socioeconomico.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise de dados socioeconômicos\n",
    "Iremos analisar os dados socioeconômicos e complementares dos discentes da Universidade Federal do Rio Grande do Norte - UFRN, a fim de entendermos melhor os perfis de pessoas que entram nessa universidade.\n",
    "\n",
    "# Sumário\n",
    "- [Base de Dados](#base-dados)\n",
    "- [Código](#codigo)\n",
    "  - [Dependências](#dependencias)\n",
    "  - [Preparando os dados](#preparando-dados)\n",
    "  - [Analises](#analises)\n",
    "\n",
    "# <a id=\"base-dados\">Base de Dados</a>\n",
    "Os dados utilizados formam retirados do site de [dados abertos da UFRN](http://dados.ufrn.br/). Foram utilizados os seguintes conjuntos de dados:\n",
    "\n",
    "- [Dados Sócio-Econômicos de Discentes](http://dados.ufrn.br/dataset/dados-socio-economicos-de-discentes)\n",
    "\n",
    "# <a id=\"codigo\">Código</a>\n",
    "Agora iremos para a parte em que tentaremos encontrar as respostas das [perguntas orientadoras](#perguntas-orientadoras).\n",
    "\n",
    "## <a id=\"dependencias\">Dependências</a>\n",
    "Nessa seção, iremos declarar as dependências necessárias para o Jupyter Notebook executar corretamente, para isso iremos: declarar as variáveis globais que iremos utilizar, importar as libs necessárias e preparar os arquivos que temos para podermos utilizá-los nas seções seguintes.\n",
    "\n",
    "- [re](https://docs.python.org/3/library/re.html)\n",
    "- [Numpy](http://www.numpy.org/) as **np**\n",
    "- [Pandas](https://pandas.pydata.org/) as **pd**\n",
    "- [Matplotlib](https://matplotlib.org/) as **plt**\n",
    "\n",
    "```sh\n",
    "conda install -c conda-forge numpy\n",
    "conda install -c conda-forge matplotlib\n",
    "conda install -c conda-forge pandas\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pacotes que iremos utilizar\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from os import listdir\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"preparando-dados\">Preparando os dados</a>\n",
    "Agora iremos criar uma variável chamada **dfs** que conterá os dataframes com os dados socioeconômicos e complementares dos discentes da UFRN. Para isso, além das funções informadas logo a seguir, iremos criar uma função chamada **make_df** que gerará um único DataFrame a partir de uma lista de arquivos.\n",
    "\n",
    "Funções que irão nos ajudar:\n",
    "- **[pandas.read_csv](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)**: Ler um arquivo [CSV](https://pt.wikipedia.org/wiki/Comma-separated_values) e gera um DataFrame a partir dele;\n",
    "  - **sep**: Especifica um delimitador (`default=','`).\n",
    "- **[pandas.concat](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html)**: Concatena vários objetos pandas;\n",
    "  - **ignore_index**: Ignora os indexs dos objetos pandas concatenados (`default=False`).\n",
    "- **[re.compile](https://docs.python.org/3/library/re.html#re.compile)**: Compila uma expressão regular;\n",
    "- **[re.search](https://docs.python.org/3/library/re.html#re.search)**: Busca por um padrão em uma string;\n",
    "- **[filter](https://www.programiz.com/python-programming/methods/built-in/filter)**: Filtra cada um dos itens de um iterável usando uma função;\n",
    "- **[list](https://www.tutorialspoint.com/python/list_list.htm)**: Pega uma sequência de valores e converte para uma lista."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_df(filenames):\n",
    "    '''Pega uma lista de arquivos e retorna um único dataframe gerado.'''\n",
    "    # Lista de dataframes\n",
    "    dfs = [pd.read_csv('data/'+filename, sep=';') for filename in filenames]\n",
    "    # Concatena todos os dataframes em um só\n",
    "    return pd.concat(dfs, ignore_index=True)\n",
    "\n",
    "# Arquivos da pasta de arquivos de dados\n",
    "files = listdir('data')\n",
    "\n",
    "# Filtro aplicado, lista de arquivos que satisfazem o regex\n",
    "files_socioeconomicos = list(filter(re.compile(r'dados-socio-economicos-20[0-9]{2}').search, files))\n",
    "\n",
    "# Variável com os dataframes que iremos usar\n",
    "df_socioeconomicos = make_df(files_socioeconomicos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A função **[pandas.DataFrame.info](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.info.html)** nos fornecerá um sumário dos dados que temos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 160366 entries, 0 to 160365\n",
      "Data columns (total 9 columns):\n",
