Mini-Projeto3.ipynb

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   "source": [
    "# <font color='blue'>Data Science Academy</font>\n",
    "# <font color='blue'>Linguagem Python e Power BI Para Manipulação de Dados</font>\n",
    "\n",
    "## <font color='blue'>Mini-Projeto 3</font>\n",
    "## <font color='blue'>Dashboard de Recursos Humanos Com Power BI e Linguagem Python</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](mini-projeto3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definição do Problema\n",
    "\n",
    "A empresa ZuZa Inc coletou dados do ano anterior com diversos atributos de funcionários de todos os setores. O objetivo é estudar a relação desses atributos e o impacto na promoção dos funcionários. Esse trabalho de análise pode ser usado mais tarde para construir modelos de Machine Learning para prever se um colaborador será ou não promovido.\n",
    "\n",
    "Essas são as perguntas que devem ser respondidas:\n",
    "\n",
    "- Pergunta 1 - Qual a Correlação Entre os Atributos dos Funcionários?\n",
    "- Pergunta 2 - Qual o Tempo de Serviço da Maioria dos Funcionários?\n",
    "- Pergunta 3 - Qual Avaliação do Ano Anterior Foi Mais Comum?\n",
    "- Pergunta 4 - Qual a Distribuição das Idades dos Funcionários?\n",
    "- Pergunta 5 - Qual o Número de Treinamentos Mais Frequente?\n",
    "- Pergunta 6 - Qual a Proporção dos Funcionários Por Canal de Recrutamento?\n",
    "- Pergunta 7 - Qual a Relação Entre a Promoção e a Avaliação do Ano Anterior?\n",
    "\n",
    "Seu trabalho é limpar e preparar os dados e então construir um dashboard no Power BI para apresentar seus resultados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregando e Instalando Pacotes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Versão da Linguagem Python Usada Neste Jupyter Notebook: 3.7.6\n"
     ]
    }
   ],
   "source": [
    "# Versão da Linguagem Python\n",
    "from platform import python_version\n",
    "print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para atualizar um pacote, execute o comando abaixo no terminal ou prompt de comando:\n",
    "# pip install -U nome_pacote\n",
    "\n",
    "# Para instalar a versão exata de um pacote, execute o comando abaixo no terminal ou prompt de comando:\n",
    "# !pip install nome_pacote==versão_desejada\n",
    "\n",
    "# Depois de instalar ou atualizar o pacote, reinicie o jupyter notebook.\n",
    "\n",
    "# Instala o pacote watermark. \n",
    "# Esse pacote é usado para gravar as versões de outros pacotes usados neste jupyter notebook.\n",
    "!pip install -q -U watermark"
   ]
  },
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   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Data Science Academy\n",
      "\n",
      "matplotlib: 3.3.3\n",
      "seaborn   : 0.10.0\n",
      "pandas    : 1.1.5\n",
      "numpy     : 1.19.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Versões dos pacotes usados neste jupyter notebook\n",
    "%reload_ext watermark\n",
    "%watermark -a \"Data Science Academy\" --iversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregando os Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 52229,
     "status": "ok",
     "timestamp": 1593698494406,
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   "outputs": [],
   "source": [
    "dadosRH = pd.read_csv('dadosRH.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_funcionario</th>\n",
       "      <th>departmento</th>\n",
       "      <th>regiao</th>\n",
       "      <th>educacao</th>\n",
       "      <th>genero</th>\n",
       "      <th>canal_recrutamento</th>\n",
       "      <th>numero_treinamentos</th>\n",
       "      <th>idade</th>\n",
       "      <th>aval_ano_anterior</th>\n",
       "      <th>tempo_servico</th>\n",
       "      <th>promovido</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>Mestrado ou Doutorado</td>\n",
       "      <td>F</td>\n",
       "      <td>Outsourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65141</td>\n",
       "      <td>Logistica</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outro</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outsourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Norte</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outro</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Tecnologia</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outro</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id_funcionario departmento    regiao               educacao genero  \\\n",
       "0           65438   Marketing   Sudeste  Mestrado ou Doutorado      F   \n",
       "1           65141   Logistica  Nordeste               Superior      M   \n",
       "2            7513   Marketing   Sudeste               Superior      M   \n",
       "3            2542   Marketing     Norte               Superior      M   \n",
       "4           48945  Tecnologia  Nordeste               Superior      M   \n",
       "\n",
       "  canal_recrutamento  numero_treinamentos  idade  aval_ano_anterior  \\\n",
       "0        Outsourcing                    1     35                5.0   \n",
       "1              Outro                    1     30                5.0   \n",
       "2        Outsourcing                    1     34                3.0   \n",
       "3              Outro                    2     39                1.0   \n",
       "4              Outro                    1     45                3.0   \n",
       "\n",
       "   tempo_servico  promovido  \n",
       "0              8          0  \n",
       "1              4          0  \n",
       "2              7          0  \n",
       "3             10          0  \n",
       "4              2          0  "
      ]
     },
     "execution_count": 10,
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   ],
   "source": [
    "dadosRH.head()"
   ]
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    {
     "data": {
      "text/plain": [
       "(54808, 11)"
      ]
     },
     "execution_count": 11,
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   ],
   "source": [
    "dadosRH.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise Exploratória, Limpeza e Transformação de Dados\n",
    "\n",
    "Vamos manipular os dados e corrigir eventuais problemas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 52608,
     "status": "ok",
     "timestamp": 1593698494813,
