Desafio_1-DigitalHouse.ipynb

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    "## Desafio_1\n",
    "\n",
    "Agora vamos praticas tudo o que aprendemos no módulo 1 utilizando dados reais do CAGED (Cadastro Geral de Empregados e Desempregados)\n",
    "\n",
    "<br>\n",
    "<img src=\"img/caged.png\" width='280' >\n",
    "<br>\n",
    "\n",
    "O Cadastro Geral de Empregados e Desempregados (CAGED) foi criado como registro permanente de admissões e dispensa de empregados, sob o regime da Consolidação das Leis do Trabalho (CLT).\n",
    "\n",
    "É utilizado pelo Programa de Seguro-Desemprego, para conferir os dados referentes aos vínculos trabalhistas, além de outros programas sociais.\n",
    "\n",
    "Este Cadastro serve, ainda, como base para a elaboração de estudos, pesquisas, projetos e programas ligados ao mercado de trabalho, ao mesmo tempo em que subsidia a tomada de decisões para ações governamentais.\n",
    "\n",
    "Visite o sitee do CAGED através [deste link](http://trabalho.gov.br/trabalhador-caged/2015-09-10-17-46-22) e acesse os dados clicando inicialemente em **ESTATÍSTICA** e depois em **MICRODADOS**, então acesse a base de dados do CAGED e baixe o último arquivo do ano de 2018 **CAGEDEST_122018.7z**. Além disso, baize também o arquivo **CAGEDEST_layout_Atualizado.xls** para o dicionário de dados.\n",
    "Chegando [NESTE LINK](ftp://ftp.mtps.gov.br/pdet/microdados/)\n",
    "Após isso, podemos começar as nossas análises:\n",
    "\n",
    "1. **Importar as bibliotecas** e ler os dados: Importe as bibliotecas necessárias e verifique a cara dos dados e suas informações básicas dos conteúdos das colunas.\n",
    "\n",
    "2. Limpeza e transformação da variável alvo **Salário Mensal**: verifique o tipo de dado da variával Salário Mensal, faça as devidas mudanças, plote a distribuição e dispersão dos dados e aplique a transformação matemática para a correção da assimetria.\n",
    "\n",
    "3. Verificação dos dados da variável **Idade**: verifique os dados da variável Idade, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados.\n",
    "\n",
    "4. Verificação dos dados da variável **Grau de Instrução**: verifique os dados da variável Grau de instrução, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados.\n",
    "\n",
    "5. Verificação dos dados da variável **Sexo**: verifique os dados da variável Sexo, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados. Depois disso, faça uma inferência estatística pra verificar se os homens ganham mais que as mulheres.\n",
    "\n",
    "6. Verificação dos dados da variável **Raça Cor**: verifique os dados da variável Raça Cor, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados. Depois disso, faça uma inferência estatística pra verificar se os brancos ganham mais que os outros grupos étnicos.\n",
    "\n",
    "7. Elaborar uma hipótese para ser rejeitada ou não-rejeitada por você de acordo com o resultado da sua análise, através da utilização dos dados do CAGED considerando as suas colunas.\n",
    "\n",
    "8. Avançado: plote um SEABORN heatmap com **todas as correlações das variávies numéricas entre si**. \n",
    "\n",
    "9. Avançado: Plote uma **regressão múltipla**.\n"
   ]
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   "source": [
    "#Importando Bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import random\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DNw3Afk_OtKE"
   },
   "source": [
    "# Importando dataframe: Local"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y_yPKOMNOtKE"
   },
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   "source": [
    "#Importanto dataframe guia - Local\n",
    "df_guia=pd.read_excel(r\"\\CAGEDEST_layout.xls\")\n",
    "#Importanto data frame - Local\n",
    "df = pd.read_csv(r'CAGEDEST_122018.txt',\n",
    "                 sep = ';',\n",
    "                 decimal = ',',\n",
    "                thousands = '.', \n",
    "                encoding='latin1', \n",
    "                 usecols = ['Admitidos/Desligados','Competência Declarada', 'Ano Declarado', 'Sexo', 'Município', 'Grau Instrução', 'Salário Mensal', 'Idade', 'Raça Cor', 'UF', 'Tempo Emprego']\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gxwasyl5OtKH"
   },
   "source": [
    "# Importando dataframe: Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MXsznURTOtKI"
   },
   "outputs": [],
   "source": [
    "#from google.colab import drive\n",
    "#drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fm4TmqstWdJ6"
   },
   "outputs": [],
   "source": [
    "#import os\n",
    "#os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RE7QdFwdOtKM"
   },
   "outputs": [],
   "source": [
    "# Importanto dataframe guia - Colab\n",
    "#df_guia = pd.read_excel(\")\n",
    "# Importanto data frame - Colab\n",
    "#df = pd.read_csv(r\"\",\n",
    " #                sep = ';',\n",
    " #                decimal = ',',\n",
    " #               thousands = '.', \n",
    " #               encoding='latin1', \n",
    "  #              usecols = ['Admitidos/Desligados','Competência Declarada', 'Ano Declarado', 'Sexo' ,'Município', 'Grau Instrução', 'Salário Mensal', 'Idade', 'Raça Cor', 'UF', 'Tempo Emprego']\n",
    "#                )"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Admitidos/Desligados</th>\n",
       "      <th>Competência Declarada</th>\n",
       "      <th>Município</th>\n",
       "      <th>Ano Declarado</th>\n",
       "      <th>Grau Instrução</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Raça Cor</th>\n",
       "      <th>Salário Mensal</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Tempo Emprego</th>\n",
       "      <th>UF</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500215</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>954.0</td>\n",
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       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500460</td>\n",
       "      <td>2018</td>\n",
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       "      <td>201812</td>\n",
       "      <td>500460</td>\n",
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       "      <td>8</td>\n",
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       "      <td>201812</td>\n",
       "      <td>500460</td>\n",
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       "   Admitidos/Desligados  Competência Declarada  Município  Ano Declarado  \\\n",
       "0                     1                 201812     500215           2018   \n",
       "1                     1                 201812     500710           2018   \n",
       "2                     1                 201812     500460           2018   \n",
       "3                     1                 201812     500460           2018   \n",
       "4                     1                 201812     500460           2018   \n",
       "\n",
       "   Grau Instrução  Idade  Raça Cor  Salário Mensal  Sexo  Tempo Emprego  UF  \n",
       "0               6     24         8           954.0     1            0.0  50  \n",
       "1               8     25         2          2500.0     1            0.0  50  \n",
       "2               2     55         8             0.0     1            0.0  50  \n",
       "3               2     52         8             0.0     1            0.0  50  \n",
       "4               2     32         8             0.0     1            0.0  50  "
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   ],
   "source": [
    "#Explorando df\n",
    "df.head()"
   ]
  },
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    "outputId": "da95a0de-1b74-4f99-d2b3-211cd39a5e83"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2256752 entries, 0 to 2256751\n",
      "Data columns (total 11 columns):\n",
      " #   Column                 Dtype  \n",
      "---  ------                 -----  \n",
      " 0   Admitidos/Desligados   int64  \n",
      " 1   Competência Declarada  int64  \n",
      " 2   Município              int64  \n",
      " 3   Ano Declarado          int64  \n",
      " 4   Grau Instrução         int64  \n",
      " 5   Idade                  int64  \n",
      " 6   Raça Cor               int64  \n",
      " 7   Salário Mensal         float64\n",
      " 8   Sexo                   int64  \n",
      " 9   Tempo Emprego          float64\n",
      " 10  UF                     int64  \n",
      "dtypes: float64(2), int64(9)\n",
      "memory usage: 189.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
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     "execution_count": 8,
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   "source": [
    "df.index"
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   "source": [
    "### 2. Limpeza e transformação da variável alvo **Salário Mensal**: verifique o tipo de dado da variável Salário Mensal, faça as devidas mudanças, plote a distribuição e dispersão dos dados e aplique a transformação matemática para a correção da assimetria.\n",
    "__________________________________________________________________________________________________________________________________________________________________________________________________________________________\n",
    "\n",
    "#### <font color='yellow'>Limpeza 1 - Removendo valores que são zero, pois são maioria e estão atrapalhando a interpretação da dispersão.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "#Limpeza 1 - Removendo valores que são zero, pois são maioria e estão atrapalhando a interpretação da dispersão.\n",
    "#df.loc[(df['Salário Mensal'] <= 0, 'Salário Mensal')]=1\n",
    "#df\n",
    "#df_limpeza_zeros = df.drop(remover_valores0_do_sal_mensal.index)\n",
    "#df_limpeza_zeros.reset_index()"
   ]
  },
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   "source": [
    "# Observação sobre a célula anterior\n",
    " - No começo do meu racíocinio, considerei que todos os campos alimentados com zero na coluna Salário Mensal deviam ser excluídos.\n",
    " - Mas ao rever a aula, a conclusão foi outra:\n",
    "     **A conclusão** foi que ao eliminar essas linhas, estarei eliminando dados importantes do meu dataset, posso estar eliminando dados que:\n",
    "- Afetem a média;\n",
    "- Pode ser um tratamento para valores nulos, de um tratamento anterior;\n",
    "\n",
    "Depende do que será feito a posteriormente com esses dados, por exemplo:\n",
    "No caso do exercício vamos aplicar um log ou até outro método que não permita \"zeros\" na tabela."
   ]
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   "source": [
    "df['Salário Mensal'].min()"
   ]
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       "  <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",
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       "      <th>Admitidos/Desligados</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>1.574103</td>\n",
       "      <td>0.494478</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Competência Declarada</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>201812.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>201812.0</td>\n",
       "      <td>201812.0</td>\n",
       "      <td>201812.0</td>\n",
       "      <td>201812.0</td>\n",
       "      <td>201812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Município</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>355386.287837</td>\n",
       "      <td>83000.731507</td>\n",
       "      <td>110001.0</td>\n",
       "      <td>314700.0</td>\n",
       "      <td>353150.0</td>\n",
       "      <td>411790.0</td>\n",
       "      <td>530010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ano Declarado</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>2018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grau Instrução</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>6.595547</td>\n",
       "      <td>1.548538</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idade</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>32.314226</td>\n",
       "      <td>10.960189</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raça Cor</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>5.254456</td>\n",
       "      <td>3.066560</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salário Mensal</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>1627.244223</td>\n",
       "      <td>2097.520547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>1271.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>142830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>1.413703</td>\n",
       "      <td>0.492497</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tempo Emprego</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>13.180299</td>\n",
       "      <td>34.134545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UF</th>\n",
       "      <td>2256752.0</td>\n",
       "      <td>35.343807</td>\n",
       "      <td>8.311311</td>\n",
       "      <td>11.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count           mean           std       min  \\\n",
       "Admitidos/Desligados   2256752.0       1.574103      0.494478       1.0   \n",
       "Competência Declarada  2256752.0  201812.000000      0.000000  201812.0   \n",
       "Município              2256752.0  355386.287837  83000.731507  110001.0   \n",
       "Ano Declarado          2256752.0    2018.000000      0.000000    2018.0   \n",
       "Grau Instrução         2256752.0       6.595547      1.548538       1.0   \n",
       "Idade                  2256752.0      32.314226     10.960189      10.0   \n",
       "Raça Cor               2256752.0       5.254456      3.066560       1.0   \n",
       "Salário Mensal         2256752.0    1627.244223   2097.520547       0.0   \n",
       "Sexo                   2256752.0       1.413703      0.492497       1.0   \n",
       "Tempo Emprego          2256752.0      13.180299     34.134545       0.0   \n",
       "UF                     2256752.0      35.343807      8.311311      11.0   \n",
       "\n",
       "                            25%       50%       75%       max  \n",
       "Admitidos/Desligados        1.0       2.0       2.0       2.0  \n",
       "Competência Declarada  201812.0  201812.0  201812.0  201812.0  \n",
       "Município              314700.0  353150.0  411790.0  530010.0  \n",
       "Ano Declarado            2018.0    2018.0    2018.0    2018.0  \n",
       "Grau Instrução              6.0       7.0       7.0       9.0  \n",
       "Idade                      23.0      30.0      39.0     103.0  \n",
       "Raça Cor                    2.0       6.0       8.0       9.0  \n",
       "Salário Mensal           1066.0    1271.0    1600.0  142830.0  \n",
       "Sexo                        1.0       1.0       2.0       2.0  \n",
