZRomkDSgz3aCs77hjWa+fLp3suVNWFqo3JFCHOVZrOKZh/WPTAQyr/p5X762KlLQTi37vl9n+Q2PBDcB2I3tdTrE1JD3Z9o0AtVzxyE+FdN0K0Pb3atO9lEVwrSLp1ZT7NOdROgXHSnqv7RnfILurPfdPUBYwfaE2vRm4xfa7m4uq++pY+x22P17Pf0NZjj0LuDQ3xaafpBdTluvfSEkumwJvtv3DRgMbkKTvAfuPGVKdS6mVvuqoz3iTdAXwwl5vvc7++XETK2y7mtxXoiT03WvT2cCJtluzoGmc+bJQejPzgHf3emajRGUruh37yhRfZns7leIyF9jeZdlfIaZCvSncuxl/XZtquS9lL4AFwH8ALxn1SQaSrrL9rL7zlYAr+ttmSieHZWw/AhxXP9rq08AC4GuUHthrKKVELwW+BOzaWGRLt1IvsVfvhzJY3StkFTNiC2BLyhXTNpIaqSc+SY9I2sj2AgBJm1CuBt9TVzuPurMk/ZCyEA7g7yg36GdcV3vurd2JqWcpBZR6O8SPZCElSdcCzx47tl5L/1406r2uLpD0Icob/1aUpLIHcKHtVzYZ16DqsNLxwPmUTs0uwCFtGVYCkPQKyk5qUK5YT20ijk723IEvs2gnpt2oOzE1GtHw7q83Z3o3Yl7Jopoto/qO/EXgG5IO6U3hlLQp5QrqxEYjW3G8krIP6WW2D6wzN74yweeMDNtnSZpDKZQH8E7b/9NkTMOy/R3gO03H0baEN6jVbZ9DuTK52faRlIUpbbIf8DpKGYU76vH+Knt7vq3JwJbG9r9RFv1cKOkuSXdRemBn2P7ksj87psif6rDkQklrU/52Nm44pmE9TIn798BWkp7fcDwTqm+iSNpZ0iWS7pP0Z0kPS/p9EzF1tefe+p2Y6g3Tsbuo91w4k7EMw/bxwPGS1qrnIz39sYPm1aJtXwTmA/dRVgu3wtJKXVPqK42yT1MWjB1LuT/2LcquTK8HntZEQF0dc98RuBZYl7IT0zrAx23/stHAJknSpba3bzqOGG11VtJGtm+p55sBa9u+ssm4hiHpKhaVut62V+ra9ssbDm2Z+qpCzrM9Z0z5hEYWUHay5277knp4H/efJJEAABEnSURBVGW8ve3aWHo2ZlidlXQm8Kx6flOzEU1KW0td966m75e0GnC5pI9TtttrZPi7U2Pukh4v6UOSDpO0pqTjJP1K0mmSntp0fMvh+00HEK1xab1ybasFdVjpP4GzJZ0G3NxwTBOyfUQ9fB0lr76NUspiY+AVTcTUqWGZWi50HrAWZQHTl4EzgL8C9rO9a3PRDUdl95w/2X5EZdvAp1O2qntogk9tnMoepO8GNrH9pjo1dcu+peUxTSRdBzyVkhD/yKK6OFs3GtgkqGWlrkdN15L7Fba3qWOPN9vepO+5JYqJjTJJ8ylvSusBPwMuAf5se79GAxuApG9Qbua93vYza7L/eZt+/21Vp54uwfbI937h0UVLS2hLdVRJzwOOZMn9m2d8jU3XxtwfhkfHHsfOjX2kgXiWh2zfL+kg4PO2Py7p8qaDGtBTbP+dpH0B6s+R+wYzwPbNkrahdAygLKK5osmYhvR9Fm24MwvYHLiesjNTG5wEvJPSuWm03EnXkvuTVTY5Vt8x9Xzz5sKaFEnamTLf/aDatnKD8Qzjz3U+fq8q51Po2+4wpo+kw4E3sWiD6a9IOsH2sQ2GNbCxNVgkbQ+8taFwJuNe2z9oOgjo3rDMXy/reY/QzuQTqT/Lu4Gf2f6YpCcD77A98psHS3oh8L8pS+B/RFmK/fe2z2syrhWBpCuBnW3/sZ6vAfyijWPuPWOLcY0ySUdTOmHfZfH9my+d8Vg6ltxPAH5AKbHZicUzvYJbvRrpbSHpcZQFKKLMWW7VEvK26s0Tt/1APZ8FXNKi5PiuvtOVgO2Bx9n+m4ZCGoqkc8dpthvY4LtrwzInUQolvUvSnym9xrPaMuYoabXerABJzwJOpmwxJkl3Um5QXt1kjMtSL6H73VYfN1HZMnDGey8roC8DF0nqFavah1JFtC3W6jteSBmDb7xOy6Bsj8wGI53quferPccXUZL91pRSuWfZ/majgS2DpLcC19g+T9LPgQ/aPrc+tytlpd5zm4xxWfp6LbMoS6+voPTctwbm2d65qdhWJPVNtlc7/wLblzUZTzSjs8l9LEk7AC+2/S9Nx7I0tR7OJ2y/eyklf0ey1O9Ykr4LfMj2VfX8mcCRbSk722aSTrH9uonaRo2kM1hGtVNn7+ChdW1YBni0fviRLJoOdh7wkVFO7PDoJiO9rQBvlPSPwCn1fH/K1mltsGUvsQPY/pWkv2wyoBXIYlMGVfa13aGhWIbRqxr6cuAvWFSmeF/gt41E1HKd7LlL+g5l7865tel1wDajXnyon8rekUdRLq9N2RH+KNt3NxrYACR9nbI6svcfdD9gTdv7NhdVt0n6B+ADwOrA/SyqR/Rn4ATb/9BUbMPoFd6aqG1USVoVeAvQK1P8U+D4JlaWdzW5L7EatS0rVOvshkMoS8ivAr7UhpID/erP0P8Hfj5wXG8GR0wfSR9tSyIfj8puXnvVktdI2hw403YrrvwknQisyuIdy4dtv3GmY+nksAzwJ0m72L4QHl0S/KeGYxrUXOAhSk99D+AvgXc0GtGQahI/pn7EzPqBxtncwvb5TQQzCe8EzpN0I+XqY1PKZvdtseOY+2I/kdTIbL2uJvdDgJPr2LuA3wF/32hEg9uqNydZ0knAxQ3HE+3y3r7jWcCzKUvhR32zC+DRbfa2oBTKA7jOdptWNz8s6Sm2/x9AXXzYSBmCTib3Oq99G5VtxrDdyDZXk/ToEIzthSnJEsOwvdjuXZI2puwS1CZbAFtS3py2kYTtkxuOaVDvAc4dc+XRyJ4SnUzuAJL2oswcmNVLkLY/3GhQg9lGi/ZcFLB6Pe+Vbl27udCGI+mxtu9vOo4V3ALK0F4rSPoQsCuldMWZlKHJCykL+kZanZm0DYvenACub+rKo1PJXdLbbH9O0vHAY4HdgBMpO8K3YnjDdluKgy2VpOdSfu9rUlanbgO82XabCkC1kqRjWTRffCVgW8oCvrZ4JSVBXmb7QJWNp78yweeMBNsPS9rX9jFA41sbdiq5Ay8EPgc81/bWKvsYHiXpU5SaMzEzjgH+BjgdyjDZeDf5YlrM6zteCHzd9s+aCmYSehvULKzDqndQdjNqi59J+hzwDcp0YKCZwmFdS+7r1cfelLv7JT0JuAt4YjMhrZhs3zLmfkGjta1XFLbnquzh+bTadH2T8UzCPJVt9r5IuRF8H/CLZkMaSm+6df8QsGnghnbXkvsh9fGM+gfyCcolqSnDBDEzbqlDM66LOg4Hrm04phVCrUE0F7iJcp9mY0kHtGEqZN3Q5aO27wGOl3QWsLbtxoc4BpXCYTNI0mOAWbbvbTqWFYWkxwOfAf4XJcH8CDjM9u8aDWwFoLI942ttX1/Pn0YZmmlDCYJW1W7vJ2l/218ZU7L4Ubb/baZjWmmmv+FMkPSh2nOn3qleqdZpiZmxpe39bG9g+wm296dFMzZabtVeYgew/X8pKybb4lJJOzYdxCQ8tj6utZSPGdfJnruky2xvN6btN5Qe5Ma292wmshWDpEttbz9RW0w9SV+i7BfcX9dnZdtvaC6qwUm6jlJ642bKDcneFOCR3klK0sdsv1/Sq2x/q+l4oLvJ/Spgh76NL2YBV9jecrzEH1NDZc/X51LKJfSXHlgb+Ns2lCtuuzoMeSh99dwpG6y3YpWnpE3Ha7d980zHMoyac7YG5o9KJ6ZrN1R7vgL8WNK/1/MDKLs0waI/+ph6q1Hmtq/C4peiv6fMX45pZvtBSacAp9i+s+l4JmG87THbsGXmWcDdwJr9iw6hucWHney5A0jaA9i9np5t+4dNxrMikbTpqPe0uqbONPkQ8DYW3Ut7GDi2JSuzAZB0E2Ve+92UxLgucDulpvubbM9vLrqJSTrN9t5NxwEdTu7RnDpD4z3AZvRdHTaxSfCKos7S2AM42PZvatuTgeMo20u2okKnpC8C3+51xiS9CHgFZW/Yz9h+TpPxDaIOLW1h+8eSVgdWsT3jVx+dTO6SdgKOpczQWA1YGfhjm+qytFktcXo8ZRHKo4uXRr3X1WaSLgNeaPt/xrTPBn7UlvtM402FrCvNt27DngyS3gQcDKxv+ym1wuXxtnef4FOnXFfH3D8HvAb4FmWj5tezaMVeTL+Fto9rOogVzKpjEzuA7TvrQrK2uE3S+4H/qOd/B/y2FuV6pLmwBnYopczyRQC2fy3pCU0E0sl57gC2b6BMAXvY9peBFzcd0wrkDElvlfRESev3PpoOquP+PMnnRs1rgY2A/wROpYy/v5Zy9f3qBuMa1IO9WXoAklZhGRt/T6euDsucT1kdeSLlZsxtwN9nKt7MqGsKxrLtJ894MCsISQ/TV6iq/ynKCu029d5bS9LHgXsoowVvB94KXGP7gzMeS0eT+6aUanKrUrbtWocy1/eGRgOLiE6rs5beCLyI8sb6Q+BEN5BoO5nco3mSnknZcGFWr61Fu+lEDK3eF7ja9tMnfPEM6OQN1TossMS7VoYFZkabd9OJmKy6Wcf1kjax/V9Nx9PJ5E6ZIdMzC3gVkBt6M6e1u+lEsyRtRJnGvAulg3YBcLjtBY0GNrj1gKslXczim3W8bKYD6WRyt33XmKZP11Ko/9REPCugtu+mE835MvA1SocMYP/a9sLGIhrOyFSf7WRyl9RfuGclSk++kz/riGr7bjrRnNl16nLPv0t6R2PRDKgWJzyEUtHyKuAk2wsbjamLN1Qlndt3upCyK80n++tcx8yQtBkt200nmiPpHEpP/eu1aV/gwCZWeA5D0jeAhyjDSHsAN9s+vNGYupjcoxljrpiW0MQmwdEudRrzscDOlDH3n1N28Wr8BuWy9JdNqAuXLm669G+nhiqWtsVVTxNbXa1gPlUfZ1GGwq6gzPXdGphH+Q8bsVS1muiM33ycAg/1DmwvHLM5fCM6ldxZVEN8S2BH4PR6/lLg4kYiWoH0NgeW9F1ge9tX1fNnAkc2GFq0RC109iaWrCg66jtJbVPruEPp0KzeX9c99dynSC0/sFevzKaktYDv235+s5GtGCRdbfsZE7VFjCXp55Rx67EVRb/TWFAt1bWee88GLF4s6c+1LWbGlZJOZPF9PHNDNQbxWNvvbzqILuhqcj8ZuFjSqfV8H2Bug/GsaA4E3gL0ZgucT9k0ImIi35O0p+0zmw6k7To5LAMgaQcW7Zd6vu3LmownIpZO0h9YtOfoGsCDlJuUjY1Zt11nkztALZLfX7hqpKdTdUVq+0Q0r5PDMpJeRpmW9yTK0vdNgOuA3NCbGantE5MmaUNgUxafLXN+cxG1Uyd77nUPzxcAP7a9naTdgP1tH9RwaCssSfNt79B0HDHaJH2MsrXeNSyaLeMmCm+1XSd77sBDtu+StJKklWyfK+nTTQe1okhtn1gO+wBb2n6w6UDarqv/4e6RtCZllsZXJd3B+FuQxfT4VN/xQuA3tGP/y2jejZQd1JLcl1NXh2XWAP5E6TXuR9lm76vjlAKOaSDpybZvHNO2ue3x9laNeJSk71D2AjiHvgRv+7DGgmqpziX3utXVj3tL4WPmSbp0bNGkjLnHICQdMF677axTGVLnhmXqVlePSFrH9r1Nx7MikfR0yoykdSS9vO+ptembkhqxNLbnSlod2CQlupdP55J7dR9wlaSzWXyrq1zaTa8tgZcA61KKtfX8gVIMKmKZJL0U+CSwGrC5pG2BD2e2zPA6NywDS1za9X5A5dJuZkja2XZ2Xoqh1e0wXwCcZ3u72vYr289sNrL26VTPXdLewEa2/089vxiYTUnwKUY0c/5W0tWUm9pnUeq5v9N2NsmOiTxk+94x9dAfaSqYNlup6QCm2PtYVMMdyqXdDsCulP0NY2a8yPbvKUM0N1H2lXxvoxFFW1wt6bXAypK2kHQsZTemGFLXkvtqtm/pO7/Q9u9qTZk1mgpqBbRqfdwL+FZubMcQ3k65Kf8g8DXgXhZVF40hdGpYBliv/8T22/pOZ89wLCuyMyRdRxmWeUvdXeeBhmOKdtjL9geBD/YaJL0K+FZzIbVTp26oSvoq5UbMF8e0vxnY1fa+zUS24pG0PnBvnZq6BrCW7dubjitG21LWSCzRFhPrWnJ/AvCflEu6S2vzDsBjgH1s/7ap2FYEkt5n++P1+FW2v9X33L/a/kBz0cUok7QHsCelTMU3+p5aG9jK9rMbCazFOpXceyS9gEXlfa+2/ZMm41lR9Pewxva20vuKZZG0DbAt8GHgn/qe+gNwru27GwmsxTqZ3KMZki7rm5v86PF45xHjkbSq7YeajqMLujZbJprlpRyPdx6xhCT2qZOee0wZSQ9Tyj0IWB24v/cUMMv2qkv73IiYWknuETFy6n4M2L6v6VjaKsMyETEyJD1L0mXA1cA1kuZLSl2ZSUhyj4hR8gXgXbY3tb0J8G7ghIZjaqUk94gYJWvYPrd3Yvs8UjpkUrpWfiAi2u1GSf8InFLP96fsqxpDSs89IkbJGyh1oL5bP2bXthhSZstERHRQhmUiYmTUCqLvo5QPeXTfXdsvaCyolsqwTEQ0TtLn6+FXgeuAzYGjKJu9XNJQWK2W5B4Ro2CL+vg42ydRttv7qe03UPZUjSFlWCYiRsGf62OvtsxtkvYC/htYv5mQ2i03VCOicb1qkJJeAlwAbAwcS6nnfpTt05f5BWIJSe4RER2UYZmIaJykf1rG07b9kRkLpiPSc4+Ixkl69zjNawAHUW6yrjnDIbVekntEjBRJawGHUxL7N4FP2b6j2ajaJ8MyETESJK0PvAvYD5gLbJ+9UycvyT0iGifpE8DLKeV9n5VNOpZfhmUionGSHgEeBBay+H67otxQXbuRwFosyT0iooNSfiAiooOS3CMiOijJPSKig5LcIyI6KMk9IqKDktwjIjro/wPItRnIxmK8ngAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phQS3abGEoCY",
        "outputId": "c463999d-5e88-4d8a-886c-d7bb22dd902c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "dados[\"('P10', 'job_situation')\"].value_counts()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empregado (CTL)                                  1073\n",
              "Empreendedor ou Empregado (CNPJ)                  234\n",
              "Estagiário                                        131\n",
              "Somente Estudante (graduação)                      85\n",
              "Desempregado, buscando recolocação                 69\n",
              "Servidor Público                                   60\n",
              "Trabalho na área Acadêmica/Pesquisador             45\n",
              "Somente Estudante (pós-graduação)                  36\n",
              "Freelancer                                         23\n",
              "Prefiro não dizer                                   6\n",
              "Desempregado e não estou buscando recolocação       3\n",
              "Name: ('P10', 'job_situation'), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CteZD8NuFEG_",
        "outputId": "18598d3f-5b33-4c4c-a1bc-903831b13f4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "dados[\"('P12', 'workers_number')\"].value_counts().sort_index()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Acima de 3000     393\n",
              "de 1 a 5           72\n",
              "de 1001 a 3000    164\n",
              "de 101 a 500      333\n",
              "de 11 a 50        204\n",
              "de 501 a 1000     172\n",
              "de 51 a 100       128\n",
              "de 6 a 10          61\n",
              "Name: ('P12', 'workers_number'), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91BHdFWRFbfQ",
        "outputId": "3acaf4c0-8a87-4514-e326-3812861e40f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "dados[\"('P13', 'manager')\"].value_counts()/n"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0    0.692351\n",
              "1.0    0.172805\n",
              "Name: ('P13', 'manager'), dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7kd5DAi3Srz",
        "outputId": "86b2c5b1-64ba-47c3-9495-a60b9d726198",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "dados['salario'].value_counts().sort_index().plot(kind = 'bar');"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K43RWbjQF8_4",
        "outputId": "c2de217e-b659-4f41-a0df-1853b711c1a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "dados[\"('P17', 'time_experience_data_science')\"].value_counts()#.sort_index().plot(kind = 'bar');"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Menos de 1 ano                            445\n",
              "de 1 a 2 anos                             343\n",
              "de 2 a 3 anos                             244\n",
              "Não tenho experiência na área de dados    221\n",
              "de 4 a 5 anos                             186\n",
              "de 6 a 10 anos                            179\n",
              "Mais de 10 anos                           147\n",
              "Name: ('P17', 'time_experience_data_science'), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGYJAZFwGL6J",
        "outputId": "0f86e194-4c04-4e76-ab5a-bc67efe1f8c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        }
      },
      "source": [
