mulheres_TI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inserção das Mulheres na área de TI nos últimos 10 anos\n",
    "\n",
    "Análise de dados do número de mulheres versus o número de homens que entram nos cursos da área de Tecnologia da Informação (TI) e também analisar a quantidades de alunos que cursam os cursos e quantos se formam.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importando os módulos\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import mode\n",
    "low_memory=False\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Leitura do dataset utilizado na análise\n",
    "df = pd.read_csv(r'df2009_df2018.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDADE</th>\n",
       "      <th>ANO</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>SITUACAO</th>\n",
       "      <th>CURSO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>2009</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Cursando</td>\n",
       "      <td>ANALISE E DESENVOLVIMENTO DE SISTEMAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>2009</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Desvinculado do curso</td>\n",
       "      <td>ANALISE E DESENVOLVIMENTO DE SISTEMAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>2009</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Cursando</td>\n",
       "      <td>COMPUTACAO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41</td>\n",
       "      <td>2009</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Matrícula trancada</td>\n",
       "      <td>ANALISE E DESENVOLVIMENTO DE SISTEMAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>2009</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Cursando</td>\n",
       "      <td>INFORMATICA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IDADE   ANO       SEXO               SITUACAO  \\\n",
       "0     29  2009  Masculino               Cursando   \n",
       "1     21  2009  Masculino  Desvinculado do curso   \n",
       "2     20  2009  Masculino               Cursando   \n",
       "3     41  2009  Masculino     Matrícula trancada   \n",
       "4     18  2009  Masculino               Cursando   \n",
       "\n",
       "                                   CURSO  \n",
       "0  ANALISE E DESENVOLVIMENTO DE SISTEMAS  \n",
       "1  ANALISE E DESENVOLVIMENTO DE SISTEMAS  \n",
       "2                             COMPUTACAO  \n",
       "3  ANALISE E DESENVOLVIMENTO DE SISTEMAS  \n",
       "4                            INFORMATICA  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizando as primeiras linhas do dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1217117, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizar o número de linhas e colunas\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IDADE        int64\n",
       "ANO          int64\n",
       "SEXO        object\n",
       "SITUACAO    object\n",
       "CURSO       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificação dos tipos de dados\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.217117e+06\n",
       "mean     2.707266e+01\n",
       "std      7.438209e+00\n",
       "min      1.300000e+01\n",
       "25%      2.100000e+01\n",
       "50%      2.500000e+01\n",
       "75%      3.100000e+01\n",
       "max      8.400000e+01\n",
       "Name: IDADE, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificação da descrição da idade\n",
    "df['IDADE'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEXO\n",
       "Feminino     25\n",
       "Masculino    25\n",
       "Name: IDADE, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Agrupamento da mediana - por sexo e idade\n",
    "df.groupby('SEXO')['IDADE'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEXO\n",
       "Feminino     26.613892\n",
       "Masculino    27.164551\n",
       "Name: IDADE, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Agrupamento da média - por sexo e idade\n",
    "df.groupby('SEXO')['IDADE'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtro dos dados por sexo\n",
    "masc = df[df['SEXO'] == 'Masculino'] \n",
    "femi = df[df['SEXO'] == 'Feminino']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDADE</th>\n",
       "      <th>ANO</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>SITUACAO</th>\n",
       "      <th>CURSO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>2018</td>\n",
       "      <td>Masculino</td>\n",
       "      <td>Cursando</td>\n",
       "      <td>ANALISE E DESENVOLVIMENTO DE SISTEMAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   IDADE   ANO       SEXO  SITUACAO                                  CURSO\n",
       "0     21  2018  Masculino  Cursando  ANALISE E DESENVOLVIMENTO DE SISTEMAS"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Moda da idade dos homens\n",
    "masc.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDADE</th>\n",
       "      <th>ANO</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>SITUACAO</th>\n",
       "      <th>CURSO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feminino</td>\n",
       "      <td>Cursando</td>\n",
       "      <td>ANALISE E DESENVOLVIMENTO DE SISTEMAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IDADE   ANO      SEXO  SITUACAO                                  CURSO\n",
       "0     20  2018  Feminino  Cursando  ANALISE E DESENVOLVIMENTO DE SISTEMAS"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Moda da idade das mulheres\n",
    "femi.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "#Histograma da idade \n",
    "df['IDADE'].plot(kind = 'hist', bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2870010570455597"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificação da Assemetria \n",