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    "outputId": "5920b0bd-1740-47c5-e80f-8ee0460291a0"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_funcionario            0\n",
       "departmento               0\n",
       "regiao                    0\n",
       "educacao               2409\n",
       "genero                    0\n",
       "canal_recrutamento        0\n",
       "numero_treinamentos       0\n",
       "idade                     0\n",
       "aval_ano_anterior      4124\n",
       "tempo_servico             0\n",
       "promovido                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.isnull().sum()"
   ]
  },
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   "execution_count": 15,
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       "    <tr>\n",
       "      <th>Ensino Medio</th>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>458</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mestrado ou Doutorado</th>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14798</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Superior</th>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
       "      <td>33404</td>\n",
       "      <td>36669</td>\n",
       "      <td>36669</td>\n",
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      "text/plain": [
       "                       id_funcionario  departmento  regiao  genero  \\\n",
       "educacao                                                             \n",
       "Ensino Medio                      805          805     805     805   \n",
       "Mestrado ou Doutorado           14925        14925   14925   14925   \n",
       "Superior                        36669        36669   36669   36669   \n",
       "\n",
       "                       canal_recrutamento  numero_treinamentos  idade  \\\n",
       "educacao                                                                \n",
       "Ensino Medio                          805                  805    805   \n",
       "Mestrado ou Doutorado               14925                14925  14925   \n",
       "Superior                            36669                36669  36669   \n",
       "\n",
       "                       aval_ano_anterior  tempo_servico  promovido  \n",
       "educacao                                                            \n",
       "Ensino Medio                         458            805        805  \n",
       "Mestrado ou Doutorado              14798          14925      14925  \n",
       "Superior                           33404          36669      36669  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
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    }
   ],
   "source": [
    "dadosRH.groupby(['educacao']).count()"
   ]
  },
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   "execution_count": 16,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='educacao', ylabel='count'>"
      ]
     },
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     "data": {
      "image/png": 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6qLTO1Cz/qKSppfjBklbmOpdLUqP2xczM6mtkT+Q6YGKn2DnAwogYAyzMxwDHA2Nymg5cDUXSAc4FDgMOBc6tJZ4s84XSep23ZWZmDdawJBIRvwDWdwpPAmbl/Cxgcik+OwqLgaGShgPHAQsiYn1EvAAsACbmst0jYnFEBDC7VJeZmTVJs6+J7B0R63L+aWDvnB8BPFUqtyZj3cXX1InXJWm6pDZJbR0dHdu3B2Zm9oY+u7CePYho0rZmRERrRLS2tLQ0Y5NmZgNCs5PIM3kqivz7bMbXAqNK5UZmrLv4yDpxMzNromYnkblA7Q6rqcDtpfiUvEtrPPBSnvaaD0yQtEdeUJ8AzM9lL0san3dlTSnVZWZmTTK4URVL+hFwFDBM0hqKu6wuAW6UdCrwJPCpLD4POAFoB14FpgFExHpJFwJLs9wFEVG7WH8axR1gQ4A7czIzsyZqWBKJiJO7WHRsnbIBnN5FPTOBmXXibcAB29NGMzPbPv7GupmZVeYkYmZmlTmJmJlZZU4iZmZWmZOImZlV5iRiZmaVOYmYmVllTiJmZlaZk4iZmVXmJGJmZpU5iZiZWWVOImZmVpmTiJmZVeYkYmZmlTmJmJlZZU4iZmZWmZOImZlV5iRiZmaVOYmYmVllfZJEJD0haaWkZZLaMranpAWSHs2/e2Rcki6X1C5phaSDSvVMzfKPSpraF/tiZjaQ9WVP5OiIGBcRrfn4HGBhRIwBFuZjgOOBMTlNB66GIukA5wKHAYcC59YSj5mZNcdb6XTWJGBWzs8CJpfis6OwGBgqaThwHLAgItZHxAvAAmBik9tsZjag9VUSCeCnku6XND1je0fEupx/Gtg750cAT5XWXZOxruJmZtYkg/tou0dGxFpJfwYskPSb8sKICEnRWxvLRDUdYN999+2tas3MBrw+6YlExNr8+yxwK8U1jWfyNBX599ksvhYYVVp9ZMa6itfb3oyIaI2I1paWlt7cFTOzAa3pSUTSOyXtVpsHJgCrgLlA7Q6rqcDtOT8XmJJ3aY0HXsrTXvOBCZL2yAvqEzJmZmZN0hens/YGbpVU2/6/RsRdkpYCN0o6FXgS+FSWnwecALQDrwLTACJivaQLgaVZ7oKIWN+83TAzs6YnkYh4DPhgnfjzwLF14gGc3kVdM4GZvd1GMzPrmbfSLb5mZtbPOImYmVllTiJmZlaZk4iZmVXmJGJmZpU5iZiZWWVOImZmVpmTiJmZVeYkYmZmlTmJmJlZZU4iZmZWmZOImZlV5iRiZmaVOYmYmVllTiJmZlaZk4iZmVXmJGJmZpU5iZiZWWV98RvrZmbdOuKKI/q6CW979555b6/U456ImZlV1u+TiKSJkh6W1C7pnL5uj5nZQNKvk4ikQcD3gOOBscDJksb2bavMzAaOfp1EgEOB9oh4LCL+CMwBJvVxm8zMBgxFRF+3oTJJJwITI+I/5+PPAodFxBmdyk0HpufD9wEPN7WhzTUMeK6vG2GV+Nj1b2/34/fnEdHSOTgg7s6KiBnAjL5uRzNIaouI1r5uh207H7v+baAev/5+OmstMKr0eGTGzMysCfp7ElkKjJG0n6SdgJOAuX3cJjOzAaNfn86KiI2SzgDmA4OAmRGxuo+b1dcGxGm7tykfu/5tQB6/fn1h3czM+lZ/P51lZmZ9yEnEzMwqGzBJRFJI+pfS48GSOiTdUaGuoZJO64U2fU7SldtbzzZs7wlJK3N6UNJFknbZjvrOkvSO3mxjqe7RklY1ou7eJOnvJa2WtELSMkmHNXh78yQNbeQ2+itJm/IY1KZtHgZJ0j6Sbuql9twt6beSVIrdJmnDNtZznqS/y/kLJH2kN9rXW/r1hfVt9ApwgKQhEfEH4KNUvx14KHAacFXnBZIGR8TGyq1svKMj4jlJu1JcCPw+MLViXWcB/wK82tMVJA2KiE0Vt/eWIulw4D8CB0XE65KGATs1aFuiuIZ5QoV1/tSINr0F/SEixm1PBRHxO+DE3mkOAC8CRwD3ZPIfvj2VRcQ3e6FNvWrA9ETSPOA/5PzJwI9qCyS9U9JMSUskPSBpUsbfn7Fl+WlzDHAJ8O6MfUvSUZJ+KWku8GCud5uk+/NT6vTSdqZJekTSEooXVy0+WtLPchsLJe3bufGS9sx6V0haLOkDGX/jk0o+XiVpdHdPRERsAL4ETM56lfuyKnsqn866jir31iRdmT2oLwP7AIskLcplJ+e6qyRdWlpng6TvSFoOHC7pm5KWZrkZtU9qkg6WtDzLnV5afxdJ12bdD0g6urt9a6LhwHMR8TpARDwXEb/LHt8wAEmtku7O+fMkXS/pV5IelfSFWkWSvpbPyQpJ52dstIrBRWcDq4BRner+r/kcrpJ0VlfrNO3ZeIvK5+x8Sb/O19D+Gf+wNvdaHpC0m0o94Hyd3yLprjxe/1Sqs+5rvY45FF89APgEcEuntm1x3DP+9/l/4h6KUTZq8etUjNSBpGOz3StV/O/aeTufqmoiYkBMwAbgA8BNwC7AMuAo4I5c/g/AKTk/FHgEeCdwBfCZjO8EDAFGA6tKdR9F0dPZrxTbM/8OoXgz70XxT+e3QEvWdS9wZZb7MTA15z8P3FZnH64Azs35Y4BlOX8e8HelcquA0XXWfwIY1im2DDgM+FtgAcWt0ntnO4eXn6MsfyXwuc71USSU2r4NBn4GTM5lAXyq83OT89cDH8v5FcBf5/y3as8x8FWK27cB9s/t7PIWeE3tms/fIxS90g/XeV5agbtLx2l5viaGAU/l8zaBolcoig92dwB/na+zPwHjOx9D4GBgJcVrdFdgNXBgvXUGygRsyuNRmz5des7OzPnTgP+d8z8Gjigdy8GU3tvA54DHgHdR/M94kiIpd/la79SeuyneWyso3lc/zfo35PKujnvt2L4D2B1oJ9/fwHUUPaVd8vXz3ozPBs7qi+d9QPVEImIFxUE8maJXUjYBOEfSMoqDvwuwL/Ar4BuSzqYYO+YPXVS/JCIeLz3+cn6iXkzxwhtD8YK6OyI6ohgw8oZS+cOBf83564Ej62zjyFxGRPwM2EvS7lvZ7a2pna89EvhRRGyKiGeAnwOHbEM9h7B53zYCP6R4Q0Dx5r65VPZoSfdJWkmRDN+voqs/NCJ+kWWuL5U/kuK0GRHxG4o383u3oW0NEUVv7mCKcdk6gBskfW4rq90eEX+IiOeARRSDiE7I6QHg1xSJckyWfzIiFtep50jg1oh4JdtxC/BXW1nn7e4PETGuNJXfX7UewP0U/wOg+BD33exVD436p6EXRsRLEfEaxVmGP6f713pnm4B7KHojQyLiidKyro77X1Ec21cj4mXqf4H6fcDjEfFIPp7VTRsaaiBdE6mZC3yb4hP2XqW4gL+NiM6DMz4k6T6K02DzJH2R4tNJZ6+8UZF0FPAR4PCIeDVPZ1S+gN0DG3nzqckebUvSbhRvqEe6KVap