       "Tempo Emprego               0.0       2.0      11.0     599.0  \n",
       "UF                         31.0      35.0      41.0      53.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 10374,
     "status": "ok",
     "timestamp": 1605283240436,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "gspHH8DzOtKp",
    "outputId": "80e48cb4-01fb-427b-e837-be3b6cdc53d5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Tempo Emprego'], \n",
    "            df['Salário Mensal'], \n",
    "            marker = \".\",\n",
    "            alpha = 0.2\n",
    "           )\n",
    "plt.title('Dispersão: Salário Mensal x Tempo de Emprego')\n",
    "plt.xlabel('Tempo Emprego (Meses)')\n",
    "plt.ylabel(\"Salário Mensal\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "executionInfo": {
     "elapsed": 10820,
     "status": "ok",
     "timestamp": 1605283240896,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "4Czvlb4WOtKu",
    "outputId": "a39df7bb-1672-4bc3-abfa-91bcfab7dfb4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['Salário Mensal'].fillna(df['Salário Mensal'].mean(), inplace=True)\n",
    "histo_sal_mensal = plt.hist(df['Salário Mensal'], \n",
    "                             bins=200, \n",
    "                             range = (0,25000))\n",
    "plt.xlabel('Salário Mensal')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 619
    },
    "executionInfo": {
     "elapsed": 11396,
     "status": "ok",
     "timestamp": 1605283241488,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "qFWr3Zn4OtKz",
    "outputId": "b89d6a1f-800e-4202-b27a-ae6900b395fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Salário Mensal'].hist(bins = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K0ZzpktwOtK5"
   },
   "source": [
    "# Observação sobre a célula anterior\n",
    "- No python, é usado todo o range da tabela e as vezes precisamos estudar uma parcela do gráfico, que é como fosse aplicado um zoom e conseguissemos entender como aquela parcela de dados se comporta\n",
    "- Ao trocar o range, variando de 25000 em 25000, fiz o teste para 25000:50000, 50000:75000, 75000:140000...\n",
    "- A conclusão que cheguei foi que em todas essa parcelas, o gráfico se comportou como uma normal descentralizada."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sZi1vipPOtK5"
   },
   "source": [
    "________________________________________________________________________________________________________________________________________________________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o8xGWTr8OtK7"
   },
   "source": [
    "### Teste de Shapiro_Wilks\n",
    "\n",
    "O test shapiro-wilks é um indicador de quão bem os nossos dados se ajustam à distribuição normal. O primeiro parâmetro da estatística W (quando está próximo de 1 a distribuição é Gaussiana), e o segundo parâmetro é o p_valor (quando p_valor é maior que 0.05, a distribuição é Gaussiana)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11389,
     "status": "ok",
     "timestamp": 1605283241491,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "7dLaZ4BSOtK8",
    "outputId": "610bcfc3-fbda-451c-c7c4-27f4dec7c57b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\arquivos de programas\\python\\python38\\envs\\mod0\\lib\\site-packages\\scipy\\stats\\morestats.py:1676: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.3946847915649414, 0.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_test = df['Salário Mensal'].iloc[0:25000]\n",
    "stats.shapiro(shap_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7leekPboOtLB"
   },
   "source": [
    "##### Parâmetro da estatística W = 0.394668 => Não está próximo de 1, não é Gaussiana\n",
    "##### P_valor = 0 => Não é maior que 0.05, Não é Gaussiana\n",
    "_______________________________________________________________________________________________________________________________\n",
    "\n",
    "# Portanto vamos padronizar e normalizar os dados\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ib_WNsLBOtLB"
   },
   "source": [
    "# Padronização"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4BomTURCOtLC"
   },
   "source": [
    "### Uso do log1p\n",
    "<font color='orange'>log1p - Soma 1 em todos os valores da coluna e quem era 0 passa a ser 1. (Deixando de ser necessário, realizar o tratamento para remover os zeros)</font>\n",
    "\n",
    "### Para números negativos\n",
    "<font color='orange'>Somar todos os valores da coluna pelo menor valor possível, o mais negativo possível da coluna, levando todos os dados para o lado positivo da curva</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qMR15MqVOtLD"
   },
   "outputs": [],
   "source": [
    "df['log_Salário'] = np.log1p(df['Salário Mensal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "executionInfo": {
     "elapsed": 12292,
     "status": "ok",
     "timestamp": 1605283242411,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "6DL1_whpOtLN",
    "outputId": "12732479-bd33-4ed3-cd3e-f37ed21ba364"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['log_Salário'].hist(bins = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 617
    },
    "executionInfo": {
     "elapsed": 12666,
     "status": "ok",
     "timestamp": 1605283242796,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "s5q728gvOtLR",
    "outputId": "943d7b96-142d-4eb6-fc68-1d003817a239"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tcKNMV/wHAOAIbeVE/99YY/FSkn+Z5E3bNSAAgL1oKyf6323V44OZ7oH5kiQXbtuIAAD2oK2c6H/feQ4EAGAv28rhy5/b7LZjjFcc2XAAAPamrRy+fEySOyXZn+TjSb6c5LuTfFOmm5Xvm213MIkoAwDYgq1E2aszhdhDxhgfS5Kq+rYkv5fknWOMX5/D+AAA9oStXBLjzCS/fCjIkmSM8dkkT0ryhO0eGADAXrLV65R9xxrL7pBpDxoAAEdoK4cvX5rk5VX1jCR/ninofijJU5I8ew5jAwDYM7YSZU9Jcl2Spye52WzZR5OcPcZ40XYPDABgL9nKdcquT3JOknOq6mZJrh5j/P3cRgYAsIdsZU9ZqupWSR6V5MQkj6yqf53k8jHGu+YxOACAvWLTJ/pX1Q8m+UCSk5PcP8lxSe6a5K1VdepcRgcAsEds5duXFyQ5d4xxv0z3vMwY49FJzkty7hzGBgCwZ2wlyu6S6QKyq70y0+FMAACO0Fai7LNJbrvG8rsm+ZvtGQ4AwN60lSi7KMkLq+qnM93n8uSqemyS30nywnkMDgBgr9jKJTGeXVXLSc5PcnyS1yT5VKbzyf7DfIYHALA3bDrKquqhSV4zxnhhVZ2Q5NgxxhfmNzQAgL1jK9cpe16S9yT5rIvGAgBsr62cU/buJD85r4EAAOxlW9lT9pUk51fVv0/ykSTXrFw5xrj7No4LAGBP2UqUvXv2CwCAbbZhlFXV9Um+c4zx6THG02fL7pHkfWOMLy9igAAAe8Hhzinbt8ayNyW5xRzGAgCwZ23lRP9D1go1AAC+AUcSZQAAbDNRBgDQwGa+fXl6VX1x1c/8bFV9ZuVGY4zf2daRAQDsIYeLso8leeSqZZ9K8rBVyw5mujE5AABHYMMoG2N894LGAQCwp23l4rHbpqoelOSMMca91ln/pCS/kuSGSV6e5N+NMa5f4BABABZqoVFWVfuSPDzJRUnet842P5nkF5P8YJKrk/xxkscmec6ChgkAsHCL/vbl+Ul+PsmFG2zzkCQvGmN8ZIzx6SS/nuShixgcAMBOWXSUPXd2yPLKDbY5KcnlKx5fMVu24w5ed83hNwIAOAILPXw5xvjkJjY7IdNhy0OuTnJcVe0bYxxc6weWlpZy4MCB7Rjihq5/xzlZWlqa++uweeajJ/PSjznpybz0M8852b9//4brd+RE/8O4OslxKx4fn+Tv1wuyJFleXp57lC0tLeWYU87N59/8uLm+Dpu3tLSU5eXlnR4Gq5iXfsxJT+aln3nPyeGirOMV/S9PcocVj0/MdAhzx+079rjDbwQAcAQ67il7VZILq+p1Sa5K8uQkF+/skAAA5qvFnrKqelNVnZ0kY4zXZro7wFsy7TV7e5Ln7tzoAADmb0f2lI0xXpbkZSse33/V+guSXLDYUQEA7JwWe8oAAPY6UQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBoQJQBADQgygAAGhBlAAANiDIAgAZEGQBAA6IMAKCBYxf5YlX1g0lekOQOSS5JcvoY48pV2+xL8oV8bTC+bIzx6EWNEwBg0RYWZVX1zUlel+SsJK9K8uQkr05y8qpNb5vkmjHGdyxqbAAAO22Rhy/vm+SqMcYrxxgHkjwzya2r6vtWbXdykksXOK5NO3jdNTs9BABgl1rk4cuTklx+6MEY4/qq+vBs+V+s2O7kJDevqvcnuVmSP05yxhjjqvWeeGlpKQcOHJjLoFe6/h3nZGlpae6vw+aZj57MSz/mpCfz0s8852T//v0brl9klJ2Q5OpVy65OcvyqZV9O8vYkT5s9fnmSi5L83HpPvLy8PPcoW1payjGnnJvPv/lxc30dNm9paSnLy8s7PQxWMS/9mJOezEs/856TTlF2dZLjVi07PskXVy4YY/zaysdV9dQk/+98h7Y5+45dPXwAgO2xyHPKLs/0rcskSVUdk+R2Sa5YuVFVPbGqvn/Fohtm2nsGALBrLXJP2VuSfFtVPSzJxUmelOSjSS5btd2JSX60qv51kv1JzkvyygWOEwBg4Ra2p2yMcU2SU5P8cpLPJvmxJD81xjhYVX9ZVafNNv2VJJ9M8qFMe9E+kOTsRY0TAGAnLPTisWOMP0tytzWW32nF76/KBif1AwDsRm6zBADQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA2IMgCABkQZAEADogwAoIFjF/liVfWDSV6Q5A5JLkly+hjjylXb3CDJbyZ5aJKvJHnOGOO8RY4TAGDRFranrKq+OcnrMgXXtyZ5c5JXr7HpY5LcI8mJSX4oyS9U1b9a0DABAHbEIg9f3jfJVWOMV44xDiR5ZpJbV9X3rdruIUkuHGN8Zozx4SQXZdprBgCway3y8OVJSS4/9GCMcX1VfXi2/C/W2y7JFUkevtET79+/fxuHuf5r3OD6Ly7ktdic/fv3m4+GzEs/5qQn89LPvOfkcM+9yCg7IcnVq5ZdneT4w2y31jaH3DhJTjvttO0Y3yZ8MDn99AW9FgCwS904yRdWL1xklF2d5LhVy45P8sXDbLfWNod8Ismtkly1HQMEAJizG2fql6+zyCi7PCsOQ1bVMUlul+nw5Ort7pDk0tnjE9fYJkly1llnHUzy8W0fKQDAfHzdHrJDFhllb0nybVX1sCQXJ3lSko8muWzVdq9KclZVvS3TocxHJ3n8AscJALBwC/v25RjjmiSnJvnlJJ9N8mNJfmqMcbCq/rKqDp0Y9rxMAXdJkncledEY47WLGicAwE7Yd/DgwZ0eAwDAnrfQK/ofjTZzFwK2R1WdmeS8JNeuWHyHJN+VdeZgo/kxd9+YqnpQkjPGGPeaPb59kpcmuUuSDyf5xTHGu+e1jq+3xpz8VJL/lOTLKzb70THGO83JfFXVP0tyQaZzoz+e5Owxxn8+0vck72XbY4N5WfPzZYzxfzrNi3tfbmALdyFge5yc5HFjjBsd+pXkc1lnDjaaH3N35KpqX1U9ItO5n/tWrPqDJP81yU2SPDfJH1XVcXNcx8wGc3Jykt9c+f/MGOOds3XmZE6q6uZJXpPkaZneX85I8h+r6k7Z5vcr72Wbt8G83DZrfL7MgqzVvDh8uYGqun+me2+eNHt8TJLPJLnPGOMvNvxhtqyqLsv0r/J3rFi27hwkucWRrDN3G6uqZyU5JcnbMv193bOq/q8k70ly09kdOVJV70/ylCRXbve6McZ/WdSf92iw1pzMlr8hye+PMX5/1fbbPl/m5Kuq6m5JHj7G+Lcrlv1ZpnOin7yd71cbrfNe9rU2mJffSPLvs+rzZba+1WeMPWUb+7q7EGTanX/Sjo1ol5r9q+PEJE+qqk9X1aVVdWo2noMjXcfGnjs7PLZyN/xJST586IN65op89e96u9fxtdaak2T61/9Dq+qTVXXF7NvtiTmZqzHGe1d98N8myR2T3DLb/37lvWyTNpiXy7L250vSbF5E2cY2excCvnE3T/L2JL+V6Y3tKZkOo2w0B0e6jg2MMT65xuJ5zIM52qS15mT2D5kPJXl5ku9O8rAkF87OqTEnC1JV/yjJGzOdh7cv/j9pYdW8LGeNz5eqOinN5sWJ/hvb7F0I+AaNMT6W5IdXLHpDVb0lyTVZfw42mh9zt72O9O/aHM3JGONL+dr/Z95RVRcn+Ykkb405mbvZOWRvzHQ+0WMyncPk/5MdtnpexhhfydqfL/dPs3mxp2xjh+4ukGTDuxDwDaqqk6vqiasW3zBTlK03BxvNj7nbXpcnuc3s7/GQQ3fbmMc6DqOqbl1V561afMNM38Q0J3NWVfdM8v8lef4Y45dmH/xH+p7kvWybrDUvG3y+HPp/pc28ONF/A7NvHP11kifnq3cheFCSk8cY/uK2UVXdLslfJPk3mf6F86Akv5vpA+HPs8YcJPnmrDM/G60zd5tTVacnecSKk8ovTfLaJL+e5CFJzk9ymzHG1fNYt7g/6dFj5ZxU1bdk+m/8KUlemOReSV6f5F5jjEvNyfxU1S0zvV+dOcZ46Yrl635m5AjfrzZa573sa20wLxt9vnw+jebFnrINbHQXgh0d2C40xvhQkp/O9AGwnOmD5idm59KsOQcbzY+5m4sHZvrW0WcyHab5yRUf1PNYxwbGGF9I8oBM55JdleTFmb55dui+weZkfh6R6ZIhz6uqLx76leTB2eb3K+9lW7LevNwj63y+dJsXe8oAABqwpwwAoAFRBgDQgCgDAGhAlAEANCDKAAAaEGUAAA24zRLQRlUdTPKAMcYbFvR6JyR5aqaLSn5nkr/NdHHJp44x/maTz/E/krxvjHHmquW/leQnk9xljPHZrf48sPfYUwbsZb+X5L5JTs90y5TTknxfklp1q6EtqapvT/KzSR68UZDNPDDJ04/0tYDdw54yYE+a3abop5Lce4zxp7PFH62qn07ykUy3LfofR/j0n0/yXWOMw96ceIzxuSN8DWCXEWXAUWN2D7tnZ7oN0Fcy3aPx340xlmfrvz/Jbyf5J5luDPyKJI8dY3z3Gk93cPbrflX19kO3RxljfLSq7pjko7PnPCHJBUn+ZZKbJflUppsdr74ZeKrq2Ex7vU5Lcouq+lyS/5Tk8WOM66vqZZned28/+/XgJOdkxeHLqvo3Sc7OtOfuY0nOG2O8/Aj/yoCjiMOXwFGhqm6S5E+THMi0F+uBSe6Z5KWz9d+S5L8luTLJDyS5MMkz1nu+Mcah+0Wek+Svq+oFVfXgqvrWMcYHV9zv8TlJ/mmmKDsxyfOSPLOq7rLG0z4xyUOT/Hym6HpCkkcn+YkV2zwkyUVJfiTJO1b9GX8mU0i+INNh1N9K8uKqOvUwfz3ALmBPGXC0OC3JMUl+bnYz4FTV6UneVVW3z3Ru2MEkvzTGuDbJB6vqTpluRLyeRyZ5X5L/O8kvJPmlJF+qql9bsSfs7UleMMb4n7PHF1TVU5PcOcmfr3q+DyQ5fYzx1tnjj1TVE2bbvm627IoxxisP/UBVrfz5xyd58Rjj+bPHV87+DGdn+gICsIuJMuBocccklxwKspn3Jrk2yZ0y7Vm6dBZkh7wzG0TZ7JDlS5K8pKpummRkirNnVtVfjTFelWnP1b+oqp/NtKfs5CQ3yhSIq5/vj6rqPlX1rNm235vkNkn+cMVmf3WYP+NzVi370yQ/s8HPALuEw5fA0eJL6yzflymQDmQL72lV9cNV9Q+HN8cYnxtj/D9JfizJu5L8+GzVS5M8f/b6r0xySqYT+dd6zqcl+aNM/+B9bZJTk1yyarNrsr61/ow3yBoBCOw+ogw4WnwwyclVddyKZXdLsn+27rIk31tV37Rq/XpukuTsqrrtyoWzvWfLSf62qr4t0+Uyfm6McfZsz9mXk3xLphhc7cxMJ/U/fozxikzf4rz1Otuu92c8ZdWyU2bLgV3O4Uugmx+oqutWLXt/koszXej1FVX19CQ3zbQHq8YYH6iqjyR5ZpLnV9UFmQ5nPjbJetcJe32SP0vy36rq7CTvzvTtygcmuXum882uyhRoD6yqv07yjzN9E3Nfkhuu8Zz/O8mpVfW2JDdO8quZ4m+tbddyfpLXVNX7k/xJkh9N8vAkD9vkzwNHMXvKgG6enuRNq3796OzbkPfLtJfqvZnO03pbpojKbP2pmc7jujTJkzKdL3Zt1jDGuC5T9Lw2yblJLs/07c07JrnnGOPDY4wDmb4tOTKdxP+K2TZ/nOmyG6udnuR7MkXkH2XaU/a762y71phen+SXM53wf1mSRyV5xBjj4s38PHB023fw4MGdHgPAN6yqvifTBVvftmLZE5Lcf4zxIzs3MoDNcfgS2C1unORPZpfJ+NNM3378lbiFEXCUcPgS2BXGGJcm+cVM551dkeSFmS4v8aKdHBfAZjl8CQDQgD1lAAANiDIAgAZEGQBAA6IMAKABUQYA0IAoAwBo4P8HNwPXy/ckVaYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histograma_padronizado = plt.hist(df['log_Salário'], \n",
    "                             bins = 200, \n",
    "                             range = (0, 25000)\n",
    "                                 )\n",
    "plt.xlabel('Log Salário')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TN9ic-3ZOtLW"
   },
   "source": [
    "# Normalização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W1WCqq9TOtLX"
   },
   "outputs": [],
   "source": [
    "df['normal_log_salário'] = ( df['log_Salário'] - df['log_Salário'].mean() ) / df['log_Salário'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "executionInfo": {
     "elapsed": 13236,
     "status": "ok",
     "timestamp": 1605283243383,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "3OpvQQFhOtLb",
    "outputId": "7fdca929-4efb-4e2a-a7a1-f76d2e70b824"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histograma_normalizado = plt.hist(df['normal_log_salário'], \n",
    "                             bins=100, \n",
    "                             #range = (25000, 50000)\n",
    "                                 )\n",
    "plt.xlabel('Normal Log Salário')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c8BB1YhEOtLg"
   },
   "source": [
    "# Refazendo o teste de shapiro, depois de padronizar e normalizar\n",
    "\n",
    "#### <font color='orange'> **Olhar aula de Inferência 2**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13481,
     "status": "ok",
     "timestamp": 1605283243639,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "I9rbic8OOtLh",
    "outputId": "e0f76b5d-fcf2-4d5e-bfd1-b3956019fe09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.505557656288147, 0.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado_normalização = stats.shapiro(df['normal_log_salário'])\n",
    "resultado_normalização"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VAa_7DwbOtLl"
   },
   "source": [
    "# Verificando a média e o desvio padrão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13472,
     "status": "ok",
     "timestamp": 1605283243640,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "QRqTVH3GOtLm",
    "outputId": "d3d922b9-d311-4d6b-e040-9107a006fabf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.3424681501685713e-15, 0.9999997784426223)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# definindo uma variável para os dados de salários transformados e normalizados\n",
    "x = df['normal_log_salário']\n",
    "# calculando a média\n",
    "mu = np.mean(x)\n",
    "# calculando o desvio-padrão populacional\n",
    "std = np.std(x)\n",
    "#Imprimindo a média e o desvio-padrão\n",
    "mu,std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GAipZGaqOtLr"
   },
   "source": [
    "# Verificando o Z-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13462,
     "status": "ok",
     "timestamp": 1605283243640,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "SWrUpRiROtLs",
    "outputId": "4f274ca6-6f4c-4e8e-f87b-fed63a40086b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.95996355029557, 1.9599635502955766)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.norm.interval(alpha=0.95,loc=mu,scale=std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "COAAVUaSOtLx"
   },
   "source": [
    "### 3. Verificação dos dados da variável **Idade**: verifique os dados da variável Idade, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13454,