    "df.IDADE.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assimetria positiva, ou seja, distribuição tem uma cauda e média está à direita.\n",
    "\n",
    "A obliquidade mede a assimetria das caudas da distribuição. Distribuições assimétricas que tem uma cauda mais \"pesada\" que a outra apresentam obliquidade. Distribuições simétricas tem obliquidade zero. Assim:\n",
    "\n",
    "- Se v>0, então a distribuição tem uma cauda direita (valores acima da média) mais pesada\n",
    "- Se v<0, então a distribuição tem uma cauda esquerda (valores abaixo da média) mais pesada\n",
    "- Se v=0, então a distribuição é aproximadamente simétrica (na terceira potência do desvio em relação à média)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9136320583452933"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.IDADE.kurtosis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Curtose-leptocurtica, possui a curva da função de distribuição mais afunilada com um pico mais alto do que a distribuição normal\n",
    "\n",
    "\n",
    "A curtose é uma medida de forma que caracteriza o achatamento da curva da função de distribuição de probabilidade\n",
    "\n",
    "- Se o valor da curtose for = 0 (ou 3, pela segunda definição), então tem o mesmo achatamento que a distribuição normal. Chama-se a estas funções de mesocúrticas\n",
    "- Se o valor é > 0 (ou > 3), então a distribuição em questão é mais alta (afunilada) e concentrada que a distribuição normal. Diz-se que esta função probabilidade é leptocúrtica, ou que a distribuição tem caudas pesadas (o significado é que é relativamente fácil obter valores que não se aproximam da média a vários múltiplos do desvio padrão)\n",
    "- Se o valor é < 0 (ou < 3), então a função de distribuição é mais \"achatada\" que a distribuição normal. Chama-se-lhe platicúrtica\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANALISE E DESENVOLVIMENTO DE SISTEMAS', 'COMPUTACAO',\n",
       "       'INFORMATICA', 'TECNOLOGIA DA INFORMACAO'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cursos compreendido no dataset\n",
    "df['CURSO'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualização dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='CURSO'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KIClUAwF1hJaioQJIpLjkPZioSAyaG25EpCCpABIpEolUZiS695cUNvUDkqKhAkikeOj0lxS6nROpTLvvYygSBxVAIsVDp7+k0FUAu8UdQiQfKoBEikfuDWVFCtHouAOI5EMFkEgRCE8rbBZ3DpE8qACSoqACSKQ4jIo7gEiexsQdQCQfKoBEmmFmVWZ2vJldEj6ON7M+MUZSASTF4ssaEFGKgQogkRxmNhKYC+wFLAgfewFzwnlx2C6m/Yq0VxW6XYsUAVXpIk1dAZzo7o9EJ5rZvsCVwN4xZFILkBST0QQ/IkQKllqARJraJLf4AXD3fwMbxpAHVABJcVGLpRQ8FUAiTZU119/HzNYhhlbTRCrTH40ALcVl07gDiLRFBZBIU38F7jCz4Y0TzCwB3A7cHEOe/4lhnyKdMTTuACJtUR8gkRzu/jsz+wnwlJn1AwxYCVzi7lfEEOlLMexTpDNUtEvBUwEk0gx3nwpMNbOB4etPYoyjAkiKjVqApODpFJhIDjP7ZuPpr7DwOcXMXjaze81s8xgiqQCSYqP/Z6XgqQASaeo84AMAM/sGcDTwXeBe4OoY8mwQwz5FOqNPIpVZN+4QIq1RASTSlLv7p+HzQ4Hr3P0Fd7+WeH7Z6te0FCP1A5KCpgJIpCkzswFmVgZ8DXg0Mm+dGPKoBUiK0ZC4A4i0RgWQSFN/AGYBM4F57j4TwMx2BBbHkKdfDPsU6azKthYws/XNbFb4eM/MFkVee+T5LDNLhetUmlnazP5rZi+a2TNmdlA4L2tmd0S2/20zuzHy+hAze8XM5plZtZkdEpl3o5l9u5mMPzCzV8PH82Y2PjKvwszOD7M05jw7Mn9lzrZONrM1Zja4heORMLPVZvZSmPF5MzuumeVmmdltrRzXKWZ2auR9LWoc28zMNjCzbM7+ose5Ks9jNT9c/mUz+1pk3jQzW2BmFpl2d77HwswOMrOZZjY3PA6XtvXew3s3/sHM3gg/i3vMrM2xqHQVmEgOd7/ezB4iaMJ/OTLrPeC4GCLph4oUoza/X9z9I2AHCL60gZXufkn4eqW779DMar8lGBh0O3evMbOhwJ6R+WPNbKS7f+FWHGa2PXAJsJ+7zw8vaHjEzN5y91eayxf2Afw/YLy7f2hmOwF3m9nO7v4e8DuC0eFHu/ua8KrRX7TylicBMwhOrd/QwjJvuvuO4f63AO40M3P3G8JpXwbKgQlm1t/dV7Wyv0b1BP0Yr2phfzvkvO98jtVp7v5PM9sb+AuwVWQTy4Ddgelmti7ND+Ta5FiY2XbAVCDp7q+aWTnwg0iult77+cBAYBt3rzez4wmO2y7u7i0dFP3DKtIMd1/k7i+5e0Nk8kDg3Bji6O9UilGX/8C2YFyuE4CfunsNgLsvcffbI4tdCpzdzOqnAue7+/xwvfnABcBprezyDIIv+g/DdV4EbgJ+nJNlTTj/E3ef0kL2EcAA4ByCL/82uftbwM+Bn0UmTyIYkPVh4OB8tkPQqn2KmeX7mbTnWD0DbJIz7TZgYvj8UODO6MxWjsXpwHnu/mq433p3jxZtTd57+DkcD5zi7vXhejcANcA+rb1JtQCJ5DCzMQS/fjYG7ia4AepUYBeCf1x7PFIM+ywJm5S/MWf8en9Z8Gzfsv+pdYZU1VldZT31lXXUVdVSX1VHbVUdDVV11FfV0lBVZw19ar2hqg6vqrOGqjr3qlqorINgGlTWYZX1WGWdW2U9VhE8yivqvayigfLyBsrKG6gob/DysgbKy5zyMqeyzL3SnApzKsHLKfLPfXVFHyed7Mwm+prZrMjrC4B5wAJ3X9HKercDPzKzLXOmjyL4u46aCfy4lW2NAl5oZp3JwJZhlnzHCJtIUBg8BWxjZkPdfUke670IbBt5fSSwXzjtp8AteWxjATAdOAa4L2feiMhx/o+7/5j2HasDCf6djHoUuCZswZlI0Irzy8j8lo7FdrT+b2xz773xc8j9f2Jm+D4epQUqgESauoagqfgZgj/uWQS/+o5q/KXXw+pj2GdJWFS/5ajnPzx54INVZ66Z0a/8kz8NGbxiTlXVGFroowHewvN85NYzLdc35t5QUU9tRT1rK+uorQz+Wxc+r6uqo66q1uur6mh81AUFGmFx5g1VtXikKKOqDg+KM7fKOqyiHirrKa+ox8obKK+op6w8KNAaC7OKsgYqypygMIPKoDijCqiyNr4/+tfVtPP4NLG6mVMzY/JYrx64GDgTeKCzIfIVnnY5CVgf2M3d38lZZBLwLXdvCPspHU7ww6rNTUf2MQ740N0XmNki4HozW8/dl+axnQuAe4BMzvQmp8DydLGZnU9w37ddc+bVExRcE4G+7p6NdAmCDhyLlt57B3J/RgWQSFN93P3G8PlrZnaSu58eY561Me6715vvGw8bV3P1Jw9z+rzbVi+ZsBZq/jlwwDM3rjuofHF5+Y6YtdmZt6u5WVltBX1qK+izuslteRvF3EDk7hX1rA2Ls9qK+qA4q6xjbVUddeZ8enfX7/UNYJiZDWqjFehmggJodmTaXGAsX+zXNxaY08p2Gtd5rJl1GrMMDE993QDcYGazCfqpfMbMRhP0kXkkLASqgPnkVwDtSNDyBUHhsG1jJ2ZgEHAYwY+2Vrn7f8OWniPy2Gc+x6qxD9BPgevD+VG3AXcBU6IT2zgWc5rZb6OW3vstRD6HnLz3t/Ym1bdApKl1zGxHM9sp7PRYk/O6p9XGsM+Ssoq+A3evufwrD9WPfaIK+nznk5W7PvzOuzs/sWDRimOXr3iyf0NDa1+SpcnM6iqsz+o+NnBFf1tv6SAbumSIbbLwS7b5WxvZVm9ubF1euIfjc10H/DFytdKXzOzwnOVqgd8Dp0QmXwKcacGNjRtvcHwWrZ9yuQi40MzWD9fZgeBCiD9Fskw1s3XC+eUEX+i5JgFT3D0RPjYGNrbIDZebE2a8BLjCgmE5jiDocJ1w9wRBP5i8+hOFziPo39OW9hyrqUCZmR2QM/0pglanW3Omt3YsLgbOMrOtw/2WmdkPW3vvYUfom4DLwuOPmR1LcPXsY7RCLUAiTb0HXNbCa6eNjnXdYHUP769Emf1f7S/2PL7hgWfOrbh5ezP6rdfQsP5pS5ftcdrSZbxRWTl/6pDBbz/Rr++IOrPN4k5bBD5te5FW5fYBetDdUwQdZ38HzDWzNcAqmr844bpwWQDcfZaZnQHcZ0GrXi1wurtH9/FnM/tD+Pwdd9/VzDYBnjYzBz4Bjnb3xuEwzia4Km22mX1C8Ld6E/BuTpaJwNdzpt0VTr8wZ/oIM3uJYMyxT4DL3f1GM9sTWOTu0W