        "dados[\"('P18', 'time_experience_before')\"].value_counts()#.sort_index().plot(kind = 'bar');"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Não tive experiência na área de TI/Engenharia de Software antes de começar a trabalhar na área de dados    555\n",
              "Menos de 1 ano                                                                                             333\n",
              "de 1 a 2 anos                                                                                              210\n",
              "de 6 a 10 anos                                                                                             188\n",
              "Mais de 10 anos                                                                                            181\n",
              "de 4 a 5 anos                                                                                              161\n",
              "de 2 a 3 anos                                                                                              137\n",
              "Name: ('P18', 'time_experience_before'), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kgg_5zxKGau1",
        "outputId": "0f2da9ee-4d31-4952-862f-d9648580357e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "dados[\"('P19', 'is_data_science_professional')\"].value_counts().plot(kind = 'pie', autopct='%1.1f%%');"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sw4JRM3qGuVA",
        "outputId": "cb098703-fd2b-4c89-b9f3-12df43f851b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        }
      },
      "source": [
        "dados.filter(regex = 'P20').sum().sort_values(ascending = False)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P20', 'linear_regression')             524\n",
              "('P20', 'decision_tree')                 441\n",
              "('P20', 'logistic_regression')           406\n",
              "('P20', 'cluster_analysis')              368\n",
              "('P20', 'random_forest')                 359\n",
              "('P20', 'neural_networks')               267\n",
              "('P20', 'nlp')                           252\n",
              "('P20', 'bayesian_inference')            224\n",
              "('P20', 'no_listed_methods')             192\n",
              "('P20', 'svms')                          180\n",
              "('P20', 'gradient_boosted_machines')     177\n",
              "('P20', 'ensemble')                      169\n",
              "('P20', 'glms')                          134\n",
              "('P20', 'cnns')                          101\n",
              "('P20', 'longitudinal_data_analysis')    101\n",
              "('P20', 'rnns')                           98\n",
              "('P20', 'joint analysis')                 79\n",
              "('P20', 'markov_chains')                  79\n",
              "('P20', 'survival_analysis')              72\n",
              "('P20', 'gans')                           24\n",
              "('P20', 'hmms')                           18\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OY0ICKkHwjp",
        "outputId": "d147261c-9330-4d78-83c5-22510f24d967",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "dados.filter(regex = 'P21').sum().sort_values(ascending = False)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P21', 'python')                 784\n",
              "('P21', 'sql_')                   714\n",
              "('P21', 'r')                      318\n",
              "('P21', 'java')                    89\n",
              "('P21', 'visual_basic_vba')        84\n",
              "('P21', 'c_c++_c#')                65\n",
              "('P21', 'scala')                   62\n",
              "('P21', 'sas_stata')               61\n",
              "('P21', 'php')                     48\n",
              "('P21', 'dotnet')                  35\n",
              "('P21', 'matlab')                  29\n",
              "('P21', 'julia')                   14\n",
              "('P21', 'no_listed_languages')      8\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWE8Hg8fH6Gk",
        "outputId": "7e462d8c-c59a-4e4f-c808-b7d6fb449dd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados[\"('P22', 'most_used_proggraming_languages')\"].value_counts()"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Python                                         459\n",
              "SQL                                            279\n",
              "R                                               91\n",
              "SAS/Stata                                       19\n",
              "Java                                            17\n",
              "Visual Basic/VBA                                12\n",
              "Não utilizo nenhuma das linguagens listadas     10\n",
              "Scala                                           10\n",
              "C/C++/C#                                         9\n",
              "Name: ('P22', 'most_used_proggraming_languages'), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBvYhkT5IfGJ",
        "outputId": "ff826147-4b1f-4e0d-f4e5-b693b63a117c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "dados.filter(regex = 'P27').sum().sort_values(ascending = False)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P27', 'microsoft_powerbi')               393\n",
              "('P27', 'tableau')                         209\n",
              "('P27', 'no_bi_tool_at_work')              179\n",
              "('P27', 'metabase')                        144\n",
              "('P27', 'google_data_studio')              129\n",
              "('P27', 'only_excel_gsheets')              105\n",
              "('P27', 'qlik_view_qlik_sense')             95\n",
              "('P27', 'other')                            83\n",
              "('P27', 'microstrategy')                    27\n",
              "('P27', 'redash')                           26\n",
              "('P27', 'ibm_analytics_cognos')             20\n",
              "('P27', 'sap_business_objects')             17\n",
              "('P27', 'superset')                         17\n",
              "('P27', 'looker')                           15\n",
              "('P27', 'oracle_business_intelligence')     10\n",
              "('P27', 'birst')                             2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNTS4fkyJHR4",
        "outputId": "6972f176-2821-4af8-bb68-a75203b66532",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "dados.filter(regex = 'P33').sum().sort_values(ascending = False)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P33', 'youtube_channels')         1014\n",
              "('P33', 'abroad_blogs')              780\n",
              "('P33', 'meetups_and_events')        762\n",
              "('P33', 'other_brasilian_blogs')     675\n",
              "('P33', 'telegram_groups')           476\n",
              "('P33', 'abroad_podcasts')           452\n",
              "('P33', 'whatsapp_groups')           385\n",
              "('P33', 'twitter')                   341\n",
              "('P33', 'other')                     166\n",
              "('P33', 'only_data_hackers')         138\n",
              "('P33', 'other_slack_channels')       93\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_IPk_DEoJNCW",
        "outputId": "c2fd07a1-6c1a-40c4-aad0-4d7285ba4fdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "dados.filter(regex = 'P34').sum().sort_values(ascending = False)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P34', 'udemy')             1125\n",
              "('P34', 'coursera')           693\n",
              "('P34', 'udacity')            527\n",
              "('P34', 'data_camp')          392\n",
              "('P34', 'kaggle_learn')       316\n",
              "('P34', 'height')             301\n",
              "('P34', 'edx')                294\n",
              "('P34', 'other')              238\n",
              "('P34', 'online_courses')     168\n",
              "('P34', 'data_quest')          64\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdx560xsJW1U",
        "outputId": "da2188b5-e49b-4fe5-95fb-0f03183eaa33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "dados[\"('P35', 'data_science_plataforms_preference')\"].value_counts().plot(kind = 'barh');"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9lQYD3_KEQ-",
        "outputId": "e7fcf1a4-f310-445f-b270-1cebb85af6a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "dados.filter(regex = 'D1').value_counts()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D1', 'living_macroregion')\n",
              "Região Sudeste                  1159\n",
              "Região Sul                       269\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGlFhqGWKwRl",
        "outputId": "a59769ce-5c48-486b-ba89-f3c10a81dea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "source": [
        "dados.filter(regex = 'D2').value_counts()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D2', 'origin_macroregion')\n",
              "Região Sudeste                  154\n",
              "Região Nordeste                  77\n",
              "Região Sul                       51\n",
              "Região Centro-Oeste              26\n",
              "Região Norte                     17\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qnXxKG_K5-B",
        "outputId": "9d4b9a83-c875-4529-f1b8-1897885861cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados.filter(regex = 'D3').value_counts()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D3', 'anonymized_degree_area')                            \n",
              "Computação / Engenharia de Software / Sistemas de Informação    1013\n",
              "Outras Engenharias                                               247\n",
              "Economia/ Administração / Contabilidade / Finanças               174\n",
              "Estatística/ Matemática / Matemática Computacional               104\n",
              "Outras                                                            92\n",
              "Marketing / Publicidade / Comunicação / Jornalismo                47\n",
              "Química / Física                                                  28\n",
              "Ciências Sociais                                                  25\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0O7hhSGK-f6",
