EDA_bus_Natal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "10f6cdb6",
   "metadata": {},
   "source": [
    "# Transporte Público da Cidade do Natal\n",
    "\n",
    "## Introdução\n",
    "\n",
    "A questão do transporte público em Natal se destaca como um tópico de profunda importância e apreensão para os moradores locais. O acesso a um serviço de transporte público de alta qualidade desempenha um papel fundamental na promoção da mobilidade urbana, viabilizando que os cidadãos se movam com facilidade e segurança por diversos pontos da cidade. Como resultado, a garantia da mobilidade urbana está intrinsecamente ligada ao conceito mais amplo de direito à cidade. \n",
    "\n",
    "No entanto, a atual situação na cidade de Natal é alarmante, caracterizada por vários desafios, que vão desde a degradação dos meios de transporte coletivo, a sobrecarga excessiva dos veículos até a carência de investimentos em aprimoramentos. Esses problemas dificultam consideravelmente o acesso a serviços essenciais como saúde e educação. Este notebook tem como objetivo realizar um estudo sobre o Transporte Público da Cidade do Natal, por meio da extração e análise de informações estatísticas relevantes acerca de mobilidade urbana da cidade do Sol.\n",
    "\n",
    "\n",
    "![images/vista-da-cidade-de-natal.jpg](images/vista-da-cidade-de-natal.jpg)\n",
    "\n",
    "## Obtenção dos Dados\n",
    "\n",
    "Os dados foram obtidos diretamente do portal [Portal da Transparêcia da Mobilidade Urbana de Natal](http://dados.natal.br/group/mobilidade-urbana), página de dados da Prefeitura Municipal do Natal. No portal, é possível acessar as fontes de dados que contém registros da bilhetagem eletrônica a partir do ano de 2018 até 2022.\n",
    "\n",
    "No portal, os dados sintéticos são apresentados por mês entre o período de 2018 até 2022, alguns relatórios sobre mudanças do transporte público ocasionado pela pandemia do COVID-19 e custos operacionais. Aqui focaremos na parte de bilhatem eletrônica.\n",
    "\n",
    "## Análise Inicial e Limpeza dos Dados\n",
    "\n",
    "Com o objetivo de entender o dataset em estudo, extraí algumas informações a respeito, como dimensões, colunas, tipos de variáveis, etc. Além disso, mostro o extenso dicionário de variáveis e verifico a qualidade dos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4838d51a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando algumas bibliotecas importantes.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622c5f22",
   "metadata": {},
   "source": [
    "## Importando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19d4d96e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Todos os nomes das colunas são iguais em todos os DataFrames.\n",
      "Nomes das colunas: Index(['Mês', 'Empresa', 'Linha', 'Estudante_Cartao', 'Estudante_BT',\n",
      "       'Vale_Transporte', 'Integracao_Plena', 'Integracao_Complementar',\n",
      "       'Gratuito_Cartao', 'Gratuito_BT', 'Inteira_Cartao', 'Inteira_Especie',\n",
      "       'Tarifa_Social', 'Qtd_Viagens'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "dados_2018 = pd.read_csv('data/raw/01-dados-be-2018-analitico.csv', delimiter = ',', encoding='unicode_escape')\n",
    "dados_2019 = pd.read_csv('data/raw/01-dados-be-2019-analitico.csv', delimiter = ',')\n",
    "dados_2020 = pd.read_csv('data/raw/01-dados-be-2020-analitico.csv', delimiter = ',')\n",
    "dados_2021 = pd.read_csv('data/raw/01-dados-be-2021-analitico.csv', delimiter = ',')\n",
    "dados_2022 = pd.read_csv('data/raw/01-dados-be-2022-analitico.csv', delimiter = ',', encoding='unicode_escape')\n",
    "\n",
    "#Verificando se os nomes das colunas são diferentes.\n",
    "cols_set = set([tuple(dados_2018.columns), tuple(dados_2019.columns), tuple(dados_2020.columns), \n",
    "                        tuple(dados_2021.columns), tuple(dados_2022.columns)])\n",
    "\n",
    "if len(cols_set) == 1:\n",
    "    print(\"Todos os nomes das colunas são iguais em todos os DataFrames.\")\n",
    "    print(\"Nomes das colunas:\", dados_2018.columns) \n",
    "else:\n",
    "    print(\"Os nomes das colunas não são iguais em todos os DataFrames.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73459972",
   "metadata": {},
   "source": [
    "## Análise Inicial dos Dados\n",
    "\n",
    "Antes de iniciar a análise, vamos combinar todos os dados em uma única tabela já que todas tem os mesmos nomes de colunas. Após isso vou obter algumas informações a respeito, como dimensões, colunas, tipos de variáveis, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60e78c2e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Mês</th>\n",