0/CYw0s40imVrk7nPM7EWg1R9yeR6rxmXdzH5H0IH5oeh0mvYjglaOhbtfaGYnA7eGnZudoBVnAq28d4LWvkuA182sAXiV4BRbq+eprY35IhKzRCozBfhV3DlKyU72+qu3V/1mUIU1bJw7z8Gn912n+k9DBi+f3Wp/oZKXqJ5c/XbcIURaohYgkRxmdmjOJAc+BGbFdFf4OAZfLGkv+tbbfrVm6geP9jn1lcH26Rc63hrYhNVrxkxYvYa1UHPnwAHP3jB4kL1bUb4j4WkZASCfK5xEYqMWIJEcZtbcAGXrAWOA77l7q+eVu1oilTmYppeZSg+opG7tnVXnPje6LDuhrWU/Litbev3gQbP/OXDAkJXlZaN7Il8BW1o9uXr9uEOItEYFkEiewk56t7v7Lj2530QqswvwbE/uU77oVxU3PXlc+UO7meXXav5WZcXbU4esm328X9/N68yGdXe+AlRdPbk6n0vWRWKjq8BE8uTub5PH/Y26gU6BxezXdZP3OLH2pFcanI/zWX6L2rrhl73/4Z4vZd8ZdtV7778yek3NU+a+rJtjFpKFcQcQaYsKIJE8mdm2BMOr97T3Ytin5HiwYZed9l17yYo1Xvlme9Ybv3rNmFsWL5kwM/tOv19+uPTZTWrrnsW9t4/ttCjuACJt0SkwkRxmdh9Nh/ldj+CGfke7+zM9nSmRyiwFhvT0fqWp/qz+5OE+p8/bxD7auaPbWFZW9vH1gwdV/3PggCGf9M7+Qr+unlw9Je4QIq3RVWAiTd0LDCUYyAuCYugjgiHu348p05vAuJj2LRGr6DtwfM0fx/2l8rJp+5W/uFdHtrFuQ8OQn3+8bI+ff7yM+ZUVb1+57uD5j/bvt3ldGwPjFRG1AEnB0ykwkaYOBu5x9yfCx5PuPgf4mOCuynFobmh4iYlTVnZC7al7/bb26KfdOzdQ5ea1dcMv+eCjvV7KvjP8z4vfr95+Tc2T5p5XX6MCNq/tRUTipQJIpKmh7l6dOzGcluj5OIAKoIJ0Xf3Xdzti7bnZOi/rko7qu61ZM/pvi5fsMTP7Tv9zP/zouU2Lt7/Q7LYWMLOV4X8TZubhPaUa5001s+PC5zea2XwzmxU+fhZOH2xmfzWzN8zszfD54Mg2V4fLzw3nVYbz9gr39/3I/nYIp50amVZhZh+YWTp8fXYkQ300j5lNyVn3VDN7NZw/I7w1Q+O8Dcys1sx+2OGjK11CBZBIU+u2Mq9vT4XIoQKoQM3wbb+8W80VFcu9X5OiuaOqoOrwT1bt8sDCd786fcGiT7+7bPmTA+sbXumq7XezhdWTq5e1c533gZOs5YEkT3P3HcLH5eG064C33H1Ldx9BcEPNayPrNN7lfDTBHcujNwGdnfN6Ek3/xvYDXgcONzNz9/MaMxDeqT4nDwBhYbMfsHO47Nf44p1rDycY1qI99/CSbqACSKSpmWZ2Qu7E8BfjCzHkASiWL7+S9D5DvjSu5upt5jQMn97V2x7c0LDuKR8v3+PpBQvH3PfOuwsOXLlqWkUwJEOh6kix/gHwKDA5n4XNbEuCu33/NjL5N8A4MxsRXdbd64HngU0ik98muOnxUAtuSX4g8EDObiYBfwQWALvm/1Y4Czix8W717r7C3W/K2e4vgE3MbNN2bFe6mAogkaZOBo43s2lmdmn4eAL4HnBSHIGy6eQygn+0pUDVUlGVXHvB+Jvq9n/Cnfru2Eeirm7YxWF/oWsWL5m9Q9BfaGl37KsTOvoj4ULg1MY7eue4OHLKaTQwkuDWNJ8d5/D5LGBUdMXwTu27AA/mbPOfBK0xuwEvEhniIlxnX+A+gruZ59VaY2aDgIHu/lYL8zcDNnL354HbgSPz2a50DxVAIjncfYm77wb8GsiGj1+7+67uHueYPDoNVgR+VXfcnj+p/dmsBmdZd+7nq2tqtrt58ZI9Xsi+M/BXH3z03Ka1tc/iHsc4VblmdmSlsGh4DvhOM7Ojp8DyPdU4Iryb/BJgsbvntqLeTlAATSIocqK+ATzu7quBO4BDWijM2uvIcL8At6HTYLFSASTSAnd/3N2vCB89ev+vFrwYdwDJT6bhq2P3W3vx8pp2DprYEZVQ+e2Vq3Z5YOHir05fsGj195ctf2pQff0rxDfI24xOrHs+cAZf7DPTnLnADmb22XdY+HyHcB583gdoBDDWzP43uoHwx0wtQX+dR3O2PwnY18yyBC1a6wP7tBU+PO210sy2aGGRScBx4XbvBcaY2VZtbVe6hwogkeKR+4+0FLA3fZPhY2uu+tK7vt7zPbXPwQ0N65708fIJ/1mwaMz9CxcvPGjlqmmV7tme2j8wr3pydYdbSd39VYIC5pttLPcG8BJwTmTyOcCL4bzosh8CKeDMZjZ1LnBG9FRaeBprAjDM3RPungB+TP6tNRcAV4bbwcwGmNmxZrY1MMDdN4ls94J2bFe6mAogkeLxLLAi7hCSv5X0G7R7zeXjHq3fcVpP73t4Xd1mF33w0V4vZt9JXLt4yZwd16zpif5CD3fBNs4juGqrLd8Dtg4vgX8T2Dqc1py7gX5mNiE60d2fdve7c5b9FvCYf/F04j3AN82sTx65rgIeB2aY2WyCAVUbCAqdu3KWvQMVQLHRrTBEikgilbkLOCTuHNJ+J5Rnnj6r4u87msU2lAK1UHvfgP4vXbvuoPp3Kip2Ir8v9Pb4evXk6tyrqUQKklqARIpLV/zClhhcU5/c7ci1v5xf30WDJnZEJVQeunLVzv9auHjX6QsWrjkh6C/0chf1F6oBnuiC7Yj0CLUAiRSRRCqzBcF9waRIDWXp+4/0OX3JIPu0YG6CuqCiYuGVQwa/8Uj/fsNrzTbv4GYeq55c/bUuDSbSjdQCJFJEsunkW6gAKmpLWO9/xtVctfXchmFdPmhiRw2rq9v0wqC/0ObXLV4yZ6c1a54w94/auRm1TkpRUQEkUnweijuAdM5aKvt8fW16/M11+3bboIkdtfOamlE3LX5/zxey7wz6zQcfPT+stvYZ3Nfksar6/khR0SkwkSKTSGX2BgphXCLpAt8oe+aFyyuvGFFmrd6DLlYrymz5TYMGvfKPQQMGLS8rG0Nw+4ioedWTq0fGEk6kg9QCJFJ8ngAWxh1Cusb9DbuOPWDtRctqvKLZ2ycUgkENPviny5ZPmL5g0faZhYsXJYPxheZHFskdSVmk4KkFSKQIJVKZi4DT4s4hXWcAn674d5/TXtvQPv5K3FnyNWOdPnOnDhn8wYvrrPP96snVb7S9hkjhUAuQSHG6Oe4A0rVW0m/QrjVXjH28fvuiuZT8K2tqRt60+P1KFT9SjFQAiRShbDpZTQdvOimFyykrO772jD0vqJ30H3fy6XhcCG6IO4BIR6gAEile18YdQLrHn+u/ufvEtee8FeegiXn6FPhH3CFEOkIFkEjxupXgC0h6oed85Mjday4v+8T7zok7SytuYcryT+IOIdIRKoBEilQ2nVwB3Bh3Duk+77He0LE1V2/5asNmBTNoYkQDcEncIUQ6SgWQSHG7CKiLO4R0n7VU9jlw7YXj/163T6ENmngvU5a/FncIkY5SASRSxLLp5NvALXHnkO53dt339/xZ7U9ecmd53FlCF8YdQKQzVACJFL80oAG9SsB9DbuNO2DthUtrvGJ+20t3q+lMWf5szBlEOkUFkEiRy6aT84C7484hPeN132zzcTVXrfeeD4lzGISLYty3SJdQASTSO5wfdwDpOZ/Qf/CuNVfs9ET9mDgGTZwL3B/DfkW6lAogkV4gm07OBB6OO4f0HKesbHJtas+Lao/s6UETf8mU5TrlKkVPBZBI73EGwaXJUkL+VH/w7pNqz36z3u29Htjdf5iy/M4e2I9It1MBJNJLZNPJWcBf4s4hPe/ZhlGjxtdcTg8Mmqgb8EqvoQJIpHc5B1gadwjpeYtZf8NxNVeNeK1h0/900y7+wZTlz7S1kJltaGa3mdmbZvaCmf3LzLY2s1Fm9piZvWZm/zWzX5qZhescZ2ZuZvtGtnNIOO3b4etp4bovm9l/zGybcHrWzDaIrLeXmd1vZseb2azwsdbMqsPn6XC5k81sjZkNzsl/kJnNNLO5ZvaSmV2aM3+Wmd2WM63KzP5gZm+E7+0eM9u0/YdYepIKIJFeJJtOfgT8Mu4cEo8aqtY5YO1Fu99at/cT7l16OnQVcGpbC4UFzV3ANHcf4e5jgTOBocC9QNrdtwG2B3YDfhRZvRqYGHk9CXg5ZxdHufv2wE3Axa1lcfcb3H0Hd98BeBfYO3ydimx/BnBoJP92wFTgaHcfCYwD3ojM/zJQDkwws/6R3Z0PDAS2cfetCK7KvLOxwJPCpAJIpPf5M02/OKSEnFl3wp6n1P7oxS4cNPECpixfmMdyewO17n514wR3fxnYGviPuz8cTvsU+AmQiqz7FLCzmVWa2QBgS2BWC/t5MpzfIWY2AhhA0GI6KTLrdOA8d381zFnv7ldF5k8Cbia44ODgcFv9gOOBU9y9PlzvBqAG2KejGaX7qQAS6WWy6WQ98LO4c0i87m4YP+7Atemlazs/aOJc8r/n13bAC81MH5U73d3fBAaY2aDGScC/gQMIiot7W9nPNwlajDpqInAbQdG1jZkNDae3lL/RkeF6t/J54bQlsMDdV+QsO5PgfUuBUgEk0gtl08kngb/GnUPi9ZoP23xczZ/WW+LrdnTQxFrgGKYsr+nKXK24jaA4mUhQZOT6u5nNAnbn81NyzV2S39Zl+pOA29y9AbgDOLytYGY2DvjQ3RcAjwI7mtl6ba0nhUsFkEjv9VMgG3cIidcKBgzetWbqjk/Vb9eRQRN/x5TlL7Zj+TnA2Gamz82dbmZbACujLSfu/jwwGtjA3V9vZjtHhf14DnH3d8JpHwFDIsusB3zYUkAzGw1sBTxiZlmCYquxNael/ITLbBuu8yYwCDgsfD7MzAbmLD823J4UKBVAIr1UNp1cARwFBXUHcYlBA2Xlx9SetefFtUdMdyff1pwZtH+E8ceAPmb2g8YJZjYGeA0Y33iVl5n1BS6n+VtqpICz2rHPacAx4XbLgaOBx1tZfhIwxd0T4WNjYGMzG07QsfosM9s63F6Zmf3QzMqAI4DRjesRnKab5O6rCDplXxbuHzM7FugXHg8pUCqARHqxbDr5NHBe3DmkMFxZf8j4o2rPeqPebUkbi64GjmXK8rr2bN/dHfgWsG94Gfwc4ALgPYKC4Rwze42g/84MgiuucrfxgLu3VsDk+i2wpZm9DLxEcNXW31pZfiLBlWpRdwET3f0V4GTgVjObB8wGtgAmAIvc/d3IOk8CI81sI4Ir3dYAr5vZfwlOqX0rPB5SoEyfj0jvlkhlygk6e+4adxYpDBvx0XuP9Dlt6QBbM7KFRU5myvI/9mgokR6mFiCRXi68Kuxo4JO4s0hhWMz6G46tuXqL1xs2aW7QxAcJTk+J9GoqgERKQDadfAs4Me4cUjhqqFpn/7UX7/6Pur2igya+CXxHNzuVUqBTYCIlJJHKXAr8PO4cUli+VfbUjEsrr96szHx/pizvzPg6IkVDBZBICUmkMmXA/cBBcWeRguJf4uPDZqSPzu0cLNJrqQASKTGJVGYw8DTQUgdYKT3nZNNJXS0oJUUFkEgJSqQyw4BngY3iziKx+1s2nTwm7hAiPU2doEVKUDadXAB8HV0ZVuoeAL4XdwiROKgAEilR2XRyFnAowQBuUnr+DRyaTSfXxh1EJA4qgERKWDad/DfBnbVXx51FetQ04H+z6aSKXylZKoBESlxYBCWBVXFnkR4xHfhGNp1U0SslTZ2gRQSARCozAfgXMCDuLNJtngX2z6aT6vslJU8tQCICQDadfAo4AHWM7q2eAQ5U8SMSUAEkIp8J7x6/H/Bx3FmkS90O7JNNJ5fHHUSkUKgAEpEvyKaTzwG7AK/GnUW6RBqYqA7PIl+kPkAi0qxwxOjbgAPjziIdUgecmE0nr407iEghUguQiDQrPF3yDeCyuLNIuy0HDlLxI9IytQCJSJsSqczxwNVAVdxZpE2vAYdl08k5cQcRKWRqARKRNmXTyRuAvYGFcWeRVl0L7KTiR6RtagESkbwlUpl1gSuAo2OOIl+0FDghm07eGXcQkWKhAkhE2i2RyhxGcEpsg7izCNOAY7LppFrnRNpBp8BEpN2y6eQdwHbAfXFnKWG1wNnA11T8iLSfWoBEpFMSqcx3gd8Dg+LOUkKeIrjEXX19RDpILUAi0inZdPJ6YBvgJkC/qLrXB8DxwJ4qfkQ6Ry1AItJlEqnMLsDlwM5xZ+llaoGpwG+y6eSymLOI9AoqgESkSyVSGQOOAM4Htog5Tm9wH3BqNp18Pe4gIr2JCiAR6RaJVKYK+CFwBrBxzHGKjQN3Aedn08kX4g4j0hupABKRbhUWQt8BfkFw5Zi0rA64Fbggm07OizuMSG+mAkhEekwilTkIOBXYJ+4sBaYGuBG4MJtOzo85i0hJUAEkIj0ukcrsRNAidBjQJ+Y4cZpDUPjcnE0nl8ScRaSkqAASkdgkUpnBBEXQdwjuNVYKQ3N8THCa64ZsOjkz7jAipUoFkIgUhEQqsxEwETgKGBtznK62BngU+CtwTzadrIk5j0jJUwEkIgUnkcpsDXyToK/QBGBgvIk65DXgwfDxRDadXB1zHhGJUAEkIgUtkcpUAF8hKIb2AXYD1ok1VPM+BKYTFj3ZdPLtmPOISCtUAIlIUUmkMn0ICqLtgTHhYxQ920q0AJgNvALMBGaq4BEpLiqARKRXCPsQbQNsTTDw4gaRx/qR57mtRw3hoz58rAAWt/B4F5iXTSdXdPPbEZFupgJIREpKODCjAw3ZdLI+7jwiEg8VQCIiIlJySmHMDREREZEvUAEkIiIiJUcFkIiIiJQcFUAiIiJSclQAiYiISMlRASQiIiIlRwWQiIiIlBwVQCIiIlJyVACJiIhIyVEBJCIiIiVHBZCIiIiUHBVAIiIiUnJUAImIiEjJUQEkIiIiJUcFkIiIiJQcFUAiIiJSclQAiYiISMlRASQiIiIlRwWQiIiIlBwVQCIiIlJyVACJiIhIyVEBJCIiIiVHBZCIiIiUHBVAIiIiUnJUAImIiEjJUQEkIiIiJUcFkIiIiJSc/we6rJSK/AnuLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico para visualizar a distribuição do número de alunos por curso.\n",
    "df['CURSO'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='SITUACAO'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico para visualizar a distribuição do número de alunos por situação.\n",
    "df[\"SITUACAO\"].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='SEXO'>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Gráfico para visualizar a distribuição do número de alunos por sexo.\n",
    "df[\"SEXO\"].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Criação de categorias para idade\n",
    "df['idade_categoria'] = pd.cut(\n",
    "    df['IDADE'],\n",
    "    bins = ( 13, 20, 30, 40, 50, 60, 70, 80, 84),\n",
    "    labels = ('13 a 20', '20 a 30', '30 a 40', '40 a 50', '50 a 60', '60 a 70', '70 a 80', '80 a 84'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico 'Faixa etária - Mulheres x Homens no Curso de TI'\n",
    "No eixo x temos as categorias de idade e no eixo y as quantidades de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Faixa etária - Mulheres x Homens no Curso de TI'}, xlabel='idade_categoria'>"
      ]
     },
     "execution_count": 20,
     "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": [
    "situacao_sexo_id = pd.crosstab(df['idade_categoria'],df['SEXO'])\n",
    "situacao_sexo_id.plot(kind='bar',width=0.5, color=['red','blue'], title='Faixa etária - Mulheres x Homens no Curso de TI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico Porcentagem Mulheres x Homens no Curso de TI - Por Curso'\n",
    "No eixo x temos os cursos e no eixo y a porcentagem de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Porcentagem Mulheres x Homens no Curso de TI - Por Curso'}, xlabel='CURSO'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "curso_sexo = pd.crosstab(df['CURSO'],df['SEXO']).apply(lambda r: r/r.sum(), axis=1) * 100\n",
    "curso_sexo.plot(kind='bar',width=0.5, color=['red','blue'], title='Porcentagem Mulheres x Homens no Curso de TI - Por Curso')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico 'Situação Mulheres x Homens no Curso de TI'\n",
    "No eixo x temos situação dos alunos e no eixo y o número de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Situação Mulheres x Homens no Curso de TI'}, xlabel='SITUACAO'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "situacao_sexo = pd.crosstab(df['SITUACAO'],df['SEXO'])\n",
    "situacao_sexo.plot(kind='bar',width=0.5, color=['red','blue'], title='Situação Mulheres x Homens no Curso de TI')"
   ]
  },
  {
   "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>SEXO</th>\n",
       "      <th>Feminino</th>\n",
       "      <th>Masculino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>10164</td>\n",
       "      <td>40309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>13887</td>\n",
       "      <td>58140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>16777</td>\n",
       "      <td>73014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>19052</td>\n",
       "      <td>86917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>20876</td>\n",
       "      <td>98899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>23483</td>\n",
       "      <td>115072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>23559</td>\n",
       "      <td>121969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>23639</td>\n",
       "      <td>129352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>24430</td>\n",
       "      <td>136797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>27247</td>\n",
       "      <td>153534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SEXO  Feminino  Masculino\n",
       "ANO                      \n",
       "2009     10164      40309\n",
       "2010     13887      58140\n",
       "2011     16777      73014\n",
       "2012     19052      86917\n",
       "2013     20876      98899\n",
       "2014     23483     115072\n",
       "2015     23559     121969\n",
       "2016     23639     129352\n",
       "2017     24430     136797\n",
       "2018     27247     153534"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curso_ano = pd.crosstab(df['ANO'],df['SEXO'])\n",