7k5ei7wOouJC/lVAa0Q8Jem8inW+JeR+3Q3cnUlxKm9+zjrvW+cvZgXFa+8fI+L75QV5SvIVtl2Vdd7uXs+/m8j/exFxiaSfACcA90o6Dniti/XetO42mgPcStETLevquJ9VYRt9ZkD1RNJM4PyIWNkpPh84s3R+/sD8+++BxyLicuB2ilNivwd262Yb7wJeyASyPzA+4/cBH5a0l6QdgU+W1vk/bD53+hngl3Xq/WUuqyWq5/KTyhPAQRk/CNivm7aR5Xal+Gd+W0S8kHV/WtIgSS0Un2qWUHzqHytp5+wtHFuqpvw8LMl9G6bid15OpujNdFb7p1q7uH8iQES8CLwoqdYD+0wX+/1eih5in4/ELOl9Kq6R1YyjeL6eoOihQHGasGxSXuPZi+KDzFKK197n8/lA0ghJf7aVzf+S4nrWOyS9E/g49V8z1gVJ746IlRFxKcVx2L+Hq/b0tV7zS+AfKV2DTV0d919QHNsh+UHvY3XqfBgYLek9+fizW2lDwwy4nkhErAEur7PoQuB/Aisk7QA8TnHnzaeAz0r6f8DTwD9ExHpJ9+YFuDuBn3Sq6y7gS5IeojjYi3Pb6/KT968o7tpYVlrnTOBaSV+jODUyrU4bzwNmSlpBcUdU7a6qm4EpklZTJKruehaLMlHuQPHp6MKM30pxSm05xafj/xYRTwNIupHiOsvjFF3vmhnAXZJ+FxFHq7ilchHFJ6yfRMTtnTceES9K+kHW9zTFm7dmWu5fUJw/rrkKuDo/6W+kuCZT/oTYV3YFrsjkupHi3PV04C+AayRdSNFLKVtB8RwNAy6M4m6g30n6C+BX+RlmA3AKxSffuiLi15Kuo/iHBsV5/ge0lRsq3uaG5OnomrsiorvbfM9ScZPGnyiuKd1JD+6eyvfxVl/rpfJBcfajc/yn9Y57HtsbKN6Lz/Lm90ht3dckTQP+TdLgLPO/ttb2RvCwJ2ZNkh8gNkTEFv9QzPqrgXg6y8zMeol7ImZmVpl7ImZmVpmTiJmZVeYkYmZmlTmJmPUyNXl0ZrO+5CRiZmaVOYmYbSNJp2jzyM7fz2/5T1P90ZnfGHU1H28ozZ+dI7Aul3RJxr6Qo7oul3Sz8vdaJO0t6daML5f0oYx3NVp0T0eZNdsuTiJm2yC/YfxpitFfx1F8q/wU4HyK5HEkMLYH9RwPTAIOi4gPArVhxm+JiEMy9hBwasYvB36e8YMovmEN8PmIOJhitOAv55A6+wCXUgxuOQ44RNLk7dlvs64MuGFPzLbTsRTjYi3NoSqGAB8iR3UFyCErtjbK8EeAayPiVYCIWJ/xAyRdRPFzBLtSjK8ERUKYkmU3AS9l/MuSPp7ztdGi9+7Untoos7dV2mOzbrgnYrZtBMwqDTf+PrYcnbXsjRF9c0y2rf3y4XXAGRHxlxS9my5HONabR4v+IMW4Zv12RGTrn5xEzLbNQuDE2ii7kvak+Ofd1ejMT7B5RN+/AXbM+QXAtNI1jz0zvhuwLuspj2S8EPgvWXaQpHfR9WjR2zrKrFllTiJm2yAiHgT+O/DTHE15AcXIr+dRjM58L8W1jJofUPxDX04xSvIrWc9dFL9t05Yjz9Z+3vh/UIzEfC/wm1I9X6H4Ma+VFD+sNJZitOjBOVr0JZRGiwZqo8wuB+7vbpRZs+3hsbPMzKwy90TMzKwyJxEzM6vMScTMzCpzEjEzs8qcRMzMrDInETMzq8xJxMzMKvv/94mqh3GoxucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dadosRH['educacao'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 275
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53067,
     "status": "ok",
     "timestamp": 1593698495290,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "BOD90Ycjj1oU",
    "outputId": "89be0cf2-edfe-416d-b509-bc0f8ec84485"
   },
   "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>id_funcionario</th>\n",
       "      <th>departmento</th>\n",
       "      <th>regiao</th>\n",
       "      <th>educacao</th>\n",
       "      <th>genero</th>\n",
       "      <th>canal_recrutamento</th>\n",
       "      <th>numero_treinamentos</th>\n",
       "      <th>idade</th>\n",
       "      <th>tempo_servico</th>\n",
       "      <th>promovido</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aval_ano_anterior</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "      <td>5887</td>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "      <td>6223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "      <td>4035</td>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "      <td>4225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "      <td>17867</td>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "      <td>18618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9509</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "      <td>11362</td>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id_funcionario  departmento  regiao  educacao  genero  \\\n",
       "aval_ano_anterior                                                          \n",
       "1.0                          6223         6223    6223      5887    6223   \n",
       "2.0                          4225         4225    4225      4035    4225   \n",
       "3.0                         18618        18618   18618     17867   18618   \n",
       "4.0                          9877         9877    9877      9509    9877   \n",
       "5.0                         11741        11741   11741     11362   11741   \n",
       "\n",
       "                   canal_recrutamento  numero_treinamentos  idade  \\\n",
       "aval_ano_anterior                                                   \n",
       "1.0                              6223                 6223   6223   \n",
       "2.0                              4225                 4225   4225   \n",
       "3.0                             18618                18618  18618   \n",
       "4.0                              9877                 9877   9877   \n",
       "5.0                             11741                11741  11741   \n",
       "\n",
       "                   tempo_servico  promovido  \n",
       "aval_ano_anterior                            \n",
       "1.0                         6223       6223  \n",
       "2.0                         4225       4225  \n",
       "3.0                        18618      18618  \n",
       "4.0                         9877       9877  \n",
       "5.0                        11741      11741  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.groupby(['aval_ano_anterior']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53064,
     "status": "ok",
     "timestamp": 1593698495293,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "2zwyIkVGkCCx",
    "outputId": "5410af54-0d77-46f9-82c0-8b6019ad8742"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='aval_ano_anterior', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dadosRH['aval_ano_anterior'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos aplicar imputação e preencher os valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53063,
     "status": "ok",
     "timestamp": 1593698495295,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "vSnmkA63kIee"
   },
   "outputs": [],
   "source": [
    "dadosRH['educacao'].fillna(dadosRH['educacao'].mode()[0], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53063,
     "status": "ok",
     "timestamp": 1593698495297,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "5fkYpTLEkP63"
   },
   "outputs": [],
   "source": [
    "dadosRH['aval_ano_anterior'].fillna(dadosRH['aval_ano_anterior'].median(), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 53058,
     "status": "ok",
     "timestamp": 1593698495299,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "RacxdeaXlI3T",
    "outputId": "1d9dd074-1b60-4691-eaae-36371c1ef0e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_funcionario         0\n",
       "departmento            0\n",
       "regiao                 0\n",
       "educacao               0\n",
       "genero                 0\n",
       "canal_recrutamento     0\n",
       "numero_treinamentos    0\n",
       "idade                  0\n",