       "      <th>Empresa</th>\n",
       "      <th>Linha</th>\n",
       "      <th>Estudante_Cartao</th>\n",
       "      <th>Estudante_BT</th>\n",
       "      <th>Vale_Transporte</th>\n",
       "      <th>Integracao_Plena</th>\n",
       "      <th>Integracao_Complementar</th>\n",
       "      <th>Gratuito_Cartao</th>\n",
       "      <th>Gratuito_BT</th>\n",
       "      <th>Inteira_Cartao</th>\n",
       "      <th>Inteira_Especie</th>\n",
       "      <th>Tarifa_Social</th>\n",
       "      <th>Qtd_Viagens</th>\n",
       "      <th>Ano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>21</td>\n",
       "      <td>13493</td>\n",
       "      <td>9506</td>\n",
       "      <td>35944</td>\n",
       "      <td>8671</td>\n",
       "      <td>0</td>\n",
       "      <td>17138</td>\n",
       "      <td>7397</td>\n",
       "      <td>6542</td>\n",
       "      <td>43727</td>\n",
       "      <td>3965</td>\n",
       "      <td>3684</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>22</td>\n",
       "      <td>4644</td>\n",
       "      <td>3371</td>\n",
       "      <td>13088</td>\n",
       "      <td>3230</td>\n",
       "      <td>0</td>\n",
       "      <td>8109</td>\n",
       "      <td>3157</td>\n",
       "      <td>2529</td>\n",
       "      <td>15363</td>\n",
       "      <td>0</td>\n",
       "      <td>1737</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>30</td>\n",
       "      <td>8519</td>\n",
       "      <td>5614</td>\n",
       "      <td>20580</td>\n",
       "      <td>5346</td>\n",
       "      <td>0</td>\n",
       "      <td>7265</td>\n",
       "      <td>3191</td>\n",
       "      <td>4415</td>\n",
       "      <td>21507</td>\n",
       "      <td>1537</td>\n",
       "      <td>2500</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>31</td>\n",
       "      <td>9631</td>\n",
       "      <td>6020</td>\n",
       "      <td>23460</td>\n",
       "      <td>7587</td>\n",
       "      <td>0</td>\n",
       "      <td>7065</td>\n",
       "      <td>2922</td>\n",
       "      <td>5491</td>\n",
       "      <td>22212</td>\n",
       "      <td>140</td>\n",
       "      <td>2478</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>41</td>\n",
       "      <td>6556</td>\n",
       "      <td>4038</td>\n",
       "      <td>20388</td>\n",
       "      <td>7924</td>\n",
       "      <td>0</td>\n",
       "      <td>5704</td>\n",
       "      <td>2791</td>\n",
       "      <td>3883</td>\n",
       "      <td>13870</td>\n",
       "      <td>0</td>\n",
       "      <td>2296</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mês    Empresa  Linha  Estudante_Cartao  Estudante_BT  Vale_Transporte  \\\n",
       "0    1  CONCEIÇÃO     21             13493          9506            35944   \n",
       "1    1  CONCEIÇÃO     22              4644          3371            13088   \n",
       "2    1  CONCEIÇÃO     30              8519          5614            20580   \n",
       "3    1  CONCEIÇÃO     31              9631          6020            23460   \n",
       "4    1  CONCEIÇÃO     41              6556          4038            20388   \n",
       "\n",
       "   Integracao_Plena  Integracao_Complementar  Gratuito_Cartao  Gratuito_BT  \\\n",
       "0              8671                        0            17138         7397   \n",
       "1              3230                        0             8109         3157   \n",
       "2              5346                        0             7265         3191   \n",
       "3              7587                        0             7065         2922   \n",
       "4              7924                        0             5704         2791   \n",
       "\n",
       "   Inteira_Cartao  Inteira_Especie  Tarifa_Social  Qtd_Viagens   Ano  \n",
       "0            6542            43727           3965         3684  2018  \n",
       "1            2529            15363              0         1737  2018  \n",
       "2            4415            21507           1537         2500  2018  \n",
       "3            5491            22212            140         2478  2018  \n",
       "4            3883            13870              0         2296  2018  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_2018['Ano'] = 2018\n",
    "dados_2019['Ano'] = 2019\n",
    "dados_2020['Ano'] = 2020\n",
    "dados_2021['Ano'] = 2021\n",
    "dados_2022['Ano'] = 2022\n",
    "\n",
    "combined_df = pd.concat([dados_2018,dados_2019, dados_2020, dados_2021, dados_2022], ignore_index=True)\n",
    "\n",
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7ec413a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colunas:  15\n",