    "curso_ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_teste = pd.DataFrame(curso_ano)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ANO</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEXO</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>Feminino</th>\n",
       "      <td>10164</td>\n",
       "      <td>13887</td>\n",
       "      <td>16777</td>\n",
       "      <td>19052</td>\n",
       "      <td>20876</td>\n",
       "      <td>23483</td>\n",
       "      <td>23559</td>\n",
       "      <td>23639</td>\n",
       "      <td>24430</td>\n",
       "      <td>27247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Masculino</th>\n",
       "      <td>40309</td>\n",
       "      <td>58140</td>\n",
       "      <td>73014</td>\n",
       "      <td>86917</td>\n",
       "      <td>98899</td>\n",
       "      <td>115072</td>\n",
       "      <td>121969</td>\n",
       "      <td>129352</td>\n",
       "      <td>136797</td>\n",
       "      <td>153534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ANO         2009   2010   2011   2012   2013    2014    2015    2016    2017  \\\n",
       "SEXO                                                                           \n",
       "Feminino   10164  13887  16777  19052  20876   23483   23559   23639   24430   \n",
       "Masculino  40309  58140  73014  86917  98899  115072  121969  129352  136797   \n",
       "\n",
       "ANO          2018  \n",
       "SEXO               \n",
       "Feminino    27247  \n",
       "Masculino  153534  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_teste.head(15).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "    '2009': [10164, 40309],\n",
    "    '2010': [13887, 58140],\n",
    "    '2011': [16777, 73014],\n",
    "    '2012': [19052, 86917],\n",
    "    '2013': [20876, 98899],\n",
    "    '2014': [23483, 115072],\n",
    "    '2015': [23559, 121969],\n",
    "    '2016': [23639, 129352],\n",
    "    '2017': [24430, 136797],\n",
    "    '2018': [27247, 153534],\n",
    "    'sexo': [0,1]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_model = pd.DataFrame(data, columns=['2009','2010','2011', '2012', '2013', '2014',\n",
    "                                       '2015', '2016', '2017', '2018', 'sexo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>sexo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10164</td>\n",
       "      <td>13887</td>\n",
       "      <td>16777</td>\n",
       "      <td>19052</td>\n",
       "      <td>20876</td>\n",
       "      <td>23483</td>\n",
       "      <td>23559</td>\n",
       "      <td>23639</td>\n",
       "      <td>24430</td>\n",
       "      <td>27247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40309</td>\n",
       "      <td>58140</td>\n",
       "      <td>73014</td>\n",
       "      <td>86917</td>\n",
       "      <td>98899</td>\n",
       "      <td>115072</td>\n",
       "      <td>121969</td>\n",
       "      <td>129352</td>\n",
       "      <td>136797</td>\n",
       "      <td>153534</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2009   2010   2011   2012   2013    2014    2015    2016    2017    2018  \\\n",
       "0  10164  13887  16777  19052  20876   23483   23559   23639   24430   27247   \n",
       "1  40309  58140  73014  86917  98899  115072  121969  129352  136797  153534   \n",
       "\n",
       "   sexo  \n",
       "0     0  \n",
       "1     1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_model.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_model.drop(['sexo'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_model['sexo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression().fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.04381424e-07, 5.93633808e-07, 7.54393702e-07, 9.10378017e-07,\n",
       "       1.04664295e-06, 1.22862466e-06, 1.32012526e-06, 1.41809167e-06,\n",
       "       1.50735205e-06, 1.69408250e-06])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.24066279804006474"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico 'Inserção Mulheres x Homens no Curso de TI entre os anos de 2009 a 2018 no Brasil'\n",
    "No eixo x temos os anos e no eixo y o número de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Inserção Mulheres x Homens no Curso de TI entre os anos de 2009 a 2018 no Brasil'}, xlabel='ANO'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "curso_ano.plot(kind='bar',width=0.5, color=['red','blue'], \n",
    "               title='Inserção Mulheres x Homens no Curso de TI entre os anos de 2009 a 2018 no Brasil' ,grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>SEXO</th>\n",
       "      <th>Feminino</th>\n",
       "      <th>Masculino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>20.137499</td>\n",
       "      <td>79.862501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>19.280270</td>\n",
       "      <td>80.719730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>18.684501</td>\n",
       "      <td>81.315499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>17.978843</td>\n",
       "      <td>82.021157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>17.429347</td>\n",
       "      <td>82.570653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>16.948504</td>\n",
       "      <td>83.051496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>16.188637</td>\n",
       "      <td>83.811363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>15.451236</td>\n",
       "      <td>84.548764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>15.152549</td>\n",
       "      <td>84.847451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>15.071827</td>\n",