       "aval_ano_anterior      0\n",
       "tempo_servico          0\n",
       "promovido              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54808, 11)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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>id_funcionario</th>\n",
       "      <th>departmento</th>\n",
       "      <th>regiao</th>\n",
       "      <th>genero</th>\n",
       "      <th>canal_recrutamento</th>\n",
       "      <th>numero_treinamentos</th>\n",
       "      <th>idade</th>\n",
       "      <th>aval_ano_anterior</th>\n",
       "      <th>tempo_servico</th>\n",
       "      <th>promovido</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educacao</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ensino Medio</th>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "      <td>805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mestrado ou Doutorado</th>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "      <td>14925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Superior</th>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "      <td>39078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id_funcionario  departmento  regiao  genero  \\\n",
       "educacao                                                             \n",
       "Ensino Medio                      805          805     805     805   \n",
       "Mestrado ou Doutorado           14925        14925   14925   14925   \n",
       "Superior                        39078        39078   39078   39078   \n",
       "\n",
       "                       canal_recrutamento  numero_treinamentos  idade  \\\n",
       "educacao                                                                \n",
       "Ensino Medio                          805                  805    805   \n",
       "Mestrado ou Doutorado               14925                14925  14925   \n",
       "Superior                            39078                39078  39078   \n",
       "\n",
       "                       aval_ano_anterior  tempo_servico  promovido  \n",
       "educacao                                                            \n",
       "Ensino Medio                         805            805        805  \n",
       "Mestrado ou Doutorado              14925          14925      14925  \n",
       "Superior                           39078          39078      39078  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.groupby(['educacao']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id_funcionario</th>\n",
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       "      <th>1.0</th>\n",
       "      <td>6223</td>\n",
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       "      <td>6223</td>\n",
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       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>4225</td>\n",
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       "      <td>4225</td>\n",
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       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>22742</td>\n",
       "      <td>22742</td>\n",
       "      <td>22742</td>\n",
       "      <td>22742</td>\n",
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       "      <td>22742</td>\n",
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       "      <td>22742</td>\n",
       "      <td>22742</td>\n",
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       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
       "      <td>9877</td>\n",
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       "    <tr>\n",
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       "      <td>11741</td>\n",
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       "      <td>11741</td>\n",
       "      <td>11741</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id_funcionario  departmento  regiao  educacao  genero  \\\n",
       "aval_ano_anterior                                                          \n",
       "1.0                          6223         6223    6223      6223    6223   \n",
       "2.0                          4225         4225    4225      4225    4225   \n",
       "3.0                         22742        22742   22742     22742   22742   \n",
       "4.0                          9877         9877    9877      9877    9877   \n",
       "5.0                         11741        11741   11741     11741   11741   \n",
       "\n",
       "                   canal_recrutamento  numero_treinamentos  idade  \\\n",
       "aval_ano_anterior                                                   \n",
       "1.0                              6223                 6223   6223   \n",
       "2.0                              4225                 4225   4225   \n",
       "3.0                             22742                22742  22742   \n",
       "4.0                              9877                 9877   9877   \n",
       "5.0                             11741                11741  11741   \n",
       "\n",
       "                   tempo_servico  promovido  \n",
       "aval_ano_anterior                            \n",
       "1.0                         6223       6223  \n",
       "2.0                         4225       4225  \n",
       "3.0                        22742      22742  \n",
       "4.0                         9877       9877  \n",
       "5.0                        11741      11741  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.groupby(['aval_ano_anterior']).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos verificar o balanceamento de classe na variável \"promovido\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 56941,
     "status": "ok",
     "timestamp": 1593698499196,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "ViwkeH9Ynnbj",
    "outputId": "5d3ba7b5-e854-44c8-89ac-d585f093ad66"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_funcionario</th>\n",
       "      <th>departmento</th>\n",
       "      <th>regiao</th>\n",
       "      <th>educacao</th>\n",
       "      <th>genero</th>\n",
       "      <th>canal_recrutamento</th>\n",
       "      <th>numero_treinamentos</th>\n",
       "      <th>idade</th>\n",
       "      <th>aval_ano_anterior</th>\n",
       "      <th>tempo_servico</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promovido</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>50140</td>\n",