      "Linhas:  4734\n"
     ]
    }
   ],
   "source": [
    "# Verificação da dimensão do dataframe\n",
    "print(\"Colunas: \", combined_df.shape[1])\n",
    "print(\"Linhas: \", combined_df.shape[0])   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2cf6f00",
   "metadata": {},
   "source": [
    "A partindo das dimensões do dataframe, vamos mostrar as variáveis presentes nele e o tipo delas. Para isso, vou imprimir o nome das colunas e a seguir os tipos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37d8d763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mês', 'Empresa', 'Linha', 'Estudante_Cartao', 'Estudante_BT',\n",
       "       'Vale_Transporte', 'Integracao_Plena', 'Integracao_Complementar',\n",
       "       'Gratuito_Cartao', 'Gratuito_BT', 'Inteira_Cartao', 'Inteira_Especie',\n",
       "       'Tarifa_Social', 'Qtd_Viagens', 'Ano'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d60289",
   "metadata": {},
   "source": [
    "Vamos juntar a coluna **Mês** e **Ano** em uma coluna **Data** e convertê-la para o tipo datetime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f46e275",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4734 entries, 0 to 4733\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   Mês                      4734 non-null   object        \n",
      " 1   Empresa                  4734 non-null   object        \n",
      " 2   Linha                    4734 non-null   object        \n",
      " 3   Estudante_Cartao         4734 non-null   int64         \n",
      " 4   Estudante_BT             4734 non-null   int64         \n",
      " 5   Vale_Transporte          4734 non-null   int64         \n",
      " 6   Integracao_Plena         4734 non-null   int64         \n",
      " 7   Integracao_Complementar  4734 non-null   int64         \n",
      " 8   Gratuito_Cartao          4734 non-null   int64         \n",
      " 9   Gratuito_BT              4734 non-null   int64         \n",
      " 10  Inteira_Cartao           4734 non-null   int64         \n",
      " 11  Inteira_Especie          4734 non-null   int64         \n",
      " 12  Tarifa_Social            4734 non-null   int64         \n",
      " 13  Qtd_Viagens              4734 non-null   int64         \n",
      " 14  Ano                      4734 non-null   object        \n",
      " 15  Data                     4734 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(11), object(4)\n",
      "memory usage: 591.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Criar uma nova coluna 'Data' juntando as colunas 'Mês' e 'Ano'\n",
    "combined_df['Data'] = combined_df['Ano'].astype(str) + '-' + combined_df['Mês'].astype(str).str.zfill(2)\n",
    "\n",
    "# Converter a coluna 'Data' para o tipo datetime\n",
    "combined_df['Data'] = pd.to_datetime(combined_df['Data'], format='%Y-%m')\n",
    "\n",
    "\n",
    "combined_df['Ano'] = combined_df['Data'].map(lambda x: datetime.strftime(x,\"%Y\"))\n",
    "\n",
    "combined_df['Mês'] = combined_df['Data'].map(lambda x: datetime.strftime(x,\"%b\"))\n",
    "\n",
    "combined_df['Linha'] = combined_df['Linha'].astype('str')\n",
    "\n",
    "combined_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a028f4",
   "metadata": {},
   "source": [
    "O dataset é composto, majoritariamente, por variáveis do tipo inteiro, string e datetime. Segue o dicionário das variáveis.\n",
    "\n",
    "**Dicionário de Variáveis**\n",
    "\n",
    "- Empresa: Nome da empresa de ônibus\n",
    "- Linha: Número do intinerário\n",
    "- Estudante_Cartao: Bilhete pago com o cartão de estudante\n",
    "- Estudante_BT: Bilhete pago com o bilhete de transporte\n",
    "- Vale_Transporte: Bilhete pago com o cartão de vale transporte\n",
    "- Integracao_Plena: Integração \n",
    "- Integracao_Complementar:\n",
    "- Gratuito_Cartao: Bilhete gratuito a partir do cartão\n",
    "- Gratuito_BT: Bilhete gratuito a partir do cartão\n",
    "- Inteira_Cartao: Bilhete pago com o cartão de vale transporte\n",
    "- Inteira_Especie: Bilhete pago em espécie\n",
    "- Tarifa_Social: Bilhete pela metade do seu valor em feriados\n",
    "- Qtd_Viagens : Quantidade de viagens\n",
    "- Data: Mês e ano\n",
    "\n",
    "A partir dessas variáveis podemos criar 5 novas que as generalizam da seguinte forma.\n",
    "\n",
    "Inteira = Vale_Transporte + Inteira_Cartao + Inteira_Especie\n",
    "\n",
    "Estudante = Estudante_Cartao + Estudante_BT\n",
    "\n",
    "Gratuidade = Gratuito_Cartao + Gratuito_BT\n",
    "\n",
    "Social = Tarifa_Social\n",
    "\n",
