cap7-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução ao Python - Capítulo 7 - Pandas\n",
    "<br>\n",
    "\n",
    "[**Pandas**](https://pandas.pydata.org/) é uma ferramenta de manipulação de dados de alto nível, construída com base no pacote **NumPy**. O pacote Pandas possui estruturas de dados bastante interessantes (_Seires_ e *DataFrames*) para manipulação de dados e por isso é muito utilizado por cientistas de dados.\n",
    "\n",
    "---\n",
    "<br>\n",
    "\n",
    "### Índice<a id='indice'></a>\n",
    "1 - [Estruturas de dados](#1)<br>\n",
    "2 - [Seleções com DataFrames](#2)<br>\n",
    "3 - [Queries com DataFrames](#3)<br>\n",
    "4 - [Iterando com DataFrames](#4)<br>\n",
    "5 - [Tratamento de dados](#5)<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PgTCVRB2D0-5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.3\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lTtI6so4D0-9"
   },
   "source": [
    "## 1. Estruturas de dados<a id='1'></a>\n",
    "### 1.1. Series\n",
    "Series são _arrays_ unidimensionais rotulados capazes de armazenar qualquer tipo de dado. Os rótulos das linhas são chamados de _**index**_. A forma básica de criação de uma Series é a seguinte:\n",
    "```\n",
    "s = pd.Series(dados, index = index)\n",
    "```\n",
    "O argumento _dados_ pode ser um dicionário, uma lista, um _array_ NumPy ou uma constante.\n",
    "\n",
    "#### 1.1.1. Criando uma Series a partir de uma lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'> ['Gol 1.0', 'Palio 1.4', 'Fiesta 1.6']\n"
     ]
    }
   ],
   "source": [
    "carros = ['Gol 1.0', 'Palio 1.4', 'Fiesta 1.6']\n",
    "print(type(carros), carros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "0       Gol 1.0\n",
      "1     Palio 1.4\n",
      "2    Fiesta 1.6\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(carros)\n",
    "print('{}\\n{}'.format(type(s), s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. DataFrames\n",
    "DataFrame é uma estrutura de dados tabular bidimensional com rótulos nas linhas e colunas. Como Series, os DataFrames são capzes de armazenar qualquer tipo de dados.\n",
    "```\n",
    "df = pd.DataFrame(dados, index = index, columns = columns)\n",
    "```\n",
    "O argumento _dados_ pode ser um dicionário, uma lista, um _array_ NumPy, uma Series ou outro DataFrame.\n",
    "\n",
    "#### 1.2.1. Criando um DataFrame a partir de uma lista de dicionários"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[{'Nome': 'Gol', 'Motor': '1.0 Turbo', 'Ano': 1993, 'Quilometragem': 44410.0, 'Zero_km': False, 'Valor': 88078.64}, {'Nome': 'Palio', 'Motor': 'Diesel', 'Ano': 1991, 'Quilometragem': 5712.0, 'Zero_km': False, 'Valor': 106161.94}, {'Nome': 'Fiesta', 'Motor': 'Diesel V8', 'Ano': 1990, 'Quilometragem': 37123.0, 'Zero_km': False, 'Valor': 72832.16}]\n"
     ]
    }
   ],
   "source": [
    "dados = [\n",
    "    {'Nome': 'Gol', 'Motor': '1.0 Turbo', 'Ano': 1993, 'Quilometragem': 44410.0, 'Zero_km': False, 'Valor': 88078.64},\n",
    "    {'Nome': 'Palio', 'Motor': 'Diesel', 'Ano': 1991, 'Quilometragem': 5712.0, 'Zero_km': False, 'Valor': 106161.94},\n",
    "    {'Nome': 'Fiesta', 'Motor': 'Diesel V8', 'Ano': 1990, 'Quilometragem': 37123.0, 'Zero_km': False, 'Valor': 72832.16}\n",
    "]\n",
    "print('{}\\n{}'.format(type(dados), dados))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "    Ano      Motor    Nome  Quilometragem      Valor  Zero_km\n",
      "0  1993  1.0 Turbo     Gol        44410.0   88078.64    False\n",
      "1  1991     Diesel   Palio         5712.0  106161.94    False\n",
      "2  1990  Diesel V8  Fiesta        37123.0   72832.16    False\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(dados)\n",
    "print('{}\\n{}'.format(type(df), df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Ano</th>\n",
       "      <th>Nome</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1993</td>\n",
       "      <td>Gol</td>\n",
       "      <td>1.0 Turbo</td>\n",
       "      <td>False</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>Palio</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>False</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990</td>\n",
       "      <td>Fiesta</td>\n",
       "      <td>Diesel V8</td>\n",
       "      <td>False</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ano    Nome      Motor  Zero_km  Quilometragem      Valor\n",
       "0  1993     Gol  1.0 Turbo    False        44410.0   88078.64\n",
       "1  1991   Palio     Diesel    False         5712.0  106161.94\n",
       "2  1990  Fiesta  Diesel V8    False        37123.0   72832.16"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# é possível alterar a ordem em que as colunas são apresentadas\n",
    "df[['Ano', 'Nome', 'Motor', 'Zero_km', 'Quilometragem', 'Valor']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.2. Criando um DataFrame a partir de um dicionário"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n",
      "{'Nome': ['Jetta Variant', 'Passat', 'Crossfox'], 'Motor': ['Motor 4.0 Turbo', 'Motor Diesel', 'Motor Diesel V8'], 'Ano': [2003, 1991, 1990], 'Quilometragem': [44410.0, 5712.0, 37123.0], 'Zero_km': [False, False, False], 'Valor': [88078.64, 106161.94, 72832.16]}\n"
     ]
    }
   ],
   "source": [
    "dados = {\n",
    "    'Nome': ['Jetta Variant', 'Passat', 'Crossfox'], \n",
    "    'Motor': ['Motor 4.0 Turbo', 'Motor Diesel', 'Motor Diesel V8'],\n",
    "    'Ano': [2003, 1991, 1990],\n",
    "    'Quilometragem': [44410.0, 5712.0, 37123.0],\n",
    "    'Zero_km': [False, False, False],\n",
    "    'Valor': [88078.64, 106161.94, 72832.16]\n",
    "}\n",
    "print('{}\\n{}'.format(type(dados), dados))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "            Nome            Motor   Ano  Quilometragem  Zero_km      Valor\n",
      "0  Jetta Variant  Motor 4.0 Turbo  2003        44410.0    False   88078.64\n",
      "1         Passat     Motor Diesel  1991         5712.0    False  106161.94\n",
      "2       Crossfox  Motor Diesel V8  1990        37123.0    False   72832.16\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(dados)\n",
    "print('{}\\n{}'.format(type(df), df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.3. Criando um DataFrame a partir de um arquivo externo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kq-JdRHFD0-_"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data/db.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mOV1rCA6D0_A",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jetta Variant</td>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2003</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', 'Travas elétricas', 'Piloto ...</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Passat</td>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crossfox</td>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DS5</td>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aston Martin DB4</td>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2006</td>\n",
       "      <td>25757.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', '4 X 4', 'Central multimídia...</td>\n",
       "      <td>92612.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Nome            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "0     Jetta Variant  Motor 4.0 Turbo  2003        44410.0    False   \n",
       "1            Passat     Motor Diesel  1991         5712.0    False   \n",
       "2          Crossfox  Motor Diesel V8  1990        37123.0    False   \n",
       "3               DS5  Motor 2.4 Turbo  2019            NaN     True   \n",
       "4  Aston Martin DB4  Motor 2.4 Turbo  2006        25757.0    False   \n",