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       "      <td>50140</td>\n",
       "      <td>50140</td>\n",
       "      <td>50140</td>\n",
       "      <td>50140</td>\n",
       "      <td>50140</td>\n",
       "      <td>50140</td>\n",
       "      <td>50140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "      <td>4668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id_funcionario  departmento  regiao  educacao  genero  \\\n",
       "promovido                                                          \n",
       "0                   50140        50140   50140     50140   50140   \n",
       "1                    4668         4668    4668      4668    4668   \n",
       "\n",
       "           canal_recrutamento  numero_treinamentos  idade  aval_ano_anterior  \\\n",
       "promovido                                                                      \n",
       "0                       50140                50140  50140              50140   \n",
       "1                        4668                 4668   4668               4668   \n",
       "\n",
       "           tempo_servico  \n",
       "promovido                 \n",
       "0                  50140  \n",
       "1                   4668  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH.groupby(['promovido']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 56939,
     "status": "ok",
     "timestamp": 1593698499199,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "Fcc-ez0voIKR",
    "outputId": "3489579a-96cb-4e6b-d336-2b2af6e12d66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='promovido', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dadosRH['promovido'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 56934,
     "status": "ok",
     "timestamp": 1593698499199,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "Dvh71WWtrPRp",
    "outputId": "c83dd245-3b86-4f11-c0e1-3ae9fbdca692"
   },
   "outputs": [],
   "source": [
    "df_classe_majoritaria = dadosRH[dadosRH.promovido==0]\n",
    "df_classe_minoritaria = dadosRH[dadosRH.promovido==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50140, 11)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_classe_majoritaria.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4668, 11)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_classe_minoritaria.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Upsample da classe minoritária\n",
    "from sklearn.utils import resample\n",
    "df_classe_minoritaria_upsampled = resample(df_classe_minoritaria, \n",
    "                                           replace = True,     \n",
    "                                           n_samples = 50140,   \n",
    "                                           random_state = 150) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "dadosRH_balanceados = pd.concat([df_classe_majoritaria, df_classe_minoritaria_upsampled])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    50140\n",
       "0    50140\n",
       "Name: promovido, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dadosRH_balanceados.promovido.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 56930,
     "status": "ok",
     "timestamp": 1593698499201,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "2Ewpso7rvTlZ",
    "outputId": "ea5e438b-d138-4a6f-e986-3e9fa7b2d729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100280 entries, 0 to 5869\n",
      "Data columns (total 11 columns):\n",
      " #   Column               Non-Null Count   Dtype  \n",
      "---  ------               --------------   -----  \n",
      " 0   id_funcionario       100280 non-null  int64  \n",
      " 1   departmento          100280 non-null  object \n",
      " 2   regiao               100280 non-null  object \n",
      " 3   educacao             100280 non-null  object \n",
      " 4   genero               100280 non-null  object \n",
      " 5   canal_recrutamento   100280 non-null  object \n",
      " 6   numero_treinamentos  100280 non-null  int64  \n",
      " 7   idade                100280 non-null  int64  \n",
      " 8   aval_ano_anterior    100280 non-null  float64\n",
      " 9   tempo_servico        100280 non-null  int64  \n",
      " 10  promovido            100280 non-null  int64  \n",
      "dtypes: float64(1), int64(5), object(5)\n",
      "memory usage: 9.2+ MB\n"
     ]
    }
   ],
   "source": [
    "dadosRH_balanceados.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 56926,
     "status": "ok",
     "timestamp": 1593698499202,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "5pzA1bDCvpk0",
    "outputId": "72049ef5-4ec9-4428-fd73-2e8995ba11c8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='promovido', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(dadosRH_balanceados['promovido'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados estão balanceados. Vamos salvar o dataset com os dados manipulados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dadosRH_balanceados.to_csv('dadosRH_modificado.csv', encoding = 'utf-8', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora carregamos os dados e seguimos com o trabalho de análise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> A partir daqui executaremos também no Power BI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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>id_funcionario</th>\n",
       "      <th>departmento</th>\n",
       "      <th>regiao</th>\n",
       "      <th>educacao</th>\n",
       "      <th>genero</th>\n",
       "      <th>canal_recrutamento</th>\n",
       "      <th>numero_treinamentos</th>\n",
       "      <th>idade</th>\n",
       "      <th>aval_ano_anterior</th>\n",
       "      <th>tempo_servico</th>\n",
       "      <th>promovido</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>Mestrado ou Doutorado</td>\n",
       "      <td>F</td>\n",
       "      <td>Outsourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65141</td>\n",
       "      <td>Logistica</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outro</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7513</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outsourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2542</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Norte</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outro</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48945</td>\n",
       "      <td>Tecnologia</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>Superior</td>\n",
