Projeto_Carros.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# O que vende um carro?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A atividade trata-se de uma análise de uma dataframe com as informações de propagandas de carros. O dataframe representaria um site, chamado Lista de Eixo de Manivela. Centenas de propagandas gratuitas de veículos são publicadas no site todos os dias. Estudarei os dados coletados nos últimos anos e determinarei quais fatores influenciaram o preço de um veículo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em primeiro lugar, será feito o pré-processamento dos dados. O dataframe será analisado, procurando-se por valores ausentes, duplicados ou com formatação incorreta, e serão tratados. Encontrado valores ausentes, será investigado se existe alguma razão, então decidirei se serão substituídos. Em seguida, investigariei a necessidade de modelagem e criação de novas colunas do dataframe, para melhorar a análise. \n",
    "\n",
    "Sendo assim, a análise do dataframe será feita estudando os parâmetros como preço, idade do veículo, quando a propaganda foi colocada, etc. Para cada parâmetro será construido histogramas e outros gráficos, investigando a distribuição dos parâmetros e a existencia de valores atípicos. \n",
    "\n",
    "Em seguida, será estudado quantos dias as propagandas foram exibidas. Descrevendo o tempo de vida útil comum de uma propaganda. Determinarei quando as propagandas foram removidas rapidamente, e quando elas foram listadas por um tempo anormalmente longo. Analisarei o número de propagandas e o preço médio para cada tipo de veículo. Para demonstrar a relação, construirei um gráfico mostrando a dependência do número de propagandas em relação ao tipo de veículo.\n",
    "\n",
    "Por fim, será visto quais fatores mais  influenciam o preço. Serão pegos os tipos populares detectados e será visto se os seus preços dependem da idade, quilometragem, condição, tipo de transmissão, etc. As categorias de variáveis categóricas analisadas precisam ter pelo menos 50 propagandas, de outro modo, seus parâmetros não serão válidos para análise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iniciação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando todas as bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregue dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando o arquivo com os dados em um DataFrame\n",
    "try:\n",
    "    df = pd.read_csv(\"vehicles_us.csv\")\n",
    "\n",
    "except:\n",
    "    df = pd.read_csv(\"/datasets/vehicles_us.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore os dados iniciais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O conjunto de dados contém os seguintes campos:\n",
    "- **`price`** -      preço do veículo\n",
    "- **`model_year`** - ano de fabricação do veículo\n",
    "- **`model`** -      modelo do veículo\n",
    "- **`condition`** -  codição do veículo\n",
    "- **`cylinders`** -  número de cilindros\n",
    "- **`fuel`** —       gasolina, diesel etc.\n",
    "- **`odometer`** —   a quilometragem do veículo quando a propaganda foi publicada \n",
    "- **`transmission`**-tipo de transmissão\n",
    "- **`paint_color`**- cor do veículo\n",
    "- **`is_4wd`** —     se o veículo é 4 por 4 (tipo Booleano)\n",
    "- **`date_posted`**— a data que a propaganda foi publicada\n",
    "- **`days_listed`**— dias desde a publicação até a retirada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51525 entries, 0 to 51524\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   price         51525 non-null  int64  \n",
      " 1   model_year    47906 non-null  float64\n",
      " 2   model         51525 non-null  object \n",
      " 3   condition     51525 non-null  object \n",
      " 4   cylinders     46265 non-null  float64\n",
      " 5   fuel          51525 non-null  object \n",
      " 6   odometer      43633 non-null  float64\n",
      " 7   transmission  51525 non-null  object \n",
      " 8   type          51525 non-null  object \n",
      " 9   paint_color   42258 non-null  object \n",
      " 10  is_4wd        25572 non-null  float64\n",
      " 11  date_posted   51525 non-null  object \n",
      " 12  days_listed   51525 non-null  int64  \n",
      "dtypes: float64(4), int64(2), object(7)\n",
      "memory usage: 5.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#informações gerais/resumidas sobre o DataFrame\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9400</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>bmw x5</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>88705.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5500</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>hyundai sonata</td>\n",
       "      <td>like new</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14900</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>chrysler 200</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>80903.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  model_year           model  condition  cylinders fuel  odometer  \\\n",
       "0   9400      2011.0          bmw x5       good        6.0  gas  145000.0   \n",
       "1  25500         NaN      ford f-150       good        6.0  gas   88705.0   \n",
       "2   5500      2013.0  hyundai sonata   like new        4.0  gas  110000.0   \n",