       "\n",
       "                                          Acessórios      Valor  \n",
       "0  ['Rodas de liga', 'Travas elétricas', 'Piloto ...   88078.64  \n",
       "1  ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "2  ['Piloto automático', 'Controle de estabilidad...   72832.16  \n",
       "3  ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07  \n",
       "4  ['Rodas de liga', '4 X 4', 'Central multimídia...   92612.10  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5D739PzJD0_B"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome              object\n",
       "Motor             object\n",
       "Ano                int64\n",
       "Quilometragem    float64\n",
       "Zero_km             bool\n",
       "Acessórios        object\n",
       "Valor            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r_qUf16LD0_D"
   },
   "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>Quilometragem</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>197.000000</td>\n",
       "      <td>258.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>58278.421320</td>\n",
       "      <td>98960.513101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>35836.733259</td>\n",
       "      <td>29811.932305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>107.000000</td>\n",
       "      <td>50742.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27505.000000</td>\n",
       "      <td>70743.512500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55083.000000</td>\n",
       "      <td>97724.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90495.000000</td>\n",
       "      <td>124633.302500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>119945.000000</td>\n",
       "      <td>149489.920000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quilometragem          Valor\n",
       "count     197.000000     258.000000\n",
       "mean    58278.421320   98960.513101\n",
       "std     35836.733259   29811.932305\n",
       "min       107.000000   50742.100000\n",
       "25%     27505.000000   70743.512500\n",
       "50%     55083.000000   97724.380000\n",
       "75%     90495.000000  124633.302500\n",
       "max    119945.000000  149489.920000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Quilometragem', 'Valor']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0R-_7PH5D0_E"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 258 entries, 0 to 257\n",
      "Data columns (total 7 columns):\n",
      "Nome             258 non-null object\n",
      "Motor            258 non-null object\n",
      "Ano              258 non-null int64\n",
      "Quilometragem    197 non-null float64\n",
      "Zero_km          258 non-null bool\n",
      "Acessórios       258 non-null object\n",
      "Valor            258 non-null float64\n",
      "dtypes: bool(1), float64(2), int64(1), object(3)\n",
      "memory usage: 12.4+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.2.3.1. Definindo umas das colunas como índice do DataFrame\n",
    "Passando o índice de uma coluna no parâmetro `index_col`, podemos alterar o índice do DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data/db.csv', sep=';', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Jetta Variant</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2003</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', 'Travas elétricas', 'Piloto ...</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin DB4</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2006</td>\n",
       "      <td>25757.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', '4 X 4', 'Central multimídia...</td>\n",
       "      <td>92612.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                              \n",
       "Jetta Variant     Motor 4.0 Turbo  2003        44410.0    False   \n",
       "Passat               Motor Diesel  1991         5712.0    False   \n",
       "Crossfox          Motor Diesel V8  1990        37123.0    False   \n",
       "DS5               Motor 2.4 Turbo  2019            NaN     True   \n",
       "Aston Martin DB4  Motor 2.4 Turbo  2006        25757.0    False   \n",
       "\n",
       "                                                         Acessórios      Valor  \n",
       "Nome                                                                            \n",
       "Jetta Variant     ['Rodas de liga', 'Travas elétricas', 'Piloto ...   88078.64  \n",
       "Passat            ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "Crossfox          ['Piloto automático', 'Controle de estabilidad...   72832.16  \n",
       "DS5               ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07  \n",
       "Aston Martin DB4  ['Rodas de liga', '4 X 4', 'Central multimídia...   92612.10  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2qa7RC03D1BD"
   },
   "source": [
    "## 2. Seleções com DataFrames<a id='2'></a>\n",
    "### 2.1. Selecioando colunas\n",
    "Para selecionar uma coluna podemos usar o seguinte comando:\n",
    "```\n",
    "df['column']\n",
    "```\n",
    "que irá retornar uma Series, mostrando que um DataFrame nada mais é que um conjunto de Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R2FWgCM_D1BO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset['Valor'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Jetta Variant        88078.64\n",
       "Passat              106161.94\n",
       "Crossfox             72832.16\n",
       "DS5                 124549.07\n",
       "Aston Martin DB4     92612.10\n",
       "Name: Valor, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Valor'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se quisermos obter um DataFrame como retorno devemos usar um par de \"colchetes\" a mais, como abaixo:\n",
    "```\n",
    "df[['column']]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset[['Valor']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jetta Variant</th>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin DB4</th>\n",
       "      <td>92612.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Valor\n",
       "Nome                       \n",
       "Jetta Variant      88078.64\n",
       "Passat            106161.94\n",
       "Crossfox           72832.16\n",
       "DS5               124549.07\n",
       "Aston Martin DB4   92612.10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Valor']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Valor</th>\n",
       "      <th>Ano</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jetta Variant</th>\n",
       "      <td>88078.64</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>106161.94</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>72832.16</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>124549.07</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin DB4</th>\n",
       "      <td>92612.10</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Valor   Ano\n",
       "Nome                             \n",
       "Jetta Variant      88078.64  2003\n",
       "Passat            106161.94  1991\n",
       "Crossfox           72832.16  1990\n",
       "DS5               124549.07  2019\n",
       "Aston Martin DB4   92612.10  2006"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# para selecionar mais de uma coluna, basta separá-las por vírgula\n",
    "dataset[['Valor', 'Ano']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7RPXPm1XD1BQ"
   },
   "source": [
    "### 2.2.  Selecionando linhas\n",
    "A seleção de linhas é similar a de colunas, com a diferença que ao invés de passarmos o nome da coluna agora passamos os índices final e inicial do recorte que queremos fazer, da mesma forma que com outros contentores (listas por exemplo):\n",