      "id_discente                          160366 non-null object\n",
      "ano                                  160366 non-null int64\n",
      "periodo                              160366 non-null int64\n",
      "renda                                92825 non-null float64\n",
      "escola_ens_medio                     92825 non-null object\n",
      "possui_bolsa_pesquisa                160366 non-null object\n",
      "possui_auxilio_alimentacao           160366 non-null object\n",
      "possui_auxilio_transporte            160366 non-null object\n",
      "possui_auxilio_residencia_moradia    160366 non-null object\n",
      "dtypes: float64(1), int64(2), object(6)\n",
      "memory usage: 11.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_socioeconomicos.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"analises\">Analises</a>\n",
    "Nessa seção, iremos realizar algumas análises dos dados de discentes, a fim de termos uma melhor visão sobre eles.\n",
    "\n",
    "Perguntas orientadoras para as nossas analises:\n",
    "- Qual a renda dos ingressantes da UFRN por ano e período?\n",
    "- Renda dos discentes é um fator importante para adquirir bolsa? \n",
    "\n",
    "Algo interessante que poderiamos analisar inicialmente seria a renda dos ingressantes. Tomemos como base a notícia **[2 em 3 alunos de universidades federais são das classes D e E](https://educacao.estadao.com.br/noticias/geral,2-em-3-alunos-de-universidades-federais-sao-das-classes-d-e-e,10000070529)**.\n",
    "\n",
    "Para a elaboração da coluna **classe**, iremos utilizar a coluna de **renda** junto aos critérios de classificação econômica presente no site da [ABEP](http://www.abep.org/criterio-brasil).\n",
    "\n",
    "Iremos também criar a coluna **ano_periodo** que será a junção de duas colunas já existentes: **ano** e **periodo**.\n",
    "\n",
    "Funções que irão nos ajudar:\n",
    "- **[pandas.Series.max](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.max.html)**: Retorna o maior valor da Series;\n",
    "- **[pandas.cut](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html)**: Segmentar e classificar os valores de objeto pandas. Essa função também é útil para ir de uma variável contínua para uma variável categórica.\n",
    "  - **labels**: Rótulos atribuidos a partir dos valores inseridos como **bins** (`default=None`);\n",
    "  - **include_lowest**: Se o primeiro intervalo esquerdo deve ser incluído ou não. (`default=False`).\n",
    "- **[pandas.Series.astype](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html)**: Aplica o cast no objeto pandas.\n",
    "- **[pandas.Series.str.cat](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.cat.html)**: Concatena as strings da Series.\n",
    "  - **sep**: Separador das strings concatenadas (`default=None`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Limites de renda\n",
    "bins = [0, 768, 1625, 2705, 4852, 9254, df_socioeconomicos['renda'].max()]\n",
    "\n",
    "# Labels\n",
    "labels = [\"D-E\", \"C2\", \"C1\", \"B2\", \"B1\", \"A\"]\n",
    "\n",
    "# Criamos a coluna classe\n",
    "df_socioeconomicos['classe'] = pd.cut(\n",
    "    df_socioeconomicos['renda'], bins, \n",
    "    labels=labels, \n",
    "    include_lowest=True\n",
    ")\n",
    "\n",
    "# Ano periodo\n",
    "df_socioeconomicos['ano_periodo'] = df_socioeconomicos['ano'].astype(str).str.cat(\n",
    "    df_socioeconomicos['periodo'].astype(str), sep='-'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usaremos a função **[pandas.DataFrame.head](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html)** para verificarmos as primeiras linhas do nosso DataFrame e ver se a coluna **classe** e **ano_periodo** foram criadas corretamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_discente</th>\n",
       "      <th>ano</th>\n",
       "      <th>periodo</th>\n",
       "      <th>renda</th>\n",
       "      <th>escola_ens_medio</th>\n",
       "      <th>possui_bolsa_pesquisa</th>\n",
       "      <th>possui_auxilio_alimentacao</th>\n",
       "      <th>possui_auxilio_transporte</th>\n",
       "      <th>possui_auxilio_residencia_moradia</th>\n",
       "      <th>classe</th>\n",
       "      <th>ano_periodo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>825312813b1a17de107a020b565b72d0</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>202.0</td>\n",
       "      <td>Todo em escola particular</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>D-E</td>\n",