    "Integração = Integracao_Plena + Integracao_Complementar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "806148a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df['Inteira'] = combined_df['Vale_Transporte'] + combined_df['Inteira_Cartao'] + combined_df['Inteira_Especie']\n",
    "\n",
    "combined_df['Estudante'] = combined_df['Estudante_Cartao'] + combined_df['Estudante_BT']\n",
    "\n",
    "combined_df['Gratuidade'] = combined_df['Gratuito_Cartao'] + combined_df['Gratuito_BT']\n",
    "\n",
    "combined_df['Social'] = combined_df['Tarifa_Social'] + combined_df['Gratuito_BT']\n",
    "\n",
    "combined_df['Integracao'] = combined_df['Integracao_Plena'] + combined_df['Integracao_Complementar']\n",
    "\n",
    "combined_df['Qtd_Passageiros'] = combined_df['Inteira'] + combined_df['Estudante'] + combined_df['Gratuidade'] + \\\n",
    "                                    + combined_df['Social'] + combined_df['Integracao']  \n",
    "\n",
    "\n",
    "\n",
    "combined_df = combined_df.drop(['Vale_Transporte', 'Inteira_Cartao', 'Inteira_Especie', 'Estudante_Cartao', \n",
    "                                'Estudante_BT', 'Gratuito_Cartao', 'Gratuito_BT', 'Tarifa_Social', 'Integracao_Plena', \n",
    "                                'Integracao_Complementar'], axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c15e0a8",
   "metadata": {},
   "source": [
    "Com as informações relacionadas a dimensão, colunas, tipos de variáveis e algumas alterações das colunas, irei imprimir as primeiras linhas do dataframe para ter uma visão inicial do conjunto após isso, vou verificar a quantidade de valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c68b5620",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mês</th>\n",
       "      <th>Empresa</th>\n",
       "      <th>Linha</th>\n",
       "      <th>Qtd_Viagens</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Data</th>\n",
       "      <th>Inteira</th>\n",
       "      <th>Estudante</th>\n",
       "      <th>Gratuidade</th>\n",
       "      <th>Social</th>\n",
       "      <th>Integracao</th>\n",
       "      <th>Qtd_Passageiros</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>21</td>\n",
       "      <td>3684</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>86213</td>\n",
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       "      <th>1</th>\n",
       "      <td>Jan</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>22</td>\n",
       "      <td>1737</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>30980</td>\n",
       "      <td>8015</td>\n",
       "      <td>11266</td>\n",
       "      <td>3157</td>\n",
       "      <td>3230</td>\n",
       "      <td>56648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>30</td>\n",
       "      <td>2500</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>46502</td>\n",
       "      <td>14133</td>\n",
       "      <td>10456</td>\n",
       "      <td>4728</td>\n",
       "      <td>5346</td>\n",
       "      <td>81165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jan</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>31</td>\n",
       "      <td>2478</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>51163</td>\n",
       "      <td>15651</td>\n",
       "      <td>9987</td>\n",
       "      <td>3062</td>\n",
       "      <td>7587</td>\n",
       "      <td>87450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan</td>\n",
       "      <td>CONCEIÇÃO</td>\n",
       "      <td>41</td>\n",
       "      <td>2296</td>\n",
       "      <td>2018</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>38141</td>\n",
       "      <td>10594</td>\n",
       "      <td>8495</td>\n",
       "      <td>2791</td>\n",
       "      <td>7924</td>\n",
       "      <td>67945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mês    Empresa Linha  Qtd_Viagens   Ano       Data  Inteira  Estudante  \\\n",
       "0  Jan  CONCEIÇÃO    21         3684  2018 2018-01-01    86213      22999   \n",
       "1  Jan  CONCEIÇÃO    22         1737  2018 2018-01-01    30980       8015   \n",
       "2  Jan  CONCEIÇÃO    30         2500  2018 2018-01-01    46502      14133   \n",
       "3  Jan  CONCEIÇÃO    31         2478  2018 2018-01-01    51163      15651   \n",
       "4  Jan  CONCEIÇÃO    41         2296  2018 2018-01-01    38141      10594   \n",
       "\n",
       "   Gratuidade  Social  Integracao  Qtd_Passageiros  \n",
       "0       24535   11362        8671           153780  \n",
       "1       11266    3157        3230            56648  \n",
       "2       10456    4728        5346            81165  \n",
       "3        9987    3062        7587            87450  \n",