    "```\n",
    "df[i:j]\n",
    "```\n",
    "Lembrando que:\n",
    "- a indexação tem início no zero, o índice $i$ é **inclusivo** e o índice $j$ **é exclusivo**, ou seja, o recorte vai da linha de indíce $i$ até a linha de indíce $j$-1;\n",
    "- se quisermos iniciar o recorte no indíce $0$ podemos omiti-lo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PdDUwPw3D1BQ"
   },
   "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",
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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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Jetta Variant</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2003</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', 'Travas elétricas', 'Piloto ...</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                           \n",
       "Jetta Variant  Motor 4.0 Turbo  2003        44410.0    False   \n",
       "Passat            Motor Diesel  1991         5712.0    False   \n",
       "Crossfox       Motor Diesel V8  1990        37123.0    False   \n",
       "\n",
       "                                                      Acessórios      Valor  \n",
       "Nome                                                                         \n",
       "Jetta Variant  ['Rodas de liga', 'Travas elétricas', 'Piloto ...   88078.64  \n",
       "Passat         ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "Crossfox       ['Piloto automático', 'Controle de estabilidad...   72832.16  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G3DMSk97D1BR"
   },
   "source": [
    "### 2.3.  Utilizando `.loc[]` para seleções\n",
    "\n",
    "Este método seleciona um grupo de linhas e colunas segundo os rótulos ou uma matriz booleana.\n",
    "```\n",
    "df.loc[[linhas], [colunas]]\n",
    "```\n",
    "\n",
    "Se queremos selecionar uma linha, basta passar o rótulo (índice não numérico) dentro dos colchetes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ftg-hNOoD1BR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Motor                                                 Motor Diesel\n",
       "Ano                                                           1991\n",
       "Quilometragem                                                 5712\n",
       "Zero_km                                                      False\n",
       "Acessórios       ['Central multimídia', 'Teto panorâmico', 'Fre...\n",
       "Valor                                                       106162\n",
       "Name: Passat, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecionado a coluna 'Passat'\n",
    "dataset.loc['Passat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xdxkDrHvD1BS"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                    \n",
       "Passat     Motor Diesel  1991         5712.0    False   \n",
       "DS5     Motor 2.4 Turbo  2019            NaN     True   \n",
       "\n",
       "                                               Acessórios      Valor  \n",
       "Nome                                                                  \n",
       "Passat  ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "DS5     ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecionado as colunas 'Passat' e 'DS5'\n",
    "dataset.loc[['Passat', 'DS5']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pMN3U1KjD1BS"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Motor      Valor\n",
       "Nome                              \n",
       "Passat     Motor Diesel  106161.94\n",
       "DS5     Motor 2.4 Turbo  124549.07"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecionado as colunas 'Passat' e 'DS5', e as colunas 'Motor' e 'Valor'\n",
    "dataset.loc[['Passat', 'DS5'], ['Motor', 'Valor']]\n",
    "# dataset.loc[['Passat', 'DS5']]['Motor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oOEO72uZD1BT",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Motor</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jetta Variant</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin DB4</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>92612.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Motor      Valor\n",
       "Nome                                        \n",
       "Jetta Variant     Motor 4.0 Turbo   88078.64\n",
       "Passat               Motor Diesel  106161.94\n",
       "Crossfox          Motor Diesel V8   72832.16\n",
       "DS5               Motor 2.4 Turbo  124549.07\n",
       "Aston Martin DB4  Motor 2.4 Turbo   92612.10"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecionando todas as linhas\n",
    "dataset.loc[:, ['Motor', 'Valor']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SYKEe3vCD1BT"
   },
   "source": [
    "### 2.4.  Utilizando `.iloc[]` para seleções\n",
    "Funciona de forma muito similar ao `.loc[]`, selecionando com base nos índices. Ou seja, se baseia na posição das informações. E a lógica de seleção dos indíces inicial e final é a mesma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U4Bru90bD1BT"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                 \n",
       "Passat  Motor Diesel  1991         5712.0    False   \n",
       "\n",
       "                                               Acessórios      Valor  \n",
       "Nome                                                                  \n",
       "Passat  ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleciona apenas a linha de indice '1'\n",
    "dataset.iloc[[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iLKUyrzND1BU"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                      \n",
       "Passat       Motor Diesel  1991         5712.0    False   \n",
       "Crossfox  Motor Diesel V8  1990        37123.0    False   \n",
       "DS5       Motor 2.4 Turbo  2019            NaN     True   \n",
       "\n",
       "                                                 Acessórios      Valor  \n",
       "Nome                                                                    \n",
       "Passat    ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "Crossfox  ['Piloto automático', 'Controle de estabilidad...   72832.16  \n",
       "DS5       ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleciona as linhas do indíce '1' ao '3'\n",
    "dataset.iloc[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KGZpixYUD1BU"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Motor</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Quilometragem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>106161.94</td>\n",
       "      <td>5712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>72832.16</td>\n",
       "      <td>37123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>124549.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Motor      Valor  Quilometragem\n",
       "Nome                                               \n",
       "Passat       Motor Diesel  106161.94         5712.0\n",
       "Crossfox  Motor Diesel V8   72832.16        37123.0\n",
       "DS5       Motor 2.4 Turbo  124549.07            NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleciona as linhas do indíce '1' ao '3', e apresenta apenas as colunas de indíce '0', '5' e '2', nesta ordem\n",
    "dataset.iloc[1:4, [0, 5, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GxdqIz4LD1BV"
   },
   "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",
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       "\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>Motor</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Quilometragem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>106161.94</td>\n",