       "      <td>84.928173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SEXO   Feminino  Masculino\n",
       "ANO                       \n",
       "2009  20.137499  79.862501\n",
       "2010  19.280270  80.719730\n",
       "2011  18.684501  81.315499\n",
       "2012  17.978843  82.021157\n",
       "2013  17.429347  82.570653\n",
       "2014  16.948504  83.051496\n",
       "2015  16.188637  83.811363\n",
       "2016  15.451236  84.548764\n",
       "2017  15.152549  84.847451\n",
       "2018  15.071827  84.928173"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curso_ano_per = pd.crosstab(df['ANO'],df['SEXO']).apply(lambda r: r/r.sum(), axis=1) * 100\n",
    "curso_ano_per"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico 'Porcentagem Mulheres x Homens no Curso de TI entre os anos de 2009 a 2018 no Brasil'\n",
    "No eixo x temos os anos e no eixo y a porcentagem de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Porcentagem Mulheres x Homens no Curso de TI entre os anos de 2009 a 2018 no Brasil'}, xlabel='ANO'>"
      ]
     },
     "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": [
    "curso_ano_per.plot(kind='bar',width=0.5, color=['red','blue'],\n",
    "                   title='Porcentagem Mulheres x Homens no Curso de TI entre os anos de 2009 a 2018 no Brasil', grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Como na descrição da Idade, percebemos que 75% está em até 31 anos, portanto, foi criada a categoria de alunos maior e menor que 31 anos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Quantos alunos acima de 31 anos estudam?\n",
    "#Criação da categoria\n",
    "\n",
    "df['alunos_idade'] = ['> 31' if x > 31 else '< 31' for x in df['IDADE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< 31    934757\n",
       "> 31    282360\n",
       "Name: alunos_idade, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['alunos_idade'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>index</th>\n",
       "      <th>idade_categoria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13 a 20</td>\n",
       "      <td>218388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20 a 30</td>\n",
       "      <td>676708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30 a 40</td>\n",
       "      <td>247817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40 a 50</td>\n",
       "      <td>60707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50 a 60</td>\n",
       "      <td>12108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60 a 70</td>\n",
       "      <td>1289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70 a 80</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80 a 84</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  idade_categoria\n",
       "2  13 a 20           218388\n",
       "0  20 a 30           676708\n",
       "1  30 a 40           247817\n",
       "3  40 a 50            60707\n",
       "4  50 a 60            12108\n",
       "5  60 a 70             1289\n",
       "6  70 a 80               93\n",
       "7  80 a 84                5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['idade_categoria'].value_counts().reset_index().sort_values('index', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Como o curso de Análise e Desenvolvimento de Sistemas apresentou o maior número de alunos do dataset\n",
    "Foi criado filtros para saber a situação do aluno por sexo no curso de ADS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.DataFrame(df.query('SITUACAO == \"Formado\" and SEXO ==\"Masculino\" and CURSO == \"ANALISE E DESENVOLVIMENTO DE SISTEMAS\"'))\n",
    "y = pd.DataFrame(df.query('SITUACAO == \"Formado\" and SEXO ==\"Feminino\" and CURSO == \"ANALISE E DESENVOLVIMENTO DE SISTEMAS\"'))\n",
    "\n",
    "z = pd.DataFrame(df.query('SITUACAO == \"Cursando\" and SEXO ==\"Masculino\" and CURSO == \"ANALISE E DESENVOLVIMENTO DE SISTEMAS\"'))\n",
    "w = pd.DataFrame(df.query('SITUACAO == \"Cursando\" and SEXO ==\"Feminino\" and CURSO == \"ANALISE E DESENVOLVIMENTO DE SISTEMAS\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatena os dataframes criados acima\n",
    "data = pd.concat([x, y, z, w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>SEXO</th>\n",
       "      <th>Feminino</th>\n",
       "      <th>Masculino</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SITUACAO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cursando</th>\n",
       "      <td>14.837883</td>\n",
       "      <td>85.162117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formado</th>\n",
       "      <td>15.913133</td>\n",
       "      <td>84.086867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SEXO       Feminino  Masculino\n",
       "SITUACAO                      \n",
       "Cursando  14.837883  85.162117\n",
       "Formado   15.913133  84.086867"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sit_sex = pd.crosstab(data['SITUACAO'],data['SEXO']).apply(lambda r: r/r.sum(), axis=1) * 100\n",
    "sit_sex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico 'Cursando X Formados no Curso de ADS'\n",