       "      <td>2015-1</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>149560a4d0e7b585febc5a4c2bae1410</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>Todo em escola particular</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>B2</td>\n",
       "      <td>2015-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>909b29d77b69ba8f3eb5f04434845c3e</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Todo em escola publica</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>B2</td>\n",
       "      <td>2015-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a975fe54fb5f07a01fad54e3b2e61808</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>Todo em escola publica</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>C2</td>\n",
       "      <td>2015-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>f318ca81231a8e38a11093471e7990ae</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>Todo em escola publica</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>B1</td>\n",
       "      <td>2015-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        id_discente   ano  periodo   renda  \\\n",
       "0  825312813b1a17de107a020b565b72d0  2015        1   202.0   \n",
       "1  149560a4d0e7b585febc5a4c2bae1410  2015        1  3500.0   \n",
       "2  909b29d77b69ba8f3eb5f04434845c3e  2015        1  4000.0   \n",
       "3  a975fe54fb5f07a01fad54e3b2e61808  2015        1  1457.0   \n",
       "4  f318ca81231a8e38a11093471e7990ae  2015        1  9000.0   \n",
       "\n",
       "             escola_ens_medio possui_bolsa_pesquisa  \\\n",
       "0  Todo em escola particular                      f   \n",
       "1  Todo em escola particular                      f   \n",
       "2     Todo em escola publica                      f   \n",
       "3     Todo em escola publica                      f   \n",
       "4     Todo em escola publica                      f   \n",
       "\n",
       "  possui_auxilio_alimentacao possui_auxilio_transporte  \\\n",
       "0                          t                         f   \n",
       "1                          f                         t   \n",
       "2                          f                         f   \n",
       "3                          t                         f   \n",
       "4                          f                         f   \n",
       "\n",
       "  possui_auxilio_residencia_moradia classe ano_periodo  \n",
       "0                                 f    D-E      2015-1  \n",
       "1                                 f     B2      2015-1  \n",
       "2                                 f     B2      2015-1  \n",
       "3                                 t     C2      2015-1  \n",
       "4                                 f     B1      2015-1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_socioeconomicos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora que temos os discentes classificados por renda, podemos criar um gráfico mostrando a taxa de ingressantes da UFRN de modo que poderemos ver a evolução dos grupos de renda.\n",
    "\n",
    "Funções que irão nos ajudar:\n",
    "- **[np.logical_and](https://docs.scipy.org/doc/numpy/reference/generated/numpy.logical_and.html)**: Aplica o operador lógico AND nos vetores booleanos passados;\n",
    "- **[plt.figure](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html)**: Cria uma nova figura;\n",
    "  - **figsize**: Define o tamanho da figura (tupla de inteiros) (`default=None`).\n",
    "- **[pandas.DataFrame.groupby](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html)**: Agrupa as Series do DataFrame;\n",
    "- **[pandas.Series.count](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.count.html)**: Conta a quantidade de valores não nulos na Series;\n",
    "- **[pandas.Series.keys](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.keys.html)**: Chaves para os índices;\n",
    "- **[pandas.Series.values](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.values.html)**: Retorna os valores da Series;\n",
    "- **[plt.plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html)**: Traça um valor (X,Y) como linha ou marcador;\n",
    "  - **label**: Define o rótulo;\n",
    "  - **linewidth**: Define a largura da linha/marcador.\n",
    "- **[plt.legend](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.legend.html)**: Coloca a legenda;\n",
    "  - **bbox_to_anchor**: Define a posição da legenda (uso conjunto com o **loc**);\n",
    "  - **loc**: A localização da legenda.\n",