       "      <td>5712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Optima</th>\n",
       "      <td>Motor 1.8 16v</td>\n",
       "      <td>86641.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Up!</th>\n",
       "      <td>Motor Diesel V6</td>\n",
       "      <td>71367.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Motor      Valor  Quilometragem\n",
       "Nome                                             \n",
       "Passat     Motor Diesel  106161.94         5712.0\n",
       "Optima    Motor 1.8 16v   86641.34            NaN\n",
       "Up!     Motor Diesel V6   71367.10            NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleciona as linhas de indíce '1', '42' e '25', as colunas de indíce '0', '5' e '2', e apresenta os dados na ordem passada\n",
    "dataset.iloc[[1, 42, 25], [0, 5, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AVIhs7uLD1BW",
    "scrolled": true
   },
   "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>Motor</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Quilometragem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jetta Variant</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>88078.64</td>\n",
       "      <td>44410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>106161.94</td>\n",
       "      <td>5712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>72832.16</td>\n",
       "      <td>37123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>124549.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin DB4</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>92612.10</td>\n",
       "      <td>25757.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Motor      Valor  Quilometragem\n",
       "Nome                                                       \n",
       "Jetta Variant     Motor 4.0 Turbo   88078.64        44410.0\n",
       "Passat               Motor Diesel  106161.94         5712.0\n",
       "Crossfox          Motor Diesel V8   72832.16        37123.0\n",
       "DS5               Motor 2.4 Turbo  124549.07            NaN\n",
       "Aston Martin DB4  Motor 2.4 Turbo   92612.10        25757.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seleciona todas as linhas e algumas colunas\n",
    "dataset.iloc[:, [0, 5, 2]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "eAbwq1oLD1BW"
   },
   "source": [
    "## 3. Queries com DataFrames<a id='3'></a>\n",
    "Vamos considerar que queremos obter todos os carros com uma certa motorização.\n",
    "\n",
    "Antes disso vamos ver outra forma de selecionar apenas um coluna. Além do comando visto acima, usando colchetes, podemos selecionar a coluna como um \"atributo\" do DataFrame desde que seu rótulo não possua espaços (\" \").\n",
    "```\n",
    "df.column\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Jetta Variant       Motor 4.0 Turbo\n",
       "Passat                 Motor Diesel\n",
       "Crossfox            Motor Diesel V8\n",
       "DS5                 Motor 2.4 Turbo\n",
       "Aston Martin DB4    Motor 2.4 Turbo\n",
       "Name: Motor, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Motor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AOZTWzehD1BW",
    "scrolled": true
   },
   "source": [
    "Vamos supor que o motor de interesse é o \"Motor Diesel\". Podemor tentar comparar a Series do bloco acima com esse motor e ver o resultado:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rO5T4h94D1BX",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Jetta Variant       False\n",
       "Passat               True\n",
       "Crossfox            False\n",
       "DS5                 False\n",
       "Aston Martin DB4    False\n",
       "Name: Motor, dtype: bool"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dataset.Motor == 'Motor Diesel').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(dataset.Motor == 'Motor Diesel').shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "STZ-l8oaD1BX"
   },
   "source": [
    "Vemos pelos blocos acima que o retorno dessa comparação é uma Series booleana com a mesma quantidade de linhas que o dataset, ou seja, o índice das linhas que possuem o motor usado na comparação recebem `True`. Então para selecionar apenas os carros com 'Motor Diesel', basta passar essa comparação como parâmetro da seleção de um DataFrame. Dessa forma, apenas os registros `True` serão retornados, ou seja, aqueles com 'Motor Diesel'.\n",
    "\n",
    "Para isso, vamos atribuir essa comparação a uma variável e passá-la como parâmetro para a seleção:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OZC8NAn6D1BX"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select = dataset.Motor == 'Motor Diesel'\n",
    "type(select)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OzXC2M40D1BY",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26, 6)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# podemos descobrir quantos registros (carros) possuem este tipo de motor\n",
    "motor_diesel = dataset[select]\n",
    "motor_diesel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Effa Hafei Picape Baú</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>102959.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Controle de estabilidade', 'Painel digital',...</td>\n",
       "      <td>125684.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sorento</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Sensor de chuva', 'Câmera de estacionamento'...</td>\n",
       "      <td>81399.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Fiesta Hatch</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2017</td>\n",
       "      <td>118895.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Sensor de estacionamento', 'Travas elétricas...</td>\n",
       "      <td>66007.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kangoo Express</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2007</td>\n",
       "      <td>29132.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Bancos de couro', 'Câmbio automático', 'Pilo...</td>\n",
       "      <td>146716.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                                \n",
       "Passat                 Motor Diesel  1991         5712.0    False   \n",
       "Effa Hafei Picape Baú  Motor Diesel  1991       102959.0    False   \n",
       "Sorento                Motor Diesel  2019            NaN     True   \n",
       "New Fiesta Hatch       Motor Diesel  2017       118895.0    False   \n",
       "Kangoo Express         Motor Diesel  2007        29132.0    False   \n",
       "\n",
       "                                                              Acessórios  \\\n",
       "Nome                                                                       \n",
       "Passat                 ['Central multimídia', 'Teto panorâmico', 'Fre...   \n",
       "Effa Hafei Picape Baú  ['Controle de estabilidade', 'Painel digital',...   \n",
       "Sorento                ['Sensor de chuva', 'Câmera de estacionamento'...   \n",
       "New Fiesta Hatch       ['Sensor de estacionamento', 'Travas elétricas...   \n",
       "Kangoo Express         ['Bancos de couro', 'Câmbio automático', 'Pilo...   \n",
       "\n",
       "                           Valor  \n",
       "Nome                              \n",
       "Passat                 106161.94  \n",
       "Effa Hafei Picape Baú  125684.65  \n",
       "Sorento                 81399.35  \n",
       "New Fiesta Hatch        66007.16  \n",