        "outputId": "a467f94d-127a-471c-afcc-383b57dd762e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        }
      },
      "source": [
        "dados.filter(regex = 'D4').value_counts()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D4', 'anonymized_market_sector')\n",
              "Tecnologia/Fábrica de Software        501\n",
              "Outras                                183\n",
              "Finanças ou Bancos                    182\n",
              "Setor Público                          89\n",
              "Educação                               86\n",
              "Indústria (Manufatura)                 78\n",
              "Varejo                                 73\n",
              "Marketing                              72\n",
              "Área da Saúde                          63\n",
              "Internet/Ecommerce                     60\n",
              "Telecomunicação                        39\n",
              "Setor Automotivo                       21\n",
              "Entretenimento ou Esportes             19\n",
              "Agronegócios                           19\n",
              "Seguros ou Previdência                 15\n",
              "Setor Alimentício                      13\n",
              "Setor Farmaceutico                      9\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SeQjIag9K-3n",
        "outputId": "3dc40574-014a-4ec6-c01a-d0414d7738fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "dados.filter(regex = 'D5').value_counts()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D5', 'anonymized_manager_level')\n",
              "Coordenador                           73\n",
              "Gerente                               60\n",
              "Team Leader/Tech Leader               53\n",
              "C-level (CDO, CIO, CTO)               38\n",
              "Head                                  26\n",
              "Supervisor                            24\n",
              "Diretor                               22\n",
              "Outras                                 9\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMIuDswULCba",
        "outputId": "e88bfd80-d431-4c50-857e-f3dc083e041c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "source": [
        "dados.filter(regex = 'D6').value_counts()"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D6', 'anonymized_role')              \n",
              "Desenvolvedor ou Engenheiro de Software    225\n",
              "Outras                                     220\n",
              "Data Scientist/Cientista de Dados          167\n",
              "Data Analyst/Analista de Dados             163\n",
              "Business Intelligence/Analista de BI       150\n",
              "Data Engineer/Engenheiro de Dados          130\n",
              "Business Analyst/Analista de Negócios       72\n",
              "Analista de Inteligência de Mercado         29\n",
              "Engenheiro                                  26\n",
              "Analista de Marketing                       19\n",
              "Engenheiro de Machine Learning              15\n",
              "DBA/Administrador de Banco de Dados         14\n",
              "Estatístico                                 11\n",
              "Economista                                  10\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ5Wq0RR6CJQ",
        "outputId": "d6f3e94f-4672-4a1d-8948-abffa8f62467",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "dados['salario'].groupby(dados['idade']).mean().plot(kind = 'bar');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E3kRkn6r9eH"
      },
      "source": [
        "## **Pergunta: Qual o perfil dos que se consideram atuar em Data Science?** <a name=\"p2\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTPVT9D3Le4h",
        "outputId": "e5fec19f-b186-4111-f3cd-772f4fb53971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('P19', 'is_data_science_professional')\"]).mean()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P19', 'is_data_science_professional')\n",
              "0    5564.207650\n",
              "1    6945.283019\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uepzm5C3Me2z",
        "outputId": "60661c9a-70d2-49cc-f1d6-3223bc203f56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "dados[\"('P1', 'age')\"].groupby(dados[\"('P19', 'is_data_science_professional')\"]).mean()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P19', 'is_data_science_professional')\n",
              "0    30.004779\n",
              "1    29.611726\n",
              "Name: ('P1', 'age'), dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNG1zf9iMpUt",
        "outputId": "a433f341-03b8-45e7-8bb4-989d3f662328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "pd.crosstab(dados[\"('D6', 'anonymized_role')\"], dados[\"('P19', 'is_data_science_professional')\"])"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>('P19', 'is_data_science_professional')</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('D6', 'anonymized_role')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Analista de Inteligência de Mercado</th>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analista de Marketing</th>\n",
              "      <td>16</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Analyst/Analista de Negócios</th>\n",
              "      <td>47</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Intelligence/Analista de BI</th>\n",
              "      <td>75</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DBA/Administrador de Banco de Dados</th>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Analyst/Analista de Dados</th>\n",
              "      <td>32</td>\n",
              "      <td>131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Engineer/Engenheiro de Dados</th>\n",
              "      <td>37</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Scientist/Cientista de Dados</th>\n",
              "      <td>5</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Desenvolvedor ou Engenheiro de Software</th>\n",
              "      <td>166</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Economista</th>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro</th>\n",
              "      <td>18</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro de Machine Learning</th>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estatístico</th>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outras</th>\n",
              "      <td>187</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "('P19', 'is_data_science_professional')    0    1\n",
              "('D6', 'anonymized_role')                        \n",
              "Analista de Inteligência de Mercado       13   16\n",
              "Analista de Marketing                     16    3\n",
              "Business Analyst/Analista de Negócios     47   25\n",
              "Business Intelligence/Analista de BI      75   75\n",
              "DBA/Administrador de Banco de Dados        9    5\n",
              "Data Analyst/Analista de Dados            32  131\n",
              "Data Engineer/Engenheiro de Dados         37   93\n",
              "Data Scientist/Cientista de Dados          5  162\n",
              "Desenvolvedor ou Engenheiro de Software  166   59\n",
              "Economista                                 6    4\n",
              "Engenheiro                                18    8\n",
              "Engenheiro de Machine Learning             0   15\n",
              "Estatístico                                2    9\n",
              "Outras                                   187   33"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfTedndnNIkl",
        "outputId": "6e5586b2-48e7-4774-aeb3-c1be9b13584c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "pd.crosstab(dados[\"('P8', 'degreee_level')\"], dados[\"('P19', 'is_data_science_professional')\"])"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>('P19', 'is_data_science_professional')</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('P8', 'degreee_level')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Doutorado ou Phd</th>\n",
              "      <td>9</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estudante de Graduação</th>\n",
              "      <td>212</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Graduação/Bacharelado</th>\n",
              "      <td>292</td>\n",
              "      <td>286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mestrado</th>\n",
              "      <td>51</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Não tenho graduação formal</th>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prefiro não informar</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pós-graduação</th>\n",
              "      <td>268</td>\n",
              "      <td>259</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "('P19', 'is_data_science_professional')    0    1\n",
              "('P8', 'degreee_level')                          \n",
              "Doutorado ou Phd                           9   41\n",
              "Estudante de Graduação                   212  162\n",
              "Graduação/Bacharelado                    292  286\n",
              "Mestrado                                  51  150\n",
              "Não tenho graduação formal                17   17\n",
              "Prefiro não informar                       1    0\n",
              "Pós-graduação                            268  259"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOgpm5WuNSzZ",
        "outputId": "6cae1720-fbdc-4efa-d240-60c7372e1ab8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "pd.crosstab(dados[\"('D3', 'anonymized_degree_area')\"], dados[\"('P19', 'is_data_science_professional')\"])"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>('P19', 'is_data_science_professional')</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('D3', 'anonymized_degree_area')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ciências Sociais</th>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Computação / Engenharia de Software / Sistemas de Informação</th>\n",
              "      <td>512</td>\n",