     "status": "ok",
     "timestamp": 1605283243641,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "b4U_vJW-OtLz",
    "outputId": "60c7acbf-a21a-497c-916b-71ae9aac7a2e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.314226374896315"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Idade'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13444,
     "status": "ok",
     "timestamp": 1605283243641,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "oQsClNSxOtL3",
    "outputId": "11cb007e-d444-4641-d4ee-a8044867e1b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Idade mínima:  10 anos\n",
      "Idade máxima:  103 anos\n",
      "Idade média:  32.314226374896315 anos\n"
     ]
    }
   ],
   "source": [
    "print('Idade mínima: ', df['Idade'].min(), 'anos')\n",
    "print('Idade máxima: ', df['Idade'].max(), 'anos')\n",
    "print('Idade média: ', df['Idade'].mean(), 'anos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "executionInfo": {
     "elapsed": 16254,
     "status": "ok",
     "timestamp": 1605283246462,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "YCixi5ZXOtL8",
    "outputId": "6f03030f-54bd-4045-f72f-5be96870db3d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Tempo Emprego'], \n",
    "            df['Idade'], \n",
    "            marker = \".\",\n",
    "            alpha = 0.2\n",
    "           )\n",
    "plt.title('Dispersão: Tempo de Emprego x Idade')\n",
    "plt.xlabel('Tempo Emprego (Meses)')\n",
    "plt.ylabel(\"Idade (Anos)\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "executionInfo": {
     "elapsed": 16540,
     "status": "ok",
     "timestamp": 1605283246761,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "WuUr1-N8OtMB",
    "outputId": "c608ec4e-4787-4cce-ad05-c98074fa0332"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histo_idade = plt.hist(df['Idade'], \n",
    "                       bins=200, \n",
    "                       #range = (0,10000000)\n",
    "                      )\n",
    "plt.xlabel('Idade')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16531,
     "status": "ok",
     "timestamp": 1605283246762,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "4q1iyfs8OtME",
    "outputId": "87f7ff27-e6bc-4a87-82b5-8f00d482cd87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9526928663253784, 0.0)"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_test2 = df['Idade'].iloc[0:25000]\n",
    "stats.shapiro(shap_test2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4oVZl17HOtMI"
   },
   "source": [
    "Parâmetro da estatística W = 0.952692 => Não está próximo de 1, é Gaussiana\n",
    "P_valor = 0 => Não é maior que 0.05, Não é Gaussiana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "executionInfo": {
     "elapsed": 16802,
     "status": "ok",
     "timestamp": 1605283247044,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "t9yFc1o0OtMJ",
    "outputId": "e79e446d-f473-47d1-9f46-4f6eeb16d604"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Idade'].hist(bins = 200\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5rS9somQOtMN"
   },
   "source": [
    "# Padronização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "executionInfo": {
     "elapsed": 17923,
     "status": "ok",
     "timestamp": 1605283248179,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "hDffmNoYOtMO",
    "outputId": "4b76fe25-862b-4170-b0dc-e5194c94b18f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VkucneTCDuLp1zMtgIo7Mbf47YXzrVB83bOyw7YxjPnt9jJ1uezfbG/c6Oxm/n9sGDt62UdZau+z9XOsvX/5Qkv+V5AO99zcmuTvJO5M8kuTLSR5K8owxLwMAmGm7unhsa+1ckmNJ3pzkm0lem+Tm1trafizbyxMEAJgGO/qapdbakQ23v5DkZVcYN/ZlAACzzNcsAQAUIMoAAAoQZQAABYgyAIACRBkAQAE7+vQlALNvcWn50u0zp49PcCZwuDhTBgBQgCgDAChAlAEAFCDKAAAKEGUAAAWIMgCAAkQZAEABogwAoABRBgBQgCgDAChAlAEAFCDKAAAKEGUAAAXMTXoCANS3uLR86faZ08cnOBOYXc6UAQAUIMoAAAoQZQAABYgyAIACRBkAQAGiDACgAFEGAFCAKAMAKECUAQAUIMoAAAoQZQAABYgyAIACRBkAQAGiDACgAFEGAFCAKAMAKECUAQAUIMoAAAoQZQDsyOLSchaXlic9DZg5ogwAoABRBgBQgCgDAChAlAEAFCDKAAAKmJv0BACYfhs/jXnm9PEJzgSmlzNlAAAFiDIAgAJEGQBAAaIMAKAAUQYAUIAoAwAoQJQBABQgygAAChBlAAAFiDIAgAJEGQBAAaIMAKAAUQYAUIAoAwAoQJQBABQwN+kJADCbFpeWL90+c/r4BGcC08GZMgCAAkQZAEABogwAoABRBgBQgCgDAChAlAEAFCDKAAAKEGUAAAWIMgCAAlzRH4AD4yr/cGXOlAEAFCDKAAAKEGUAAAWIMgCAArzRn0PDG4wBqMyZMgCAAkQZAEABogwAoABRBgBQgCgDACjApy8BmCifjIYBZ8oAAAoQZQAABYgyAIACRBkAQAGiDACgAFEGAFCAKAMAKECUAQAUIMoAAApwRX8AynGVfw4jZ8oAAAoQZQAABYgyAIACRBkAQAGiDACgAFEGAFCAKAMAKECUAQAUIMoAAAoQZQAABYz0NUu991cn+dUkz0/y9STvbq2d7r2/PMmdSV6Y5ItJbmmtfW19nbEvAwCYVUPPlPXen5Xko0nem+TaJG9N8pu99x9I8vEkH1i//74kH1lf5ynjXgbA4bS4tHzZd2HCrBrlTNlzk3y4tfap9Z8/3Xv/apIfTvJYa205SXrv70/ytt77S5L85XEva619aUzPmUNq7fy5UuusnT830tidbG+c44aNHbadccxnr4+x022Psr3NY3Yzh3HNa7+2vdvfzUE+HsyiI2traztaoff+vCRfSfKvkrystfb3Nyz7wyS/kkFc/eg4l7XWfnvzXE6ePPn0JI/ec889WV1d3dHzAAA4SPPz87npppuS5NoTJ058e/Pykd5TdlHv/fuS3JvkriRHkpzdNORskmuSPHUfll3RysqKKFu3sLCQlZWVSU+jpKPHTl26/a17b9v3dYaNP3rsVNbOn8uFz9++7T4bdQ7jHrdx7Fbjhm1nJ7+HvT7Gbuaw1bqjbG/zPtvNHMY1r/3a9rD7tpvLXrc96uPthP8uTp9Z3Wfz8/PbLh85ytbfQ3ZvBu/zeksG7y27etOwa5I8nkFIjXsZ7MmRuc3/ak12nerjho0dtp1xzGevj7HTbe9me+NeZyfj92vb+/k8x/V4MItGuiRG7/0VST6b5Ddaa29qrX03yQMZfELy4pirMvh05oP7tAwAYGYNPVPWe78uySeT/GJr7a4Ni34nyTN6729McneSdyZ5JMmXkzy0D8sAAGbWKC9f/lySo0nu6L3fseH+f5LkWAbXFLsjyR8lubm1tpbkXO993MsAIEkuXSLjzOnjE54JjM/QKGutvS/J+7YZ8rIrrPeFcS8DAJhVvmYJAKAAUQYAUIAoAwAoQJQBABSwoyv6A0BVG7+03KcymUbOlAEAFCDKAAAKEGUAAAWIMgCAAkQZAEABogwAoABRBgBQgCgDAChAlAEwcxaXli+7mCxMA1EGAFCAKAMAKECUAQAUIMoAAAoQZQAABYgyAIACRBkAQAGiDIBDwbXLqE6UAQAUIMqYWv7qBWCWiDIAgAJEGQBAAaIMAKAAUQYAUIAoAwAoQJQBABQgygAAChBlAAAFzE16AgBwkDZedPrM6eMTnAlczpkyAIACRBkAQAGiDACgAFEGAFCAKAMAKECUAQAUIMoAAAoQZQAABYgyAIACRBkAQAG+ZgmAQ89XL1GBM2UAAAWIMgCAAkQZAEABogwAoABRBgAbLC4tX/bGfzgoogwAoABRBgBQgCgDAChAlAEAFCDKAAAKEGUAAAWIMgCAAkQZAEABogwAoABRBgBDuMo/B0GUAQAUIMoAAAqYm/QEAGBabHwJ88zp4xOcCbPImTIAgAJEGQBAAaIMAKAAUQYAUIAoAwAoQJQBABQgygAAChBlAAAFuHgsAOyBC8oyLs6UAQAUIMoAAAoQZQAABYgyAIACRBkAQAGijIlaXFq+7JNLAHBYiTIAGBN/aLIXogwAoABRBgBQgCgDAChAlAEAFOC7LwFgzHwfJrvhTBkAQAGiDACgAFEGAFCAKAOAA+LismxHlAEAFCDKAAAKcEkMADhgLpnBVpwpAwAoQJQBABQgygAAChBlAAAFiDIAmCDXLuMiUQYAUIAoAwAoQJQBQDFe0jycRBkAQAGu6A8ARbny/+HiTBkATAEvac6+kmfKeu8vT3Jnkhcm+WKSW1prX5vopNiSv+IADpb/7s6uclHWe39Kko8nOZHkt5K8K8lHktw4wWkBQFlCbTaUi7Ikr0ryWGttOUl67+9P8rbe+0taa1+a7NQAoLatXuIUatOhYpRdn+SBiz+01i703h9ev3/LKJufnz+gqdU3Pz9/oL+Pqy6sXPbYu13/INbdzVz3ss6w8VddWMnahf+XI0P22ahzGPe4jWO3GjdsOzv5Pez1MXYzh63WHWV7m/fZbuYwrnnt17aH3bfdXPa67Z0+3pXmsHGdi/9st82dPL9R1t3JdnY6bjdzeObf/ffbLn/0vn+aJLn2x379CfdNwkH/v+ygDHtOR9bW1g5oKqPpvd+e5IbW2us33PeZJHe11j60cezJkyf/SpI/PdgZAgDsyXNOnDjx9c13VjxTdjbJ1ZvuuybJ41uM/bMkz0ny2H5PCgBgDJ6WQb88QcUoeyDJrRd/6L1fleT5SR7cPPDEiRNrSZ5QmgAARX37SgsqRtnvJHlG7/2NSe5O8s4kjyT58kRnBQCwj8pdPLa1di7JsSRvTvLNJK9NcnNrrdab3wAAxqjcG/0BAA6jii9fMkTv/dVJfjWD99p9Pcm7W2unN405ksHr1hvPhn6otfYLBzZRLum9/3SSf5Hk2Rm8b/KtrbXPbRrzPUk+kOR4ku8m+TettV8+6LkyMOI+c5wV03u/Icn9SV7cWnto0zLHWEFD9tmhOsZE2ZTpvT8ryUeT/OMk9yZ5dZKP9d5vbK09vGHo9yc511r73glMkw167y9KcirJK1tr9/feb81gHz5709C3JPmRJC9K8vTBqv1/ttY+fqATZif7zHFWSO99LskHkzz5CkMcY8WMsM8O1TFW7j1lDPXcJB9urX2qtfbd1tqnk3w1yQ9vGndjkj866MnxRK21B5P8pfX/uT8pydEM3i+52euT/OvW2jfWA/vfZfAXPQdsB/vsxjjOKnlXkt/bZrljrJ5h++zGHKJjzJmyKdNa+4Mkf3Dx597785LckCd+OvXGJM/qvf+PJM9M8p8yePnFNd0moLX2eO/9pUn+MMn5JD+xxbDLvs0ig8vA3LrFOA7AiPvsxjjOSui9/2CS1yV5WZK3X2GYY6yQEffZjTlEx5gzZVOs9/59GbyEeVdr7SubFn8nyecy+C7RFyf53gz+KmRyvpzkKUl+PslHe++bT8c/NYOLJ190NoMLJzM5w/aZ46yA9bOZH0zypvVP8F+JY6yIHeyzQ3WMOVM2pXrvP5BBkN2XwfskLtNa+5ebxr8nyX89mNmxldba6vrND/be357klUk+vGHI5m+zuNI3WXBAhu0zx1kZ70nyu5s/iLEFx1gdI+2zw3aMOVM2hXrvr0jy2SS/0Vp7U2vtu1uM+aX1U8MXPTmDvzg4YL33Y733/7jp7icleXTTfQ8keeGGn1+ULb7Jgv036j5znJVxc5Jbe++P9t4fXb/v/t776zeNc4zVMdI+O2zHmDNlU6b3fl2STyb5xdbaXdsMfVGS1/TefyrJfJJfTrJ8AFPkib6Q5BW9959M8qkMXgqbzyCsN/qtJCd675/J4GWWX0jyjoOcKJeMus8cZwW01q7f+HPvfS3JD22+vEIcY2XsYJ8dqmPMmbLp83MZfBLsjt774xv++Zne+1d6729YH/e2JP8nyUMZ/CX4x0nePZkpH26ttT9P8pNJ3pfkG+u3f7y1dnbTPrsjg68Z+2KS309yqrX2sYOfMTvYZ46z4hxj0+cwH2Ou6A8AUIAzZQAABYgyAIACRBkAQAGiDACgAFEGAFCAKAMAKECUAQAUIMoAAAoQZQAABfx/zDdirLgHNcIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['log_Idade'] = np.log(df['Idade'])\n",
    "df['log_Idade'].hist(bins = 200\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qfgDngUmOtMS"
   },
   "source": [
    "# Normalização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Guw5NE4VOtMT"
   },
   "outputs": [],
   "source": [
    "df['normal_log_idade'] = ( df['log_Idade'] - df['log_Idade'].mean() ) / df['log_Idade'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 606
    },
    "executionInfo": {
     "elapsed": 18437,
     "status": "ok",
     "timestamp": 1605283248710,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "gRJKx84HOtMi",
    "outputId": "2fbd5cfa-69f9-4402-c155-aace57eb4571"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histograma_normalizado = plt.hist(df['normal_log_idade'], \n",
    "                             bins=200, \n",
    "                            # range = (0, 25000)\n",
    "                                 )\n",
    "plt.xlabel('Normal Log Idade')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gu97JxdMOtMm"
   },
   "source": [
    "# Correlação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "executionInfo": {
     "elapsed": 18429,
     "status": "ok",
     "timestamp": 1605283248712,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "cNIMXgKAOtMm",
    "outputId": "3a56b552-25f6-4a01-8ef6-9ed00f975fe9"
   },
   "outputs": [
    {
     "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>Idade</th>\n",
       "      <th>Salário Mensal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>954.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idade  Salário Mensal\n",
       "0     24           954.0\n",
       "1     25          2500.0\n",
       "2     55             0.0\n",
       "3     52             0.0\n",
       "4     32             0.0"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ex3 = df[['Idade','Salário Mensal']]\n",
    "df_ex3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19306,
     "status": "ok",
     "timestamp": 1605283249598,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "TJcJiITsOtMp",
    "outputId": "58cad33e-ef6a-43da-a03a-46e60afe88ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.29049665848735234, pvalue=0.0)"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Idade'].corr(df['Salário Mensal']) \n",
    "stats.spearmanr(df['Idade'], df['Salário Mensal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6atE5UZ5OtMs"
   },
   "source": [
    "#### <font color='orange'> corr menor que 0.3, desprezivel... Vamos plotar uma dispersão para tirar mais conclusões</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "executionInfo": {
     "elapsed": 22020,
     "status": "ok",
     "timestamp": 1605283252323,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "bzKxdJxLOtMs",
    "outputId": "e453533d-c0f2-4843-d676-4de9919cd945"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Idade'], \n",
    "            df['Salário Mensal'], \n",
    "            marker = \".\",\n",
    "            alpha = 0.2\n",
    "           )\n",
    "plt.title('Dispersão: Idade x Salário Mensal')\n",
    "plt.xlabel('Idade (Anos)')\n",
    "plt.ylabel(\"Salário Mensal\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rAGMqTaaOtMv"
   },
   "outputs": [],
   "source": [
    "# As duas váriaveis estão bem distribuidas entre si, mostrando que não tem muita correlação...\n",
    "# Parece que a unica coisa que é possível analisar nesse gráfico acima é a linha de salário de 130k reais, que são pessoas que trabalham com Buffet (Bem estranho)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zLR14AnQOtMz"
   },
   "source": [
    "### 4. Verificação dos dados da variável Grau de Instrução: verifique os dados da variável Grau de instrução, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "executionInfo": {
     "elapsed": 22008,
     "status": "ok",
     "timestamp": 1605283252326,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "oDFqQY5FOtMz",
    "outputId": "3632dfc3-16b1-4e6b-ce45-ec28d38b4ed0"
   },
   "outputs": [
    {
     "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>Categoria</th>\n",
       "      <th>Valores na fonte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Analfabeto</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Até 5ª Incompleto</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5ª Completo Fundamental</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6ª a 9ª Fundamental</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fundamental Completo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Médio Incompleto</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Médio Completo</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Superior Incompleto</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Superior Completo</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MESTRADO</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DOUTORADO</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>IGNORADO</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Categoria  Valores na fonte\n",
       "0                Analfabeto                 1\n",
       "1         Até 5ª Incompleto                 2\n",
       "2   5ª Completo Fundamental                 3\n",
       "3       6ª a 9ª Fundamental                 4\n",
       "4      Fundamental Completo                 5\n",
       "5          Médio Incompleto                 6\n",
       "6            Médio Completo                 7\n",
       "7       Superior Incompleto                 8\n",
       "8         Superior Completo                 9\n",
       "9                  MESTRADO                10\n",
       "10                DOUTORADO                11\n",
       "11                 IGNORADO                -1"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grau_Instr_Bibl = {'Categoria': ['Analfabeto', 'Até 5ª Incompleto', '5ª Completo Fundamental', '6ª a 9ª Fundamental', 'Fundamental Completo', 'Médio Incompleto', 'Médio Completo', 'Superior Incompleto', 'Superior Completo', 'MESTRADO', 'DOUTORADO', 'IGNORADO'],\n",
    "                   'Valores na fonte': [1,2,3,4,5,6,7,8,9,10,11,-1]\n",
    "                  }\n",
    "Grau_Instr_Bibli = pd.DataFrame(data=Grau_Instr_Bibl)\n",
    "Grau_Instr_Bibli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22002,
     "status": "ok",
     "timestamp": 1605283252328,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "Cw-ZteW3RT5_",
    "outputId": "4c92b084-62ab-47b5-83f8-1da979ad39c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Analfabeto',\n",
       " 2: 'Até 5ª Incompleto',\n",
       " 3: '5ª Completo Fundamental',\n",
       " 4: '6ª a 9ª Fundamental',\n",
       " 5: 'Fundamental Completo',\n",
       " 6: 'Médio Incompleto',\n",
       " 7: 'Médio Completo',\n",