       "      <td>M</td>\n",
       "      <td>Outro</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_funcionario departmento    regiao               educacao genero  \\\n",
       "0           65438   Marketing   Sudeste  Mestrado ou Doutorado      F   \n",
       "1           65141   Logistica  Nordeste               Superior      M   \n",
       "2            7513   Marketing   Sudeste               Superior      M   \n",
       "3            2542   Marketing     Norte               Superior      M   \n",
       "4           48945  Tecnologia  Nordeste               Superior      M   \n",
       "\n",
       "  canal_recrutamento  numero_treinamentos  idade  aval_ano_anterior  \\\n",
       "0        Outsourcing                    1     35                5.0   \n",
       "1              Outro                    1     30                5.0   \n",
       "2        Outsourcing                    1     34                3.0   \n",
       "3              Outro                    2     39                1.0   \n",
       "4              Outro                    1     45                3.0   \n",
       "\n",
       "   tempo_servico  promovido  \n",
       "0              8          0  \n",
       "1              4          0  \n",
       "2              7          0  \n",
       "3             10          0  \n",
       "4              2          0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('dadosRH_modificado.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100280, 11)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 1 - Qual a Correlação Entre os Atributos dos Funcionários?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "corr = dataset.corr()\n",
    "sns.heatmap(corr, cmap = \"RdPu\", linewidths = 0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 2 - Qual o Tempo de Serviço da Maioria dos Funcionários?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 59017,
     "status": "ok",
     "timestamp": 1593698501319,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "0eqrfprLVPac",
    "outputId": "ce9ee07b-771b-421d-94a4-3ab7945316b0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.distplot(dataset['tempo_servico'], color = 'green')\n",
    "plt.title('Distribuição do Tempo de Serviço dos Funcionários', fontsize = 15)\n",
    "plt.xlabel('Tempo de Serviço em Anos', fontsize = 15)\n",
    "plt.ylabel('Total')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 3 - Qual Avaliação do Ano Anterior Foi Mais Comum?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 571
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 59013,
     "status": "ok",
     "timestamp": 1593698501320,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "VuXeNqr3ZpvU",
    "outputId": "a3c50ca1-7bf5-4ec6-ef29-06c54e982fa3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "dataset['aval_ano_anterior'].value_counts().sort_values().plot.bar(color = 'blue', figsize = (10, 5))\n",
    "plt.title('Distribuição da Avaliação do Ano Anterior dos Funcionários', fontsize = 15)\n",
    "plt.xlabel('Avaliações', fontsize = 15)\n",
    "plt.ylabel('Total')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 4 - Qual a Distribuição das Idades dos Funcionários?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 585
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 59011,
     "status": "ok",
     "timestamp": 1593698501322,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "xSOr6f5qZ5cR",
    "outputId": "7820d569-ce44-4ea7-88be-5010d10e6942"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IeIBMIpC9n86RtAAQcE5EXFdVrNZPPa39YGZdpdKBchFxMXBxw7YT6m6vILu0Nj7vsbLtZmY2dDyS2tqbV6oza5l27cVkZmYt5gRhZmalnCDMzKyU2yCsZ+6tZDasuQRhZmalnCDMzKyUE4SZmZVygjAzs1JupLbO5KnBzSrnEoSZmZVygjAzs1JOEGZmVsoJwszMSjlBmJlZKScIMzMrVWmCkDRV0kJJiyQdX/L4KEkXFo/PkTS+2H6kpPl1l9WSJlUZq5mZra2yBCFpBLl06AHAbsARknZr2O0o4MGImAicBpwCEBEXRMSkiJgEvAu4LSLmVxWrmZmtq8oSxBRgUUTcGhErgZnAtIZ9pgHnFrcvAvaXpIZ9jiiea2ZmQ6jKBLEDcFfd/cXFttJ9ImIV8DAwumGfw4Dvl72ApOmS5kmat3Tp0kEJ2szMUls3UkvaG1geEX8rezwizoyIyRExecyYMUMcnZlZd6syQSwBdqy7P7bYVrqPpJHAlsCyuscPp4fSg5mZVavKBDEX2EXSBEkbkV/2sxr2mQW8u7h9CDA7IgJA0gbAobj9wcysJSqbzTUiVkmaAVwCjADOjogFkk4G5kXELOAs4HxJi4AHyCRS8yrgroi4taoYrcv0tESqZ3k1Wy+VTvcdERcDFzdsO6Hu9grg7T0893fAy6uMz8zMetbWjdRmZtY6XjBouKm6GuYJ4DpgVXF/Z2C7QTq2mQ0pJwgbHKuBK8huB482PPZ84HXAS4DGYZBm1racIGzgngC+AdwMTASOBp5NliKuBf4AnA7sytrdEMysrTlB2MAsB/4PuAP4F+AVrF1KmAq8Efgj8DPgc8BDwEnAVkMYp5n1mxupbf09QU6xeCfwAWBfyquQNgBeDZwM7Ad8lSxNnE1WTZlZW3KCsPXzNHAGOcPWB4FJTTxnc+BI4GpgF3Iu31cB11cTopkNjBOE9V8AFwA3kpOxv6ifz9+TrHI6G7ipuP9vwIpBjNHMBswJwvrvUuBK4C1km8P62AB4L7CwuP4icCLZRdbM2oIThPXPDcBPgJcBbxuE440GvkUmnE3JJabOI9s3zKylnCCsefeTX+bbk1MsDuaYhlcAnyJ7Pf2J7O10V6/PMLOKOUFYc1aSYxkgG6VHVfAaGwIHA8eRYyhOAf5SweuYWVOcIKxvAZxPrt5xFFD12kwTgf8AJgDnAD8tYjCzIeUEYX2bDVxFtjnsMUSvuQVwDDm24lfkslEeM2E2pDyS2np3G3AROY/SAUP82iPIbrSbk6uKrADeg3/WmA2RSj9qkqZKWihpkaTjSx4fJenC4vE5ksbXPfZiSX+WtEDS9ZI2rjJWK/E0WbW0JdkVtRVfzAL+CZgGzCFLEq5uMhsSlX3kJY0gOy0eAOwGHCFpt4bdjgIejIiJ5KQNpxTPHQl8Fzg6InYHXgM8VVWs1oPZZLvDYcAmLY7lzWQPpz+Q3WzNrHJV/iacAiyKiFsjYiW5tvS0hn2mAecWty8C9pckcnq36yLirwARsSwinq4wVmt0J/Bz4MU0N43GUDiInNPpEjJRmFmlqkwQO7B2T/bFxbbSfSJiFfAwOXRqVyAkXSLpGkn/XvYCkqZLmidp3tKlSwf9DxjWPklW5RxO+6zhIDKe3cmfG4taG45Zt2vX5r6R5LyfRxbXB0vav3GniDgzIiZHxOQxY6ruezmM3AX8kPy1PrrFsTTagKyYHE1OFvhga8Mx62ZVJoglwI5198cW20r3KdodtgSWkaWNP0TE/RGxHLgYeGmFsVq9M8gupa9pcRw92YwcrPckcBbu/mpWkSoTxFxgF0kTJG1EVg7MathnFjlpA8AhwOyICLKW+UWSNi0Sx6vJWYCsaivIdavfBmzd4lh6sz3ZeP534PIWx2LWpSpLEEWbwgzyy/5G4AcRsUDSyZIOLHY7CxgtaRHwCeD44rkPAqeSSWY+cE1E/LKqWK3OD4GlwEdaHUgTXkGOz/gpcHdrQzHrRpUOlIuIi8nqofptJ9TdXgG8vYfnfpfs6mpD6avAC4D9gVtbHEtfBLyTXL70HHKyv3ZtVTPrQP442Rp/I8tsH6J9ei71ZQuy8vJOchEiMxs0nmrD1vgpmRhKy3RtbDI5LuJn5DoVmzfxnDN72D59sIIy63wuQdgaPwVeDjy3xXH0V218xBOs2w3CzNabE4Slu4CrydHKnWgHsq/bH/BCQ2aDxAnC0s+K64NaGcQAvQ3YGPhFqwMx6w5OEJZ+CryQnOSkU21G9r6aTzZam9mAOEEYPA78js4uPdTsT84861EzZgPmBGFwPbn2w0EtjmMwbIpLEWaDxAnCcpz7GLK7aDeolSIu7mtHM+uNE8RwF8DNZA+gbnk3bEr+PfOB+1sbilkn65avBFtfy4AHyC/UbvJqcnzE71sdiFnncoIY7m4urrstQTwH2BO4gmyEN7N+c4IY7m4mu4fu3upAKvA6YDlwQasDMetMThDD3c3ALnTnO+F55HJU/0e2tZhZv3Tj14I16wGyDaKTB8f1RsBrgQW4LcJsPVSaICRNlbRQ0iJJx5c8PkrShcXjcySNL7aPl/SEpPnF5ZtVxjls1dofujVBAOxFLmT7rVYHYtZ5KksQkkYAXwcOAHYDjpC0W8NuRwEPRsRE4DTglLrHbomIScXl6KriHNZuJruE7tDqQCq0EfAu4EdkacnMmlZlCWIKsCgibo2IlcBMYFrDPtOAc4vbFwH7S+qUpWo639/p3vaHetOBJ4HzWx2IWWep8qthB9aeeHkx6/5WfWafYg3rh4HRxWMTJF0r6feSXln2ApKmS5onad7SpUsHN/pu9zhwH7BzqwMZAi8i17k4EzdWm/VDu/52vAcYFxF7Ap8Avidpi8adIuLMiJgcEZPHjBkz5EF2tDuK651aGsXQmU5OKXJlqwMx6xxVLjm6hOxkWDO22Fa2z2JJI8nmxGUREWSlABFxtaRbyKbUeRXGO7zUEsS4lkYxdA4FjiFLEfv1sl/ZUqRehtSGqR4ThKSX9vbEiLimj2PPBXaRNIFMBIcD72jYZxbwbuDPwCHA7IgISWOAByLiaUk7kzXlt/bxetYfdwDbkIPkhoPNgCOBs4EvtzYUs07RWwniS708FuQ41Z53iFglaQZwCTACODsiFkg6GZgXEbOAs4DzJS0ie+UfXjz9VcDJkp4CVgNHR8QDTf1F1pzbgYmtDmKITQdOB75LrjxnZr3qMUFExGsHevCIuJiGSZcj4oS62yuAt5c870dkx0SrwiPAgwyf9oeaSeS4iDOBj5AD6cysR021QUjagxzL8Mzvrog4r6qgrGLDrYG63nTg/WSF5fNaHItZm+uzF5OkzwBfLS6vBb4AHFhxXFalO8hfz8Olgbre4cDmwB9bHYhZ+2umBHEI8BLg2oh4r6RtyVpc61R3AM9leNbDb86axupDyZHkfSnr2QTu3WRdr5lxEE9ExGpgVTEW4T7W7r5qneYOhmf1Us104ClgTqsDMWtvzSSIeZK2Iqc7uxq4huyWap3oQXK8+nBOEC8lq9f+iEdWm/WizyqmiPhQcfObkn4NbBER11UbllXmzuJ6OCcIgFeSCwndDkxobShm7aqZRurLa7cj4vaIuK5+m3WYxcV1N8/g2oy9gFG4sdqsF72NpN6YbMLbWtKzWdNrfAv89dIZyhpXlwBjGJ4N1PU2IZPEVeRInE1aG45ZO+qtBPEBss3hBWS7w9XF5WfA16oPzSpRNqfucPVKYCVuUTPrQY8JIiK+EhETgOMiYkLd5SUR4QTRiVaSfdDGtjqQNjGenO58Njmhi5mtpZleTGdI+qiki4rLDEkbVh6ZDb67yV47ThBrvB5YCrjbhdk6mkkQ3wBeVlzXbp9eZVBWkVoDtRPEGpPIJap+0+I4zNpQb43UI4tV3vaKiJfUPTRb0l+rD80G3RKy587ovnYcRkaQE8hcRHYBHo7Tj5j1oLcSxFXF9dOSnpnWrFif4elKo7JqLAa2p33XEWyV/cjEeWmrAzFrL70NlKt1az0O+K2k2oI944H3VhmUVSDIBPGyVgfShjYhSxG/Bt6ABxGaFXr7LTlG0ifIWtozyL4es8kpN/Zs5uCSpkpaKGmRpONLHh8l6cLi8TmSxjc8Pk7SY5KOa/LvsZ48BCzH7Q89mQo8i6xq8vQbZkDvCWIEOffls8iShorLyGJbrySNAL4OHECuJXGEpN0adjsKeDAiJgKnAac0PH4q8Ku+/wzrkxuoe7cJ8BbgZtyjyazQWxXTPRFx8gCOPQVYFBG3AkiaCUwDbqjbZxpwYnH7IuBrklSsS30QcBvw+ABisBpPsdG3VwG/BX4M7E7fM5WVjVT3FODWRXorQQx0QcYdgLvq7peN4X1mn6LH1MPAaEmbA58EThpgDFazhOy95CklejaCXP3kXuAXLY7FrA30liD2H7Io1nUicFpEPNbbTpKmS5onad7SpUuHJrJO5Sk2mvNiYF+ywXpRi2Mxa7Heptp4YIDHXsLaCwuNLbaV7iNpJLAlsAzYG/iCpNuBY4BPS5pREuOZETE5IiaPGTNmgOF2saeAf+D2h2YdSpa2zgaeaHEsZi1UZY/4ucAukiZI2ohcDXhWwz6zgHcXtw8BZkd6ZUSMj4jxwJeB//L8TwNwDznXkBNEczYG/hV4ADgPz9Nkw1ZlCaJoU5gBXALcCPwgIhZIOlnSgcVuZ5FtDouATwDrdIW1QeAG6v57HvDP5DzGv2xxLGYt0ueKcgMRERcDFzdsO6Hu9gpyNv7ejnFiJcENJ4uBDYFtWh1Ih3k9WQn6C3IEugcZ2jDjSReGgyV4io31IeBIckrwc1izXKvZMOGvjG5Xm2LD7Q/rZ0Pgg+SQ0W+QHbHNhgkniG73CPAYThADsQXwIXLI5ulkrzCzYcAJotu5gXpwjAPeQ47t/2FrQzEbKpU2UlsbcIIYPC8jZ3u9DHg+/Wu09rQc1oFcguh2S4CtyDp0G7iDgQnk+AgP3rcu5wTR7Zbg9ofBNAJ4P/nJ+TZeOsu6mhNEN1tFjqJ2ghhco4F3AreTczaZdSkniG52D/kL1+0Pg+9lwF7kIDqPj7Au5QTRzWoN1ONaGkX3OoJcOusc3PXVupITRDe7C0+xUaXNgHcBdwM/b3EsZhVwguhmd5HtD/4vV+dFwH7ApcAtLY7FbJD5q6Nb1abY2LGvHW3A3g48h6xq8gK51kWcILrVncBy3INpKGxMjrJeCny6taGYDSYniG41v7h2CWJo7Aq8Bvgq8OfWhmI2WJwgutV8crpqd3EdOgeTJbb3AU+2OBazQVBpgpA0VdJCSYskrbNanKRRki4sHp8jaXyxfYqk+cXlr5IOrjLOrjSf7L00qsVxDCcbA98EbgD+u8WxmA2CyhKEpBHA14EDgN2AIyTt1rDbUcCDETEROA04pdj+N2ByREwCpgJnSPLEgv0xH1cvtcKbgXcA/wUsaHEsZgNUZQliCrAoIm6NiJXATGBawz7TgHOL2xcB+0tSRCwv1rSG/F0WFcbZfR4ip4FwgmiNL5NrSLwPz9VkHa3KBLED2RO/ZjHr1og/s0+REB4mZ7pB0t6SFgDXA0fXJYxnSJouaZ6keUuXemrNZ/y1uHYPptYYQyaJv5BlaLMO1baN1BExJyJ2J2e8+ZSkjUv2OTMiJkfE5DFjxgx9kO3q2uLaJYjWOZKsXP00nqvJOlaVCWIJa39FjS22le5TtDFsCSyr3yEibiQXzdyjski7zVxge/JsWmuIXJ50NXBsi2MxW09VJoi5wC6SJkjaCDgcmNWwzyzg3cXtQ4DZERHFc0YCSNoJeAFZq27NuIpsAbLW2gn4FNm6dlOLYzFbD5X1DIqIVZJmAJeQy6ycHRELJJ0MzIuIWcBZwPmSFgEPkEkEcnab4yU9Rf4G+1BE3F9VrF1lGbCI7B9mrXcccDZwIfCf5CehpmwZUvBSpNY2Ku06GhEXAxc3bDuh7vYKciabxuedD5xfZWxda25xvTfw91YGYgBsQnbgPhj4PfC61oZj1h9t20ht62kOWf/9slYHYs+YBryQnBL80RbHYtYPThDdZg45LHGLVgdizxBwGLAC+FmLYzHrByeIbhK4gbpdbQe8FrgCd3u1juEE0U1uJRup9251IFbqbcDm5JwCnhvAOoATRDe5qrh2gmhPm5CN1beQVYFmbc4T4HWTOeSXkIcUtq99gD8APwYmkTONNSrr/uqur9YCLkF0kzlk7yWn/fa1ATna52EaOoCbtR9/lXSDM8keMnOB/el5AJa1hwlkSeI3wL7Atq0Nx6wnLkF0i0Xk1NIvbHUg1pSDgY3Iye5XtzgWsx44QXSLG8ny4MRWB2JN2ZKsaroFuLTFsZj1wAmiW9wEPI/8VWqdYW+yzWgWHhthbckJohs8Qi7H5OqlziJy3YjNgW8Dy1sbjlkjJ4huUJtK2gmi82xGLk16P9m5wEuUWhtxgugGNwGbAuNaHYitl13JksSNeJS1tRV3c+10AdxALqnkdN+59gXuJRusn0VOy6G6x712hLVApV8pkqZKWihpkaTjSx4fJenC4vE5ksYX298g6WpJ1xfXnkW/J4uAB8kEYZ3tYDJR/JKcGtwlCWuxykoQkkYAXwfeQDahzpU0KyJuqNvtKODBiJgo6XDgFHJi5PuBt0XE3ZL2IFel26GqWDvaT4vr3VsZhA2KDYB3Frd/SSaIA1m7JGE2hKosQUwBFkXErRGxkqxdndawzzRyqBDkyr37S1JEXBsRdxfbFwCbSBpVYayd6/vkyNytWx2IDYpaktiPnIrjZ7gkYS1TZYLYAbir7v5i1i0FPLNPRKwiZ6gZ3bDPPwPXRMSTjS8gabqkeZLmLV26dNAC7xgLgWuBya0OxAbVBmSj9X7Ar8hSopOEtUBbN2tK2p2sdvpA2eMRcWZETI6IyWPGjBna4NrBTLL6wQmi+9SSxCuBX5P/a0/JYUOsyl5MS4Ad6+6PLbaV7bNY0khyAoJlAJLGAj8B/iUibqkwzs5S680SwOnALsBWLYvGqlRLEhsDlwGPAe/FfQ9tyFT5VpsL7CJpApkIDgfe0bDPLODdwJ+BQ4DZERGStiKb6Y6PiCsrjLFzLQb+Aby+1YFYpUR+Mp5FriGxHDga6K1Fzl1ibZBUVsVUtCnMIHsg3Qj8ICIWSDpZ0oHFbmcBoyUtAj4B1LrCziCnnTtB0vzisk1VsXakq8j/3ktbHYgNiTcB/0J+kk4jSxNmFau0sBoRF9OwLEpEnFB3ewXw9pLnfQ74XJWxdbSnyDLXHuQ8PjY87EtOzfEt4IvAx1objnW/tm6kth5cCzwKvKbFcdjQmwR8lBwc+QWyJ5tZRZwgOtFvgW3w5HzD1fOBY8mS5H5ka59ZBZwgOs2dwK3Aq/F/bzgbB/wbWcX4WrKXk9kg81dMp/kduSjQK1och7XetsCVwM7AW8ixEmaDyD2qO8kysvfSy8npvc22B/5Aztn0DnIWM68qaIPEJYhOcjpZ7+y5ba3eVmRn8gOBj5Cjizw1hw0CJ4hOsQL4Gtm1dfsWx2LtZxNyusujyCGmP8JJwgbMVUyd4gJy5PQRrQ7E2tZIcozELWSj9WpylFFtuvCyEdYeXW29cILoBAGcCrwELwxkvRM5qc0GwOVkkjgMrylh68UJohP8mlxW9DzgiRbHYu1PwKFkkvgNmSRqSaOR522yXrgNohN8kVw547BWB2IdozbJ3xuB3wPfBVa1NCLrQC5BtLv5wGxyVQx3X7T+EPBPwIZkw/W95MoqW7YyKOskLkG0uy+Ro2Vd5Lf1IbL76/vItRs/T/7oMGuCSxDtbDE5OvbDeFEgG5i9gO3ICfZPB15MVlkOxlrm7h3VtVyCaGf/RzYwelpnGwxjgf8kV3lfCJxA/gB5pJVBWTurNEFImippoaRFko4veXyUpAuLx+dIGl9sHy3pt5Iek/S1KmNsWw8BZ5ANjRNaG4p1kRFkw/VJ5HxevyeTxizcQ87WUVmCkDQC+DpwALAbcISk3Rp2Owp4MCImkutknVJsXwH8P+C4quJre98gf9mtk1bNBsGzgXcCJ5Kj838J/Ac5lfzTrQvL2kuVbRBTgEURcSuApJnANLJHf8008i0KOVHA1yQpIh4HrpA0scL42tdyMl0eAOzZ4lisu21LthfcQU7PMZOc/G8hud5I/QA7tysMO1VWMe1A9puoWVxsK92nWMP6YWB0hTF1hm+Ts3J+utWB2LCxE/Bx4GhgJfAVsgfdzXhOp2Gso3sxSZpO8btm3LhxLY5mAOp7gawiy1SvIlcLMxsqIkusewBXkKvJfwl4DjnNy2bAeDKZbEe2Z/SXezx1lCoTxBJgx7r7Y4ttZfssljSSHMKzrNkXiIgzKd5ykydP7o7fOVeQ6w279GCtsiG5St2+5HKmfyXfl7+t22ck+cndCXiS/HS/mFwKF3qewsM6SpUJYi6wi6QJZCI4nFzSpN4s4N3An8n+OrMjoju+6NfHCuAXwC5kTxOzVtqITBL7smYdkjvqLrcX1zeSn+AfkiWLl5DJYgLNVWJ7Pqi2VVmCiIhVkmaQS5mMAM6OiAWSTgbmRcQsctjO+ZIWAQ+QSQQASbcDWwAbSToIeGNE3EA3uwx4lBwY59k3rZ1sSDZav7DksTPJNrPryNLGpeQEk88iE8ULycbw0eRKiH5vd4xK2yAi4mKyJrN+2wl1t1eQM9aXPXd8lbG1nYfJBPFSPO7BOs/WZAnjdWQvvAVksriGXDe7ZiPyZ9+WddfPJRfBGkcufGRto6MbqbvKz8li/EEtjsNsoDYlp/bYixxTcTewlGxdfLi4PEJOHngjWbUKWbLYAdiVbCx/mnUbwl0dNaScINrBzcAfgf3JorhZtxhBNmbv2MPjQc4acDdwG7CI/CzMJqcoP5hsnXw1/rZqAZ/yVnsCOJ8sok9rcSxmQ03kqO5nA7sX21YAfyNLGucD3wTGkJMLbgrsjNsxhogTRKudBNwHHAOMam0oZm1hY2AyWW30BNngPZMcQLqC/DE1pbhsVzynP91qXR3VNCeIVvo9uVrcvpT3DjEb7jYhq5kOJtstPgbMAX5Fdn/ZkUwmLyhuNzN4z4P1muYE0Sp3k0XmieT6wWbWuy2AfYrLw8A84CrgJ8Xjo8hG7u3IUsYWZFfb+uv+rso4zJOJE8RQqr3ZVgGnko1zR5NFajNr3pZkp479yWRxM9nAfTc5HuPRHp43irW72NZub0b+WNuFnFrEACeIobea7J1xC7kM5PatDces423Jmm61NU+RSeKRkutHyKSymByvsQL4ad1znwe8HHgD8DiZPIYpJ4ihFOSk5n8G3srab2gzGzwbkiWBZkoDT5IlkVvIxQjmAJcDF5BThewKTCouz+7lOAOdf6oNq66cIIZKkPMsXU6ONn1ra8Mxs8Iockmz3YC3FduCbOM4AbiW7EU1k5zl4EFytuWdyHFLK8nR4/eypnRSK62sJKuUIb9tR5FJZnTx/M0r/csGzAliKDxFzq/0C3KZx7fjftxm7UxkCb/Wg+oeMlFcS/OrPG5ANorXvmWfIhNG/XSkO5LzVb124CFXwQmiav8A3kXOs3QAcCAVrwRuZoNuu+LyZnJa0SXF9SNkddZGZGngWaxp+N6UdT/rT5MljKVko/pNZHfdS4E7gc/QVo3kThBVWU0O7Pkk2dB1FmuKmmbWuZpt2ygzou75zwfeQlZNXUKuQ/9D4Gxg6sDDHAz+LTvY7gG+QNZnfoBs2LoO+NcWxmRm7eu55Ko4V5GJ4wDgI2TjeYu5BDFQ/0v2wf57cX1PsX0/cunQw3B7g5n1bU+yYfzTwGlkb6ofkMu8togTRH8FOZHYRcWltoTRKHKgzd7kP/q5ZP3kt1oQo5l1po3JQbSvAt5D1kB8gRwz1YL6nkpfUtJUSQslLZK0Ttu/pFGSLiwenyNpfN1jnyq2L5T0pirj7NNqsvj3H+ScLy8GPkeuv/vPwKfIjP9Rsnj43NaEaWZd4iBysaU9yarqV5JToK8e2jAqK0FIGgF8nRyPuBiYK2lWw7KhRwEPRsRESYcDpwCHSdqNXH50d3Ks8W8k7RoRT1cVL0+TdX4PkT0MlgDXk93aLieXVBxBdkf7BPkP3BYvzm5m1diZTArnAceRg/kmkN3kJ5HfjtuQvaY2ppKq7CqrmKYAiyLiVgBJM8kVD+oTxDSyph6ywuZrklRsnxkRTwK3FWtWTyHHIA+uueSw+p4y847Am8jubW8iB7iYmQ0FkQ3Yh5KTEp5FVkE19og8FLiwgpePiL73Wp8DS4cAUyPifcX9dwF7R8SMun3+VuyzuLh/C1mLfyLwl4j4brH9LOBXEXFRw2tMZ80A9ecDCyv5Y3q3NVm+sDV8Ttblc7Iun5O1tep87BQRY8oe6OhG6og4kxZX8kiaFxGTWxlDu/E5WZfPybp8TtbWjuejykbqJay9Eu3YYlvpPpJGkrVpy5p8rpmZVajKBDEX2EXSBEkbkY3Osxr2mUXWsEEuTT47ss5rFnB40ctpAjlL+1UVxmpmZg0qq2KKiFWSZpCDyEcAZ0fEAkknA/MiYhbZ5HJ+0Qj9AJlEKPb7AdmgvQr4cKU9mAbG/ZjW5XOyLp+TdfmcrK3tzkdljdRmZtbZPBeTmZmVcoIwM7NSThD9IGlHSb+VdIOkBZI+Vmx/jqTLJP29uO5tYcKuIWljSVdJ+mtxPk4qtk8opk5ZVEylslGrYx1qkkZIulbSL4r7w/qcSLpd0vWS5kuaV2wblp+bGklbSbpI0k2SbpS0T7udEyeI/lkFHBsRu5Hjrz9cTAtyPHB5ROxCTszR7JpTne5J4HUR8RJy8P9USS8np0w5LSImkgs0HtW6EFvmY8CNdfd9TuC1ETGprq//cP3c1HwF+HVEvAB4Cfl+aatz4gTRDxFxT0RcU9x+lPyH7kBODXJusdu55ExNXS/SY8XdDYtLkKtu10a9D5vzUSNpLLkUzLeL+2KYn5MeDMvPDYCkLck5W88CiIiVEfEQbXZOnCDWUzHz7J7krO3bRkRtJYh7yWn8hoWiKmU+cB+5sOotwEMRUZstZjGZRIeTLwP/zpoZvkbjcxLApZKuLqbIgWH8uSGn3VsKnFNURX5b0ma02TlxglgPkjYHfgQcExGP1D9WDPQbNn2HI+LpiJhEjnafQk6IPmxJeitwX0Rc3epY2sx+EfFSckL8D0t6Vf2Dw+1zQ45BeylwekTsSS5MvFZ1UjucEyeIfpK0IZkcLoiIHxeb/yFpu+Lx7chf08NKUTz+LbAPsFUxdQoMv2lS9gUOlHQ7MJOsWvoKw/ucEBFLiuv7yHlJpzC8PzeLgcURMae4fxGZMNrqnDhB9ENRl3wWcGNEnFr3UP2UIe8GfjbUsbWCpDGStipub0Ku/XEjmSgOKXYbNucDICI+FRFjI2I8OTPA7Ig4kmF8TiRtJulZtdvAG8l1GYfl5wYgIu4F7pL0/GLT/uTMEW11TjySuh8k7Qf8kVxKqFa//GnWrB47DrgDODQiHmhJkENI0ovJhrQR5I+NH0TEyZJ2Jn89P4dccumdxdoew4qk1wDHRcRbh/M5Kf72nxR3RwLfi4jPSxrNMPzc1EiaRHZk2Ai4FXgvxeeINjknThBmZlbKVUxmZlbKCcLMzEo5QZiZWSknCDMzK+UEYWZmpZwgzEpIOlHS/X3sM0/Sdwbp9e6XdOJgHMtssDhBmJlZKScIMzMr5QRh1gRJe0i6UtKKYnGXA0v22UfSLEn3SHq8WBznyJL9XlUssrSimN30FT285rSiGmuFpHslfaGYC8xsSIzsexez4a2YZ+oS4H7gHcAm5JTem5NzCtXsBFwJfBNYQU7cd46k1RHx/eJY2wO/Aq4i52baHrgA2LThNQ8Fvg+cQU7n8jzgv8kfdcdV8GearcMJwqxv7wW2AfaOiMWQS2gCV9TvFBEza7eLiR3/QM7c+n7yyx7gGDJ5vCUilhf7Pg58t+G5/wucFxEfqtv+JPB1Sf8dEcsG9080W5ermMz6NgW4upYcACLiShqmYpb0bEn/J+kO4KniMh3YteFYl9WSQ+EnrG1XcrK2H0gaWbsAs4GNgT0G6e8y65VLEGZ9ey7l8/I3bvsOuVb5Z8mpmx8BPkguI1l/rOvqnxQRyyU9Vrdp6+L64h7i2bGpqM0GyAnCrG/3Ur5S3ja1G5I2Bt4KfDgivlm3vbGUfm/984p9NiXbM2pq0ztPJ6cGb3Rb05GbDYAThFnf5gJHShpb1waxL2t/0Y8iq2yfWeOhWCTnQNZeNnIu8K+SNq2rZjq44fUWkivOjY+Ibw3qX2LWD04QZn07B/hP4JfFaOdNyGqkZ0ZaR8TDkuYCJ0h6hFxQ6njgYWCLumN9Gfgw8AtJp5K9mD4FPFF3rNWSjgXOl7QF2etpJbAzcBBwSEMbhlkl3Eht1ofiy/hN5MLyM4HPAMeSK37Vewe5Mth55DrUPypu1x9rCfBmsp3hR8CHgHcCyxv2u5Bsu5gE/BD4cbHvNWSyMKucV5QzM7NSLkGYmVkpJwgzMyvlBGFmZqWcIMzMrJQThJmZlXKCMDOzUk4QZmZWygnCzMxK/X8pfmo8oEQ4FwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.distplot(dataset['idade'], color = 'magenta')\n",
    "plt.title('Distribuição da Idade dos Funcionários', fontsize = 15)\n",
    "plt.xlabel('Idade', fontsize = 15)\n",
    "plt.ylabel('Total')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 5 - Qual o Número de Treinamentos Mais Frequente?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 60206,
     "status": "ok",
     "timestamp": 1593698502526,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "Inh7kNo7aXic",
    "outputId": "7a650a60-b8f4-424d-9eef-1a12087fc39d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.violinplot(dataset['numero_treinamentos'], color = 'red')\n",
    "plt.title('Número de Treinamentos Feitos Pelos Funcionários', fontsize = 15)\n",
    "plt.xlabel('Número de Treinamentos', fontsize = 15)\n",
    "plt.ylabel('Frequência')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 6 - Qual a Proporção dos Funcionários Por Canal de Recrutamento?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outro          55375\n",
       "Outsourcing    42358\n",
       "Indicado        2547\n",
       "Name: canal_recrutamento, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['canal_recrutamento'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "dataset['canal_recrutamento'].value_counts()\n",
    "fatias = [55375, 42358, 2547]\n",
    "labels = \"Outro\", \"Outsourcing\", \"Indicação\"\n",
    "colors = ['purple', 'lime', 'yellow']\n",
    "explode = [0, 0, 0]\n",
    "plt.pie(fatias, labels = labels, colors = colors, explode = explode, shadow = True, autopct = \"%.2f%%\")\n",
    "plt.title('Percentual de Funcionários Por Canal de Recrutamento', fontsize = 15)\n",
    "plt.axis('off')\n",
    "#plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pergunta 7 - Qual a Relação Entre a Promoção e a Avaliação do Ano Anterior?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 680
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 60188,
     "status": "ok",
     "timestamp": 1593698502530,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "eq77o58mcJC9",
    "outputId": "d27c140f-afdf-4ebf-d4fe-45ae9069e5ae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "data = pd.crosstab(dataset['aval_ano_anterior'], dataset['promovido'])\n",
    "data.div(data.sum(1).astype(float), axis = 0).plot(kind = 'bar', \n",
    "                                                   stacked = True, \n",
    "                                                   figsize = (16, 9), \n",
    "                                                   color = ['blue', 'magenta'])\n",
    "plt.title('Relação Entre Avaliação do Ano Anterior e a Promoção', fontsize = 15)\n",
    "plt.xlabel('Avaliação do Ano Anterior', fontsize = 15)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1178,
     "status": "ok",
     "timestamp": 1593700867232,
     "user": {
      "displayName": "work data",
      "photoUrl": "",
      "userId": "01648815589157946143"
     },
     "user_tz": -330
    },
    "id": "XpKTjlk68MN7"
   },
   "source": [
    "# Fim"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMB3LsuTvymI4FpU/rgfBud",
   "collapsed_sections": [],
   "name": "HR Analytics Promotion.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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}