              "      <td>501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Economia/ Administração / Contabilidade / Finanças</th>\n",
              "      <td>84</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estatística/ Matemática / Matemática Computacional</th>\n",
              "      <td>20</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marketing / Publicidade / Comunicação / Jornalismo</th>\n",
              "      <td>30</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outras</th>\n",
              "      <td>57</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outras Engenharias</th>\n",
              "      <td>104</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Química / Física</th>\n",
              "      <td>10</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "('P19', 'is_data_science_professional')               0    1\n",
              "('D3', 'anonymized_degree_area')                            \n",
              "Ciências Sociais                                     15   10\n",
              "Computação / Engenharia de Software / Sistemas ...  512  501\n",
              "Economia/ Administração / Contabilidade / Finanças   84   90\n",
              "Estatística/ Matemática / Matemática Computacional   20   84\n",
              "Marketing / Publicidade / Comunicação / Jornalismo   30   17\n",
              "Outras                                               57   35\n",
              "Outras Engenharias                                  104  143\n",
              "Química / Física                                     10   18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkFnPB6usUks"
      },
      "source": [
        "## **Pergunta: Qual o perfil do profissional empregado como Cientista de Dados?** <a name=\"p3\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTiT-y-IOYwb"
      },
      "source": [
        "cds = dados[dados[\"('D6', 'anonymized_role')\"] == 'Data Scientist/Cientista de Dados']"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtAM7CZYOzu0",
        "outputId": "4ee51e06-9189-4f25-a3e7-b2fc55a79e1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"idade\"]).mean()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "idade\n",
              "[18,24]     3964.285714\n",
              "[25,30]     6333.333333\n",
              "[31,40]    10486.842105\n",
              "[41,50]     8666.666667\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hutxtaj0OzvM",
        "outputId": "80dcff20-fd8d-4744-d5ec-4345d3d7c07f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('P22', 'most_used_proggraming_languages')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P22', 'most_used_proggraming_languages')\n",
              "R             4722.22\n",
              "Python        6861.79\n",
              "Scala         7000.00\n",
              "SQL           7107.14\n",
              "Java         10000.00\n",
              "SAS/Stata    13833.33\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LH0QIEXpOpq3",
        "outputId": "e350ef39-d79d-4575-fc21-bee9222320e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        " pd.crosstab(cds[\"('P22', 'most_used_proggraming_languages')\"], cds[\"('P17', 'time_experience_data_science')\"])"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>('P17', 'time_experience_data_science')</th>\n",
              "      <th>Mais de 10 anos</th>\n",
              "      <th>Menos de 1 ano</th>\n",
              "      <th>Não tenho experiência na área de dados</th>\n",
              "      <th>de 1 a 2 anos</th>\n",
              "      <th>de 2 a 3 anos</th>\n",
              "      <th>de 4 a 5 anos</th>\n",
              "      <th>de 6 a 10 anos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('P22', 'most_used_proggraming_languages')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Java</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Python</th>\n",
              "      <td>6</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>41</td>\n",
              "      <td>23</td>\n",
              "      <td>20</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>R</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAS/Stata</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SQL</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scala</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "('P17', 'time_experience_data_science')     Mais de 10 anos  Menos de 1 ano  \\\n",
              "('P22', 'most_used_proggraming_languages')                                    \n",
              "Java                                                      0               0   \n",
              "Python                                                    6              25   \n",
              "R                                                         1               3   \n",
              "SAS/Stata                                                 1               0   \n",
              "SQL                                                       1               2   \n",
              "Scala                                                     0               0   \n",
              "\n",
              "('P17', 'time_experience_data_science')     Não tenho experiência na área de dados  \\\n",
              "('P22', 'most_used_proggraming_languages')                                           \n",
              "Java                                                                             0   \n",
              "Python                                                                           1   \n",
              "R                                                                                0   \n",
              "SAS/Stata                                                                        0   \n",
              "SQL                                                                              0   \n",
              "Scala                                                                            0   \n",
              "\n",
              "('P17', 'time_experience_data_science')     de 1 a 2 anos  de 2 a 3 anos  \\\n",
              "('P22', 'most_used_proggraming_languages')                                 \n",
              "Java                                                    0              0   \n",
              "Python                                                 41             23   \n",
              "R                                                       4              8   \n",
              "SAS/Stata                                               0              1   \n",
              "SQL                                                     4              5   \n",
              "Scala                                                   1              1   \n",
              "\n",
              "('P17', 'time_experience_data_science')     de 4 a 5 anos  de 6 a 10 anos  \n",
              "('P22', 'most_used_proggraming_languages')                                 \n",
              "Java                                                    1               0  \n",
              "Python                                                 20               7  \n",
              "R                                                       2               0  \n",
              "SAS/Stata                                               0               1  \n",
              "SQL                                                     2               0  \n",
              "Scala                                                   0               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBFUKaf0RyAG",
        "outputId": "88ef1617-f263-4c06-fb9c-0912c951f6ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "cds['salario'].groupby(cds['idade']).mean().plot(kind = 'bar');"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAvNNsE4s_7k"
      },
      "source": [
        "## **Pergunta: Vale a pena fazer pós-graduação?** <a name=\"p4\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkctaNmIRV8G",
        "outputId": "004f6be3-23c4-4c0b-cdcc-ccaf06c5c2f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('P8', 'degreee_level')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P8', 'degreee_level')\n",
              "Prefiro não informar           2500.00\n",
              "Estudante de Graduação         2770.83\n",
              "Graduação/Bacharelado          5767.05\n",
              "Pós-graduação                  7573.92\n",
              "Não tenho graduação formal     7593.75\n",
              "Mestrado                       8936.31\n",
              "Doutorado ou Phd              12191.18\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJMdDqs2Q1Ec",
        "outputId": "0abf06cd-893d-4159-8ea7-e2d46feacee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('P8', 'degreee_level')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P8', 'degreee_level')\n",
              "Estudante de Graduação         3722.22\n",
              "Graduação/Bacharelado          5483.33\n",
              "Mestrado                       7824.32\n",
              "Não tenho graduação formal     8750.00\n",
              "Pós-graduação                  8883.33\n",
              "Doutorado ou Phd              12166.67\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8l--ajdatJd2"
      },
      "source": [
        "## **Pergunta: Há desigualdade nos salários em relação ao gênero na área de dados no Brasil?** <a name=\"p5\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7zRvrn6RS-U",
        "outputId": "76765595-c267-4a33-8e86-1665dabaafbd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('P2', 'gender')\"]).mean()"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P2', 'gender')\n",
              "Feminino     5812.500000\n",
              "Masculino    6926.573427\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8D3qQn8S2Az",
        "outputId": "9565865b-2738-4a0a-8d7f-77c132f84650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('P2', 'gender')\"]).mean()"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P2', 'gender')\n",
              "Feminino     5343.173432\n",
              "Masculino    6490.438247\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjUkm5VKtfiE"
      },
      "source": [
        "## **Pergunta: Quais são as habilidades mais predominantes nos Cientistas de Dados?** <a name=\"p6\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Jsyw4JtUi9h",