       " 8: 'Superior Incompleto',\n",
       " 9: 'Superior Completo',\n",
       " 10: 'MESTRADO',\n",
       " 11: 'DOUTORADO',\n",
       " -1: 'IGNORADO'}"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = {1: 'Analfabeto', 2: 'Até 5ª Incompleto', 3: '5ª Completo Fundamental', 4: '6ª a 9ª Fundamental', 5: 'Fundamental Completo', 6: 'Médio Incompleto', 7: 'Médio Completo', 8: 'Superior Incompleto', 9: 'Superior Completo', 10: 'MESTRADO', 11: 'DOUTORADO', -1: 'IGNORADO'}\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rsq98pQC8fAO",
    "outputId": "3708f503-fa12-4ae8-a66d-121fe13bc0c8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Admitidos/Desligados</th>\n",
       "      <th>Competência Declarada</th>\n",
       "      <th>Município</th>\n",
       "      <th>Ano Declarado</th>\n",
       "      <th>Grau Instrução</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Raça Cor</th>\n",
       "      <th>Salário Mensal</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Tempo Emprego</th>\n",
       "      <th>UF</th>\n",
       "      <th>log_Salário</th>\n",
       "      <th>normal_log_salário</th>\n",
       "      <th>log_Idade</th>\n",
       "      <th>normal_log_idade</th>\n",
       "      <th>NovaCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500215</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
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       "      <td>50</td>\n",
       "      <td>6.861711</td>\n",
       "      <td>-0.338373</td>\n",
       "      <td>3.178054</td>\n",
       "      <td>-0.734573</td>\n",
       "      <td>Médio Incompleto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500710</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>7.824446</td>\n",
       "      <td>0.751796</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>-0.610896</td>\n",
       "      <td>Superior Incompleto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500460</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.108356</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>1.777875</td>\n",
       "      <td>Até 5ª Incompleto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500460</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.108356</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>1.607942</td>\n",
       "      <td>Até 5ª Incompleto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500460</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8.108356</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>0.137010</td>\n",
       "      <td>Até 5ª Incompleto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256747</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>5510.0</td>\n",
       "      <td>2</td>\n",
       "      <td>447.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8.614501</td>\n",
       "      <td>1.646430</td>\n",
       "      <td>4.077537</td>\n",
       "      <td>1.990571</td>\n",
       "      <td>Superior Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256748</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>954.0</td>\n",
       "      <td>2</td>\n",
       "      <td>215.0</td>\n",
       "      <td>22</td>\n",
       "      <td>6.861711</td>\n",
       "      <td>-0.338373</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>1.777875</td>\n",
       "      <td>Médio Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256749</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>61</td>\n",
       "      <td>8</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>202.0</td>\n",
       "      <td>22</td>\n",
       "      <td>6.908755</td>\n",
       "      <td>-0.285103</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>2.091570</td>\n",
       "      <td>Médio Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256750</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>1365.0</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22</td>\n",
       "      <td>7.219642</td>\n",
       "      <td>0.066936</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>-0.998190</td>\n",
       "      <td>Superior Incompleto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256751</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>220770</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>996.0</td>\n",
       "      <td>2</td>\n",
       "      <td>69.0</td>\n",
       "      <td>22</td>\n",
       "      <td>6.904751</td>\n",
       "      <td>-0.289637</td>\n",
       "      <td>3.295837</td>\n",
       "      <td>-0.377729</td>\n",
       "      <td>Superior Completo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2256752 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Admitidos/Desligados  Competência Declarada  Município  \\\n",
       "0                           1                 201812     500215   \n",
       "1                           1                 201812     500710   \n",
       "2                           1                 201812     500460   \n",
       "3                           1                 201812     500460   \n",
       "4                           1                 201812     500460   \n",
       "...                       ...                    ...        ...   \n",
       "2256747                     2                 201812     221100   \n",
       "2256748                     2                 201812     221100   \n",
       "2256749                     2                 201812     221100   \n",
       "2256750                     2                 201812     221100   \n",
       "2256751                     2                 201812     220770   \n",
       "\n",
       "         Ano Declarado  Grau Instrução  Idade  Raça Cor  Salário Mensal  Sexo  \\\n",
       "0                 2018               6     24         8           954.0     1   \n",
       "1                 2018               8     25         2          2500.0     1   \n",
       "2                 2018               2     55         8             0.0     1   \n",
       "3                 2018               2     52         8             0.0     1   \n",
       "4                 2018               2     32         8             0.0     1   \n",
       "...                ...             ...    ...       ...             ...   ...   \n",
       "2256747           2018               9     59         8          5510.0     2   \n",
       "2256748           2018               7     55         9           954.0     2   \n",
       "2256749           2018               7     61         8          1000.0     2   \n",
       "2256750           2018               8     22         2          1365.0     1   \n",
       "2256751           2018               9     27         2           996.0     2   \n",
       "\n",
       "         Tempo Emprego  UF  log_Salário  normal_log_salário  log_Idade  \\\n",
       "0                  0.0  50     6.861711           -0.338373   3.178054   \n",
       "1                  0.0  50     7.824446            0.751796   3.218876   \n",
       "2                  0.0  50     0.000000           -8.108356   4.007333   \n",
       "3                  0.0  50     0.000000           -8.108356   3.951244   \n",
       "4                  0.0  50     0.000000           -8.108356   3.465736   \n",
       "...                ...  ..          ...                 ...        ...   \n",
       "2256747          447.0  22     8.614501            1.646430   4.077537   \n",
       "2256748          215.0  22     6.861711           -0.338373   4.007333   \n",
       "2256749          202.0  22     6.908755           -0.285103   4.110874   \n",
       "2256750           16.0  22     7.219642            0.066936   3.091042   \n",
       "2256751           69.0  22     6.904751           -0.289637   3.295837   \n",
       "\n",
       "         normal_log_idade              NovaCol  \n",
       "0               -0.734573     Médio Incompleto  \n",
       "1               -0.610896  Superior Incompleto  \n",
       "2                1.777875    Até 5ª Incompleto  \n",
       "3                1.607942    Até 5ª Incompleto  \n",
       "4                0.137010    Até 5ª Incompleto  \n",
       "...                   ...                  ...  \n",
       "2256747          1.990571    Superior Completo  \n",
       "2256748          1.777875       Médio Completo  \n",
       "2256749          2.091570       Médio Completo  \n",
       "2256750         -0.998190  Superior Incompleto  \n",
       "2256751         -0.377729    Superior Completo  \n",
       "\n",
       "[2256752 rows x 16 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NovaCol'] = df['Grau Instrução'].map(s)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 21978,
     "status": "aborted",
     "timestamp": 1605283252343,
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    "id": "WmdmTxvcaLfh",
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['NovaCol'], \n",
    "            df['Salário Mensal'], \n",
    "            marker = \".\",\n",
    "            alpha = 0.2\n",
    "           )\n",
    "plt.title('Dispersão: Grau Instrução x Salário Mensal')\n",
    "plt.xlabel('Grau de Instrução')\n",
    "plt.ylabel(\"Salário Mensal\")\n",
    "plt.rcParams['figure.figsize'] = (100,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 21969,
     "status": "aborted",
     "timestamp": 1605283252344,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "QqKQyL8KOtM5",
    "outputId": "bdcbed85-2fc9-45a3-d258-fd847129a9a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18470180695812546"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlação\n",
    "df['Salário Mensal'].corr(df['Grau Instrução'])\n",
    "# Essa variável não tem uma alta correlação,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SYwthDJ8fAP"
   },
   "source": [
    "### 5. Verificação dos dados da variável **Sexo**: verifique os dados da variável Sexo, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados. Depois disso, faça uma inferência estatística pra verificar se os homens ganham mais que as mulheres.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CbUg7emM8fAP",
    "outputId": "0dbaf6e5-38de-4be7-af6a-f196de25ed58"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Sexo'], \n",
    "            df['Salário Mensal'], \n",
    "            marker = \".\",\n",
    "            alpha = 0.2\n",
    "           )\n",
    "plt.title('Dispersão: Salário Mensal x Tempo de Emprego')\n",
    "plt.xlabel('Sexo (Masc \"01\"/Feminino \"02\")')\n",
    "plt.ylabel(\"Salário Mensal\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZTZN8zua8fAP",
    "outputId": "9be32f3c-5ccc-4241-cb20-2be2b2fe711b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histo_sexo = plt.hist(df['Sexo'], \n",
    "                       bins=200, \n",
    "                       #range = (0,10000000)\n",
    "                      )\n",
    "plt.xlabel('Idade')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Z5h49NK08fAP",
    "outputId": "f2679e6f-247a-4352-8f86-3f05d4385f85"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04239430463953994"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correlação\n",
    "df['Salário Mensal'].corr(df['Sexo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SMCzUo788fAP",
    "outputId": "fb86396d-2adf-41c6-b510-b2fe9ac2b386"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sexo'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CTyY2Uwd8fAP",
    "outputId": "50186b79-39f3-4b6b-c296-e2dd73ea3ba6"
   },
   "outputs": [
    {
     "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>Sexo</th>\n",
       "      <th>Salário Mensal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1701.940527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1521.385129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sexo  Salário Mensal\n",
       "Sexo                      \n",
       "1        1     1701.940527\n",
       "2        2     1521.385129"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df[['Sexo', 'Salário Mensal']].groupby(df['Sexo'])\n",
    "media = grouped.mean()\n",
    "media"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-ChJT7qe8fAQ",
    "outputId": "2d07b259-f298-4928-9601-5d24b27bf45d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media['Sexo'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W-fhYrbd8fAQ",
    "outputId": "eeb518c0-fddb-4241-c7b3-cc8da5283711"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'Masculino', 2: 'Feminino'}"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = {1: 'Masculino', 2:'Feminino'}\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lb4yGNgx8fAQ",
    "outputId": "2f577d88-5feb-4004-a0ad-00c7d88c694b"
   },
   "outputs": [
    {
     "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>Sexo</th>\n",
       "      <th>Salário Mensal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genero</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>1</td>\n",
       "      <td>1701.940527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feminino</th>\n",
       "      <td>2</td>\n",
       "      <td>1521.385129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sexo  Salário Mensal\n",
       "Genero                         \n",
       "Masculino     1     1701.940527\n",
       "Feminino      2     1521.385129"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media['Genero'] = media['Sexo'].map(j)\n",
    "media.drop(columns = 'Sexo')\n",
    "media.set_index('Genero')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "N2OwMFgC8fAQ",
    "outputId": "a1d81366-7443-47f6-cad5-079e54b2e78a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Soma dos salários dos homens 2251883464.0 reais\n",
      "Soma dos salários dos mulheres 1420403191.0 reais\n"
     ]
    }
   ],
   "source": [
    "df_homens = df.loc[df['Sexo'] == 1]\n",
    "df_mulheres = df.loc[df['Sexo'] == 2]\n",
    "print('Soma dos salários dos homens', df_homens['Salário Mensal'].sum(), \"reais\")\n",
    "print('Soma dos salários dos mulheres', df_mulheres['Salário Mensal'].sum(), \"reais\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6HIFM1dE8fAQ"
   },
   "outputs": [],
   "source": [
    "media_M = media['Salário Mensal'][2]\n",
    "media_H = media['Salário Mensal'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Z4pwhAA8fAQ",
    "outputId": "fa098815-cc52-40f3-fd32-4280884e0838"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Admitidos/Desligados</th>\n",
       "      <th>Competência Declarada</th>\n",
       "      <th>Município</th>\n",
       "      <th>Ano Declarado</th>\n",
       "      <th>Grau Instrução</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Raça Cor</th>\n",
       "      <th>Salário Mensal</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Tempo Emprego</th>\n",