       "3   1500      2003.0      ford f-150       fair        8.0  gas       NaN   \n",
       "4  14900      2017.0    chrysler 200  excellent        4.0  gas   80903.0   \n",
       "\n",
       "  transmission    type paint_color  is_4wd date_posted  days_listed  \n",
       "0    automatic     SUV         NaN     1.0  2018-06-23           19  \n",
       "1    automatic  pickup       white     1.0  2018-10-19           50  \n",
       "2    automatic   sedan         red     NaN  2019-02-07           79  \n",
       "3    automatic  pickup         NaN     NaN  2019-03-22            9  \n",
       "4    automatic   sedan       black     NaN  2019-04-02           28  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# amostragem dos dados\n",
    "display(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price             int64\n",
       "model_year      float64\n",
       "model            object\n",
       "condition        object\n",
       "cylinders       float64\n",
       "fuel             object\n",
       "odometer        float64\n",
       "transmission     object\n",
       "type             object\n",
       "paint_color      object\n",
       "is_4wd          float64\n",
       "date_posted      object\n",
       "days_listed       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificando o tipo de dado da coluna\n",
    "df.dtypes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'model_year', 'model', 'condition', 'cylinders', 'fuel',\n",
       "       'odometer', 'transmission', 'type', 'paint_color', 'is_4wd',\n",
       "       'date_posted', 'days_listed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nome das colunas\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>odometer</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51525.000000</td>\n",
       "      <td>47906.000000</td>\n",
       "      <td>46265.000000</td>\n",
       "      <td>43633.000000</td>\n",
       "      <td>25572.0</td>\n",
       "      <td>51525.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12132.464920</td>\n",
       "      <td>2009.750470</td>\n",
       "      <td>6.125235</td>\n",
       "      <td>115553.461738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.55476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10040.803015</td>\n",
       "      <td>6.282065</td>\n",
       "      <td>1.660360</td>\n",
       "      <td>65094.611341</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.20427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1908.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>113000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16839.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>155000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375000.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>990000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>271.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price    model_year     cylinders       odometer   is_4wd  \\\n",
       "count   51525.000000  47906.000000  46265.000000   43633.000000  25572.0   \n",
       "mean    12132.464920   2009.750470      6.125235  115553.461738      1.0   \n",
       "std     10040.803015      6.282065      1.660360   65094.611341      0.0   \n",
       "min         1.000000   1908.000000      3.000000       0.000000      1.0   \n",
       "25%      5000.000000   2006.000000      4.000000   70000.000000      1.0   \n",
       "50%      9000.000000   2011.000000      6.000000  113000.000000      1.0   \n",
       "75%     16839.000000   2014.000000      8.000000  155000.000000      1.0   \n",
       "max    375000.000000   2019.000000     12.000000  990000.000000      1.0   \n",
       "\n",
       "       days_listed  \n",
       "count  51525.00000  \n",
       "mean      39.55476  \n",
       "std       28.20427  \n",
       "min        0.00000  \n",
       "25%       19.00000  \n",
       "50%       33.00000  \n",
       "75%       53.00000  \n",
       "max      271.00000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Informações quantitativas sobre o DataFrame\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na primeira visualização foi encontrado alguns problemas que necessitam maiores investigações: \n",
    "\n",
    "- As colunas **`model_year`, `cylinders`, `odometer`, `paint_color`** e **`is_4wd`** possuem valores ausentes. \n",
    "- As colunas **`model_year`** e **`date_posted`** estão com os tipos *float64* e *object*, respectivamente, quando deveriam estar com o tipo *datetime*.\n",
    "- As colunas **`model_year`**, **`is_4wd`**  e  **`cylinders`** estão com os tipos *float64*, quando deveriam ser do tipo *int64*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Será vista a quantidade de valores ausentes e a existência de valores dupicados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de valores nan:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "is_4wd         25953\n",
       "paint_color     9267\n",
       "odometer        7892\n",
       "cylinders       5260\n",
       "model_year      3619\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Proporcao de valores nan:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "is_4wd         0.503697\n",
       "paint_color    0.179854\n",
       "odometer       0.153168\n",
       "cylinders      0.102086\n",