        "outputId": "b61c410d-3900-4dea-8c5b-7ed5b8e7b2cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "cds.filter(regex = 'P21').sum().sort_values(ascending = False)"
      ],
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P21', 'python')                 155\n",
              "('P21', 'sql_')                   130\n",
              "('P21', 'r')                       66\n",
              "('P21', 'scala')                   15\n",
              "('P21', 'sas_stata')               14\n",
              "('P21', 'visual_basic_vba')        10\n",
              "('P21', 'java')                     7\n",
              "('P21', 'c_c++_c#')                 5\n",
              "('P21', 'matlab')                   4\n",
              "('P21', 'julia')                    2\n",
              "('P21', 'dotnet')                   2\n",
              "('P21', 'php')                      1\n",
              "('P21', 'no_listed_languages')      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2CfHJr7VtrY-"
      },
      "source": [
        "## **Pergunta: Quais são os salários em relação às linguagens mais utilizadas?** <a name=\"p7\"></a>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMSFReRpUpyt",
        "outputId": "c96c6dea-e6b4-4a6e-e2eb-9583ab8942b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('P22', 'most_used_proggraming_languages')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 194,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P22', 'most_used_proggraming_languages')\n",
              "R             4722.22\n",
              "Python        6861.79\n",
              "Scala         7000.00\n",
              "SQL           7107.14\n",
              "Java         10000.00\n",
              "SAS/Stata    13833.33\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGvDCVh8UwdT",
        "outputId": "ea69ea53-e364-4573-845e-9b15ea503fb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('P22', 'most_used_proggraming_languages')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P22', 'most_used_proggraming_languages')\n",
              "C/C++/C#                                       5125.00\n",
              "Visual Basic/VBA                               6125.00\n",
              "Não utilizo nenhuma das linguagens listadas    6650.00\n",
              "SQL                                            6720.08\n",
              "Python                                         6841.84\n",
              "R                                              7769.23\n",
              "Scala                                          7900.00\n",
              "Java                                           8970.59\n",
              "SAS/Stata                                      9866.67\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGVlrHOyt3Jc"
      },
      "source": [
        "## **Pergunta: Quais são os salários em relação ao tipo de emprego?** <a name=\"p8\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANsmrtvRVGnT",
        "outputId": "f46aec92-95a6-44b3-f12c-2f601b859aef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('D6', 'anonymized_role')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('D6', 'anonymized_role')\n",
              "Data Scientist/Cientista de Dados    6766.47\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6wXX4StVQ4f",
        "outputId": "55eaeeef-a741-4a70-96a5-a575ed80599f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('D6', 'anonymized_role')\"]).describe().sort_values(by = 'mean').round(2)"
      ],
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('D6', 'anonymized_role')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Analista de Marketing</th>\n",
              "      <td>19.0</td>\n",
              "      <td>3157.89</td>\n",
              "      <td>2000.37</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analista de Inteligência de Mercado</th>\n",
              "      <td>29.0</td>\n",
              "      <td>3775.86</td>\n",
              "      <td>2407.53</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>10000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Economista</th>\n",
              "      <td>10.0</td>\n",
              "      <td>4200.00</td>\n",
              "      <td>4559.48</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>5875.0</td>\n",
              "      <td>14000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DBA/Administrador de Banco de Dados</th>\n",
              "      <td>14.0</td>\n",
              "      <td>4571.43</td>\n",
              "      <td>2637.43</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>2750.0</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>10000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outras</th>\n",
              "      <td>220.0</td>\n",
              "      <td>4702.27</td>\n",
              "      <td>4161.00</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>25000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Intelligence/Analista de BI</th>\n",
              "      <td>150.0</td>\n",
              "      <td>4886.67</td>\n",
              "      <td>2979.88</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>14000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Analyst/Analista de Dados</th>\n",
              "      <td>163.0</td>\n",
              "      <td>5036.81</td>\n",
              "      <td>2890.26</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>18000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Desenvolvedor ou Engenheiro de Software</th>\n",
              "      <td>225.0</td>\n",
              "      <td>5133.33</td>\n",
              "      <td>3648.63</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>18000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Analyst/Analista de Negócios</th>\n",
              "      <td>72.0</td>\n",
              "      <td>5652.78</td>\n",
              "      <td>3274.57</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>14000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estatístico</th>\n",
              "      <td>11.0</td>\n",
              "      <td>6636.36</td>\n",
              "      <td>5436.41</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>2000.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>14000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Scientist/Cientista de Dados</th>\n",
              "      <td>167.0</td>\n",
              "      <td>6766.47</td>\n",
              "      <td>4563.73</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>3500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>25000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro</th>\n",
              "      <td>26.0</td>\n",
              "      <td>7634.62</td>\n",
              "      <td>5254.63</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>3875.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>22500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro de Machine Learning</th>\n",
              "      <td>15.0</td>\n",
              "      <td>7700.00</td>\n",
              "      <td>6108.54</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>4250.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>25000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Engineer/Engenheiro de Dados</th>\n",
              "      <td>130.0</td>\n",
              "      <td>8150.00</td>\n",
              "      <td>4918.19</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>5000.0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>25000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         count     mean      std     min  \\\n",
              "('D6', 'anonymized_role')                                                  \n",
              "Analista de Marketing                     19.0  3157.89  2000.37  1000.0   \n",
              "Analista de Inteligência de Mercado       29.0  3775.86  2407.53  1000.0   \n",
              "Economista                                10.0  4200.00  4559.48  1000.0   \n",
              "DBA/Administrador de Banco de Dados       14.0  4571.43  2637.43  1500.0   \n",
              "Outras                                   220.0  4702.27  4161.00  1000.0   \n",
              "Business Intelligence/Analista de BI     150.0  4886.67  2979.88  1000.0   \n",
              "Data Analyst/Analista de Dados           163.0  5036.81  2890.26  1000.0   \n",
              "Desenvolvedor ou Engenheiro de Software  225.0  5133.33  3648.63  1000.0   \n",
              "Business Analyst/Analista de Negócios     72.0  5652.78  3274.57  1000.0   \n",
              "Estatístico                               11.0  6636.36  5436.41  1000.0   \n",
              "Data Scientist/Cientista de Dados        167.0  6766.47  4563.73  1000.0   \n",
              "Engenheiro                                26.0  7634.62  5254.63  1000.0   \n",
              "Engenheiro de Machine Learning            15.0  7700.00  6108.54  1000.0   \n",
              "Data Engineer/Engenheiro de Dados        130.0  8150.00  4918.19  1500.0   \n",
              "\n",
              "                                            25%     50%      75%      max  \n",
              "('D6', 'anonymized_role')                                                  \n",
              "Analista de Marketing                    1500.0  2500.0   5000.0   7000.0  \n",
              "Analista de Inteligência de Mercado      1500.0  3500.0   5000.0  10000.0  \n",
              "Economista                               1500.0  1500.0   5875.0  14000.0  \n",
              "DBA/Administrador de Banco de Dados      2750.0  3500.0   7000.0  10000.0  \n",
              "Outras                                   1500.0  3500.0   5000.0  25000.0  \n",
              "Business Intelligence/Analista de BI     2500.0  5000.0   7000.0  14000.0  \n",
              "Data Analyst/Analista de Dados           3500.0  5000.0   7000.0  18000.0  \n",
              "Desenvolvedor ou Engenheiro de Software  2500.0  5000.0   7000.0  18000.0  \n",
              "Business Analyst/Analista de Negócios    3500.0  5000.0   7000.0  14000.0  \n",
              "Estatístico                              2000.0  5000.0  12000.0  14000.0  \n",
              "Data Scientist/Cientista de Dados        3500.0  5000.0  10000.0  25000.0  \n",