       "      <th>UF</th>\n",
       "      <th>log_Salário</th>\n",
       "      <th>normal_log_salário</th>\n",
       "      <th>log_Idade</th>\n",
       "      <th>normal_log_idade</th>\n",
       "      <th>NovaCol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
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       "      <td>Superior Incompleto</td>\n",
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       "      <td>2.890372</td>\n",
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       "      <td>Fundamental Completo</td>\n",
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       "      <td>2018</td>\n",
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       "      <td>Médio Completo</td>\n",
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       "      <td>500210</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>8</td>\n",
       "      <td>2123.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "      <td>7.661056</td>\n",
       "      <td>0.566779</td>\n",
       "      <td>3.806662</td>\n",
       "      <td>1.169908</td>\n",
       "      <td>6ª a 9ª Fundamental</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>201812</td>\n",
       "      <td>500210</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
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       "      <td>0.566779</td>\n",
       "      <td>3.433987</td>\n",
       "      <td>0.040822</td>\n",
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       "      <td>201812</td>\n",
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       "      <td>2018</td>\n",
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       "      <td>28</td>\n",
       "      <td>8</td>\n",
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       "      <th>2256727</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
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       "      <td>45</td>\n",
       "      <td>8</td>\n",
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       "      <td>109.0</td>\n",
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       "      <td>3.806662</td>\n",
       "      <td>1.169908</td>\n",
       "      <td>Superior Completo</td>\n",
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       "    <tr>\n",
       "      <th>2256728</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
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       "      <td>44</td>\n",
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       "      <td>22</td>\n",
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       "      <td>3.784190</td>\n",
       "      <td>1.101822</td>\n",
       "      <td>Superior Completo</td>\n",
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       "    <tr>\n",
       "      <th>2256729</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221000</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>51</td>\n",
       "      <td>4</td>\n",
       "      <td>10849.0</td>\n",
       "      <td>1</td>\n",
       "      <td>373.0</td>\n",
       "      <td>22</td>\n",
       "      <td>9.291920</td>\n",
       "      <td>2.413518</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>1.549112</td>\n",
       "      <td>Médio Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256745</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>2641.0</td>\n",
       "      <td>1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>22</td>\n",
       "      <td>7.879291</td>\n",
       "      <td>0.813902</td>\n",
       "      <td>4.143135</td>\n",
       "      <td>2.189310</td>\n",
       "      <td>Médio Incompleto</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>442609 rows × 16 columns</p>\n",
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      ],
      "text/plain": [
       "         Admitidos/Desligados  Competência Declarada  Município  \\\n",
       "1                           1                 201812     500710   \n",
       "17                          1                 201812     500800   \n",
       "21                          1                 201812     500325   \n",
       "23                          1                 201812     500210   \n",
       "24                          1                 201812     500210   \n",
       "...                       ...                    ...        ...   \n",
       "2256725                     2                 201812     221120   \n",
       "2256727                     2                 201812     221100   \n",
       "2256728                     2                 201812     221100   \n",
       "2256729                     2                 201812     221000   \n",
       "2256745                     2                 201812     221100   \n",
       "\n",
       "         Ano Declarado  Grau Instrução  Idade  Raça Cor  Salário Mensal  Sexo  \\\n",
       "1                 2018               8     25         2          2500.0     1   \n",
       "17                2018               5     18         8          1700.0     1   \n",
       "21                2018               7     35         2          5000.0     1   \n",
       "23                2018               4     45         8          2123.0     1   \n",
       "24                2018               6     31         8          2123.0     1   \n",
       "...                ...             ...    ...       ...             ...   ...   \n",
       "2256725           2018               7     28         8          3000.0     1   \n",
       "2256727           2018               9     45         8          5475.0     1   \n",
       "2256728           2018               9     44         8          5798.0     1   \n",
       "2256729           2018               7     51         4         10849.0     1   \n",
       "2256745           2018               6     63         8          2641.0     1   \n",
       "\n",
       "         Tempo Emprego  UF  log_Salário  normal_log_salário  log_Idade  \\\n",
       "1                  0.0  50     7.824446            0.751796   3.218876   \n",
       "17                 0.0  50     7.438972            0.315298   2.890372   \n",
       "21                 0.0  50     8.517393            1.536468   3.555348   \n",
       "23                 0.0  50     7.661056            0.566779   3.806662   \n",
       "24                 0.0  50     7.661056            0.566779   3.433987   \n",
       "...                ...  ..          ...                 ...        ...   \n",
       "2256725           26.0  22     8.006701            0.958176   3.332205   \n",
       "2256727          109.0  22     8.608130            1.639215   3.806662   \n",
       "2256728          148.0  22     8.665441            1.704112   3.784190   \n",
       "2256729          373.0  22     9.291920            2.413518   3.931826   \n",
       "2256745           62.0  22     7.879291            0.813902   4.143135   \n",
       "\n",
       "         normal_log_idade               NovaCol  \n",
       "1               -0.610896   Superior Incompleto  \n",
       "17              -1.606157  Fundamental Completo  \n",
       "21               0.408506        Médio Completo  \n",
       "23               1.169908   6ª a 9ª Fundamental  \n",
       "24               0.040822      Médio Incompleto  \n",
       "...                   ...                   ...  \n",
       "2256725         -0.267547        Médio Completo  \n",
       "2256727          1.169908     Superior Completo  \n",
       "2256728          1.101822     Superior Completo  \n",
       "2256729          1.549112        Médio Completo  \n",
       "2256745          2.189310      Médio Incompleto  \n",
       "\n",
       "[442609 rows x 16 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Homens que ganham mais que a média das mulheres\n",
    "df_homens.loc[df['Salário Mensal'] >= media_M]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HvS5QqgL8fAQ",
    "outputId": "5b5a0c0b-37a0-414d-9ce6-0c388331f2ec"
   },
   "outputs": [
    {
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       "      <td>3.091042</td>\n",
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       "      <th>126</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2256734</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>3066.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8.028455</td>\n",
       "      <td>0.982810</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>-0.610896</td>\n",
       "      <td>Médio Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256737</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>3615.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8.193124</td>\n",
       "      <td>1.169275</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>1.549112</td>\n",
       "      <td>Superior Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256746</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>8</td>\n",
       "      <td>5724.0</td>\n",
       "      <td>2</td>\n",
       "      <td>48.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8.652598</td>\n",
       "      <td>1.689569</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>0.493855</td>\n",
       "      <td>Superior Completo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256747</th>\n",
       "      <td>2</td>\n",
       "      <td>201812</td>\n",
       "      <td>221100</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>5510.0</td>\n",
       "      <td>2</td>\n",
       "      <td>447.0</td>\n",
       "      <td>22</td>\n",
       "      <td>8.614501</td>\n",
       "      <td>1.646430</td>\n",
       "      <td>4.077537</td>\n",
       "      <td>1.990571</td>\n",
       "      <td>Superior Completo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147899 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Admitidos/Desligados  Competência Declarada  Município  \\\n",
       "40                          1                 201812     500830   \n",
       "61                          1                 201812     500740   \n",
       "96                          1                 201812     500370   \n",
       "104                         1                 201812     500793   \n",
       "126                         1                 201812     500370   \n",
       "...                       ...                    ...        ...   \n",
       "2256730                     2                 201812     221100   \n",
       "2256734                     2                 201812     221100   \n",
       "2256737                     2                 201812     221100   \n",
       "2256746                     2                 201812     221100   \n",
       "2256747                     2                 201812     221100   \n",
       "\n",
       "         Ano Declarado  Grau Instrução  Idade  Raça Cor  Salário Mensal  Sexo  \\\n",
       "40                2018               7     24         8          2898.0     2   \n",
       "61                2018               6     25         2          2206.0     2   \n",
       "96                2018               9     28         2          1900.0     2   \n",
       "104               2018               8     22         8          2131.0     2   \n",
       "126               2018               9     26         2          2851.0     2   \n",
       "...                ...             ...    ...       ...             ...   ...   \n",
       "2256730           2018               9     60         2         16079.0     2   \n",
       "2256734           2018               7     25         8          3066.0     2   \n",
       "2256737           2018               9     51         2          3615.0     2   \n",
       "2256746           2018               9     36         8          5724.0     2   \n",
       "2256747           2018               9     59         8          5510.0     2   \n",
       "\n",
       "         Tempo Emprego  UF  log_Salário  normal_log_salário  log_Idade  \\\n",
       "40                 0.0  50     7.972121            0.919019   3.178054   \n",
       "61                 0.0  50     7.699389            0.610186   3.218876   \n",
       "96                 0.0  50     7.550135            0.441176   3.332205   \n",
       "104                0.0  50     7.664816            0.571036   3.091042   \n",
       "126                0.0  50     7.955776            0.900510   3.258097   \n",
       "...                ...  ..          ...                 ...        ...   \n",
       "2256730          483.0  22     9.685332            2.859004   4.094345   \n",
       "2256734            6.0  22     8.028455            0.982810   3.218876   \n",
       "2256737            8.0  22     8.193124            1.169275   3.931826   \n",
       "2256746           48.0  22     8.652598            1.689569   3.583519   \n",
       "2256747          447.0  22     8.614501            1.646430   4.077537   \n",
       "\n",
       "         normal_log_idade              NovaCol  \n",
       "40              -0.734573       Médio Completo  \n",
       "61              -0.610896     Médio Incompleto  \n",
       "96              -0.267547    Superior Completo  \n",
       "104             -0.998190  Superior Incompleto  \n",
       "126             -0.492070    Superior Completo  \n",
       "...                   ...                  ...  \n",
       "2256730          2.041491    Superior Completo  \n",
       "2256734         -0.610896       Médio Completo  \n",
       "2256737          1.549112    Superior Completo  \n",
       "2256746          0.493855    Superior Completo  \n",
       "2256747          1.990571    Superior Completo  \n",
       "\n",
       "[147899 rows x 16 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mulheres que ganham mais que a média dos homens\n",
    "df_mulheres.loc[df['Salário Mensal'] >= media_H]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_y9Yr0NX8fAQ"
   },
   "source": [
    "### 6. Verificação dos dados da variável **Raça Cor**: verifique os dados da variável Raça Cor, plote uma distribuição e uma dispersão, calcule a correlação com a variável salário mensal e interprete os resultados. Depois disso, faça uma inferência estatística pra verificar se os brancos ganham mais que os outros grupos étnicos.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wJMUv9Lp8fAQ"
   },
   "outputs": [],
   "source": [
    "l = {1: 'Indigena', 2: 'Branca', 4:'Preta', 6:'Amarela', 8:'Parda', 9:'Não identificado', -1:'Ignorado'}\n",
    "df['RaçaCor_Lib'] = df['Raça Cor'].map(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "CcbZf4648fAQ",