       "model_year     0.070238\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quantidade de valores ausentes\n",
    "columns_nan = df.isna().sum().sort_values(ascending=False)\n",
    "columns_nan_porc = columns_nan / df['price'].count()\n",
    "\n",
    "print('Quantidade de valores nan:')\n",
    "display(columns_nan.head())\n",
    "\n",
    "print('\\nProporcao de valores nan:')\n",
    "display(columns_nan_porc.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna **`is_4wd`** possui a maior quantidade de valores ausentes, com 25953, representando 50% dos valores da coluna. A segunda coluna com mais valores ausentes é  **`paint_color`** com 9267 ou 18% da coluna. As outras três colunas com valores ausentes possuem entre 7% a 15%. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Valores duplicados\n",
    "\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não existem valores duplicados no dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigarei se a algum problema com os valores únicos nas colunas categóricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model\n",
      "['bmw x5' 'ford f-150' 'hyundai sonata' 'chrysler 200' 'chrysler 300'\n",
      " 'toyota camry' 'honda pilot' 'kia sorento' 'chevrolet silverado 1500'\n",
      " 'honda accord' 'ram 1500' 'gmc yukon' 'jeep cherokee'\n",
      " 'chevrolet traverse' 'hyundai elantra' 'chevrolet tahoe' 'toyota rav4'\n",
      " 'chevrolet silverado' 'jeep wrangler' 'chevrolet malibu' 'ford fusion se'\n",
      " 'chevrolet impala' 'chevrolet corvette' 'jeep liberty' 'toyota camry le'\n",
      " 'nissan altima' 'subaru outback' 'toyota highlander' 'dodge charger'\n",
      " 'toyota tacoma' 'chevrolet equinox' 'nissan rogue'\n",
      " 'mercedes-benz benze sprinter 2500' 'honda cr-v' 'jeep grand cherokee'\n",
      " 'toyota 4runner' 'ford focus' 'honda civic' 'kia soul'\n",
      " 'chevrolet colorado' 'ford f150 supercrew cab xlt'\n",
      " 'chevrolet camaro lt coupe 2d' 'chevrolet cruze' 'ford mustang'\n",
      " 'chevrolet silverado 3500hd' 'nissan frontier crew cab sv'\n",
      " 'subaru impreza' 'jeep grand cherokee laredo' 'nissan versa'\n",
      " 'ford f-250 sd' 'chevrolet silverado 1500 crew' 'ford f250 super duty'\n",
      " 'chevrolet camaro' 'ford mustang gt coupe 2d' 'subaru forester'\n",
      " 'ford explorer' 'ford f-350 sd' 'ford edge' 'nissan maxima' 'ford f-250'\n",
      " 'nissan sentra' 'ford f150' 'chevrolet suburban' 'ford expedition'\n",
      " 'dodge grand caravan' 'ford taurus' 'acura tl' 'ford f350 super duty'\n",
      " 'ford ranger' 'gmc sierra' 'hyundai santa fe' 'ford escape'\n",
      " 'gmc sierra 2500hd' 'honda civic lx' 'gmc sierra 1500' 'honda odyssey'\n",
      " 'cadillac escalade' 'volkswagen jetta' 'toyota corolla'\n",
      " 'chrysler town & country' 'volkswagen passat' 'toyota prius'\n",
      " 'buick enclave' 'ford fusion' 'toyota tundra' 'ram 3500' 'ram 2500'\n",
      " 'nissan frontier' 'chevrolet silverado 2500hd' 'jeep wrangler unlimited'\n",
      " 'ford f-250 super duty' 'gmc acadia' 'toyota sienna' 'ford focus se'\n",
      " 'ford f250' 'dodge dakota' 'ford f350' 'chevrolet trailblazer'\n",
      " 'ford econoline' 'nissan murano']\n",
      "\n",
      "\n",
      "\n",
      "condition\n",
      "['good' 'like new' 'fair' 'excellent' 'salvage' 'new']\n",
      "\n",
      "\n",
      "\n",
      "fuel\n",
      "['gas' 'diesel' 'other' 'hybrid' 'electric']\n",
      "\n",
      "\n",
      "\n",
      "transmission\n",
      "['automatic' 'manual' 'other']\n",
      "\n",
      "\n",
      "\n",
      "type\n",
      "['SUV' 'pickup' 'sedan' 'truck' 'coupe' 'van' 'convertible' 'hatchback'\n",
      " 'wagon' 'mini-van' 'other' 'offroad' 'bus']\n",
      "\n",
      "\n",
      "\n",
      "paint_color\n",
      "[nan 'white' 'red' 'black' 'blue' 'grey' 'silver' 'custom' 'orange'\n",
      " 'yellow' 'brown' 'green' 'purple']\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x in df.columns:\n",
    "    if df[x].dtype == 'object' and x!='date_posted':\n",
    "        print(x)\n",
    "        print(df[x].unique())\n",
    "        print('\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parece haver duplicatas implícitas na coluna **`model`**, irá requerir maior investigação. Parece não haver nenhum problema de caixa alta ou variáveis iguais escritas de forma diferente nas outras colunas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusões e próximos passos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A primeira investigação do dataframe indentificou alguns problemas que precisam serem corrigidos ante da análise. Sendo esse problemas, valores ausentes nas colunas **`model_year`, `cylinders`, `odometer`, `paint_color`** e **`is_4wd`**. O tipo de dado das colunas **`model_year`**, **`date_posted`**, **`is_4wd`**  e  **`cylinders`** estão incorretos.  Na coluna **`model`** existem  duplicatas implícitas.\n",
    "\n",
    "\n",
    "Em relação as colunas com variáveis quantitativas, a primeira vista, para não haver valores atípicos Não foram encontrados valores duplicados. Não foram encontrados problemas de formatação nos nomes das colunas.\n",
    "\n",