              "Engenheiro                               3875.0  7000.0  10000.0  22500.0  \n",
              "Engenheiro de Machine Learning           4250.0  7000.0  10000.0  25000.0  \n",
              "Data Engineer/Engenheiro de Dados        5000.0  7000.0  10000.0  25000.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhmrBEq4uHBF"
      },
      "source": [
        "## **Pergunta: Quais são os salários em relação as plataformas de ensino preferidas?** <a name=\"p9\"></a>\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IUn-2D8Vm3z",
        "outputId": "1996ea30-a6f4-42f9-c0c3-955e9196af2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('P35', 'data_science_plataforms_preference')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P35', 'data_science_plataforms_preference')\n",
              "Kaggle Learn               4859.65\n",
              "Alura                      5289.47\n",
              "Nunca fiz cursos online    5705.67\n",
              "Udemy                      5803.87\n",
              "Udacity                    6143.84\n",
              "edX                        6222.22\n",
              "DataQuest                  6368.42\n",
              "DataCamp                   6937.96\n",
              "Coursera                   7649.43\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pizVJFoTV2JN",
        "outputId": "cb4d92e2-21d0-4a15-a5ba-3bda19a6912d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('P35', 'data_science_plataforms_preference')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P35', 'data_science_plataforms_preference')\n",
              "Alura                      3166.67\n",
              "Nunca fiz cursos online    4785.71\n",
              "Udacity                    5724.14\n",
              "Udemy                      6500.00\n",
              "DataCamp                   6621.21\n",
              "Kaggle Learn               7333.33\n",
              "DataQuest                  7500.00\n",
              "Coursera                   7641.30\n",
              "edX                        9857.14\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NB00Wv6ukqc"
      },
      "source": [
        "## **Pergunta: Quais são os salários em relação tempo de experiência?** <a name=\"p10\"></a>\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWy9a2eUWCAi",
        "outputId": "ba059ef3-e044-4fe9-b83d-9de009dc78b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "cds['salario'].groupby(cds[\"('P17', 'time_experience_data_science')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P17', 'time_experience_data_science')\n",
              "Não tenho experiência na área de dados     2500.00\n",
              "Menos de 1 ano                             3953.12\n",
              "de 1 a 2 anos                              5290.00\n",
              "de 2 a 3 anos                              6768.29\n",
              "Mais de 10 anos                            9611.11\n",
              "de 4 a 5 anos                             10700.00\n",
              "de 6 a 10 anos                            11666.67\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xODlxRwcWNBN",
        "outputId": "b77c6dff-f200-44ba-de0a-f624f62e4430",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "dados['salario'].groupby(dados[\"('P17', 'time_experience_data_science')\"]).mean().sort_values().round(2)"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('P17', 'time_experience_data_science')\n",
              "Menos de 1 ano                             3961.64\n",
              "Não tenho experiência na área de dados     4648.15\n",
              "de 1 a 2 anos                              5125.41\n",
              "de 2 a 3 anos                              6290.75\n",
              "de 4 a 5 anos                              8374.25\n",
              "de 6 a 10 anos                             9384.39\n",
              "Mais de 10 anos                           10711.54\n",
              "Name: salario, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1d9TAy2uoKc"
      },
      "source": [
        "## **Pergunta: Como os cargos se distribuem em relação ao tempo de experiência?** <a name=\"p11\"></a>\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGNSo1bRuuvf",
        "outputId": "2edef7b9-3171-4fcc-ea3c-b1250828976b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        }
      },
      "source": [
        "pd.crosstab(dados[\"('D6', 'anonymized_role')\"], dados[\"('P17', 'time_experience_data_science')\"])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>('P17', 'time_experience_data_science')</th>\n",
              "      <th>Mais de 10 anos</th>\n",
              "      <th>Menos de 1 ano</th>\n",
              "      <th>Não tenho experiência na área de dados</th>\n",
              "      <th>de 1 a 2 anos</th>\n",
              "      <th>de 2 a 3 anos</th>\n",
              "      <th>de 4 a 5 anos</th>\n",
              "      <th>de 6 a 10 anos</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('D6', 'anonymized_role')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Analista de Inteligência de Mercado</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analista de Marketing</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Analyst/Analista de Negócios</th>\n",
              "      <td>8</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Intelligence/Analista de BI</th>\n",
              "      <td>16</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>26</td>\n",
              "      <td>19</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DBA/Administrador de Banco de Dados</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Analyst/Analista de Dados</th>\n",
              "      <td>6</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>23</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Engineer/Engenheiro de Dados</th>\n",
              "      <td>13</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>22</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Scientist/Cientista de Dados</th>\n",
              "      <td>9</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>50</td>\n",
              "      <td>41</td>\n",
              "      <td>25</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Desenvolvedor ou Engenheiro de Software</th>\n",
              "      <td>13</td>\n",
              "      <td>55</td>\n",
              "      <td>51</td>\n",
              "      <td>50</td>\n",
              "      <td>17</td>\n",
              "      <td>13</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Economista</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro</th>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro de Machine Learning</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estatístico</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outras</th>\n",
              "      <td>13</td>\n",
              "      <td>65</td>\n",
              "      <td>67</td>\n",
              "      <td>29</td>\n",
              "      <td>18</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "('P17', 'time_experience_data_science')  Mais de 10 anos  Menos de 1 ano  \\\n",
              "('D6', 'anonymized_role')                                                  \n",
              "Analista de Inteligência de Mercado                    0               9   \n",
              "Analista de Marketing                                  0               7   \n",
              "Business Analyst/Analista de Negócios                  8              18   \n",
              "Business Intelligence/Analista de BI                  16              45   \n",
              "DBA/Administrador de Banco de Dados                    3               1   \n",
              "Data Analyst/Analista de Dados                         6              44   \n",
              "Data Engineer/Engenheiro de Dados                     13              29   \n",
              "Data Scientist/Cientista de Dados                      9              32   \n",
              "Desenvolvedor ou Engenheiro de Software               13              55   \n",
              "Economista                                             1               4   \n",
              "Engenheiro                                             2               7   \n",
              "Engenheiro de Machine Learning                         0               4   \n",
              "Estatístico                                            2               4   \n",
              "Outras                                                13              65   \n",
              "\n",
              "('P17', 'time_experience_data_science')  Não tenho experiência na área de dados  \\\n",
              "('D6', 'anonymized_role')                                                         \n",
              "Analista de Inteligência de Mercado                                           1   \n",
              "Analista de Marketing                                                         4   \n",
              "Business Analyst/Analista de Negócios                                         6   \n",
              "Business Intelligence/Analista de BI                                          2   \n",
              "DBA/Administrador de Banco de Dados                                           2   \n",
              "Data Analyst/Analista de Dados                                                1   \n",
              "Data Engineer/Engenheiro de Dados                                             1   \n",
              "Data Scientist/Cientista de Dados                                             1   \n",
              "Desenvolvedor ou Engenheiro de Software                                      51   \n",
              "Economista                                                                    2   \n",
              "Engenheiro                                                                    7   \n",
              "Engenheiro de Machine Learning                                                0   \n",
              "Estatístico                                                                   0   \n",
              "Outras                                                                       67   \n",
              "\n",
              "('P17', 'time_experience_data_science')  de 1 a 2 anos  de 2 a 3 anos  \\\n",