       "Kangoo Express         146716.91  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "motor_diesel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se quisermos fazer uma consulta combinada, basta colocar cada condição dentro de parênteses `( )` e separá-las pelos operadores lógicos `&` e `|`:\n",
    "```\n",
    "df[(condição1) & (condição2) & (condição3) ...]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Sorento</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Sensor de chuva', 'Câmera de estacionamento'...</td>\n",
       "      <td>81399.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cielo Hatch</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Painel digital', 'Central multimídia', 'Câme...</td>\n",
       "      <td>145197.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camry</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', 'Rodas de liga', 'Sensor ...</td>\n",
       "      <td>138597.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin Virage</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', 'Controle de tração', 'Câ...</td>\n",
       "      <td>97290.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Série 7 Sedã</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Vidros elétricos', 'Travas elétricas', 'Roda...</td>\n",
       "      <td>67539.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                              \n",
       "Sorento              Motor Diesel  2019            NaN     True   \n",
       "Cielo Hatch          Motor Diesel  2019            NaN     True   \n",
       "Camry                Motor Diesel  2019            NaN     True   \n",
       "Aston Martin Virage  Motor Diesel  2019            NaN     True   \n",
       "Série 7 Sedã         Motor Diesel  2019            NaN     True   \n",
       "\n",
       "                                                            Acessórios  \\\n",
       "Nome                                                                     \n",
       "Sorento              ['Sensor de chuva', 'Câmera de estacionamento'...   \n",
       "Cielo Hatch          ['Painel digital', 'Central multimídia', 'Câme...   \n",
       "Camry                ['Travas elétricas', 'Rodas de liga', 'Sensor ...   \n",
       "Aston Martin Virage  ['Travas elétricas', 'Controle de tração', 'Câ...   \n",
       "Série 7 Sedã         ['Vidros elétricos', 'Travas elétricas', 'Roda...   \n",
       "\n",
       "                         Valor  \n",
       "Nome                            \n",
       "Sorento               81399.35  \n",
       "Cielo Hatch          145197.70  \n",
       "Camry                138597.27  \n",
       "Aston Martin Virage   97290.18  \n",
       "Série 7 Sedã          67539.79  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[(dataset.Motor == 'Motor Diesel') & (dataset.Zero_km == True)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_XKXcWO-D1BY"
   },
   "source": [
    "### 3.1. Utilizando o método `query()`\n",
    "Esta é uma forma mais \"elegante\" de realizar seleções, aqui todas as comparações ficam dentro de aspas simples (`'`) e são separadas pelos operadores lógicos `and` e `or`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AB9JirSoD1BY"
   },
   "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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Sorento</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Sensor de chuva', 'Câmera de estacionamento'...</td>\n",
       "      <td>81399.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cielo Hatch</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Painel digital', 'Central multimídia', 'Câme...</td>\n",
       "      <td>145197.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camry</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', 'Rodas de liga', 'Sensor ...</td>\n",
       "      <td>138597.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin Virage</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', 'Controle de tração', 'Câ...</td>\n",
       "      <td>97290.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Série 7 Sedã</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Vidros elétricos', 'Travas elétricas', 'Roda...</td>\n",
       "      <td>67539.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                              \n",
       "Sorento              Motor Diesel  2019            NaN     True   \n",
       "Cielo Hatch          Motor Diesel  2019            NaN     True   \n",
       "Camry                Motor Diesel  2019            NaN     True   \n",
       "Aston Martin Virage  Motor Diesel  2019            NaN     True   \n",
       "Série 7 Sedã         Motor Diesel  2019            NaN     True   \n",
       "\n",
       "                                                            Acessórios  \\\n",
       "Nome                                                                     \n",
       "Sorento              ['Sensor de chuva', 'Câmera de estacionamento'...   \n",
       "Cielo Hatch          ['Painel digital', 'Central multimídia', 'Câme...   \n",
       "Camry                ['Travas elétricas', 'Rodas de liga', 'Sensor ...   \n",
       "Aston Martin Virage  ['Travas elétricas', 'Controle de tração', 'Câ...   \n",
       "Série 7 Sedã         ['Vidros elétricos', 'Travas elétricas', 'Roda...   \n",
       "\n",
       "                         Valor  \n",
       "Nome                            \n",
       "Sorento               81399.35  \n",
       "Cielo Hatch          145197.70  \n",
       "Camry                138597.27  \n",
       "Aston Martin Virage   97290.18  \n",
       "Série 7 Sedã          67539.79  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.query('Motor == \"Motor Diesel\" and Zero_km == True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ciJ4dw_FD1BZ"
   },
   "source": [
    "## 4. Iterando com DataFrames<a id='4'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TKAB-g5KD1BZ"
   },
   "source": [
    "Neste primeiro momento, vamos conhecer algumas maneiras mais rudimentares e não tão eficientes para iterar sobre datasets.\n",
    "\n",
    "Primeiro vamos ver o que acontece quando simplesmente tentamos iterar os itens de um DataFrame através do `for`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eFTNtPRWD1Ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Motor\n",
      "Ano\n",
      "Quilometragem\n",
      "Zero_km\n",
      "Acessórios\n",
      "Valor\n"
     ]
    }
   ],
   "source": [
    "for item in dataset:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ele apenas percore os rótulos das colunas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. método `iterrows()`\n",
    "Este método retorna um iterador. Vamos converter este iterador para uma lista e investigá-lo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generator"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset.iterrows())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "\n",
      "Tamanho da lista: 258\n"
     ]
    }
   ],
   "source": [
    "lista = list(dataset.iterrows())\n",
    "print('{}\\n\\nTamanho da lista: {}'.format(type(lista), len(lista)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Jetta Variant',\n",
       " Motor                                              Motor 4.0 Turbo\n",
       " Ano                                                           2003\n",
       " Quilometragem                                                44410\n",
       " Zero_km                                                      False\n",
       " Acessórios       ['Rodas de liga', 'Travas elétricas', 'Piloto ...\n",
       " Valor                                                      88078.6\n",