    "Primeiro serão tratados os valores ausentes e então as duplicatas implicitas. Os tipos de dados das colunas serão as últimas a serem tratadas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correção dos valores ausentes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Serão tratados os valores ausentes de cada coluna. Começando com uma investigação do motivo de haver valores ausentes nas colunas. As colunas são: \n",
    "- **`is_4wd`** - Com 25953 valores ausentes ou 50% dos valores da coluna.\n",
    "- **`paint_color`** - Com 9267 valores ausentes ou 18% dos valores da coluna.\n",
    "- **`odometer`** -Com 7892 valores ausentes ou 15% dos valores da coluna.\n",
    "- **`cylinders`** -Com 5260 valores ausentes ou 10% dos valores da coluna.\n",
    "- **`model_year`** -Com 3619 valores ausentes ou 7% dos valores da coluna."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigação da coluna **`is_4wd`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>is_4wd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>25572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   is_4wd\n",
       "0  count  25572.0\n",
       "1   mean      1.0\n",
       "2    std      0.0\n",
       "3    min      1.0\n",
       "4    25%      1.0\n",
       "5    50%      1.0\n",
       "6    75%      1.0\n",
       "7    max      1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Valores iguais a 0(\"nao\"):\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Valores quantitativos da coluna\n",
    "display(df['is_4wd'].describe().reset_index())\n",
    "\n",
    "# Valores iguais a zero na coluna\n",
    "print('\\nValores iguais a 0(\"nao\"):')\n",
    "print(len(df.query('is_4wd == 0')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna **`is_4wd`** representa se o veículo é 4 por 4, com 1 sendo *sim* e 0 *não*. Não há nenhum valor 0 na coluna, logo, suponho que os valores ausentes são de variáveis 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>is_4wd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>count</td>\n",
       "      <td>51525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean</td>\n",
       "      <td>0.496303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>std</td>\n",
       "      <td>0.499991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50%</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>75%</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        is_4wd\n",
       "0  count  51525.000000\n",
       "1   mean      0.496303\n",
       "2    std      0.499991\n",
       "3    min      0.000000\n",
       "4    25%      0.000000\n",
       "5    50%      0.000000\n",
       "6    75%      1.000000\n",
       "7    max      1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de valores ausentes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correção dos valores da coluna is_4wd\n",
    "df['is_4wd'].fillna(0,inplace=True)\n",
    "\n",
    "# Verificação da coluna is_4wd\n",
    "display(df['is_4wd'].describe().reset_index())\n",
    "\n",
    "# Quantidade de valores ausentes\n",
    "print('\\nQuantidade de valores ausentes:')\n",
    "display(df['is_4wd'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valores ausentes da coluna **`is_4wd`** foram substituídos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigação da coluna **`paint_color`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9400</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>bmw x5</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11500</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>kia sorento</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>104174.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-07-16</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18990</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>ram 1500</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>140742.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5250</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>toyota rav4</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>154000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-08-22</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11499</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>chevrolet malibu</td>\n",
       "      <td>like new</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>11999</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>toyota camry le</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>35721.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-09-13</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>9500</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>toyota highlander</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>149799.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14999</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>dodge charger</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>63037.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-05-11</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10499</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>chrysler 300</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>88042.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  model_year              model  condition  cylinders fuel  odometer  \\\n",
       "0    9400      2011.0             bmw x5       good        6.0  gas  145000.0   \n",
       "3    1500      2003.0         ford f-150       fair        8.0  gas       NaN   \n",