    "- **[plt.xlabel](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xlabel.html)**: Define o rótulo do eixo X;\n",
    "- **[plt.ylabel](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.ylabel.html)**: Define o rótulo do eixo Y;\n",
    "- **[plt.title](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html)**: O título da figura;\n",
    "- **[plt.show](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html)**: Mostra a figura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DataFrame para o gráfico (filtro de 2014 - 2017)\n",
    "df_graph_classe = df_socioeconomicos[np.logical_and(\n",
    "    df_socioeconomicos['ano'] > 2013, df_socioeconomicos['ano'] < 2018\n",
    ")]\n",
    "\n",
    "# Definimos o tamanho da figura que terá o gráfico\n",
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "# Linhas do gráfico para cada classe\n",
    "for label in labels:\n",
    "    classe_count = df_graph_classe[df_graph_classe['classe'] == label].groupby('ano_periodo')['id_discente'].count()\n",
    "    plt.plot(classe_count.keys(), classe_count.values, label=label, linewidth=4)\n",
    "\n",
    "# Demais configurações do gráfico\n",
    "plt.legend(bbox_to_anchor=(1.02, 1), loc='upper left')\n",
    "plt.xlabel('Ano-Período')\n",
    "plt.ylabel('Quantidade de ingressantes')\n",
    "plt.title('Classe social dos alunos por ano-período')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DEBATER**.\n",
    "- [\"O futuro não ia ser assim\": Pobreza extrema volta a crescer no Brasil](https://brasil.elpais.com/brasil/2018/05/22/politica/1526941832_202640.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora iremos construir 4 gráficos de barra, um para cada bolsa/auxílio que temos registrado, que são:\n",
    "- Bolsa de Pesquisa\n",
    "- Auxílio Residência Moradia\n",
    "- Auxílio Alimentação\n",
    "- Auxílio Transporte\n",
    "\n",
    "Funções que irão nos ajudar:\n",
    "- **[plt.bar](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html)**: Cria um gráfico de barra. Primeiro passamos os índices das barras, depois o tamanho (os valores que cada barra terá) e em seguida a largura de cada barra;\n",
    "  - **alpha**: Define a transparência das barras (`default=None`);\n",
    "  - **color**: Define a cor das barras;\n",
    "  - **label**: O rótulo para as barras.\n",
    "- **[plt.xticks](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xticks.html)**: Definir os locais e rótulos do eixo X.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Índices de barras que teremos no gráfico\n",
    "index = np.arange(len(labels))\n",
    "# Tamanho da barra\n",
    "bar_width = 0.35\n",
    "# Opacidade\n",
    "opacity = 0.4\n",
    "\n",
    "# Auxílios\n",
    "auxilios = ['possui_bolsa_pesquisa', 'possui_auxilio_residencia_moradia',\n",
    "            'possui_auxilio_alimentacao', 'possui_auxilio_transporte']\n",
    "# Títulos do gráfico\n",
    "title = [ 'Bolsa de Pesquisa', 'Auxílio Residência Moradia', 'Auxílio Alimentação', 'Auxílio Transporte' ]\n",
    "\n",
    "for i in range(len(auxilios)):\n",
    "    # Criamos uma nova figura\n",
    "    fig = plt.figure(i+1)\n",
    "    \n",
    "    # Definimos o tamanho da figura que terá o gráfico\n",
    "    plt.figure(figsize=(15,8))\n",
    "\n",
    "    # Pegamos a quantidade de discentes por grupo que possuem ou não o auxilio\n",
    "    classes_auxilios = {\n",
    "        't': df_graph_classe[\n",
    "            df_graph_classe[auxilios[i]] == 't'\n",
    "        ].groupby('classe')['id_discente'].count(),\n",
    "        'f': df_graph_classe[\n",
    "            df_graph_classe[auxilios[i]] == 'f'\n",
    "        ].groupby('classe')['id_discente'].count()\n",
    "    }\n",
    "\n",
    "    # Barra dos que possuem o auxílio\n",
    "    plt.bar(index, classes_auxilios['t'].values, bar_width,\n",
    "                    alpha=opacity, color='b', label='Possui')\n",
    "\n",
    "    # Barra dos que não possuem o auxílio\n",
    "    plt.bar(index + bar_width, classes_auxilios['f'].values, bar_width,\n",
    "                    alpha=opacity, color='r', label='Não possui')\n",
    "\n",
    "    # Demais configurações do gráfico\n",
    "    plt.xlabel('Classificação de renda')\n",
    "    plt.ylabel('Quantidade de discentes')\n",
    "    plt.title('Discentes por renda que possuem ou não {}'.format(title[i]))\n",
    "    plt.xticks(index + bar_width / 2, labels)\n",
    "    plt.legend()\n",
    "\n",
    "# Exibimos todos os gráficos de uma só vez\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}