       "4        8495    2791        7924            67945  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f0fe90f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mês                0\n",
       "Empresa            0\n",
       "Linha              0\n",
       "Qtd_Viagens        0\n",
       "Ano                0\n",
       "Data               0\n",
       "Inteira            0\n",
       "Estudante          0\n",
       "Gratuidade         0\n",
       "Social             0\n",
       "Integracao         0\n",
       "Qtd_Passageiros    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando se existe nulos\n",
    "combined_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "737f7e32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# Este trecho de código detecta se há algum valor ausente que está sendo representado por '?', células em branco ou N/A.\n",
    "for i in combined_df.columns.tolist():\n",
    "    if ('?' in combined_df[i].tolist() or ' ' in combined_df[i].tolist() or 'N/A' in combined_df[i].tolist()):\n",
    "        flag = True\n",
    "    else:\n",
    "        flag = False\n",
    "print(flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ef0222",
   "metadata": {},
   "source": [
    "Veja que o dataframe não possui valores NaN. Isso é algo positivo, pois valores NaN frequentemente indicam dados faltantes, incompletos ou corrompidos. Por outro lado, antes de criar o DataFrame, os dados podem ter sido pré-processados para tratar e preencher valores ausentes. E mais, os dados podem estar formatados de maneira que os valores ausentes não sejam representados como NaN, mas sim como outros valores específicos. \n",
    "\n",
    "### Informações Estatísticas da BIlhatem eletrônica\n",
    "\n",
    "Com base na compreensão dos dados, é viável extrair as informações estatísticas pertinentes e analisar esses dados. Inicialmente, examinarei a distribuição dos dados, visando identificar possíveis valores discrepantes, desvios na distribuição e conjecturar premissas para efetuar a devida depuração."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b7df07a",
   "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>Qtd_Viagens</th>\n",
       "      <th>Inteira</th>\n",
       "      <th>Estudante</th>\n",
       "      <th>Gratuidade</th>\n",
       "      <th>Social</th>\n",
       "      <th>Integracao</th>\n",
       "      <th>Qtd_Passageiros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4734.000000</td>\n",
       "      <td>4734.000000</td>\n",
       "      <td>4734.000000</td>\n",
       "      <td>4734.000000</td>\n",
       "      <td>4734.000000</td>\n",
       "      <td>4734.000000</td>\n",
       "      <td>4734.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1606.769751</td>\n",
       "      <td>42637.646388</td>\n",
       "      <td>13117.014575</td>\n",
       "      <td>9136.909590</td>\n",
       "      <td>3112.812632</td>\n",
       "      <td>5424.599704</td>\n",
       "      <td>73428.982890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1195.102276</td>\n",
       "      <td>40171.685579</td>\n",
       "      <td>14329.480314</td>\n",
       "      <td>8859.558221</td>\n",
       "      <td>3308.382218</td>\n",
       "      <td>6038.809405</td>\n",
       "      <td>69336.763948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>12172.500000</td>\n",
       "      <td>2904.750000</td>\n",
       "      <td>2313.500000</td>\n",
       "      <td>655.000000</td>\n",
       "      <td>1256.250000</td>\n",
       "      <td>19731.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1414.500000</td>\n",
       "      <td>33639.000000</td>\n",
       "      <td>8464.000000</td>\n",
       "      <td>6863.500000</td>\n",
       "      <td>2228.000000</td>\n",
       "      <td>3497.500000</td>\n",
       "      <td>56894.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2343.000000</td>\n",
       "      <td>60790.000000</td>\n",
       "      <td>18422.250000</td>\n",
       "      <td>13303.000000</td>\n",
       "      <td>4431.500000</td>\n",
       "      <td>7517.000000</td>\n",
       "      <td>105990.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6080.000000</td>\n",
       "      <td>296193.000000</td>\n",
       "      <td>95265.000000</td>\n",
       "      <td>63065.000000</td>\n",
       "      <td>30060.000000</td>\n",
       "      <td>50538.000000</td>\n",
       "      <td>470369.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Qtd_Viagens        Inteira     Estudante    Gratuidade        Social  \\\n",
       "count  4734.000000    4734.000000   4734.000000   4734.000000   4734.000000   \n",
       "mean   1606.769751   42637.646388  13117.014575   9136.909590   3112.812632   \n",