       "8   11500      2012.0        kia sorento  excellent        4.0  gas  104174.0   \n",
       "12  18990      2012.0           ram 1500  excellent        8.0  gas  140742.0   \n",
       "21   5250      2007.0        toyota rav4       good        6.0  gas  154000.0   \n",
       "25  11499      2017.0   chevrolet malibu   like new        4.0  gas       NaN   \n",
       "31  11999      2017.0    toyota camry le  excellent        4.0  gas   35721.0   \n",
       "34   9500      2008.0  toyota highlander       good        6.0  gas  149799.0   \n",
       "35  14999      2015.0      dodge charger       good        6.0  gas   63037.0   \n",
       "36  10499      2013.0       chrysler 300       good        NaN  gas   88042.0   \n",
       "\n",
       "   transmission    type paint_color  is_4wd date_posted  days_listed  \n",
       "0     automatic     SUV         NaN     1.0  2018-06-23           19  \n",
       "3     automatic  pickup         NaN     0.0  2019-03-22            9  \n",
       "8     automatic     SUV         NaN     1.0  2018-07-16           19  \n",
       "12    automatic  pickup         NaN     1.0  2019-04-02           37  \n",
       "21    automatic     SUV         NaN     0.0  2018-08-22            8  \n",
       "25    automatic   sedan         NaN     0.0  2018-07-26           43  \n",
       "31    automatic   sedan         NaN     0.0  2018-09-13           25  \n",
       "34    automatic     SUV         NaN     0.0  2018-06-04          121  \n",
       "35    automatic   sedan         NaN     0.0  2018-05-11           37  \n",
       "36    automatic   sedan         NaN     0.0  2018-05-05           22  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualização da coluna paint_color com valores ausentes\n",
    "df.query('paint_color.isna()').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>paint_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>10029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>black</td>\n",
       "      <td>7692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>silver</td>\n",
       "      <td>6244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grey</td>\n",
       "      <td>5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>blue</td>\n",
       "      <td>4475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>red</td>\n",
       "      <td>4421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>green</td>\n",
       "      <td>1396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>brown</td>\n",
       "      <td>1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>custom</td>\n",
       "      <td>1153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>yellow</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>orange</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>purple</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  paint_color\n",
       "0    white        10029\n",
       "1      NaN         9267\n",
       "2    black         7692\n",
       "3   silver         6244\n",
       "4     grey         5037\n",
       "5     blue         4475\n",
       "6      red         4421\n",
       "7    green         1396\n",
       "8    brown         1223\n",
       "9   custom         1153\n",
       "10  yellow          255\n",
       "11  orange          231\n",
       "12  purple          102"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Value_counts na coluna paint_color\n",
    "display(df['paint_color'].value_counts(dropna=False).reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna **`paint_color`** representa a cor do veículo. Possui os valores ausentes como a segunda categoria com mais comum e não parece ter relação aos valores ausentes das outras colunas. Intuitivamente a cor do carro deve possuir relação com o modelo, com cada modelo possuindo uma cor mais frequente. Sendo assim, para não deletar os dados foi decidido por preencher essa a coluna **`paint_color`** utilizando a moda, maior frequência absoluta, da coluna **`model`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>paint_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>13835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>black</td>\n",
       "      <td>9897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>silver</td>\n",
       "      <td>7998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grey</td>\n",
       "      <td>5843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blue</td>\n",
       "      <td>4729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>red</td>\n",
       "      <td>4705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>green</td>\n",
       "      <td>1554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>brown</td>\n",
       "      <td>1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>custom</td>\n",
       "      <td>1153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>yellow</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>orange</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>purple</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  paint_color\n",
       "0    white        13835\n",
       "1    black         9897\n",
       "2   silver         7998\n",
       "3     grey         5843\n",
       "4     blue         4729\n",
       "5      red         4705\n",
       "6    green         1554\n",