       " Name: Jetta Variant, dtype: object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lista[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Jetta Variant'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Motor                                              Motor 4.0 Turbo\n",
       "Ano                                                           2003\n",
       "Quilometragem                                                44410\n",
       "Zero_km                                                      False\n",
       "Acessórios       ['Rodas de liga', 'Travas elétricas', 'Piloto ...\n",
       "Valor                                                      88078.6\n",
       "Name: Jetta Variant, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[0][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Até aqui pudemos ver que o iterador resultante é um conjunto de tuplas, no caso como convertemos para uma lista, agora temos uma lista de tuplas.\n",
    "\n",
    "Essas tuplas são compostas por dois elementos, o índice (nome do carro) e toda a informação do carro. Assim então, podemos usar um `for` para desempacotar estas tuplas.\n",
    "\n",
    "Vamos calcular a quilometragem média (até o ano de 2019) e colocar essa informação em uma nova coluna:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in dataset.iterrows():\n",
    "    if ( 2019 - row['Ano'] != 0 ):\n",
    "        dataset.loc[index, 'km_media'] = row['Quilometragem'] / ( 2019 - row['Ano'] )\n",
    "    else:\n",
    "        dataset.loc[index, 'km_media'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "      <th>km_media</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>Jetta Variant</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2003</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', 'Travas elétricas', 'Piloto ...</td>\n",
       "      <td>88078.64</td>\n",
       "      <td>2775.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passat</th>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "      <td>204.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crossfox</th>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "      <td>1280.103448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aston Martin DB4</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2006</td>\n",
       "      <td>25757.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', '4 X 4', 'Central multimídia...</td>\n",
       "      <td>92612.10</td>\n",
       "      <td>1981.307692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                              \n",
       "Jetta Variant     Motor 4.0 Turbo  2003        44410.0    False   \n",
       "Passat               Motor Diesel  1991         5712.0    False   \n",
       "Crossfox          Motor Diesel V8  1990        37123.0    False   \n",
       "DS5               Motor 2.4 Turbo  2019            NaN     True   \n",
       "Aston Martin DB4  Motor 2.4 Turbo  2006        25757.0    False   \n",
       "\n",
       "                                                         Acessórios  \\\n",
       "Nome                                                                  \n",
       "Jetta Variant     ['Rodas de liga', 'Travas elétricas', 'Piloto ...   \n",
       "Passat            ['Central multimídia', 'Teto panorâmico', 'Fre...   \n",
       "Crossfox          ['Piloto automático', 'Controle de estabilidad...   \n",
       "DS5               ['Travas elétricas', '4 X 4', 'Vidros elétrico...   \n",
       "Aston Martin DB4  ['Rodas de liga', '4 X 4', 'Central multimídia...   \n",
       "\n",
       "                      Valor     km_media  \n",
       "Nome                                      \n",
       "Jetta Variant      88078.64  2775.625000  \n",
       "Passat            106161.94   204.000000  \n",
       "Crossfox           72832.16  1280.103448  \n",
       "DS5               124549.07     0.000000  \n",
       "Aston Martin DB4   92612.10  1981.307692  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. outros métodos: `items()` e `itertuples()`\n",
    "Também existem outras formas de se iterar sobre um DataFrame que valem ser citadas, como:\n",
    "- `items()`: Itera pelas colunas do DataFrame, utilizando uma tupla, onde o primeiro item é o nome da coluna e o segundo é uma Series com todo o conteúdo da coluna. Mais informações [aqui](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.items.html);\n",
    "- `itertuples()`: Este método itera pelas linhas do DataFrame, utilizando um tipo de tupla nomeada com o conteúdo de cada linha do DataFrame. Mais informações [aqui](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.itertuples.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HnUzHFQPD1Ba"
   },
   "source": [
    "## 5. Tratamento de dados<a id='5'></a>\n",
    "### 5.1. Investigando o _dataset_ com `info()`\n",
    "Vamos passar agora pelo que é, em geral, a primeira etapa de qualquer processo de análise de dados, a limpeza e o tratamento dos dados. Para iniciar esta estapa devemos verificar \"como está\" o nosso dataset, e, uma forma simples e rápida de fazer é através do método `info()`. Com isso podemos descobrir os tipos de dados em cada coluna e se existem linhas sem valores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FRxBWoBGD1Ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 258 entries, Jetta Variant to Macan\n",
      "Data columns (total 7 columns):\n",
      "Motor            258 non-null object\n",
      "Ano              258 non-null int64\n",
      "Quilometragem    197 non-null float64\n",
      "Zero_km          258 non-null bool\n",
      "Acessórios       258 non-null object\n",
      "Valor            258 non-null float64\n",
      "km_media         258 non-null float64\n",
      "dtypes: bool(1), float64(3), int64(1), object(2)\n",
      "memory usage: 24.4+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HHSwH0ZaD1Bc"
   },
   "source": [
    "Pelo _output_ acima vemos que 61 linhas não possuem valor na coluna 'Quilometragem'. É importante tratar esses registros pois se formos usar um modelo de regressão, por exemplo, essas linhas serão descartadas e não contabilizadas.\n",
    "\n",
    "O Pandas oferece a possibilidade de selecionar linhas quando alguma coluna tiver valor nulo, faremos isso usando o método `isna()`, que retorna uma Series booleana com valor True para todas a linhas que tiverem valor nulo. Seu uso é feito da seguinte forma:\n",
    "```\n",
    "df.column_nama.isna() # quando o nome da coluna NÃO TIVER espaços, retorna uma Series\n",
    "\n",
    "df.['column name'].isna() # quando o nome da coluna TIVER espaços, retorna uma Series\n",
    "\n",
    "df.[['column name']].isna() # para retornar uma DataFrame\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G27pxgZ5D1Bd",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Jetta Variant       False\n",
       "Passat              False\n",
       "Crossfox            False\n",
       "DS5                  True\n",
       "Aston Martin DB4    False\n",
       "Name: Quilometragem, dtype: bool"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Quilometragem.isna().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DhlRbxy2D1Bd",
    "scrolled": true
   },
   "source": [
    "Para selecionar os registros do dataset com valores nulo na coluna 'Quilometragem' basta passar essa condição para a seleção:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "      <th>km_media</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</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>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Câmbio automático', 'Câmera de estacionament...</td>\n",