              "('D6', 'anonymized_role')                                               \n",
              "Analista de Inteligência de Mercado                  9              6   \n",
              "Analista de Marketing                                4              1   \n",
              "Business Analyst/Analista de Negócios               13             15   \n",
              "Business Intelligence/Analista de BI                26             26   \n",
              "DBA/Administrador de Banco de Dados                  2              4   \n",
              "Data Analyst/Analista de Dados                      40             30   \n",
              "Data Engineer/Engenheiro de Dados                   25             24   \n",
              "Data Scientist/Cientista de Dados                   50             41   \n",
              "Desenvolvedor ou Engenheiro de Software             50             17   \n",
              "Economista                                           1              0   \n",
              "Engenheiro                                           4              4   \n",
              "Engenheiro de Machine Learning                       4              3   \n",
              "Estatístico                                          3              0   \n",
              "Outras                                              29             18   \n",
              "\n",
              "('P17', 'time_experience_data_science')  de 4 a 5 anos  de 6 a 10 anos  \n",
              "('D6', 'anonymized_role')                                               \n",
              "Analista de Inteligência de Mercado                  2               2  \n",
              "Analista de Marketing                                1               2  \n",
              "Business Analyst/Analista de Negócios                7               5  \n",
              "Business Intelligence/Analista de BI                19              16  \n",
              "DBA/Administrador de Banco de Dados                  0               2  \n",
              "Data Analyst/Analista de Dados                      23              19  \n",
              "Data Engineer/Engenheiro de Dados                   22              16  \n",
              "Data Scientist/Cientista de Dados                   25               9  \n",
              "Desenvolvedor ou Engenheiro de Software             13              26  \n",
              "Economista                                           0               2  \n",
              "Engenheiro                                           0               2  \n",
              "Engenheiro de Machine Learning                       1               3  \n",
              "Estatístico                                          1               1  \n",
              "Outras                                              14              14  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPbvwb0sqJfk"
      },
      "source": [
        "## **Pergunta: Como os profissionais estão distribuídos em seus empregos em relação às suas formações?** <a name=\"p12\"></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l2LsjB5jphxo",
        "outputId": "8b59bf0f-85bb-417b-82dd-48fae038952d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        }
      },
      "source": [
        "pd.crosstab(dados[\"('D6', 'anonymized_role')\"], dados[\"('D3', 'anonymized_degree_area')\"])"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>('D3', 'anonymized_degree_area')</th>\n",
              "      <th>Ciências Sociais</th>\n",
              "      <th>Computação / Engenharia de Software / Sistemas de Informação</th>\n",
              "      <th>Economia/ Administração / Contabilidade / Finanças</th>\n",
              "      <th>Estatística/ Matemática / Matemática Computacional</th>\n",
              "      <th>Marketing / Publicidade / Comunicação / Jornalismo</th>\n",
              "      <th>Outras</th>\n",
              "      <th>Outras Engenharias</th>\n",
              "      <th>Química / Física</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>('D6', 'anonymized_role')</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Analista de Inteligência de Mercado</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analista de Marketing</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Analyst/Analista de Negócios</th>\n",
              "      <td>3</td>\n",
              "      <td>27</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business Intelligence/Analista de BI</th>\n",
              "      <td>2</td>\n",
              "      <td>102</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DBA/Administrador de Banco de Dados</th>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Analyst/Analista de Dados</th>\n",
              "      <td>3</td>\n",
              "      <td>76</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Engineer/Engenheiro de Dados</th>\n",
              "      <td>0</td>\n",
              "      <td>107</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Scientist/Cientista de Dados</th>\n",
              "      <td>3</td>\n",
              "      <td>79</td>\n",
              "      <td>13</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Desenvolvedor ou Engenheiro de Software</th>\n",
              "      <td>0</td>\n",
              "      <td>199</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Economista</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro</th>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engenheiro de Machine Learning</th>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estatístico</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Outras</th>\n",
              "      <td>4</td>\n",
              "      <td>125</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>23</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "('D3', 'anonymized_degree_area')         Ciências Sociais  \\\n",
              "('D6', 'anonymized_role')                                   \n",
              "Analista de Inteligência de Mercado                     0   \n",
              "Analista de Marketing                                   0   \n",
              "Business Analyst/Analista de Negócios                   3   \n",
              "Business Intelligence/Analista de BI                    2   \n",
              "DBA/Administrador de Banco de Dados                     0   \n",
              "Data Analyst/Analista de Dados                          3   \n",
              "Data Engineer/Engenheiro de Dados                       0   \n",
              "Data Scientist/Cientista de Dados                       3   \n",
              "Desenvolvedor ou Engenheiro de Software                 0   \n",
              "Economista                                              0   \n",
              "Engenheiro                                              0   \n",
              "Engenheiro de Machine Learning                          0   \n",
              "Estatístico                                             0   \n",
              "Outras                                                  4   \n",
              "\n",
              "('D3', 'anonymized_degree_area')         Computação / Engenharia de Software / Sistemas de Informação  \\\n",
              "('D6', 'anonymized_role')                                                                               \n",
              "Analista de Inteligência de Mercado                                                      7              \n",
              "Analista de Marketing                                                                    0              \n",
              "Business Analyst/Analista de Negócios                                                   27              \n",
              "Business Intelligence/Analista de BI                                                   102              \n",
              "DBA/Administrador de Banco de Dados                                                     12              \n",
              "Data Analyst/Analista de Dados                                                          76              \n",
              "Data Engineer/Engenheiro de Dados                                                      107              \n",
              "Data Scientist/Cientista de Dados                                                       79              \n",
              "Desenvolvedor ou Engenheiro de Software                                                199              \n",
              "Economista                                                                               1              \n",
              "Engenheiro                                                                               9              \n",
              "Engenheiro de Machine Learning                                                          13              \n",
              "Estatístico                                                                              0              \n",
              "Outras                                                                                 125              \n",
              "\n",
              "('D3', 'anonymized_degree_area')         Economia/ Administração / Contabilidade / Finanças  \\\n",
              "('D6', 'anonymized_role')                                                                     \n",
              "Analista de Inteligência de Mercado                                                      7    \n",
              "Analista de Marketing                                                                    5    \n",
              "Business Analyst/Analista de Negócios                                                   16    \n",
              "Business Intelligence/Analista de BI                                                     8    \n",
              "DBA/Administrador de Banco de Dados                                                      1    \n",
              "Data Analyst/Analista de Dados                                                          21    \n",
              "Data Engineer/Engenheiro de Dados                                                        4    \n",
              "Data Scientist/Cientista de Dados                                                       13    \n",
              "Desenvolvedor ou Engenheiro de Software                                                  4    \n",