    "No eixo x temos a situação e no eixo y a porcentagem de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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27Fj69+/PMcccQ8+ePRk0aBAVFz7279+fiosSW7duzfDhw+nTpw+77747c+bMAWDGjBnst99+7Lzzzuy///6899579b6tRZ2FEhE/jYieEdE7IgZHxH8iYnpE9IuI7hFxbET8p96rMzOrwX/+8/mK5pMLLzyKpUu/5Prrz+Xaax/gj3+cwLe/fRq33DJ8xfzNmjXn7rvHc/TRZ/GjHw3g5ptvZvLkydx1113Mnz9/teW/+uqr3HDDDUydOpXp06fzwgsvrDbP4sWL2X333XnttdfYZ599uPXWWwE499xzGTJkCJMmTWLQoEEMGzas3rfffaGYWW5VbkJ5++3JTJ8+mbPPPhCA5cuX0a5dxxXT99nnCAC6d9+Jbt160bFjmtatWzfef/992rZtu8ry+/XrR+fOnQEoKytjxowZ7LXXXqvM07x5cw4//HAA+vbty5NPPgmkOwc99NBDAAwePLhBOslygJvZeiTo1q0Xd9zxUpVTmzVLd/HZaKONaN68xYrxG220EUuXLl1t/oq7/kDqwraqeZo1a7biZsTVzdNQ3BeKma03tt12exYsmMukSSnAly79knfeKc0ddvbYYw/uu+8+IN2rc++99673dXgP3MzWG82aNeeaax7gV78axqJFn7J06VIGDjyfr32t8W/S8Nvf/pZTTz2V66+/nvbt23PnnXfW+zrcnWyOuTtZKyV3J9sw3J2smdkGwAFuZpZTDnAzs5xygJuZ5ZQD3MwspxzgZmY55QA3s9z6+tfFZZedtGJ46dKlHHhge37wg8MbfN0zZsygd+/eAIwfP75B+jqpjS/kMbN6Ud/XJbz8cu3ztGq1Ce+8M5kvvvicli1bMW7ck7RvX+XNwRpUeXk55SU4qdx74GaWa3vu+S1eeOG/AXjiiXs5+OCBK6ZNmfIPTjvtGwwatAunnbYHM2a8CcA770xhyJB+lJWVsfPOOzNt2jQA7r77bnbeeWf69OnD4MGDATjllFN44IEHViyzorvZQmPHjl3RodXll1/OaaedRv/+/enWrRs33njjivlGjBhB79696d27NzfccMNab7v3wM0s1w466ARuu+1K9trrcKZNm8QRR5zGxInPAbDttj0ZOfI5mjZtyrhx/8ctt/wX1133IA899HtOOOE8LrtsEEuWLGHZsmVMmTKFq666ihdffJF27drx8ccf17mmN954gzFjxrBw4UK23357vve97zFp0iTuvPNOxo0bR0Sw2267se+++7LLLrvUeT21Brik7YH7C0Z1A34C3J2N7wLMAI6LiAV1rsTMrA569NiZWbNm8Le/3cuee35rlWmLFn3KFVcM4b33piGJpUu/BGCnnb7BHXdcTfPmM1fc1OHpp5/m2GOPpV27dgBsscUWda7psMMOo0WLFrRo0YItt9ySOXPm8Pzzz3PUUUexySabAHD00Ufz3HPPrVWAF3NHnjcjoiwiyoC+wL+Bh4FLgKciogfwVDZsZtbo9t77CG688UerNJ8A/P73l9G37ze5//7JjBjx/1iy5AsADjnkREaMeIxWrVrxrW99i6effrraZTdt2pTly5cDsHz5cpYsWVJrPcV0Q1sf1rQNfH/gnYh4l3Sn+lHZ+FHAkfVYl5lZ0Y444jTOOOOndO++0yrjFy/+lC23TAc1H3/8rhXjZ86cTqdO3Rg2bBgDBgxg0qRJ7LfffvzlL39ZcWeeiiaULl26MGHCBAAee+wxvvzyyzrVuPfee/PII4/w73//m8WLF/Pwww+vdRezaxrgJwD3Zo87RMSs7PFsoMNaVWJmVkcdOnTmhBNWP41v8OCLuPnmHzNo0C4sW7ZyL/j//m80xx/fm7KyMiZPnszJJ59Mr169GD58OPvuuy99+vThggsuAODMM8/kmWeeoU+fPrz00ksrmkDW1K677sopp5xCv3792G233TjjjDPWqvkE1qA7WUnNgQ+BXhExR9InEdGmYPqCiNi8iucNBYYCbLPNNn3ffffdtSrYVnJ3slZK7k62YTRUd7KHAq9ExJxseI6kjtnCOwIfVfWkiBgZEeURUd6+ffs1WJ2ZmdVkTQJ8ICubTwAeA4Zkj4cAj9ZXUWZmVruiAlzSJsCBwEMFo68BDpQ0DTggGzYzs0ZS1IU8EbEYaFtp3HzSWSlmtoGKiBV3ZLe1t6a3uPSl9GZWJy1btmT+/PlrHDpWtYhg/vz5tGzZsujn+FJ6M6uTzp07M3PmTObOnbvatHnzSlBQHbz+eqkrWFXLli3p3Llz0fM7wM2sTpo1a0bXrl2rnLbjjo1cTB3l/cuDm1DMzHLKAW5mllMOcDOznHKAm5nllAPczCynHOBmZjnlADczyykHuJlZTjnAzcxyygFuZpZTDnAzs5xygJuZ5ZQD3Mwsp4q9I08bSQ9IekPS65K+IWkLSU9Kmpb9Xu2GxmZm1nCK3QP/DfDXiOgJ9AFeBy4BnoqIHsBT2bCZmTWSWgNc0leAfYDbASJiSUR8AgwARmWzjQKObJgSzcysKsXsgXcF5gJ3SnpV0m3ZTY47RMSsbJ7ZQIeqnixpqKTxksZXdecOMzOrm2ICvCmwK/C7iNgFWEyl5pJIN8Wr8t4WETEyIsojorx9+/ZrW6+ZmWWKCfCZwMyIGJcNP0AK9DmSOgJkvz9qmBLNzKwqtQZ4RMwG3pe0fTZqf2Aq8BgwJBs3BHi0QSo0M7MqFXtT43OBeyQ1B6YDp5LCf7Sk04F3geMapkQzM6tKUQEeEROB8iom7V+v1ZiZWdF8JaaZWU45wM3McsoBbmaWUw5wM7OccoCbmeWUA9zMLKcc4GZmOeUANzPLKQe4mVlOOcDNzHLKAW5mllMOcDOznHKAm5nllAPczCynHOBmZjlVVH/gkmYAC4FlwNKIKJe0BXA/0AWYARwXEQsapkwzM6tsTfbAvxkRZRFRcWOHS4CnIqIH8BSVbnRsZmYNa22aUAYAo7LHo4Aj17oaMzMrWrEBHsATkiZIGpqN6xARs7LHs4EOVT1R0lBJ4yWNnzt37lqWa2ZmFYq9qfFeEfGBpC2BJyW9UTgxIkJSVPXEiBgJjAQoLy+vch4zM1tzRe2BR8QH2e+PgIeBfsAcSR0Bst8fNVSRZma2uloDXNImkjateAwcBEwGHgOGZLMNAR5tqCLNzGx1xTShdAAellQx/58j4q+SXgZGSzodeBc4ruHKNDOzymoN8IiYDvSpYvx8YP+GKMrMzGrnKzHNzHLKAW5mllMOcDOznHKAm5nllAPczCynHOBmZjnlADczyykHuJlZTjnAzcxyygFuZpZTDnAzs5xygJuZ5ZQD3MwspxzgZmY55QA3M8upogNcUhNJr0p6PBvuKmmcpLcl3S+pecOVaWZmla3JHvh5wOsFw9cCv46I7sAC4PT6LMzMzGpWVIBL6gwcBtyWDQvYD3ggm2UUcGQD1GdmZtUodg/8BuAiYHk23Bb4JCKWZsMzgU71W5qZmdWkmLvSHw58FBET6rICSUMljZc0fu7cuXVZhJmZVaGYPfA9gSMkzQDuIzWd/AZoI6nipsidgQ+qenJEjIyI8ogob9++fT2UbGZmUESAR8SPI6JzRHQBTgCejohBwBjgmGy2IcCjDValmZmtZm3OA78YuEDS26Q28dvrpyQzMytG09pnWSkixgJjs8fTgX71X5KZmRXDV2KameWUA9zMLKcc4GZmOeUANzPLKQe4mVlOOcDNzHLKAW5mllMOcDOznHKAm5nllAPczCynHOBmZjnlADczyykHuJlZTjnAzcxyygFuZpZTDnAzs5wq5qbGLSX9Q9JrkqZIuiIb31XSOElvS7pfUvOGL9fMzCoUswf+H2C/iOgDlAGHSNoduBb4dUR0BxYApzdYlWZmtppibmocEbEoG2yW/QTp7vQPZONHAUc2RIFmZla1otrAJTWRNBH4CHgSeAf4JCKWZrPMBDpV89yhksZLGj937tx6KNnMzKDIAI+IZRFRBnQm3ci4Z7EriIiREVEeEeXt27evW5VmZraaNToLJSI+AcYA3wDaSKq4q31n4IP6Lc3MzGpSzFko7SW1yR63Ag4EXicF+THZbEOARxuoRjMzq0LT2mehIzBKUhNS4I+OiMclTQXuk3QV8CpwewPWaWZmldQa4BExCdilivHTSe3hZmZWAr4S08wspxzgZmY55QA3M8spB7iZWU45wM3McsoBbmaWUw5wM7OccoCbmeWUA9zMLKcc4GZmOeUANzPLKQe4mVlOOcDNzHLKAW5mllMOcDOznCrmjjxbSxojaaqkKZLOy8ZvIelJSdOy35s3fLlmZlahmD3wpcAPI2JHYHfgbEk7ApcAT0VED+CpbNjMzBpJrQEeEbMi4pXs8ULS/TA7AQOAUdlso4AjG6hGMzOrwhq1gUvqQrq92jigQ0TMyibNBjpU85yhksZLGj937ty1qdXMzAoUHeCSWgMPAudHxGeF0yIigKjqeRExMiLKI6K8ffv2a1WsmZmtVFSAS2pGCu97IuKhbPQcSR2z6R2BjxqmRDMzq0oxZ6EIuB14PSJGFEx6DBiSPR4CPFr/5ZmZWXWaFjHPnsBg4J+SJmbj/gu4Bhgt6XTgXeC4BqnQzMyqVGuAR8TzgKqZvH/9lmNmZsXylZhmZjnlADczyykHuJlZTjnAzcxyqpizUMxsXaHqzidY11R5XZ/VM++Bm5nllAPczCyn3IRSFX9NNbMc8B64mVlOOcDNzHLKAW5mllMOcDOznHKAm5nllAPczCynHOBmZjlVzB157pD0kaTJBeO2kPSkpGnZ780btkwzM6usmD3wu4BDKo27BHgqInoAT2XDZmbWiGoN8Ih4Fvi40ugBwKjs8SjgyPoty8zMalPXNvAOETErezwb6FBP9ZiZWZHW+iBmRAQ1dMohaaik8ZLGz507d21XZ2ZmmboG+BxJHQGy3x9VN2NEjIyI8ogob9++fR1XZ2ZmldU1wB8DhmSPhwCP1k85ZmZWrGJOI7wXeAnYXtJMSacD1wAHSpoGHJANm5lZI6q1P/CIGFjNpP3ruRYzM1sDvhLTzCynHOBmZjnlADczyykHuJlZTjnAzcxyygFuZpZTDnAzs5xygJuZ5ZQD3MwspxzgZmY55QA3M8spB7iZWU45wM3McsoBbmaWUw5wM7OccoCbmeXUWgW4pEMkvSnpbUmX1FdRZmZWuzoHuKQmwM3AocCOwEBJO9ZXYWZmVrO12QPvB7wdEdMjYglwHzCgfsoyM7Pa1HpPzBp0At4vGJ4J7FZ5JklDgaHZ4CJJb67FOm0VagfMK3UVNZFKXYGVxrr/2YRcfT63rWrk2gR4USJiJDCyodezIZI0PiLKS12HWWX+bDaOtWlC+QDYumC4czbOzMwawdoE+MtAD0ldJTUHTgAeq5+yzMysNnVuQomIpZLOAf4GNAHuiIgp9VaZFcNNU7au8mezESgiSl2DmZnVga/ENDPLKQe4mVlOOcDNzHLKAW5mllMO8PVY1l9NVeP9vts6x5/LNeezUNZTkjaKiOXZ4xHAImCTiPhh5elmjUmSIiIkHQn0BDoCIyNiSsW00laYH/6Pt54qCO87gbbAC6QLr16TtLnD20qhILz3AK4A5gOfAM9LOtrhvWa8B76eycJ5QfZ4a+A24DsRsSgb93vgi4g4v3RV2oao0mfzOuCjiPhlNnwI8DPg+IiYXsIyc8V74OsRSc2AH0m6UtJmEfE+8AXwjYLZ7gCal6RA22BJ+ibwa0m7ZKOmAV0rpkfEX4FJQKsSlJdbDvD1SzNSU8nGwCWSNgFGAaMlDZbUEfgu4OYTazSSRGommQYMlbQ78CCwt6TbJG0hqT/pHgNflKrOPHITynpCUtOsf5rOwE+AcuBF4EqgC/BL4C1g04g4vmSF2gal8KCkpB8B+5C+AV4HPAv8mbQjuQVwQ0S4Q7w14ABfj2SnDf4DuJ20J7MtqZvf64E3s4NHTSNiaQnLtA2QpLOAk4FrgN2BdsAzEXFP1vS3SUR8UsISc8lNKOuX7sCsiLglIu4g7d20AH4HDILUi2QJ67MNkKSmpPvmXpbtYY8AJgDnZwczNwE+LWGJueUAX7/MBraSdCFARLwJPAx8SDpAZNYosnZvYMVOwwJguKROETEvIv4AzAFmRsQnPn2wbtyEkmMF59TuQHovp2YHgwaQ+mi/kXQa4d3ZHrlZg5PUJCKWZVdWdoyIDyS1An5MuibhAWAr4DTg0Oym6FYHDvCcqriSUtJBpAOUrYD7gYdIZ6EMAtoA0yNieMkKtQ1KpSuA/0I6FtMWuJy0U7ELcBLpW+FlEfF6iUpdLzjAc0zSlsCtwCWkr6MjSKdr3RkRr0lqFhFflrBE20BJ+iXpbJMfAh8DzwO3RsRDklqQssenDK4lt4HnVNbGOBjYAVgeER8DZwFLgV9I2t3hbY1N0kaS2pLCezhwN2nvezTwR0nnARs5vOuHAzxHCg8MAQL+nv38UNLXI+KLiPgR8CTwbilqtA1TQU+CERHzSXve2wBbRMSvIuJO0o3QF0fE56Wqc33jJpScqHRBxM6kPe23gK8Cp5DO9/4fXwhhja3goGVX4EhgakT8TVJr4I/AVGBz0p73WSUsdb3jPfCcKAjv80lt3d8nHbBcTjqIORU4SVLPUtVoG6YsvFsBj5GuRbhZ0k+ArwC3kA5edgDOLV2V6yfvgedI1iHQL4H9SJcil5HONBkcEf+QVB4R40tXoW1IKp1xsg1wUkT8XFIP0ufzHdKByzd9QL1heA98HVfpLiVBuhz5QKB7ROwGvAS8IKm/w9saU0F4/w74BXCapB0iYhrpVMGvAtdIau3wbhhNS12AVa+gbbEV0CUixmaBfhzp6D7Aa8DrpNO0zBqVpKNIzSZXkK4EvlrSTRHxNKlJb9eKvuit/jnA11HZQctl2eCfSUfwX88u3vkIOCTrefA04Bvu48Qam6Q9geOBP0bE85LeAo4GfiBpu4j4PfBqSYtczznA11EFBy1HkDqo+rmk5qTmkztIzV+bAqdGxEelq9Q2JAVXAHcnHYvpQNqZeCIiZkv6E+n+q51g5efYGoYPYq7Dslui/Zh0dH8rYFfgKFJPbt9xu6I1poLw3hj4b+Ac0vUGt5NC+/qIeMMHLBuPD2KuYyou1pE0gNSfyRjSqVh9gdsjohPpfetSqhptw1RwI+yfAu8D07L27RNJ3cHeJKmfw7vxuAllHZP1Lrgt6evp6Ih4QdKYiJgHK24GuyQ70m/W4Ap6vRRp56ENsBOwn6Sx2WXxF0i6BFhYwlI3OG5CWUdU+iP5b2B7YFBE/L1g2hnAyRGxT2mrtQ2RpK1Id5JfKulU0jfEG4GnImJxaavbMDnA1zGS2pAukx8FtAROiIiFBdM3LRw2a0gFOw/HAWcDM0kdVZ0L9AYuJd005A/uoKrxuQ18HSJpCHAV0CkivkPqM/lvknasmMfhbY0pC++epM/lhaQbZj8PPAJMIZ3/vbHDuzQc4OuWCcDnwPck7R0RZwKPA2MkfbW0pdmGRNI1kr5eMGpsRPwDmEFqNnkW6B8RYyLiF6Wo0RzgJSOphaTuSnaRtGVETAauBeaSQvzbEfFz0h/K7NJWbBuKrM/u/hHxcjZqHnCApGERsSw7t3sxUJ7Nr2oWZQ3MbeAlIuk+UnewfyWd290TGB4Rk7I/iD8CPYCLI2JsyQq1DYqkgaSmkr5Z88kppO4agtR9w0RSf/M/BgZGxGslKtXwHnhJSKq48OGnEfEicAPpK+nPJB2W7eGMA15weFsjWwj0ysJ7J+B84IuImAjsQzrfe3PgAod36XkPvJFl7Yq/jIh9K41vBxxKur/lVNIe+b7ZrdLMGk0W3M+Rbkh8YET8U1Ir30ln3eM98MY3n9RrG7Cyu9jsQp1HgfNI54Ef6fC2UoiIfwJdSQG+Rzbu86wvHluHOMAb3yfA17K2RQr6lgDYEdiLdAXmO6UpzwwiYgGwHXB81uRHRCwpbVVWmQO8kWV71b8A9pI0KBv372zyz4CFBcNmJZMF9v4AkkaXuByrgtvAS0BSM9KddfYi9SsxhtT3yYKIOLWEpZlVSdJXIuLTUtdhq3KAl0h2quDXSHfxbg58GBF3lbImM8sXB7iZWU65DdzMLKcc4GZmOeUANzPLKQe4mVlOOcDNzHLKAW65IWm4pCmSJkmaKGk3SWMllUsal417T9Lc7PFESV0kLaq0nFMk3VRp3MSsh8jCcc2yfrGnSXpF0kuSDi2YXiYpJB1S6Xm9JD0t6c3suZe5y1VrCL6pseWCpG8AhwO7RsR/ss6/VvTNERG7ZfOdApRHxDkFz61t2TsATYC9JW1ScH/HnwEdgd7ZOjsAhZ2QDSTdnWYgqVtgJLUCHgO+FxFPZN0kPAh8H7i5jptvViUHuOVFR2BeRPwHVnT+VWs4F2kgqf/1HYABwJ+z4D0T6FqwzjnA6Gy9Ao4FDgSek9Qyu63YiaRugJ/InvNvSecAY3GAWz1zE4rlxRPA1pLeknSLpH1rfUbxjgfuA+4lhTlAd+C9iPismufsAfwr63RsLHBYNr4X6dZ4K2TztJa0WT3WbOYAt3yIiEVAX2Ao6ZZz91f06FjXRQJIKift2b8HPAXsImmLIp4/kBT6ZL8H1jCvWYNwE4rlRkQsI+3tjpX0T2BIkU/9XFLzgu5QtyDd5xFS8PaUNCMb3gz4DnAPsI2kzSrvhUtqks0zQNJwQEBbSZuSbsaxT6X5uwGLatibN6sT74FbLkjaXlKPglFlwLtFPv0Z4KRsOa2A44Ax2c00jgN2ioguEdGF1AY+MOvS93bgNxU3MpDUXtKxpC5WJ0XE1tnztiUdqDyKFPx7STqgYH03AtfVfevNquYAt7xoDYySNFXSJNLNLy4v8rnnAUdLmgj8HfhLRDwL7A18EBEfFsz7LLCjpI7ApaTmmqmSJgOPA5+R9tofrrSOB0nB/znpn8Clkt4E/gm8DNyEWT1zb4RmZjnlPXAzs5xygJuZ5ZQD3MwspxzgZmY55QA3M8spB7iZWU45wM3McsoBbmaWU/8fLBxVPQhqKqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (12, 6))\n",
    "sit_sex.plot(kind='bar',width=0.5, color=['red','blue'], title='Cursando X Formados no Curso de ADS')\n",
    "plt.xticks(rotation = 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>SITUACAO</th>\n",
       "      <th>Cursando</th>\n",
       "      <th>Formado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>90.411962</td>\n",
       "      <td>9.588038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>87.475000</td>\n",
       "      <td>12.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>87.001977</td>\n",
       "      <td>12.998023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>87.829746</td>\n",
       "      <td>12.170254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>87.414197</td>\n",
       "      <td>12.585803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>86.000541</td>\n",
       "      <td>13.999459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>84.413776</td>\n",
       "      <td>15.586224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>83.934510</td>\n",
       "      <td>16.065490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>85.161905</td>\n",
       "      <td>14.838095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>85.511183</td>\n",
       "      <td>14.488817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "SITUACAO   Cursando    Formado\n",
       "ANO                           \n",
       "2009      90.411962   9.588038\n",
       "2010      87.475000  12.525000\n",
       "2011      87.001977  12.998023\n",
       "2012      87.829746  12.170254\n",
       "2013      87.414197  12.585803\n",
       "2014      86.000541  13.999459\n",
       "2015      84.413776  15.586224\n",
       "2016      83.934510  16.065490\n",
       "2017      85.161905  14.838095\n",
       "2018      85.511183  14.488817"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sit_ano = pd.crosstab(data['ANO'],data['SITUACAO']).apply(lambda r: r/r.sum(), axis=1) * 100\n",
    "sit_ano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gráfico 'Situação Cursando X Formados no Curso de ADS nos anos de 2009 à 2018'\n",
    "No eixo x temos os anos e no eixo y a porcentagem de alunos por sexo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Situação Cursando X Formados no Curso de ADS nos anos de 2009 à 2018'}, xlabel='ANO'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "sit_ano.plot(kind='bar',width=0.5, color=['purple','black'], \n",
    "             title='Situação Cursando X Formados no Curso de ADS nos anos de 2009 à 2018')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.12"
  }
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