    "outputId": "66bb4de2-2b39-41ad-cf05-52ca39055bb2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qE+u6oiRJ+6KEBgULP6mH5q7pHKcqzkSStBKU0KBg4Sf10NpxTY7XVpyJJGklKKFBwcJPkiRpPyihQcHCT+qhhLEYkiQtJws/qYcSxmJIkrScxqp40hjjI4DnhhDuMe/2I4CfAY8JIcR82/HAu4ETgYuAp4QQvjOobVJbc9c0jDu5Q5K0cuzXFr8YYy3G+GTgHKC2wF3+DZjfr/Yh4AvABuAtwCdijAcMcJs0z0JvVUmShs/+7uo9DXgC8Mb5G2KMfw3MAb/tuO2WwPHAGSGEmRDCe4CrgPsNYtvg/mwNI7t6JUkrzf4u/N6Su3d/1XljjPEo4BXA0+fd/wTgohDCTMdtF+TbB7FN2mPV+hQn1lebhyRJy2S/jvELIVzRY9M7gZeHEK6MMXbevhbYNu++24A1A9rW0+TkJDMzM4vdZZ9NTk4O9PG1l3ZsBGDNzo3gvimKn5UyuV/K4z4p0yD3y/j4+KLbK5nc0SmP+dsRQvjgApu3AfPH3q0Bpge0raepqamBFn6Tk5NMTTmJoCTjR96F1VO/YMfkCcxc/r2q01HmZ6VM7pfyuE/KNOj9slThV8JyLo8CQoxxU4xxE3A0abLFi4HzgeNijI2O+9+C1DU7iG3SbnNb/5Di9FUVZyJJ0vKovMUvhPCAzv/HGDcCT+5YzmUj8LIY4xuAxwKHAV8OIWxb7m2D/ls1XGqt2RSZqzgTSZKWRwktfks5Cbgnaebtc4GHhRC2DXCbBJRxTUVJkpZTrdVqVZ1D0U4//fT1wKazzz7bMX4jyP1SHvdJmdwv5XGflGl/jPE7+eSTAQ465ZRTNs/fPgwtfpIkSVoGFn6SJEkjwsJPkiRpRFj4SZIkjQgLP6mH2uqDu6IkScPOwk/qoT6xritKkjTsLPykHlzHT5K00lj4SUuqVZ2AJEnLwsKvAI4lK5NdvZKklcbCrwAWGGWyq1eStNJY+BXAAqNMrR3X5HhtxZlI0t6xJ6lMJewXC78CWGBIkpaTPUllKmG/WPhJkrTC2JNUphL2i4WfJEkrjD1JZSphv1j4SZIkjQgLP6mHEgbhSpK0nCz8pB4ah9yiK0qSNOws/KQeGqsPydEWP0nSymDhJ/Uwt+PqFLdfXXEmkiQtDws/qYe5qy9I8ZoLKs5EkqTlYeEnLalWdQKSJC0LCz+phxJWWJek68NVCdSLhZ/UQwkrrEvDwCKjPH5xVS8WftKS7OqVFmORUR6/uKoXCz+pB09mUn8sMspTwqXBVCYLP6kHT2ZSfywypOFh4Sf1MHHXl+f4ioozkSRpeVj4ST1MTEx0RUmShp2FXwHq64/riirDrl27uqIkScPOwq8AjbWHdkWVYfZn7+qKkiQNOwu/AsxtvSrF6SsrzkSdmtOX53hZxZlIkrQ8LPwK0Nx8cYpbNlabiLo4U1GStNJY+Ek9eDUCSdJKY+En9eACzpKklcbCT+rBBZwlSSuNhZ/Ug2P8JEkrjYWfJEnSiLDwkyRJGhEWfpIkSSPCwk+SJGlEWPhJkiSNCAs/SZKkEWHhJ0mSNCIs/CRJkkaEhZ8kSdKIsPCTJEkaERZ+BaitPrgrSpIkDYKFXwHqE+u6oiRJ0iBY+BWguWs6x6mKM5GkvWevhTQ8LPwK0NpxTY7XVpyJJO09ey2k4WHhJ0naJ/ZaSMPDwk+StE/stZCGh4WfJEnSiLDwkyRJGhEWfpIkSSPCwk+SJGlEWPgVwDWwJEnLyfOKerHwK4BrYEmSlpPnFfVi4VcA18CSJC0nzyvqZayKJ40xPgJ4bgjhHvn/JwL/BNwWuAo4LYTwzrzteODdwInARcBTQgjfGdS2KrR2XAPjk66BJUlaFp5X1Mt+bfGLMdZijE8GzgFq+bZx4NPAB4CDgUcCp8UY/zT/2oeALwAbgLcAn4gxHjDAbZIkSSvS/u7qPQ14AvDGjtuOBL4bQnh7CGEuhPAj4GvA3WKMtwSOB84IIcyEEN5DahG83yC27Ye/X5IkqTL7u/B7S+7e/VX7hhDCJSGEk9r/jzEeBNwDOA84AbgohDDT8RgX5NsHsa0StdUbuqLK4H6RNKw8fqmX/TrGL4RwxWLbY4yTwKeA7wCfBx4HbJt3t23AGmDtALb1NDk5yczMzGJ32QezAKwbn4XxyQE9h/ae+6VUk5PujxK5X0ri8atkg/ysjI+PL7q9kskdC4kxHgV8Fvgt8KgQQivGuA2YP/ZuDTBNKtaWe1tPU1NTAyv8aqs3sG58lumZBq0dmwbyHNp77pcyTU5OMjXlTMXSuF/K4vGrXIP+rCxV+BWxnEuM8RakVr5vAg8PIezIm84HjosxNjrufgtS1+wgtlWiPevKD2dZ3C+ShpXHL/VSeeEXY1xH6tZ9dwjh2SGEufa2EMIvgI3Ay2KMEzHGk4HDgC8PYtvg/1pJkqTqVF74kZZvuQnwvBjjdMe/l+ftJwH3JM28fS7wsBDCtgFukyRJWpFqrVar6hyKdvrpp68HNp199tkDnNzh+JhSuV/K4z4pk/ulPO6TMu2PMX4nn3wywEGnnHLK5vnbS2jxk4rkRc4lDSuPX+rFwk/qwYucSxpWHr/Ui4Wf1IMXOZc0rDx+qRcLP2lJtaoTkKS90tpxTY7XVpyJSmPhJ/VQX3dkV5QkadhZ+Ek91Gq1HCtORJKkZWLhJ/UwN3VZV5QkadhZ+Ek9OEZGkrTSWPhJPbgOliRppbHwk3pwHSxJ0kpj4Sf14DpYkqSVxsKvAHYpls5pvZKklcHCrwB2KZbJdfwkSSuNhV8B7FIsk+v4SZJWGgu/ArhsSJlcx0+StNJY+BXAMX6ls8lPkrQyWPgVwDF+ZXK/SJJWGgu/AjjGr0zuF0nSSmPhVwDH+JXJ/SJJWmks/CRJkkaEhZ/Ug5NuJEkrjYWf1IOTOyRJK42Fn9SDkzskSSuNhZ/Ug5M7JEkrjYWf1INj/CQNK49f6sXCT+rBMX6ShpXHL/Vi4Sf10KqN5dioOBNJ2juOUVYvFn5SD/WJtTn6jVnScHGMsnqx8CtAff1xXVGFGF/XHSUtyPFk5XGfqBcLvwI01h/TFVWImekc7SqRFuN4svK4T9SLhV8J5nbmuL3aPNRlbuqyrihpYY4nK4/7RL1Y+BVgbvp3Of6+4ky0sFrVCUhFczxZedwn6sXCrwC11myOcxVnok52lUiSVhoLvwLYJF8ml3ORJK00Fn5FsUuxJLbESpJWGgu/AtTXHdkVVQZbYiVJK42FXwFqtVqOFSeiLg6OlvrjmnHS8LDwK4DLhkgaZk6EkoaHhV8BbFmSNMwcFiENDws/SdI+8curNDws/CRJkkaEhZ8kSdKIsPCTJEkaERZ+kiRJI8LCT5IkaURY+Ek9uCitJGmlsfCTenBRWknSSmPhJ/XQqo2lSKPiTCRJWh4WflIPtdZsisxVnIkkScvDwk/qwctQSZJWGgs/qQcvQyVJWmks/CRJkkaEhV8B6uuP64qSJEmDYOFXgMbaQ7uiJEnSIFj4FWBu61UpTl9ZcSaSJGkls/ArQHPzxSlu2VhtIpIkaUWz8JMkSRoRFn4F8JqwkiRpf7DwK4DXhJUkSfuDhV8BvEJEmWyJlSStNGNVPGmM8RHAc0MI98j/Px54N3AicBHwlBDCd6rYVoXWjmtgfNIrRBSmPrEOWtdSn1jHXL6KhyRJw2y/tvjFGGsxxicD5wC1jk0fAr4AbADeAnwixnhARdskwJZYSdLKs7+7ek8DngC8sX1DjPGWwPHAGSGEmRDCe4CrgPvt72375yW4rsZht+2KKkPjkFvkeELFmUhlq63e0BUllWt/d/W+JYRwSozxZOCe+bYTgItCCDMd97sg397Yz9s+1SvxyclJZmZmem3eNzs2ArBmx0aYnBzMc2jvbfkZAAds+Zn7pTCT7o/CzAKwbnwWxt03JfGzUqZB7pfx8fFFt+/Xwi+EcMUCN68Fts27bRuwpoJtPU1NTQ2s8Bs76m4csOVnbJ+8NbOX/+9AnkN7b89+uRWzl1c2BFTzTE5OMjVl93tJ6gcey9rW1WzlYJpTl1SdjjI/K2Ua9H5ZqvArYVbvNmD++Lo1wHQF2yrRmrosxenLq0pBC5i7+oIUr/lVxZlIZavRTLHWqjgTSUspofA7HzguxtjouO0WpO7X/b2tEk4iKJPrK0r98RgmDY/KC78Qwi+AjcDLYowTefzfYcCX9/e2/fH3LqSVlwpxOZeyeDKT+uMxTBoelRd+2UmkyR5XAc8FHhZC2FbRNmme2tJ3kSRpCNRaLcdkLOb0009fD2w6++yzBzerFwfhlqhx4NGsaV3LttoG5rb8pup0lPlZKZP7pTzukzLtj8kdJ598MsBBp5xyyub520tp8ZOKY1ev1B8vbygNDws/qQfHLUn9cSJUeSzG1YuFn9SDB06pP7aOl8diXL1Y+Ek9eOCU+mPreHksxtWLhZ/UgwdOScPKYly9WPhJS3I5F0nSymDhJ/VgV68kaaWx8JN6Wb2hO0qSNOQs/Arg7NEy1cdWdUVJkoadhV8B7FIs09zWq1KcvrLiTCRJWh4WfgVw9miZmpsvTnHLxmoTkSRpmVj4FcBp95IkaX+w8JN6qK8/ritKkjTsLPykHhprD+2KkiQNOwu/Ajirt0xO7pAkrTQWfgVwVm+ZnNwhSVppLPwK4KzeMtkSK/XHz4o0PCz8CuCs3jI1Jo/qipIWZq+FNDws/KQeWq1WV5S0MHstpOFh4Sf10JrZluPWijORymavhTQ8LPykHmrja3JcW3EmkiQtDws/qYdarZZjxYlIkrRMLPykHpq7tnZFSZKGnYWf1EOtNZvjXMWZSJK0PCz8pB5atbEUaVSciSRJy8PCT+phd4sftvhJklYGCz+pB9cmkyStNBZ+Ug/1A2+c49EVZyJJe6e+/riuKLVZ+Ek9jK07sitK0rBorD20K0ptFn4F8ALnZZqdvjzFqUsrzkSS9s7c1qtSnL6y4kxUGgu/AniB8zK1dqaxfS3X8ZMW5ZfX8jQ3X5zilo3VJqLiWPgVwEkEZbKrROqPX16l4WHhVwAvcF4mu0qk/vjlVRoeFn5SD62dm1LctaXaRKSh4YWtpdJZ+BXA8TFlqufZvHVn9UqLsqtXGh4WfgXwoFmmWq2WY8WJSIWzq1caHhZ+BfCgWaZmns3b3OmsXmkxjlOWhoeFXwE8aJbJa/VKklYaCz+ph1ZtLMdGxZlIkrQ8LPykHurrjsjxBhVnIknS8rDwk3qo51nW9VUbKs5EkqTlYeEn9dAec9naeU3FmUiStDws/KQeZq8+P8cLKs5EkvaO68OqFws/aUku5CdpuLg+rHqx8CuA38zK5IFT6k99/XFdUdVzfVj1YuFXAAuMMnnglPrTWHtoV1T1XB9WvVj4FcACo0weOKX+zG29KsXpKyvORNJSxnptiDH+AWj18yAhhMOXLaMR1NpxDYxPWmBIGkrNzRfD5CTNLRurTkXSEnoWfsAL9lsWkiRJGriehV8I4b39PECMcWL50pEkSdKgLNbit1uM8TDgpcBtgPaFS2vAKuBWwEGDSG5U1NcfB80/UF9/XOoykSRJK05avWOG2uqDd48j39/6ndzxTuAk4JfAPYDzgO3AXYFXDSa10eGMuDK5zI4kaTmVsIpHv4XfvYEnhBD+HvgZcE4I4UHAG4A/HVRyo8IZcWUq4QMqSdeHX1zLVMIqHv0WfquAi/LPvwROzD+fDdx9mXMaOe3uXWfElaVVG8uxscQ9JaksfnEtUwnLhPVb+F1A6uIF+AWpixdgHXDAcicllaA+sTZHD5yShksJLUsqU1+TO4AzgPfEGBvAB4HzYow14I+Arw8qOalS4+tgR46SNERcH1a99NXiF0I4B7gX8LMQwoXAnwMHk4q+vx1YdlKFarVajhUnIknSMum3xY8Qwrkd//0msBm4IISwZdmzkgrQarVyrDgRSZKWSb/r+N0MeBdwCmlW77mk9fu2xBgfNK8o1F4qYV0fXZctfpKklabfFr9/ArYAG4GTgRsCtwCeBLwJuNu+JhJjvAdwFnDT/DwvDCF8McZ4F+DtwM2BHwMnhxB+lX9n2bdVoT6xDlrXUp9Yx5yFXzFa+VLVNvhJklaKfmf13gP4hxDC74CHAp/KY/3eCdx+X5OIMY4BHwdeHEI4EHgl8LEY4wH59jeRrg7yReAj+XdWL/e2quxeNgSXDSlKa16UJGnI9Vv47QBWxxgPJC3Y/Pl8+42A5ZgydChwCDCRZws3gZ2kCSVbQgjvDyHMAK8Djokx3o60qPRyb6tErTWbInNVpaAF2NUrSVpp+i38vkharPnTwDbgczHGPwPeA3xsX5PILYnvBj4FzJBa4J4AnACc33G/OdJC0icMaFslWnk3tFpWGCWprz86x2MqzkQqW231hq4oqVz9jvF7KvBa4FjgYSGErTHGm5OKvpfvaxJ5fcDNpG7kLwKPAt4LvJVUaHbaBqwB1g5gW0+Tk5PMzMz08ddcD62rAVjLNTA5OZjn0N777WcBmPjtZ5lwvxRl0v1RmNRrsW58FsbdNyXxs1KmQe6X8fHxRbf3VfiFEKaB58677a3XO6vregRwmxDC8/L/z4kxPok0umr+lUHWANOkYm25t/U0NTU1sMKvtnoD68ZnmZ5p0NqxaSDPob236viHM/G7L7PziPuw68JPVp2OssnJSaamvBpBSTyGlcnPSpkGvV+WpfDLkyyeAtwZGAe6+iRDCI++nvm13Sg/bqdZ4GrSzNt2Hg3gZqRLyG2jY/HoZdpWkfbL2W/Pu/aHmSt/ygQwe+VPq05FKlprx7X5KhGbqk5F0hL67er9d+DhwBdIy7ostwi8Lsb4l8CHSVcGuSup2Hx5jPGJwDnAi4FLSGsJXggcsszbKuFyLmVy0o0kaaXpt/B7GHBSCOGLg0gihPDTGONjgNcA7yAVZw8LIVwSY3wwac29s4CfAI8MIbSA7QPYVonmrmkY92LapakffAJcfS71DScwt+U3VacjSdI+67fwmyItqjwwIYRPAJ9Y4PYfkLqYF/qdZd9WBS+mXabG+mPh6nNpHHQsM5dUnY0kSfuu30FlbwJOizEeOshkpKLM5snfM1urzUOSpGXSb4vfo4HbAb+PMU4Buzo3hhAOX+7EpKrtuvTbjAO7Lv2fqlORJGlZ9Fv4/fNAs5AK1Nx8MUxO0tyysepUJElaFv2u4/feQSciSZKkweq3xY8Y46OBF5DW1bsj8GzgtyGENw0oN0mSJC2jviZ3xBhPBv4V+DgwkW8+H3hVjPFFg0lNkiRJy6nfWb3PB54eQngDpNVsQwj/BjwJePqAcpMkSdIy6rfwuynw/QVu/yFwg+VLRypHbfXBXVGSpGHXb+F3AXCfBW5/NKnLV1px6uuO7IqSNCz84qpe+p3c8VLgv2KMd86/87QY482AhwCPHFRyUpXqa4+AnZdQX3O4V+uVNFS8Brx66avFL4TweeCPgNXAz4D7ATuAu4YQPjW49EaD38zK1Fh3wxQnb1hxJpK0d1q1sRwbFWei0vS9nEsI4efAyYNLZXT5zaxQ9fzxqI9Xm4ck7aVaazZH+yvUrWfhF2M8o98HCSG4pMs+aO6ahnFo7pqqOhV1aOVr9Da9Vq+kIeN5Rb0s1uL3AqAJ/AiYAmo97tda7qRGTWvHNTA+SWvHtVWnog6tbVelH7b9odpEJGkveV5RL4sVfs8BTgLuDETgo8CnQgib90dioySN7Zuhtvrg9GFVEWY3X8IqYHbTxqpTkSRpWfSc3BFC+KcQwr2BY4HPAI8Bfhtj/HyM8ckxxkP3U44rXn1iXVdUYWr9rnokSVLZljyjhRCuCiH8ewjhQcCNgXOABwIXxhi/EmN8xqCTXOmau6ZzdCxGSRprD+2KkiQNu71qygghbA4hfAB4IXAGcCfgnwaR2GjqNYxSVZjbmsb4zU1fWXEmkiQtj76Xc4kx3gF4OPAXwK2Ac4FXAR8bRGKjxOVcytTauQnGobVrS9WpSJK0LBZbzqUG3INU6D0cOBL4KvCvwCdCCL/fHwmOAqfdl2nskBNgy3mMHXICM5d9u+p0JEnaZ4u1+P0eOBD4GnAa8GlgU3tjjHFN++cQwrbBpDcanHZfqMaq7ihpQfX1x0HzD9TXH0dz88VVpyNpEYuN8TsUmADuT2rl+y1pPb/Of9M5SivO3OZLUtz064ozkcrmRChpeCzW4nfv/ZbFiHMdvzI5xk/qjxOhpOHRs/ALIXx9fyYyypzcUSbH+En9aW6+GCYnaW7ZWHUqkpbgyrQFcB2/MtXXHtkVJUkadhZ+BWh37zq5oyy1Wjt6OWpJ0spg4Sf10JzdmeLMroozkSRpeex14RdjPDDGuGEQyYyq+vrjuqLKUBtLy7jUxl3ORVqMxzBpePRd+MUYnx5j/C1wLXBVjPGKGOOLB5fa6HAphLLZ0SstzmOYNDz6KvxijC8gLeL8T6SredwTeAvw4hjjcwaW3YhwKYQyzU1dBkBz6tKKM5HK5jFMGh79Xqv3mcDTQgj/2XHbt2KMvwFeDbx12TMbIa4XV6a5ay6A8Rwl9eRyLtLw6Ler9zDgewvc/n3gRsuXzmiqT6zriipNreoEJElaFv0WfucBj1rg9kcD5y9fOqPJdfzKVF93ZFeUJGnY9dvV+zLg8zHGuwHn5tvuDjwAOGkQiY2S1o5rYHzSdfwKU191IOyE+qpJ5qpORoCXN5SkfdVXi18I4cvAfYGdwOOBRwKbgTuHED4zuPSkCo0dkGJjTbV5aDeHRUjSvum3xY8QwjeBbw4wF6ko9bFU8NXaBaAq19w1DeMOi5Ck66tn4Rdj/DDw5BDClvxzTyGERy97ZiPE7qtCrTkcgNraIypORG0Oi5CkfbNYi99W9qxdu3U/5DKyGpNHwY6NNCaPYtbCrxiNfMWOhlfukCStED0LvxDCEzv++xXg8yGEqwaf0uhptVpdUWWY3XIZDWB2sws4S4ux10IaHv0u53IWcMggExllzenLc7ys4kzUqbn54hS3/LriTKSyOelGGh79Fn7fAR42yERGWfsbsuOWCjO+rjtKWpBrkUrDo99ZvU3gtBjjy4GNwPbOjSGEP1rmvKTK1Wq1HCtORCqck27KY/e7eum38PtO/ieNjD1jLytORJL2Un1iHbSupT6xjjkLP3Xoq/ALIbxq0IlIxZmZztHuK0nDxTUv1UtfhV+M8QDgKcBtgEa+uQasAu4YQjhhMOlJ1Wnc+mT4wak0bv1EZr/2/KrTkaS+2f2uXnpO7ogxHtfx37cDrwaOAP4G2ADcGfhL4L8GmeAoqK8/riuqDBMTq7uiJEnDbrFZvT+MMbZn8j4EeFwI4aHABcArQwi3Ad4D3HjAOa54jbWHdkWVYW5utitKkjTsFiv8XgOcFWOcBNYBP8m3/xy4U/75zUAYXHqjYW5rWhd7bvrKijNRp7lLvwXA7KVeolqStDL0LPxCCG8EbgpMk5ZwuU3edD57Cr8msH6A+Y2EPQsFb6w2EXVpbk8FeWv71RVnIknS8lh0ckcIYRYgxvh24D9ijCcDHwf+J8Z4FXBv4PuDTnKlc72lMjXzvmha+EmSVoi+rtwRQngz8AxgcwjhR/nnk4BtpNm+2gde7qhMtVYzRVzIT5K0MvS7gDMhhP/s+Pls4OwB5DOSXG+pTI1DbgmXbaRxyC2Z/cN5VacjSdI+61n4xRg/3O+DhBAevTzpjCbXWypTbfVBXVGSpGG3WIvf1v2WhVSg1o5NKW63IJckrQw9C78QwhP3ZyKjrL7+OGj+gfr643bP8FX1mlOXpjh9acWZSGVzgpo0PPq9ZFsNeCgLX7LtTiGEBwwmvdHQWHsoTP2BxtpDLfwK0mq1cqw4Ealw9Yl10LqW+sQ65iz8pKL1O7njTcCzgF8CtyYt5nwMcBDwroFkNkJcwLlQtfYPVn7SYpygJg2PvpZzAR4DPDGEcDvg18BfAUcBnyEt6aJ94ALOZaqPrU1xfG3FmUhla3fvOkFNKl+/hd/BwDfyz+cBfxRC2AG8Cnj4APKSqtdYlePqavOQJGmZ9NvVexlwNPAb4ALg9sAHgM3AYcuRSIzxWODtwB8DVwMvCyF8IMZ4PPBu4ETgIuApIYTv5N9Z9m1SW61W74qSJA27fs9o/wF8IMZ4L+BzwN/my7edRhr3t0/y5JFPAt8ljRv8S+DtMcabAB8CvgBsAN4CfCLGeED+1UFsk5LdLX6rqs1DkqRl0m/h9wrg34ADQwjfAN4KnEWa5fuMZcjjbsB64JUhhLnc+nYXYB1wPHBGCGEmhPAe4CrgfjHGWy73tmX4O7SC1MZXd0VJkoZdX129IYQ54A0d/38VaXzfcjkR+DnwTzHGRwNXAi/J+V0UQpjpuO8FwAmkZWWWe9unlvFv6lvjsNvCjo00Drstc14arBi1VQfluL7aRKTC1Q88FlpXUz/wWCepSYVbtPCLMR5Imrzx8RDCVO6SfSHwQOAPwBtDCN9dhjw2AA8AngfcCLg38FHgdK47a3gbsAZYO4BtPU1OTjIzM7PYXa6/HRsBWLNjI0xODuY5tPd+9V4Axn71PibdL0VxfxSmdTUAa1tXewwrjJ+VMg1yv4yPjy+6fbFr9d6UNJN3EvgWMEXq4n0m8GFgC/DlGOMDQgjf3sc8dwKXhxDOyv//Yozxm6SV1OaPvVsDTJOKteXe1tPU1NTACr/GYbdlzY6NbFt1NHNX/Xwgz6G9t+pWf8XEbz/Lrhs9iJ2//FDV6SibnJxkasr14kpSP/BY1rauZisbaE79pup0lPlZKdOg98tShd9iY/xeA/wQOCKEcHGM8XDg6cBHQgiPCSE8ldTde+oy5Pl/wGRuUWxrkGYNHxdjbHTcfgtS1+z5A9hWiXb3rkVfWWYu+25XlLSwdveuRZ9UvsUKv/sCrwohbM//f2C+//s67vMl0iSMffUlYDvwohhjI8b4Z8DdgU8AG4GXxRgn8kziw4AvhxB+sdzbluHv0ApSG0+9/7UJF3CWJK0MixV+60mTLNruDcwAX+u4bYo91+693kII2/Lj34+0ht9bgMeGEC4BTgLuSZp5+1zgYfn+DGibBEBtVRqDUZtYV3EmkiQtj8Umd2wkzXT9Te4WfSDwjXkF0r1Jl3DbZyGEC4CwwO0XAffp8TvLvk1q85JtUn9qqw8GZqitPnj35dsklWmxFr+zScurPBZ4B3AE8Lb2xhjjPYDXAf85yASlqtRWH5TjhmoTkQpXz63idVvHpeIt1uJ3JukavWcBTeAlIYSPA8QYzwKeRVpy5cxBJ7nS+W25TLWJya4oaWGt2hi0oFXb55E/kgasZ+GXF21+Uf433zuBd4cQfjygvEZKfWIdtK6lPrGOOQu/grTmRUkLqbVmc5yrOBNJS+nryh3zhRC8vMQy2v1ted/nyWgZtZpzOc5WnIlUtuauaRiH5i7XjJNK1++1ejVAu78t47flkuxezsXJHdKi2kNUWjuurTgTSUux8CtAc9d0jn5bLkqr1R0lSRpyFn4FqK06KEdnj5akueniHC+sOBNJkpaHhV8BGmsP7YoqQ3NuR467Ks5EKltamWBPlFQuC78CzG29KsXpK5e4p/ansSNO7IqSFtaYPKorSiqXhV8BWjs3pbhrS7WJqEv7ih31PMlD0sJaeRxsy/GwxbAVVr1Y+BVg/Kg/7ooqw9xMujrh7IyXcZYW05y+PMfLKs5EbfV1R3ZFqc3CrwCNyRt2RZWhNpvG+NVnd1aciVQ2l3MpT61Wy7HiRFQcC78CzE1fkeLU5RVnok7NrWm/tFszJGlYzE1d1hWlNgu/Asxc+j8pXvY/FWeiTq3c0tdyVq+kIWMrrHqx8CuAH9AyjR1+264oaWFOJJCGh4VfATxolqk+cWCOkxVnIpXNiQTS8LDwK0DjkFt0RZWh2eqOkhbmRAJpeFj4FaCxLi9+utZvyyWpt2ZznKs4E6lszV1bu6Kkcln4FaD9Jdlvy2WZ3XZ1ilv/UHEmUtlq+UtSzS9JUvEs/Aowmxc9nXXafVnaV1KZma42D6lwzV3TOU5VnImkpVj4FaA5/fuuqDLU1xzWFSUtzJUJpOFh4VeA+kS+JuyqdRVnok61sdU5rqo4E0mSloeFXwkm1nVHFWFuJnVbze2yq1dajEtSScPDwq8ArZ1TXVGF2Pr77ihpQS5JJQ0PC78CtGa25ehSCCWpNVIXr1290uLqjQNyXF1xJpKWYuFXAJdCKNTq9d1R0oJmN29McdOvq01E0pIs/ArQqo2lSKPiTNRl/MAcvWSbtJjm5otT3LKx2kQkLcnCrwATx9wnxWPvW3Em6lRbvSFHB6xLi6mvP64rSiqXhV8BGutu2BVVhrHGRFeUtLDG2kO7oqRyWfgVYHb6ihS9ckdRmrM7u6Kkhc1tvSrF6SsrzkTSUiz8CrDr/z6W4q8+XnEm6jSXxy3Nbbqo4kyksjnGTxoeFn5FqVWdgDq0ZrenOLej4kyksjnGTxoeFn4FqOcrdtS9ckdRGvkavQ2v1SstyjF+0vCw8CuAy7mUqdVYk+MBFWcilc0xftLwsPArQH3dESlO3qDiTNSl1u5692MiLcYxfuXx+snqxTNaAbzcUZn2XLLN5VwkDReHEJWphILcwq8Aczuu7ooqQ60+1hUlLayEk5m6NXdN5zhVcSbqVEJBbuFXgNrYmhzXVpyJOrV7ep1rLS2uhJOZurV2XJPjtRVnok4lFOQWfgVoTB6V4rqjKs5EneZ2buuKkhZWwslMGgYlFOQWfgWojaWxfbXxVRVnok6tnanrvbXLLnhpMSWczCT1x8KvAM18sGxu96BZlJmt3VGSpCFn4VeA2at+meMvKs5EnVq7o6P8JEkrg4VfSWrujpLUVm/I8aBqE5EkaZlYaRTAyx2VyZmKUn9czkUaHhZ+BWjO7sxxR8WZqEs9X6qtvqbaPKTC+SVJGh4WfgVoTB6d4zEVZ6JO9YkDuqKkhXm98fLYCqteLPwK0FhzcFdUGWqNRleUtLD6RFp8vr7KFr9S2AqrXiz8CjA7dUWKWy6vOBN1mpuZ7YqSFtZqtbqiquei2mUqoSXWwq8AzWvOT/HaCyrORJ1qs9M5euCUFtOcvjzHyyrORG0uql2mElpiLfwK4DezMjXnduU4U3EmUtksMqT+lHC+t/ArQG3VQTluqDYRdWnlD2g7SpK0L0r4kmThV4DxG9ypK6oMrZ2bc9xUbSJS4UoYtyQNgxI+KxZ+BaitPbwrqhDtK3Z45Q5pUY3Jo7qipIU5xk9JewyZY8mKMpa/kY2tPqTiTKTCja/rjqpcCS1Lui7H+AmAuU0XpXjthRVnom61HF2iQlrUTB4HO+MEtVKU0LKk63KMnwCY2/TrFDf/uuJM1Glubq4rSlrY3NRlXVHVK6FlSWWy8CtAbXxNjmsrzkSdxhr1HMcqzkQaFrWl76L9zH2ibhZ+BaitmkzRJvmiNBvjOVr4SYuprzuyK6p6dvWqFwu/AtTyN7JazW9mJWnUxnOcqDgTqWyNtTfI8YiKM1GbXb3qpaimjBjjrYAfArcJIVwYY7wL8Hbg5sCPgZNDCL/K9132bVVp7tySox/QotTzx6PeqDYPqXSNVTmurjYP7dbacQ2MT3o1FV1HMS1+McYx4D3Aqvz/1cDHgTcBBwFfBD4yqG1V8jqXhWrXew1bYqVFze3McUe1eWg3l3NRL8UUfsBLgG91/P/ewJYQwvtDCDPA64BjYoy3G9C2ypQwvVvXNbcrnczmdu6sOBOpbK285JELH5XDMX7qpYjCL8Z4e+AvgZd13HwCcH77PyGEOeCifPsgtlVm/Oj7dEWVodacTbE1W3EmUtnqBx7TFVW9Vj69t5zVq3kqH+MXY5wgdfE+NYSwPcbY3rQW2Dbv7tuANQPatqjJyUlmZgZ0ZY1rvwfA6mu/x+rJycE8h/bej14PQP1Hr2fS/VIU90dhNqYRMxMbP8KE+6YMrasBWNu6BtwnxRnkMWx8fHzR7ZUXfsArgK+FEP5n3u3bgAPm3bYGmB7QtkVNTU0NrPCbOO7BrPrDN9h56J+w69efH8hzaO9N3vsM+P5r4P+9nKmvvqjqdJRNTk4yNeVEqJKsOv7hTPzuy+w84j7suvCTVacjoH7gsaxtXc1WDqY5dUnV6ajDoI9hw1D4PRK4YYzxSR23/RB4GmnmLQAxxgZwM+ACUgH3t8u8rTJzV52X4tU/rzINzdNinFqOknqbufKnTACzV/606lSU1WimWHPkpbpVXviFELrG18UYW8AdgcuAN8UYnwicA7wYuAT4GXAhcMgyb6vO6g2w/doUt/ym0lS0R63W7IqSFtYeB1vDyxuWolUbgxa0cDkqdSticsdCQgjbgQcDzwCuBu4PPDKE0BrEtv3713Wr5+n2dafdF6WZD5hND5zSolwsuDz1iXQJ0PoqZ/WqW+UtfvOFEGodP/8AuHOP+y37tsrsXgNre7V5qEt9rNEVJS3MxYLL02q1uqLUVmyL30gZz9/Ixg+sNg91a7a6o6QF1dcf1xVVvdbMthy3VpyJSmPhV4DGmoO7ogrRvlSbl2yTFjW2/tiuqOrtHnfZctyluln4FWBuW7pyx9zWqyrORF1aze4oaUHNPEylOTt/mVRVxXGX6sXCrwC1XFi4vnphmrnga/qNWVrMnm5FxymXwkuBqhcLvwLU1x7eFVWIRu7ibRQ3B0oqytjk0SkeeHTFmUhaioVfAVpz6YogzdldFWeiLnPN7ihpQa28WHDL8WRS8Sz8CtDansf27XCMX1Eate4oaUHNnZtz3FJxJmpzpnWZanm93lqF6/Za+JVgIi/j4nIuZWmvf+U6WNKiGofeJsXDblNxJmprrD+mK6oM9XVHdsVKcqjsmbVb/YCDUlyzodpE1K2WPx41W/ykxYytPrArqgBeGKBI9VUH5jhZXQ6VPbN2a+XlXJrbrq44E3VrF3x+TKTFzG66BICZa39dcSZqm5v+XY6/rzgTdWoPh2jurG6ZHc9oJTj0dgDUDr19xYlI0t6bvernAMxd/cuKM1GbCziXaffSR7uqu6KKhV