              "Economista                                                                               7    \n",
              "Engenheiro                                                                               0    \n",
              "Engenheiro de Machine Learning                                                           0    \n",
              "Estatístico                                                                              0    \n",
              "Outras                                                                                  22    \n",
              "\n",
              "('D3', 'anonymized_degree_area')         Estatística/ Matemática / Matemática Computacional  \\\n",
              "('D6', 'anonymized_role')                                                                     \n",
              "Analista de Inteligência de Mercado                                                      1    \n",
              "Analista de Marketing                                                                    1    \n",
              "Business Analyst/Analista de Negócios                                                    2    \n",
              "Business Intelligence/Analista de BI                                                     6    \n",
              "DBA/Administrador de Banco de Dados                                                      1    \n",
              "Data Analyst/Analista de Dados                                                          10    \n",
              "Data Engineer/Engenheiro de Dados                                                        2    \n",
              "Data Scientist/Cientista de Dados                                                       24    \n",
              "Desenvolvedor ou Engenheiro de Software                                                  2    \n",
              "Economista                                                                               0    \n",
              "Engenheiro                                                                               0    \n",
              "Engenheiro de Machine Learning                                                           1    \n",
              "Estatístico                                                                              9    \n",
              "Outras                                                                                   6    \n",
              "\n",
              "('D3', 'anonymized_degree_area')         Marketing / Publicidade / Comunicação / Jornalismo  \\\n",
              "('D6', 'anonymized_role')                                                                     \n",
              "Analista de Inteligência de Mercado                                                      4    \n",
              "Analista de Marketing                                                                   10    \n",
              "Business Analyst/Analista de Negócios                                                    3    \n",
              "Business Intelligence/Analista de BI                                                     4    \n",
              "DBA/Administrador de Banco de Dados                                                      0    \n",
              "Data Analyst/Analista de Dados                                                           4    \n",
              "Data Engineer/Engenheiro de Dados                                                        0    \n",
              "Data Scientist/Cientista de Dados                                                        2    \n",
              "Desenvolvedor ou Engenheiro de Software                                                  0    \n",
              "Economista                                                                               0    \n",
              "Engenheiro                                                                               0    \n",
              "Engenheiro de Machine Learning                                                           0    \n",
              "Estatístico                                                                              0    \n",
              "Outras                                                                                   5    \n",
              "\n",
              "('D3', 'anonymized_degree_area')         Outras  Outras Engenharias  \\\n",
              "('D6', 'anonymized_role')                                             \n",
              "Analista de Inteligência de Mercado           2                   8   \n",
              "Analista de Marketing                         2                   0   \n",
              "Business Analyst/Analista de Negócios         4                  15   \n",
              "Business Intelligence/Analista de BI          6                  20   \n",
              "DBA/Administrador de Banco de Dados           0                   0   \n",
              "Data Analyst/Analista de Dados               13                  28   \n",
              "Data Engineer/Engenheiro de Dados             1                  12   \n",
              "Data Scientist/Cientista de Dados             6                  30   \n",
              "Desenvolvedor ou Engenheiro de Software       6                  11   \n",
              "Economista                                    0                   2   \n",
              "Engenheiro                                    1                  16   \n",
              "Engenheiro de Machine Learning                0                   1   \n",
              "Estatístico                                   0                   0   \n",
              "Outras                                       26                  23   \n",
              "\n",
              "('D3', 'anonymized_degree_area')         Química / Física  \n",
              "('D6', 'anonymized_role')                                  \n",
              "Analista de Inteligência de Mercado                     0  \n",
              "Analista de Marketing                                   0  \n",
              "Business Analyst/Analista de Negócios                   1  \n",
              "Business Intelligence/Analista de BI                    1  \n",
              "DBA/Administrador de Banco de Dados                     0  \n",
              "Data Analyst/Analista de Dados                          4  \n",
              "Data Engineer/Engenheiro de Dados                       0  \n",
              "Data Scientist/Cientista de Dados                       6  \n",
              "Desenvolvedor ou Engenheiro de Software                 0  \n",
              "Economista                                              0  \n",
              "Engenheiro                                              0  \n",
              "Engenheiro de Machine Learning                          0  \n",
              "Estatístico                                             0  \n",
              "Outras                                                  3  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOeaZt4spLYh"
      },
      "source": [
        "#outra maneira equivalente de fazer \n",
        "cds.groupby([\"('D6', 'anonymized_role')\", \"('D3', 'anonymized_degree_area')\"]).count().unstack().fillna(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9endw8VCq5UB"
      },
      "source": [
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tgj7d_HWWwX"
      },
      "source": [
        "São inuméras as possibilidades de análise, como por exemplo, a comparação da idade e salário através do gráfico dispersão. Veja que mesmo os pontos se espalhando pelo gráfico todo, há uma maior concentração que evidencia uma relação linear positiva, sugerindo assim que os salários são maiores de acordo com a idade da pessoa. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohztbWbw8vhk",
        "outputId": "27905f29-7d47-4084-e462-cd9d13b8d3db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x1 = dados[\"('P1', 'age')\"]\n",
        "x2 = dados[\"salario\"]\n",
        "\n",
        "plt.scatter(x1, x2, alpha = 0.10, edgecolors='black');"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IlbaBG6BlN_C"
      },
      "source": [
        "## **Conclusões**  <a name=\"conc\"></a>\n",
        "\n",
        "Acredito que esse exemplo foi capaz de mostrar a versatilidade da biblioteca pandas para fazer análises de dados. Conseguimos extrair muita informação relevante para a nossa área utilizando comandos simples quase sempre em uma linha. Com tempo e destreza, fazer análises de dados em python pode ser uma tarefa bastante rápida e ágil. \n",
        "\n",
        "Para entregar essa análise de dados como um case de portfólio, é recomendado dar uma atenção especial para a visualização da informação e a maneira de apresentar a informação obtida dos dados, o famoso storytelling. \n",
        "\n",
        "Veja que fazer boas análises exploratórias nos faz criar intimidade com os dados. Com essa proximidade maior, é possível pensar em perguntas melhores, mais relevantes para se fazer. Essa é uma das razões que faz a análise exploratória de dados uma etapa fundamental numa implementação de Machine Learning, como vocês verão mais a frente no curso. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mgfOYx7W6e9"
      },
      "source": [
        "## **Exercício**  <a name=\"exer\"></a>\n",
        "\n",
        "Aprofunde e responda ainda mais perguntas baseado nesse conjunto de dados. Por exemplo, qual é a idade média dos usuários de R? E dos usuários de SAS? Qual a plataforma de ensino mais utilizada pelos engenheiros de dados? A discrepância entre os salários de homens e mulheres é maior ou menor em cargos de gestão?\n",
        "\n",
        "Utilize os demais conjuntos de dados que você conhece e utilize o python para extrair informações dele. Procure responder perguntas relevantes sobre o contexto de negócios que se encontra o conjunto de dados. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VatAgq9SlOIa"
      },
      "source": [
        "## **Referências**  <a name=\"refs\"></a>\n",
        "\n",
        "- [Dados da pesquisa Data Hackers no Kaggle](https://www.kaggle.com/datahackers/pesquisa-data-hackers-2019)\n",
        "\n",
        "- [Script da análise desses dados pelo Mario Filho](https://www.kaggle.com/mariofilho/live26-https-youtu-be-zseefujo0zq)\n"
      ]
    }
  ]
}