       "      <td>56445.20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J5</th>\n",
       "      <td>Motor V6</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Sensor crepuscular', 'Painel digital', 'Roda...</td>\n",
       "      <td>53183.38</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>Motor 1.0 8v</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['4 X 4', 'Piloto automático', 'Central multim...</td>\n",
       "      <td>88552.39</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Série 1 M</th>\n",
       "      <td>Motor V8</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Controle de estabilidade', 'Central multimíd...</td>\n",
       "      <td>94564.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                       \n",
       "DS5        Motor 2.4 Turbo  2019            NaN     True   \n",
       "A5         Motor 4.0 Turbo  2019            NaN     True   \n",
       "J5                Motor V6  2019            NaN     True   \n",
       "A3            Motor 1.0 8v  2019            NaN     True   \n",
       "Série 1 M         Motor V8  2019            NaN     True   \n",
       "\n",
       "                                                  Acessórios      Valor  \\\n",
       "Nome                                                                      \n",
       "DS5        ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07   \n",
       "A5         ['Câmbio automático', 'Câmera de estacionament...   56445.20   \n",
       "J5         ['Sensor crepuscular', 'Painel digital', 'Roda...   53183.38   \n",
       "A3         ['4 X 4', 'Piloto automático', 'Central multim...   88552.39   \n",
       "Série 1 M  ['Controle de estabilidade', 'Central multimíd...   94564.40   \n",
       "\n",
       "           km_media  \n",
       "Nome                 \n",
       "DS5             0.0  \n",
       "A5              0.0  \n",
       "J5              0.0  \n",
       "A3              0.0  \n",
       "Série 1 M       0.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.Quilometragem.isna()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 7)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset.Quilometragem.isna()].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observamos que de fato são 61 linhas que possuem o valor da 'Quilometragem' nulo.\n",
    "\n",
    "### 5.2. Preenchendo valores com `fillna()`\n",
    "Outro ponto que pode ser \"observado\" com uma inspeção visual (ta-dum-tsss), é que o registros com quilometragem nula são todos zero km e fabricados em 2019. Podemos verificar isso com uma seleção combinada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OL04QU5RD1Be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 7)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[(dataset.Quilometragem.isna()) & (dataset['Zero_km'] == True) & (dataset.Ano == 2019)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fTH2JkGDD1Bf",
    "scrolled": true
   },
   "source": [
    "Como os datasets retornados possuem a mesma dimensão, podemos assumir que isso é uma regra. Portanto, faz sentido substituir o valor nulo por um número zero, digamos. Isso pode ser feito através do método `fillna()`:\n",
    "```\n",
    "df.fillna(valor[,inplace=False, ...])\n",
    "```\n",
    "Este método pode ser aplicado no dataset todo ou em apenas uma coluna. Se o parâmetro `inplace` não for especificado, será retornado um dataset com a modificação, mas o dataset \"original\" não será modificado, para isso devemos passar esse parâmetro como True. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4-ZvhXjvD1Bj"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Jetta Variant       44410.0\n",
       "Passat               5712.0\n",
       "Crossfox            37123.0\n",
       "DS5                     0.0\n",
       "Aston Martin DB4    25757.0\n",
       "Palio Weekend       10728.0\n",
       "A5                      0.0\n",
       "Name: Quilometragem, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Quilometragem.fillna(0).head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MQUw7i1OD1Bk",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataset.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U4Cse7miD1Bl"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "      <th>km_media</th>\n",
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       "    <tr>\n",
       "      <th>Nome</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DS5</th>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>['Câmbio automático', 'Câmera de estacionament...</td>\n",
       "      <td>56445.20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>J5</th>\n",
       "      <td>Motor V6</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>['Sensor crepuscular', 'Painel digital', 'Roda...</td>\n",
       "      <td>53183.38</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>Motor 1.0 8v</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>['4 X 4', 'Piloto automático', 'Central multim...</td>\n",
       "      <td>88552.39</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Série 1 M</th>\n",
       "      <td>Motor V8</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>['Controle de estabilidade', 'Central multimíd...</td>\n",
       "      <td>94564.40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Motor   Ano  Quilometragem  Zero_km  \\\n",
       "Nome                                                       \n",
       "DS5        Motor 2.4 Turbo  2019            0.0     True   \n",
       "A5         Motor 4.0 Turbo  2019            0.0     True   \n",
       "J5                Motor V6  2019            0.0     True   \n",
       "A3            Motor 1.0 8v  2019            0.0     True   \n",
       "Série 1 M         Motor V8  2019            0.0     True   \n",
       "\n",
       "                                                  Acessórios      Valor  \\\n",
       "Nome                                                                      \n",
       "DS5        ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07   \n",
       "A5         ['Câmbio automático', 'Câmera de estacionament...   56445.20   \n",
       "J5         ['Sensor crepuscular', 'Painel digital', 'Roda...   53183.38   \n",
       "A3         ['4 X 4', 'Piloto automático', 'Central multim...   88552.39   \n",
       "Série 1 M  ['Controle de estabilidade', 'Central multimíd...   94564.40   \n",
       "\n",
       "           km_media  \n",
       "Nome                 \n",
       "DS5             0.0  \n",
       "A5              0.0  \n",
       "J5              0.0  \n",
       "A3              0.0  \n",
       "Série 1 M       0.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset.query('Zero_km == True').shape)\n",
    "dataset.query('Zero_km == True').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8L4wum05D1Bl",
    "scrolled": true
   },
   "source": [
    "### 5.3. Eliminando registros com `dropna()`\n",
    "Agora vamos considerar o cenário em que é melhor eliminar as linhas com valores nulos. Isso pode ser feito com o método `dropna()`:\n",
    "```\n",
    "df.dropna([,subset=None, inplace=False, ...])\n",
    "```\n",
    "se as linhas forem \"dropadas\" devido a valores nulos em apenas algumas colunas, o parâmetro `subset` deve receber uma lista com os rótulos das colunas que devem ser avaliadas.\n",
    "\n",
    "Mas primeiro vamos reimportar os dados do arquivo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8iLOc-U3D1Bm"