       "std    1195.102276   40171.685579  14329.480314   8859.558221   3308.382218   \n",
       "min       1.000000       0.000000      0.000000      0.000000      0.000000   \n",
       "25%     614.000000   12172.500000   2904.750000   2313.500000    655.000000   \n",
       "50%    1414.500000   33639.000000   8464.000000   6863.500000   2228.000000   \n",
       "75%    2343.000000   60790.000000  18422.250000  13303.000000   4431.500000   \n",
       "max    6080.000000  296193.000000  95265.000000  63065.000000  30060.000000   \n",
       "\n",
       "         Integracao  Qtd_Passageiros  \n",
       "count   4734.000000      4734.000000  \n",
       "mean    5424.599704     73428.982890  \n",
       "std     6038.809405     69336.763948  \n",
       "min        0.000000         0.000000  \n",
       "25%     1256.250000     19731.500000  \n",
       "50%     3497.500000     56894.500000  \n",
       "75%     7517.000000    105990.500000  \n",
       "max    50538.000000    470369.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.describe(exclude=['object', 'datetime64'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6582274e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x2000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_aux = combined_df[['Inteira', 'Estudante','Gratuidade', 'Social','Integracao', 'Qtd_Passageiros']]\n",
    "\n",
    "df_aux.hist(bins=10, figsize=(25,20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "06fcd969",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média de bilhete inteiro: 42637.65\n",
      "Média de bilhete Estudante: 13117.01\n",
      "Média de bilhete Gratuidade: 9136.91\n",
      "Média de bilhete Social: 3112.81\n",
      "Média de bilhete Integracao: 5424.6\n",
      "Média de Qtd Passageiros: 73428.98\n"
     ]
    }
   ],
   "source": [
    "print('Média de bilhete inteiro:', round(combined_df.Inteira.mean(), 2))\n",
    "print('Média de bilhete Estudante:', round(combined_df.Estudante.mean(), 2))\n",
    "print('Média de bilhete Gratuidade:', round(combined_df.Gratuidade.mean(), 2))\n",
    "print('Média de bilhete Social:', round(combined_df.Social.mean(), 2))\n",
    "print('Média de bilhete Integracao:', round(combined_df.Integracao.mean(), 2))\n",
    "print('Média de Qtd Passageiros:', round(combined_df.Qtd_Passageiros.mean(), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "22bad686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entradas_por_ano = combined_df.groupby('Ano')['Linha'].nunique()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "entradas_por_ano.plot(kind='bar', color='purple')\n",
    "plt.xlabel('Ano')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5020538f",
   "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>Linha</th>\n",
       "      <th>Qtd_Passageiros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>73</td>\n",
       "      <td>20214367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>38</td>\n",
       "      <td>14646611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>13909598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>50</td>\n",
       "      <td>12848577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>40</td>\n",
       "      <td>11490078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>600</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>551</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>738</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Linha  Qtd_Passageiros\n",
       "92    73         20214367\n",
       "40    38         14646611\n",
       "33    33         13909598\n",
       "52    50         12848577\n",
       "43    40         11490078\n",
       "..   ...              ...\n",
       "78   600               23\n",
       "59   551                8\n",
       "96  7452                0\n",
       "94   740                0\n",
       "93   738                0\n",
       "\n",
       "[115 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df.groupby(['Linha'])['Qtd_Passageiros'].sum().reset_index().sort_values(by='Qtd_Passageiros',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aaf3f9e1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=combined_df, x=\"Ano\", y=\"Inteira\")\n",
    "sns.lineplot(data=combined_df, x=\"Ano\", y=\"Estudante\")\n",
    "sns.lineplot(data=combined_df, x=\"Ano\", y=\"Gratuidade\")\n",
    "sns.lineplot(data=combined_df, x=\"Ano\", y=\"Social\")\n",
    "sns.lineplot(data=combined_df, x=\"Ano\", y=\"Integracao\")\n",
    "sns.lineplot(data=combined_df, x=\"Ano\", y=\"Qtd_Passageiros\")\n",
    "\n",
    "plt.xlabel(\"Ano\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": 5
}