Visualização de dados.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução ao Pandas - Viagens do Governo | Visualização de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Esse notebook usa uma [versão tratada](https://github.com/anicelysantos/introducao-jornalismo-de-dados/blob/main/Tratamento%20de%20dados.ipynb) da base de [viagens de funcionários do governo](http://www.portaltransparencia.gov.br/viagens) disponibilizado no portal da transparência.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exemplo simples de uso da biblioteca matplotlib**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['Janeiro', 'Fevereiro','Março','Abril','Maio']\n",
    "y = [200000, 500000, 550000, 700000, 900000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bd01d7f190>]"
      ]
     },
     "execution_count": 10,
     "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": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_viagens = pd.read_csv('viagens_tratado_2019.csv', encoding = 'latin-1', sep=';', parse_dates = ['Período - Data de início','Período - Data de fim'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "cidades = df_viagens.groupby(['cidade_destino'])['Valor passagens'].sum().sort_values(ascending=False).round(1).head(10).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>cidade_destino</th>\n",
       "      <th>Valor passagens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brasília</td>\n",
       "      <td>90418769.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Informações protegidas por sigilo</td>\n",
       "      <td>48160610.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>30880616.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>São Paulo</td>\n",
       "      <td>26407457.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sem informação</td>\n",
       "      <td>22756133.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Manaus</td>\n",
       "      <td>9059780.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Recife</td>\n",
       "      <td>7213480.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Belo Horizonte</td>\n",
       "      <td>6901541.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Curitiba</td>\n",
       "      <td>6668706.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Belém</td>\n",
       "      <td>6296614.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      cidade_destino  Valor passagens\n",
       "0                           Brasília       90418769.5\n",
       "1  Informações protegidas por sigilo       48160610.6\n",
       "2                     Rio de Janeiro       30880616.7\n",
       "3                          São Paulo       26407457.1\n",
       "4                     Sem informação       22756133.1\n",
       "5                             Manaus        9059780.3\n",
       "6                             Recife        7213480.2\n",
       "7                     Belo Horizonte        6901541.9\n",
       "8                           Curitiba        6668706.0\n",
       "9                              Belém        6296614.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cidades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Utilizando a biblioteca no meu dataframe**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'gastos com passagens')"
      ]
     },
     "execution_count": 46,
     "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": [
    "#kind é o tipo do gráfico, figsize é o tamanho do gráfico, title é o título do gráfico, legend é a legenda do gráfico \n",
    "#fontsize é o tamanho da fonte\n",
    "\n",
    "cidades.plot(x = 'cidade_destino', y = 'Valor passagens', kind = 'bar', figsize = (6,4), title = 'Gastos com passagens Governo Federal 2019', \n",
    "             legend = False, fontsize = 12)\n",
    "\n",
    "#Mudar a legenda dos eixos\n",
    "\n",
    "plt.xlabel('cidades de destino')\n",
    "plt.ylabel('gastos com passagens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fazendo a mesma plotagem, mas usando variáveis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'gastos com passagens')"
      ]
     },
     "execution_count": 47,
     "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": [
    "x = cidades['cidade_destino']\n",
    "y = cidades['Valor passagens']\n",
    "#color muda a cor do desenho no gráfico. Pode passar um nome ou um codigo hexadecimal\n",
    "plt.bar(x,y, color = '#000080')\n",
    "\n",
    "#xticks indica a direção da legenda no eixo desejado. No caso do exemplo é eixo x\n",
    "plt.xticks(x, rotation = 'vertical', fontsize = 12)\n",
    "\n",
    "#Titulo dos eixos\n",
    "plt.xlabel('cidades de destino', color = 'r')\n",
    "plt.ylabel('gastos com passagens', color = 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gráfico de linha para fazer uma série temporal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigilo = df_viagens[df_viagens['cidade_destino'].str.contains('sigilo')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "policia_federal = sigilo[sigilo['Código órgão solicitante'] == 30108]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\anice\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4296: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(\n"
     ]
    }
   ],
   "source": [
    "policia_federal.rename(columns = {'Período - Data de início':'data_inicio','Período - Data de fim':'data_fim'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort_index para sequenciar, no caso daqui os meses. reset_index() para organizar o indice do dataframe\n",
    "df_policiaFederal = policia_federal.data_inicio.dt.month.value_counts().sort_index().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>3478</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>4245</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>4599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>3158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  data_inicio\n",
       "0       1         4218\n",
       "1       2         4673\n",
       "2       3         4803\n",
       "3       4         4074\n",
       "4       5         5144\n",
       "5       6         3669\n",
       "6       7         3915\n",
       "7       8         3976\n",
       "8       9         3478\n",
       "9      10         4245\n",
       "10     11         4599\n",
       "11     12         3158"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_policiaFederal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna 'index' ficou com as informações do mês e a coluna 'data_inicio' com as informações da quantidade de viagens. Para consertar vou renomear as colunas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_policiaFederal.rename(columns = {'index':'mês','data_inicio':'viagens'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>3158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mês  viagens\n",
       "0     1     4218\n",
       "1     2     4673\n",
       "2     3     4803\n",
       "3     4     4074\n",
       "4     5     5144\n",
       "5     6     3669\n",
       "6     7     3915\n",
       "7     8     3976\n",
       "8     9     3478\n",
       "9    10     4245\n",
       "10   11     4599\n",
       "11   12     3158"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_policiaFederal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 66,
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     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Coloquei para aparecer o nome do mês ao invés do número\n",
    "x = ['janeiro', 'fevereiro', 'março', 'abril','maio', 'junho', 'julho', 'agosto',\n",
    "                 'setembro', 'outubro', 'novembro','dezembro']\n",
    "\n",
    "y = df_policiaFederal['viagens']\n",
    "\n",
    "#linestyle é para mudar o estilo de linha\n",
    "plt.plot(x,y, linestyle = 'dotted', color = '#000080')\n",
    "plt.xticks(x, rotation = 'vertical')"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}