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('data/db.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(258, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jetta Variant</td>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2003</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', 'Travas elétricas', 'Piloto ...</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Passat</td>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crossfox</td>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DS5</td>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aston Martin DB4</td>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2006</td>\n",
       "      <td>25757.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', '4 X 4', 'Central multimídia...</td>\n",
       "      <td>92612.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Nome            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "0     Jetta Variant  Motor 4.0 Turbo  2003        44410.0    False   \n",
       "1            Passat     Motor Diesel  1991         5712.0    False   \n",
       "2          Crossfox  Motor Diesel V8  1990        37123.0    False   \n",
       "3               DS5  Motor 2.4 Turbo  2019            NaN     True   \n",
       "4  Aston Martin DB4  Motor 2.4 Turbo  2006        25757.0    False   \n",
       "\n",
       "                                          Acessórios      Valor  \n",
       "0  ['Rodas de liga', 'Travas elétricas', 'Piloto ...   88078.64  \n",
       "1  ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "2  ['Piloto automático', 'Controle de estabilidad...   72832.16  \n",
       "3  ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07  \n",
       "4  ['Rodas de liga', '4 X 4', 'Central multimídia...   92612.10  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset.shape)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(61, 7)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DS5</td>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Travas elétricas', '4 X 4', 'Vidros elétrico...</td>\n",
       "      <td>124549.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A5</td>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Câmbio automático', 'Câmera de estacionament...</td>\n",
       "      <td>56445.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>J5</td>\n",
       "      <td>Motor V6</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Sensor crepuscular', 'Painel digital', 'Roda...</td>\n",
       "      <td>53183.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>A3</td>\n",
       "      <td>Motor 1.0 8v</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['4 X 4', 'Piloto automático', 'Central multim...</td>\n",
       "      <td>88552.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Série 1 M</td>\n",
       "      <td>Motor V8</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>['Controle de estabilidade', 'Central multimíd...</td>\n",
       "      <td>94564.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Nome            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "3         DS5  Motor 2.4 Turbo  2019            NaN     True   \n",
       "6          A5  Motor 4.0 Turbo  2019            NaN     True   \n",
       "13         J5         Motor V6  2019            NaN     True   \n",
       "17         A3     Motor 1.0 8v  2019            NaN     True   \n",
       "21  Série 1 M         Motor V8  2019            NaN     True   \n",
       "\n",
       "                                           Acessórios      Valor  \n",
       "3   ['Travas elétricas', '4 X 4', 'Vidros elétrico...  124549.07  \n",
       "6   ['Câmbio automático', 'Câmera de estacionament...   56445.20  \n",
       "13  ['Sensor crepuscular', 'Painel digital', 'Roda...   53183.38  \n",
       "17  ['4 X 4', 'Piloto automático', 'Central multim...   88552.39  \n",
       "21  ['Controle de estabilidade', 'Central multimíd...   94564.40  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset[dataset.Quilometragem.isna()].shape)\n",
    "dataset[dataset.Quilometragem.isna()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acima temos o dataset original (com 258 linhas) e um DataFrame com todos as linhas que possuem valor nulo na coluna 'Quilometragem'.\n",
    "\n",
    "Vamos usar o método `dropna()` considerando apenas a coluna 'Quilometragem':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.dropna(subset = ['Quilometragem'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(197, 7)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jetta Variant</td>\n",
       "      <td>Motor 4.0 Turbo</td>\n",
       "      <td>2003</td>\n",
       "      <td>44410.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', 'Travas elétricas', 'Piloto ...</td>\n",
       "      <td>88078.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Passat</td>\n",
       "      <td>Motor Diesel</td>\n",
       "      <td>1991</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Central multimídia', 'Teto panorâmico', 'Fre...</td>\n",
       "      <td>106161.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crossfox</td>\n",
       "      <td>Motor Diesel V8</td>\n",
       "      <td>1990</td>\n",
       "      <td>37123.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Piloto automático', 'Controle de estabilidad...</td>\n",
       "      <td>72832.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aston Martin DB4</td>\n",
       "      <td>Motor 2.4 Turbo</td>\n",
       "      <td>2006</td>\n",
       "      <td>25757.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Rodas de liga', '4 X 4', 'Central multimídia...</td>\n",
       "      <td>92612.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Palio Weekend</td>\n",
       "      <td>Motor 1.8 16v</td>\n",
       "      <td>2012</td>\n",
       "      <td>10728.0</td>\n",
       "      <td>False</td>\n",
       "      <td>['Sensor de estacionamento', 'Teto panorâmico'...</td>\n",
       "      <td>97497.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Nome            Motor   Ano  Quilometragem  Zero_km  \\\n",
       "0     Jetta Variant  Motor 4.0 Turbo  2003        44410.0    False   \n",
       "1            Passat     Motor Diesel  1991         5712.0    False   \n",
       "2          Crossfox  Motor Diesel V8  1990        37123.0    False   \n",
       "4  Aston Martin DB4  Motor 2.4 Turbo  2006        25757.0    False   \n",
       "5     Palio Weekend    Motor 1.8 16v  2012        10728.0    False   \n",
       "\n",
       "                                          Acessórios      Valor  \n",
       "0  ['Rodas de liga', 'Travas elétricas', 'Piloto ...   88078.64  \n",
       "1  ['Central multimídia', 'Teto panorâmico', 'Fre...  106161.94  \n",
       "2  ['Piloto automático', 'Controle de estabilidad...   72832.16  \n",
       "4  ['Rodas de liga', '4 X 4', 'Central multimídia...   92612.10  \n",
       "5  ['Sensor de estacionamento', 'Teto panorâmico'...   97497.73  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset.shape)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Nome</th>\n",
       "      <th>Motor</th>\n",
       "      <th>Ano</th>\n",
       "      <th>Quilometragem</th>\n",
       "      <th>Zero_km</th>\n",
       "      <th>Acessórios</th>\n",
       "      <th>Valor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Nome, Motor, Ano, Quilometragem, Zero_km, Acessórios, Valor]\n",
       "Index: []"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset[dataset.Quilometragem.isna()].shape)\n",
    "dataset[dataset.Quilometragem.isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: right\"> <a href=\"#indice\">voltar ao topo </p>"
   ]
  }
 ],
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   "name": "Python para Data Science - Pandas.ipynb",
   "provenance": []
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