8BJtYd0hUlaZjU8nAIR0WUwwWcy7S7IKe6gtzCrwCzO7fl6DUVJQ2fVh4P26p5ecPyWI2XpFUby7G6z4qFXwGaV/0kxT/8tOJMJGnvNdbdqCuqeo3Jo7qiylBCF7yFXwFq42tTnFhbcSbq1poXJS2kdkBakaC22pUJStHKy1C1XI6qKCV0wVv4FaDVfiNUOMtHC6nNi5IWUpvdkaJLhxSjOX15jpdVnIk6lXANZQu/ArSnd7cchCtpCM3lk9jcjk3VJqLdSigwVCYLvwI0Dr11V5SkoeKoCGloWPgVoDGxLsXxdRVnom6ezaR+NCZvmOK6G1aciaSlWPgVYC5f4Hxu16ZqE5Gk62PNYSmuPbTaPCQtycKvAK2pS3N0EG5ZnNwh9aPWauZo67hUOgu/EuSu3t1RkoZIcyrNIJ3zy6tUPAu/AjTWHN4VJWmYNJ1BKg0NC78CtGrjKTJWcSaStPfqq13AuTS11Qd3RanNwq8ArW1XpLj9ioozUae5udmuqOp5MitTbXWa1FFffUjFmaitnocO1R1CpHks/ArQygOj21FlaDTqXVHV82RWptr4qhxXV5yJ2lq11IPUolFxJiqNZ7QCjE3eOMejK85EXebmuqMqV8J1LnVdrcaaHA+oOBO11Vqpp6KGxy91s/ArQKuev5nVHONXlMZ4d1TlvAxVmRr13Dpet3WpFH5JUi8WfgVo1lM3SbOxquJMJGnv7b5W73YL8vK4Dqm6WfgVYDyPj2lHSRomrS2/BaC55TcVZ6I2x8OqFwu/AszN7OyKkjRMmrPbAGjNba84E7U5uUO9WPgVoDmbDpbNGQ+akoZPa2ZbV1T1nNyhXiz8CjA2nmbCjU24FIKk4VMbT7N6a2PO6i2FkzvUi4VfAVqNia4oScOkfeWO+gEurF0KZ8CXqYRF6C38ClDPCzfXXcBZWlQJB01dV21isitKWlh93ZFdsZIcKntm7TbTbOVYcSJS4ZypWKh2S58tfsXwS1KZaqvaX5KqO4ZZ+BVgfGJVV5S0MMctlamer9hRbzhOuRSNg2/RFVWGWl5XsVarbn1FC78CtGbncpytOBOpbI5bKlNz+9U5XlVxJmqrj63O0QaFkrRarRyry8HCrwBzzbkc7euVFmP3VaFWHZjjQZWmoT1mN1+S4qaN1SaiLu2Wvgob/Cz8SjA2NtYVJS3MMX5lGj8gzeodz7N7VZCap/mSNHduybG64Sq+IwqQ53bQrLLtVxoCjvEr08zMTIqzuyrORG1j64/piipDc/ryHC+rLAcLvxI086Xamh40pcU4xq9Mte1XdkVVr5Vb+lpU2Keo66jl4RC1VdW1jlv4FWDP5I6ZijORpL3Xqo91RVWv1b5+8qyX0SvJ2Ppju2IVLPwKUMtj+2pj4xVnIkl7r75qfY4HVZuI9sjDIpiZrjYPdWnlWR1VDuyy8CtAvZGm29frroElLcZZvWWq1Vd1RVVvbuqyrqgytGbaLbHbK8vBwq8EjUaKY41q85AK56zeQjXGu6MqVz/wxjkeXXEm6tJugZ1xVu9oa0+3d9q9tChn9ZapThqnXG/NVZyJ2sbytWDHKrwmrK6ruWtrV6yClUYB5nalWb1zO3dWnIk0LJypWJJdW9Ns3l3bfl9xJmqby1+O5nZurjgTdapPrM3Ra/UKPJdJS7Crt0y1xkSOjvErRr4iVKXXBtN17LlkW3X7xcKvAI2J1V1R0sLs6i3T+OqDuqKqVz/gkBSdCFWWeh7LX6tuTL+FXwGaef2+puv4SYtyAecyNfMSFU17LcoxuyPFuR3V5qEu9bG8isd4da3jFn4FqNfa0SZ5SUOo2czRY1gpmrN5EoELOBeltuaGXbEKFn4FaOWDZatZcSKSdD20cuHXajqrtxS1xpquqDLU1x7eFSvJobJn1m613NJXs8VPWpQLOBeqmYepNGerzUO71cfX5HhAxZmoUwmX0rPwK0Cr5bdlqR+NyaO6osrQqte7oqrXqrWjAy9LMvuHn6V45XmV5eCntACtPLvHC5xLiythKQRdVyMfuxp1rz5Uilr+jNQcQ1SUxmS6kkrjwGMqy8HCrwD1Rq0rSlpYc/ryHL3+aEmac6mLtzlrr0Up5rZf3RVVhvr6o7tiJTlU9szq0JgXJS3E5VzK1JxNVx1qunRIMZpb01VUmtNeTaUo7SFdFY6HtfArQbuhz8kdkoZQPa9JVXeMXzEa626Q4mR1y4ZoAXM7u2MFihlUFmO8L3AmcDPgt8BLQwifjDHeBXg7cHPgx8DJIYRf5d9Z9m2VaI9Xsu6TNIwmJrujKtc48i6w5TwaR96Fmcu+XXU6asuXN6TCyxsW8fUsxng48F/AK4GDgOcCH4gx3hr4OPCmfPsXgY/k31m93Nsq05515ewrSUOokU9iDa/VW4zGgUd3RZWhNZMW1m7ly09WoYjCDzgG+FAI4dMhhGYI4UvA/wH/D9gSQnh/CGEGeB1wTIzxdsC9B7CtIvV5UZKGx+4lqVpO7ijFnu53GxRKUssNPLVadef7IiqNEML3QghPa/8/xngccCvgRsD5HfebAy4CTsj/lntbNdrXVJx1YHRR3C9Sf+Z25ej1xksxs2M6x6mKM1Gn2upDUjzgkMpyKGaMX1uM8QbAZ4F3k6Y9zF/eehuwBlg7gG09TU5OMjMzoIPa907dHScnHSNTDPdLsdwfhfnhawGo/fC17ptS/Ph0AMZ/fAbj7pNyXPAuAMYueNfAPivj4+OLbi+q8Mtj+j5LGnf3bNJYv/nXm1kDTJOKteXe1tPU1NTACr/J+7wxFRl3PpWprzx/IM+hved+Kc/4kXdh9dQv2DF5K2Yu/07V6Shbd68zqP3gNbTu9HKmv/aiqtMRsPoOT2P8onOYuelj2fHjf6s6HWWrb/9Uxi/+D2Zu8lh2/HQw+2Wpwq+Irl6AGOOfAN8E3hZCeGoIoUnqkr15x30apFm/FwxoWzXGVndHlcH9Up6xNd1RRag1xruiqjd75U9y/GnFmahTazZP7pitbnJHES1+McYbAZ8CXhBCeHfHpq8Ch8QYnwicA7wYuAT4GXDhALZJKlgzL+Dc9GoERWk107gcrw5Wjlr+clTzS1JRamvTuoq1dUdWlkMRhR/wZGADcFaM8ayO258JPJi05t5ZwE+AR4YQWsD2GONyb5NUsvYVO3ZuqjQNdauNjXVFVa9+wAa4FuqrN1Sdijo0xld3xSoU8SkNIZwKnLrIXe7c4/d+sNzbJJWruWsaxqG5y5mKRdm9CL3fn8uR94VXhCrK7NTvmABmt1xeWQ7FjPGTpKV4rd5CuQh9eVYd1B1Vhp352LVrc2UpWPhJkvZNezHaChelVbdG7uJt2NVblNrE2q5YBT+lkqR91JoXVbVmPr0736YsrQOO6IpVsPCTJO2b5lx3VOVqzR057qo4E3UaX72+K1bBwk+StG+are6oyrXG1uVYXZeirquZ1zxqVvglycJP0tBYdbu/64oqxNh4d1TlGuNrc1xXcSbq1MrXs241q7uutYWfpKExsf5GXVFSD/VGjp7mS1LP42Cr3Cu+IyQNjV2bLwVg56bfVJyJVLa5mW05bq84E3Wam0tdvHOzdvVK0pJ2/vSdAOw6710VZyKVrTa3K8edFWeiTvW5dK3eenNrdTlU9sySJGkwGqtyrO7SYLquuV25JXanhZ8kLam+/riuKGlh9fE1XVFlaBx4dIrrj6ksBws/SUOjsfbQrihpYbX6RI7OtC5JY9VkV6yChZ+koTG39aoUp6+sOBOpbLV6LUdP80WpzYsV8B0haWg0N1+c4paN1SYiFa7VbObo1VSKUsDVDS38JA2N2uqDu6KkXryKihZm4SdpaNQn1nVFSb20C79mpVlovur7ei38JA2NVm0sRRoVZyKVrTm7M8ddFWeiTu1r9HqtXknqQ601myKOW5IWV+HsAfVUz5NuqpxzY+EnaWjUDz6hK0paWK0x0RVVhvaV2iq8YpuFn6Th0VhzcFeUtLB6YzxHC7+SjI2P5Vjd+ooWfpKGRnNXuuB8c+e2ijORCtcY644qQ63eHStg4SdpeOQxfjjGT1pcc7Y7qhDNeXH/s/CTNDT2rOO3oeJMpMLVx7qjytBsdccKWPhJGhqt7dd0RUm9FHCJCF1XezpvhdN6LfwkDY1aXri5Nl7dBc6lodBqdUcVwgWcJalvjXU3SHHyBhVnIhWugEkEKpPvCElDY67VzNHJHZKG0Nxsd6yAhZ+k4bEjj+3bcW21eUjS9dFodMcKWPhJGhq1yeO6oiQNF8f4SVLfxg9Y3xUlSXvHwk/S0JjdOZ3ijumKM5Gk4WThJ2loNKcvA2Bu66UVZyJJw8nCT9LQaKxJV+4YW3NIxZlI0nCy8JM0NFr5km2tVQdXnIkkDScLP0lDY6yxOsWx1RVnIknDycJP0vBojHVHSdJesfCTJEkaERZ+kiRJI8LCT5IkaURY+EmSJI0ICz9Jw6M11x0lSXvFwk/S8Kg1uqMkaa9Y+EmSJI0ICz9JkqQRYeEnSZI0Iiz8JEmSRoSFnyRJ0oiw8JMkSRoRFn6SJEkjwsJPkiRpRFj4SZIkjQgLP0mSpBFh4SdJkjQiLPwkSZJGhIWfJEnSiLDwkyRJGhEWfpIkSSPCwk+SJGlEWPhJkiSNCAs/SZKkETFWdQJViTHeBXg7cHPgx8DJIYRfVZqUJEnSAI1ki1+McTXwceBNwEHAF4GPVJmTJEnSoI1k4QfcG9gSQnh/CGEGeB1wTIzxdpVkM7ujO6oM7pfyuE/K5H4pj/ukTAXsl1qr1arsyasSY/wH4J4hhId33PZ94IwQwoc773v66aevBzZ99KMfZWZmZv8mKkmStBfGx8d5xCMeAXDQKaecsnn+9lEd47cW2Dbvtm3Aml6/MDU1NbDCb9U93sDEj1/Pztu/hF3feulAnkN7z/1SHvdJmdwv5XGflGl/7Jfx8fFFt49qV+824IB5t60BpivIhZ3ffAmAH87CuF/K4z4pk/ulPO6TMpWwX0a18DufNJsXgBhjA7gZcEFlGUmSJA3YqHb1fhU4JMb4ROAc4MXAJcDPKs1KkiRpgEayxS+EsB14MPAM4Grg/sAjQwijN9NFkiSNjFFt8SOE8APgzlXnIUmStL+MZIufJEnSKLLwkyRJGhEWfpIkSSPCwk+SJGlEWPhJkiSNCAs/SZKkEWHhJ0mSNCIs/CRJkkaEhZ8kSdKIsPCTJEkaERZ+kiRJI8LCT5IkaURY+EmSJI0ICz9JkqQRYeEnSZI0Iiz8JEmSRoSFnyRJ0ogYqzqBYTE+Pj7wxx/0c2jvuV/K4z4pk/ulPO6TMg16vyz12BZ+SzsQ4HGPe1zVeUiSJPXrQGDz/Bst/JZ2KXA0sKXqRCRJkvpwIKl+uY5aq9Xaz7lIkiSpCk7ukCRJGhEWfpIkSSPCwk+SNDAxxmOrzkHd3CejzckdyyjG2AK2Aa38bwb4PPDMEMKmfXzs1wI3CiGcvI9prnj5oPZrYGu+qQZcDLwwhPCFqvIaJTHGjcCTQwhxL37nA8CFIYRTY4yfBz4YQnjvoHIsWT6WvDmE8LyO28ZIx5SbhBA2xhj/BPgQsIY0c+81IYR37eXzvBQ4LoTw5AW27d4f+/B3PBu4C/D4GOPRwC+A9SGEuRjjO4DHAf8L/Aq4LITwmuv7XPOe98nA40MI91qOx6vacp5bYowPBV4A/Okyp7lixRi/DNwGuHEIYVeFebSA40MIF+7L41j4Lb/bt3dKjPFA4BPA24DHVJnUiDoohDAbY6wBDwH+K8Z44xDCtVUnpsWFEP6s6hwK8JwY48dCCN9aaGO+/ah9eYIQwuv35ff7cEjHc/0GWNex7W+BPwkhnDvgHFaK5Tq3HIy9fX2LMd4EuClwHvAI4D+rzWjfWfgNUAhhS4zxo8AzAGKMxwNnAXcgTbWOpG+lUzHGrwEbgQcBnwKeA7yTVLBcBJwP7MyPczjwL6Rv0ocB3wEeF0K4bD/9aUMlhNACPh1jBDg+xngG132tzwLuAxxBapV4YgjhvBjjqcCxpBPs3YD/A54SQvg+QIzx74HnA+uBrwJPCiFcG2N8EHAqcDOgCbwrhHDKfvhzi5Lf118HHgncCPg26b16TYzxCOBs4B7A90jv7ws7fu8DIYR/jzHeDvh34Bb5seaAT4YQzs6fhX8m7btrgVeHEN6fH2Mj8K/A04ANwBeAk0MIOxf7LA7w5dhbZwNnxxhvH0LYOn/jYu+xGONdgTcBtya1fp8SQvjiAo9xKnCzEMLjl9gf6/LjPTTf/s8hhDPztq+xwD4G7g68FKjl/fSUnMs4cBWp+Igxxn/I9700hPCyGOMhpOPbnwHTwBkhhLfGGBvAacDDgSOB3wDPCCF8NbeGvhF4Qn7sL3f8jRPAa4HH5+f8NPC8wvb1Xlng3PI1Oo5pIYQn59f12aRi+9Ok49xNgLcD4zHGC0IIt/BYtaQnAZ8lnWefQS788mv+38DfADcmfXYi8GbS+eDVIYQ35/s+AXge6VyyDTgtv6ePBb4LfAV4APBo4Cf0OKZ16vWY/fxBVv0DlLs2Hks6KEIq5M4lFRE3AY4nvWnabgkcR9qZrycVITcCngg8sON+ZwCbSB/UI0hN/88dzF8x/GKM9RjjY4FrgJ/mmztf6xcCNyA15W8ALgBe0fEQfwW8GjiU9KF8fX7ch5BObH8OHA5sB87MJ8kPAi8OIRwM3B/4hxjjrQf4Z5bsL0nv3+NI7+en5dvfQTpJH0o6Md93/i/GGMdJLRsfyff7D9KJv+0DpP16I+AvgNNijHfp2P4Q4M7AicC9SN/YYenPYgnOAK4GTp+/YbH3WC7g/pv0Nx4CvJjU2n38Es+32P54E+kzcnNSYXhyjPEvO7ZfZx+HED5D+qx8KIRw/84nCiEclH+8fQjhHfPy+DfSuekoUnfkS3O39l/nnO5CKtY/lV8jSAXOfYATgADcr+PxXkPa93fK229IKvqH1gLnFug4puXj3TNJ74ubkIYDnBlCOI/0+Ts3F30eqxYRY6yTvkycTToG3SbGeJuOuzyW9B49EXgq8Hek88hjgNNjjGtijDclvd8eH0JYn+9zZozxoPwYhwE/IH2Z+TpLH9Po4zEXZYvf8vthjLFJGlc2RToAvzhv+xvg98ABpIPa1aSDadtnQgjTADHGk0itR1uAH8cY30++ikh+vK2kg+ONSW+SzsdRclVu5TsAmABODSHsyLd1vtb/RCqed5EOkpuA23U8ztdDCN/M9/0wqTUC0rezd4cQfpq3/T3pQ7wduEMI4eLcejFJarm4AfDzgf215XpvCOG3ADHGzwE3izGuJrVO3CqEsAP4ch7XN9/dgFXAP+aW2w/m15kY4w1JJ/uT8mP8LMb4HuBk0rdzgLeFEK4Gro4xfoP0ZQmW/iyWYI500vlhbt35Zse2xd5jdwPODyG8J9/3C/m1/UtSQXcdi+2PPFTir4E75uPRlhjjm0mv84fyQ1xnH1+fPzjn8VDgNvnzOR1jvA9wBelL2+dJ4xmPJh1f2/vsJOCsEMLv8uP8c34cSK2PTwkhXJG3vQj4UYzxySGEueuTZ0UWO7dA9zHtCcAbO7qGXw78NMb4jHmP6bFqcfcHNoUQvgcQYzwHeDqpqAZ4fwjhSuDKGOMVpNbS6Rjjf5Natm8A/Ba4bQjhNzHGGwCzedshpM84pPHM2/s8prHEY25a6o+y8Ft+d1xk4OWtgc+Rxlj8mNQcXOvY/ruOn48AOrtuLwFum3++Makp+CakcQcHkCYvqNuhIYRZgBjjHYCPxhjb3Tudr/UGUvfHicAvSc3mnfvlDx0/z7CnpfwIOj6MIYSrSC0mxBgfEWN8bn6s7+fH63zMUbLQ63cw6fgz/z0+31HA5bnom3+/G+fHujQX8wANUpf7Ys8NS38WixBCOD/G+ArgPaT3Z/v2uUXeY4dx3dfyN6QWhF4W2x+HAauBczte5zqp27at1+u8tzaQTmC78wgh/BwgxngY6XN6T1IX9OXs2WcLHS/b5r8evyF9ETyM7uNA6RY7t0D333JjUgvQGzpuq5N6JnZb4n2kNA71ZjHG9mu7GqjHGNtd4dd03HeOfHm0EEIzf1bqpKLs2bkY/wN7zhkLnfv7OabR52P2ZFfvfpLHmXwEeFEI4cgQwoO4brHWeXL7HelbbdsNO34+Bzg7hHB4COG+pDECWkQI4cfAx9nTBdT5Wv8raUzTYSGEPyEVBP24jNQ8D6Tm9xjjS2KMdyd1H989hHA8qTtA3a4hFQi93uNtlwJH5VantnYB8ztgB3BICOGg3H14POlg3VOfn8WSvIn0Xntj+4Yl3mOXAsfMe4xjgSsXeY7F9sfVedutOl7nm5BaCJfblfm5Oj9Xj82tfq8j9XQcEUL4I1L3W9tix8v5r8ex+TlW2iSv+eePp3Tsr8NJvRhd7wGPVb3lFtCHkFrQ75D/nUD6EvH4fLd+Ln32V8CDgVuGEG5NGl40X/tx+j2m9fOYPdnit/+sIn1bmI57Zpk+kDSebCH/AbwsxvhdUnPx37CnIDmIvFRJ/uA+no7BzLqumGZmPYw0fuLe8zYfBGzL39JuBTyLdLJbygdJg+/PIU3AeQXphPIT0re/HTHGVaTumINILRkCcpf7x4DXxhj/Brgj6TPxs3l3PZfUHfW8GONb833+mNSl8psY4/eB18QYX0Uam/YlUuvYmYs8/d5+FiuV35cnk1om2w6i93vsc8BbY4xPBN5PGvP2YNJYt17P0XN/5FahDwFviDE+Mz/HR0ldgc/s9ZjZTvYMUennb52LMf4X8KqYlmS5AWlf/lX++zYDczHGG+e/uf2Z+g/S2LTPkk6iz2JPK8oHgFNjjD8ivZdOAz4RQtjZb15D6BzgBTHGb5G6yV9Peo/flrRPJvP9DsJjVS9/DfwghPCTzhtjWubo6fT/xeEg0jCiXXlMZXvM7jip5W63vTimLfaYS7LFbz/JM8ieQ2ppuJo0E/TdpAG5C3klaSbvr4GPAZ/p2PZ00htjM/BW0ozHXo8zyjbFGKdz9+7/kGa2vW6B+z0feFy+30dILQk3jWliQU8hhP8mnUw/QzrJTJC+eX2BNBbp/0jdSjcjFebuo25PJ3VjXEmaCffZ+XfIXfWPJh2EryGNefs+6aAHqSC4BalF7Iek1/1Niz3p9fgsVi6E8CvgJR039XyP5TGNDyYNNr+W1FL4V/NPYAtYbH88i1QgXJj/bSS1FC3ls8CdY4zfWfKe3c/VJLWsfA14VR5jeyqpIN1M6vr6KHBQ7gJ+B+k4+RPSrOIvdTze60njI39AOp5eRRoMv5K9G/gwabLAVaRhAiflIRNfB9bk7ssv4rGqlyeRvtzPdw5pAscf9/k47yW9ly8jvc7bSV9we73G/RzT9vYxu9RarX5aKiVp/4sxrgVODB1r2eUi4pXBxbglaa/Z4iepZDPA52OM9wKIMT6QtKTI/1aZlCQNK1v8JBUtpvUSzyRN6riItPjuV6rNSpKGk4WfJEnSiLCrV5IkaURY+EmSJI0ICz9JkqQR4QLOkgTEGDdy3SteTJPWhnthCOHc/ZTHfwKfCyG8P///PqQ18/6ItM7eT4DXhhC+1PtRJGlhtvhJ0h4vJV3u64aky4bdh3QJpc/EGCcX+8XlkK8kch/yAsQxxqeSFkH+FukatXclLUb8+RjjIwadj6SVxxY/SdpjKoTQebH7K/Ll0n5LutTfpwb8/CcCvw8h/C7GeAzwFuAZIYT3dNznZTHGQ4E3xxg/ma9uIkl9sfCTpMW1r+k6l68kcibwcOAw0qX63hZCeD1AjLFOurTaU4BDSJeXe3YI4bwY4xjwKuBxwFGkS9D9J/D8EMJcfo77Af+df34s6ZJr710gp1eRLlM2l5/3ZsA/kloFm6TLlz0vhDCVF7/+L9L1Pp8C/GcI4Wn79pJIGlYWfpLUQ25ZewupwPsG6Rq2dyYVflcBjwDOiDF+PoTwI+AVwDNJ18n9KfD/kbpljwNeQLrm8F+TrrP5p6Rrqn4d+Hh+yvsDZ+Sf70C6SHxzfl4hhCuAK3KOG0hdwd8E7kEqON+eH/tR+VcOIV2H9Y44xEcaaRZ+krTHG2OMp+Wf66TJFN8EQm49+x/g7SGEH+b7nBljfAVwmxjjj4FnAK8JIXwMIMb4LODVwMHAL4CTQwhfz7+7Mcb4QtIF3z8eYzwAuBOpwATYAFzZR86Py3n+TQhhe37ek4H/jTEe33G/00IIF+3FayFpBbLwk6Q93gB8AFhFKuJOAk4NIfw8b38f8JAY4+OBW5Ba5daRCq9DSd2/32s/WAhhK/D8/N9PxBjvGWM8Pf/ubYHjgI/m7fcEvt8u3kgtihv6yPlWwI87fo+cwy7g1sCmfNvFfTyWpBXOJn9J2uOqEMKFIYSfhxCeSWrt+3SM8cZ5+7uBt5Fm+r4fuDt7CqtdOdYWeuAY4yuBT5C+cH8MeDDw4467dI7vg1S8/b8803f+Y90qxvi5GOPROZeF1EgFadv2HveTNEIs/CSpt2cAs8DbYoyHACeTulRfGkL4IGnix3qgFkLYDPyeNI4OgBjjqhjjpTHGu5PG+D0/hPD8EML7gI2kdQPbhd39ycu4ZB8BDszPOd8/kFobLwd+CdwhdxW33RkYz9skaTe7eiWphxDCVTHGFwP/DvwJMAWcFGP8NWmdvzNJhduq/CtvJC23shG4gDS5YyfwA+Ay4MExxm+QCrpTSV25q2KMNwQOp6MFMIRwaYzxJcDbY4w3IE0AWQU8GXgS8BchhNkY4zmkSSXvizG+ijSe8G1ADCH8IsZ4+CBeG0nDyRY/SVrcu4FvA28FHg8E0kSN95G6Zj9HmpQB8CbSMiufBv6PVBw+KISwk9RydxPgPFKX70bgXfl3A/DlEEKr84lDCG8hLevyYOBc4GukySAhhPCpfJ9twANILY/fI40Z/AZpfKIkdam1Wq2l7yVJ6luM8V+Bm5K6hX9fdT6S1GaLnyQto45Fnm9Eaq2TpGJY+EnS8voH0qSKKeDDFeciSV3s6pUkSRoRtvhJkiSNCAs/SZKkEWHhJ0mSNCIs/CRJkkaEhZ8kSdKIsPCTJEkaEf8/H+fmYTWj7UkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['RaçaCor_Lib'], \n",
    "            df['Salário Mensal'], \n",
    "            marker = \".\",\n",
    "            alpha = 0.2\n",
    "           )\n",
    "plt.title('Dispersão: Salário Mensal x Raça/Cor')\n",
    "plt.xlabel('Raça/Cor')\n",
    "plt.ylabel(\"Salário Mensal\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uYwKhXEH8fAR",
    "outputId": "cb286730-dc18-49f8-80f7-2e170e379b7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Admitidos/Desligados                  2\n",
       "Competência Declarada            201812\n",
       "Município                        520870\n",
       "Ano Declarado                      2018\n",
       "Grau Instrução                        7\n",
       "Idade                                30\n",
       "Raça Cor                              8\n",
       "Salário Mensal                      954\n",
       "Sexo                                  1\n",
       "Tempo Emprego                        19\n",
       "UF                                   52\n",
       "log_Salário                     6.86171\n",
       "normal_log_salário            -0.338373\n",
       "log_Idade                        3.4012\n",
       "normal_log_idade             -0.0585204\n",
       "NovaCol                  Médio Completo\n",
       "RaçaCor_Lib                       Parda\n",
       "Name: 142830, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Salário Mensal'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "O6oYrjPE8fAR",
    "outputId": "2c2f6b7f-b6eb-458a-9e49-d3f44e531670"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Admitidos/Desligados                    1\n",
       "Competência Declarada              201812\n",
       "Município                          500215\n",
       "Ano Declarado                        2018\n",
       "Grau Instrução                          6\n",
       "Idade                                  24\n",
       "Raça Cor                                8\n",
       "Salário Mensal                        954\n",
       "Sexo                                    1\n",
       "Tempo Emprego                           0\n",
       "UF                                     50\n",
       "log_Salário                       6.86171\n",
       "normal_log_salário              -0.338373\n",
       "log_Idade                         3.17805\n",
       "normal_log_idade                -0.734573\n",
       "NovaCol                  Médio Incompleto\n",
       "RaçaCor_Lib                         Parda\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Salário Mensal'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZGXCNZ6E8fAR",
    "outputId": "83e2e19a-8881-472a-8977-f7b268964452"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.07772690357307918"
      ]
     },
     "execution_count": 58,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Salário Mensal'].corr(df['Raça Cor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DQRo4mqm8fAR",
    "outputId": "32a81b2b-f954-4143-fbc8-316496c0db19"
   },
   "outputs": [
    {
     "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>Salário Mensal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RaçaCor_Lib</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amarela</th>\n",
       "      <td>2111.330215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Branca</th>\n",
       "      <td>1824.911142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indigena</th>\n",
       "      <td>1767.240125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Não identificado</th>\n",
       "      <td>1497.205830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parda</th>\n",
       "      <td>1459.304828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Preta</th>\n",
       "      <td>1451.797236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Salário Mensal\n",
       "RaçaCor_Lib                     \n",
       "Amarela              2111.330215\n",
       "Branca               1824.911142\n",
       "Indigena             1767.240125\n",
       "Não identificado     1497.205830\n",
       "Parda                1459.304828\n",
       "Preta                1451.797236"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_rc = df[['RaçaCor_Lib', 'Salário Mensal']].groupby(df['RaçaCor_Lib'])\n",
    "media_rc = grouped_rc.mean()\n",
    "media_rc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eRSbSwJZ8fAR",
    "outputId": "9d51a42d-5dcc-47da-9f55-7de341810578"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2111.330215239306"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal_amarelo = media_rc['Salário Mensal'][0]\n",
    "sal_amarelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Irfzy36A8fAR",
    "outputId": "53a1e723-5f48-4402-f5ce-a7a642f2f972"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amarelos que ganham mais que a maior média por raça: 2990 pessoas\n",
      "Brancos que ganham mais que a maior média por raça: 252801 pessoas\n",
      "Indigenas que ganham mais que a maior média por raça: 2290 pessoas\n",
      "Não Identificados que ganham mais que a maior média por raça: 55591 pessoas\n",
      "Pardos que ganham mais que a maior média por raça: 144456 pessoas\n",
      "Pretos que ganham mais que a maior média por raça: 22984 pessoas\n"
     ]
    }
   ],
   "source": [
    "print('Amarelos que ganham mais que a maior média por raça:', df.loc[df['RaçaCor_Lib'] == 'Amarela'].loc[df['Salário Mensal'] >= media_H].shape[0],'pessoas')\n",
    "print('Brancos que ganham mais que a maior média por raça:', df.loc[df['RaçaCor_Lib'] == 'Branca'].loc[df['Salário Mensal'] >= media_H].shape[0],'pessoas')\n",
    "print('Indigenas que ganham mais que a maior média por raça:', df.loc[df['RaçaCor_Lib'] == 'Indigena'].loc[df['Salário Mensal'] >= media_H].shape[0],'pessoas')\n",
    "print('Não Identificados que ganham mais que a maior média por raça:', df.loc[df['RaçaCor_Lib'] == 'Não identificado'].loc[df['Salário Mensal'] >= media_H].shape[0],'pessoas')\n",
    "print('Pardos que ganham mais que a maior média por raça:', df.loc[df['RaçaCor_Lib'] == 'Parda'].loc[df['Salário Mensal'] >= media_H].shape[0],'pessoas')\n",
    "print('Pretos que ganham mais que a maior média por raça:', df.loc[df['RaçaCor_Lib'] == 'Preta'].loc[df['Salário Mensal'] >= media_H].shape[0],'pessoas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y_KqeK6x8fAS"
   },
   "source": [
    "#### Os brancos ganham mais que os outros grupos étnicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F5zSfhWR8fAS"
   },
   "source": [
    "### 7. Elaborar uma hipótese para ser rejeitada ou não-rejeitada por você de acordo com o resultado da sua análise, através da utilização dos dados do CAGED considerando as suas colunas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "executionInfo": {
     "elapsed": 2290,
     "status": "ok",
     "timestamp": 1605713014443,
     "user": {
      "displayName": "Luis Henrique Batista Arantes",
      "photoUrl": "",
      "userId": "13194031754074181800"
     },
     "user_tz": 180
    },
    "id": "Zis5tlo18fAS"
   },
   "source": [
    "##### <font color='orange'> Hipótese Nula ($H_0$): Existem 45% ou mais pessoas admitidas do que demitidas no mercado de trabalho, de 2018 </font>\n",
    "##### <font color='orange'> Hipótese Alternativa ($H_1$): Existem 45% menos pessoas admitidas do que demitidas no mercado de trabalho, de 2018 </font>\n",
    "##### <font color='red'> Nível de significância de 5% </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iGH77y5fHhBP",
    "outputId": "3eb00d7a-d8e0-4013-d492-cf1f755c2ef2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histo_AD = plt.hist(df['Admitidos/Desligados'], \n",
    "                       bins=200, \n",
    "                       #range = (0,10000000)\n",
    "                      )\n",
    "plt.xlabel('Admitidos/Desligados')\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.rcParams['figure.figsize'] = (10,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aIxwLNDyHhBQ"
   },
   "outputs": [],
   "source": [
    "#Admissão = 1\n",
    "#Desligamento = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "u8xWLXagHhBQ"
   },
   "outputs": [],
   "source": [
    "#média\n",
    "mean = df['Admitidos/Desligados'].mean()\n",
    "#desvio padrão amostral\n",
    "std = df['Admitidos/Desligados'].std()\n",
    "#número de observações\n",
    "n = len(df['Admitidos/Desligados'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "e6Ufj8VYHhBQ",
    "outputId": "94f116d0-7351-49e3-9d3d-951079f116da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5740822722087529, 1.5741227587383781)"
      ]
     },
     "execution_count": 65,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# intervalo de confiança\n",
    "# da tabela Z-Score: 0.05 / 2 = 0.025 --> 0.0615\n",
    "mean - 0.0615*std/np.sqrt(n), mean + 0.0615*std/np.sqrt(n) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZqsPc-GCHhBQ",
    "outputId": "35580666-913d-45f1-af16-ea52d1172a69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5740818749872005, 1.5741231559599305)"
      ]
     },
     "execution_count": 66,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando o intervalo de confiança pelo Scipy.Stats\n",
    "st.norm.interval(alpha=0.05, loc=mean, scale=std/np.sqrt(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "moj9x1eeHhBR"
   },
   "outputs": [],
   "source": [
    "# isolando os limites de valor inferior e superior\n",
    "limite_inferior = st.norm.interval(alpha=0.05, loc=mean, scale=std/np.sqrt(n))[0]\n",
    "limite_superior = st.norm.interval(alpha=0.05, loc=mean, scale=std/np.sqrt(n))[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EXO6-GomHhBR",
    "outputId": "63a9daa1-32df-4476-c6b6-0c25fafdd92c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5740818749872005, 1.5741231559599305)"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limite_inferior, limite_superior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rVXIsgNfHhBR",
    "outputId": "e093f8bd-2334-4fbb-8ffa-d9bd75d89ab2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 69,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_H0 = 0.45\n",
    "limite_inferior < p_H0 < limite_superior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "NYuGcLMBHhBS",
    "outputId": "6e353a82-9dea-44fd-c29a-c1e95a55b9d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sided_p_value = st.norm(loc=mean, scale=std/np.sqrt(n)).sf(0.45) \n",
    "sided_p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EEipn88MHhBS",
    "outputId": "0451f518-e42d-4aaf-e5d0-625faae4b20a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value = 2*sided_p_value\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4i6CKm-vHhBS",
    "outputId": "a2a6e443-c3b6-437c-98aa-13af54baf579"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=318.3776024547895, pvalue=0.0)"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(a=df['Admitidos/Desligados'] == 2,\n",
    "                b=df['Admitidos/Desligados'] == 1, \n",
    "                equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "g6QnnDJiHhBS"
   },
   "outputs": [],
   "source": [
    "#Conclusão: que existem mais pessoas desligadas do que empregadas, no mercado de trabalho."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CZGvJojtHhBT"
   },
   "source": [
    "#### 8. Avançado: plote um SEABORN heatmap com **todas as correlações das variávies numéricas entre si**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fTsMEWm4HhBT"
   },
   "outputs": [],
   "source": [
    "df_heatmap = df.drop(columns = ['NovaCol', 'log_Salário', 'normal_log_salário', 'log_Idade', 'normal_log_idade', 'RaçaCor_Lib', 'Competência Declarada', 'Ano Declarado' ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8o071NEAHhBT",
    "outputId": "88631e46-de5c-4cff-ceca-66c50029742e"
   },
   "outputs": [
    {
     "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>Admitidos/Desligados</th>\n",
       "      <th>Município</th>\n",
       "      <th>Grau Instrução</th>\n",
       "      <th>Idade</th>\n",
       "      <th>Raça Cor</th>\n",
       "      <th>Salário Mensal</th>\n",
       "      <th>Sexo</th>\n",
       "      <th>Tempo Emprego</th>\n",
       "      <th>UF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Admitidos/Desligados</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009665</td>\n",
       "      <td>-0.031150</td>\n",
       "      <td>0.110487</td>\n",
       "      <td>-0.039206</td>\n",
       "      <td>0.044920</td>\n",
       "      <td>-0.013509</td>\n",
       "      <td>0.332575</td>\n",
       "      <td>0.009635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Município</th>\n",
       "      <td>0.009665</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012695</td>\n",
       "      <td>-0.012762</td>\n",
       "      <td>-0.149845</td>\n",
       "      <td>0.026354</td>\n",
       "      <td>0.027401</td>\n",
       "      <td>-0.005550</td>\n",
       "      <td>0.999744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grau Instrução</th>\n",
       "      <td>-0.031150</td>\n",
       "      <td>-0.012695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.110878</td>\n",
       "      <td>-0.062680</td>\n",
       "      <td>0.184702</td>\n",
       "      <td>0.205090</td>\n",
       "      <td>0.041182</td>\n",
       "      <td>-0.013805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idade</th>\n",
       "      <td>0.110487</td>\n",
       "      <td>-0.012762</td>\n",
       "      <td>-0.110878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036528</td>\n",
       "      <td>0.151294</td>\n",
       "      <td>-0.066302</td>\n",
       "      <td>0.276203</td>\n",
       "      <td>-0.013023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raça Cor</th>\n",
       "      <td>-0.039206</td>\n",
       "      <td>-0.149845</td>\n",
       "      <td>-0.062680</td>\n",
       "      <td>-0.036528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077727</td>\n",
       "      <td>-0.059259</td>\n",
       "      <td>-0.092636</td>\n",
       "      <td>-0.147059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salário Mensal</th>\n",
       "      <td>0.044920</td>\n",
       "      <td>0.026354</td>\n",
       "      <td>0.184702</td>\n",
       "      <td>0.151294</td>\n",
       "      <td>-0.077727</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042394</td>\n",
       "      <td>0.201733</td>\n",
       "      <td>0.024928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sexo</th>\n",
       "      <td>-0.013509</td>\n",
       "      <td>0.027401</td>\n",
       "      <td>0.205090</td>\n",
       "      <td>-0.066302</td>\n",
       "      <td>-0.059259</td>\n",
       "      <td>-0.042394</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009666</td>\n",
       "      <td>0.026933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tempo Emprego</th>\n",
       "      <td>0.332575</td>\n",
       "      <td>-0.005550</td>\n",
       "      <td>0.041182</td>\n",
       "      <td>0.276203</td>\n",
       "      <td>-0.092636</td>\n",
       "      <td>0.201733</td>\n",
       "      <td>0.009666</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UF</th>\n",
       "      <td>0.009635</td>\n",
       "      <td>0.999744</td>\n",
       "      <td>-0.013805</td>\n",
       "      <td>-0.013023</td>\n",
       "      <td>-0.147059</td>\n",
       "      <td>0.024928</td>\n",
       "      <td>0.026933</td>\n",
       "      <td>-0.005795</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Admitidos/Desligados  Município  Grau Instrução  \\\n",
       "Admitidos/Desligados              1.000000   0.009665       -0.031150   \n",
       "Município                         0.009665   1.000000       -0.012695   \n",
       "Grau Instrução                   -0.031150  -0.012695        1.000000   \n",
       "Idade                             0.110487  -0.012762       -0.110878   \n",
       "Raça Cor                         -0.039206  -0.149845       -0.062680   \n",
       "Salário Mensal                    0.044920   0.026354        0.184702   \n",
       "Sexo                             -0.013509   0.027401        0.205090   \n",
       "Tempo Emprego                     0.332575  -0.005550        0.041182   \n",
       "UF                                0.009635   0.999744       -0.013805   \n",
       "\n",
       "                         Idade  Raça Cor  Salário Mensal      Sexo  \\\n",
       "Admitidos/Desligados  0.110487 -0.039206        0.044920 -0.013509   \n",
       "Município            -0.012762 -0.149845        0.026354  0.027401   \n",
       "Grau Instrução       -0.110878 -0.062680        0.184702  0.205090   \n",
       "Idade                 1.000000 -0.036528        0.151294 -0.066302   \n",
       "Raça Cor             -0.036528  1.000000       -0.077727 -0.059259   \n",
       "Salário Mensal        0.151294 -0.077727        1.000000 -0.042394   \n",
       "Sexo                 -0.066302 -0.059259       -0.042394  1.000000   \n",
       "Tempo Emprego         0.276203 -0.092636        0.201733  0.009666   \n",
       "UF                   -0.013023 -0.147059        0.024928  0.026933   \n",
       "\n",
       "                      Tempo Emprego        UF  \n",
       "Admitidos/Desligados       0.332575  0.009635  \n",
       "Município                 -0.005550  0.999744  \n",
       "Grau Instrução             0.041182 -0.013805  \n",
       "Idade                      0.276203 -0.013023  \n",
       "Raça Cor                  -0.092636 -0.147059  \n",
       "Salário Mensal             0.201733  0.024928  \n",
       "Sexo                       0.009666  0.026933  \n",
       "Tempo Emprego              1.000000 -0.005795  \n",
       "UF                        -0.005795  1.000000  "
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_heatmap.corr()\n",
    "#sns.heatmap(df_heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w022HbgJHhBT",
    "outputId": "67e35402-eeac-4d1b-be16-f47da85fb3ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 84,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 7)) \n",
    "\n",
    "sns.heatmap(\n",
    "    df_heatmap,\n",
    "   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "tRCrmWpCHhBU"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "desafio1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
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 "nbformat": 4,
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}