       "7    brown         1223\n",
       "8   custom         1153\n",
       "9   yellow          255\n",
       "10  orange          231\n",
       "11  purple          102"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de valores ausentes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['paint_color'].fillna( df.groupby(['model'])['paint_color'].transform(lambda x : x.mode()[0]), inplace = True)\n",
    "\n",
    "display(df['paint_color'].value_counts(dropna=False).reset_index())\n",
    "\n",
    "print('\\nQuantidade de valores ausentes:')\n",
    "display(df['paint_color'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valores ausentes da coluna **`paint_color`** foram substituídos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigação da coluna **`cylinders`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9200</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>honda pilot</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>147191.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10499</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>chrysler 300</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>88042.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>7500</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>toyota tacoma</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>5200</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>toyota highlander</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>green</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-12-20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>30000</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>ford mustang</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>manual</td>\n",
       "      <td>convertible</td>\n",
       "      <td>red</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>subaru impreza</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>74000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>blue</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-08-07</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6950</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>chevrolet tahoe</td>\n",
       "      <td>excellent</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>186021.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>black</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-10-30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8499</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>bmw x5</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>104482.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>14800</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>kia sorento</td>\n",
       "      <td>like new</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>brown</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-02-27</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>25900</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>chevrolet silverado 1500 crew</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     price  model_year                          model  condition  cylinders  \\\n",
       "9     9200      2008.0                    honda pilot  excellent        NaN   \n",
       "36   10499      2013.0                   chrysler 300       good        NaN   \n",
       "37    7500      2005.0                  toyota tacoma       good        NaN   \n",
       "59    5200      2006.0              toyota highlander       good        NaN   \n",
       "63   30000      1966.0                   ford mustang  excellent        NaN   \n",
       "72    3650         NaN                 subaru impreza  excellent        NaN   \n",
       "75    6950      2005.0                chevrolet tahoe  excellent        NaN   \n",
       "87    8499      2009.0                         bmw x5       good        NaN   \n",
       "119  14800      2016.0                    kia sorento   like new        NaN   \n",
       "126  25900      2012.0  chevrolet silverado 1500 crew       good        NaN   \n",
       "\n",
       "      fuel  odometer transmission         type paint_color  is_4wd  \\\n",
       "9      gas  147191.0    automatic          SUV        blue     1.0   \n",
       "36     gas   88042.0    automatic        sedan       black     0.0   \n",
       "37     gas  160000.0    automatic       pickup       white     0.0   \n",
       "59     gas  186000.0    automatic          SUV       green     0.0   \n",
       "63     gas   51000.0       manual  convertible         red     0.0   \n",
       "72     gas   74000.0    automatic        sedan        blue     1.0   \n",
       "75     gas  186021.0    automatic          SUV       black     1.0   \n",
       "87     gas  104482.0    automatic          SUV        blue     1.0   \n",
       "119    gas       NaN    automatic          SUV       brown     0.0   \n",
       "126  other       NaN        other       pickup       white     1.0   \n",
       "\n",
       "    date_posted  days_listed  \n",
       "9    2019-02-15           17  \n",
       "36   2018-05-05           22  \n",
       "37   2018-07-22           44  \n",
       "59   2018-12-20            2  \n",
       "63   2019-01-23           17  \n",
       "72   2018-08-07           60  \n",
       "75   2018-10-30            3  \n",
       "87   2019-03-04           52  \n",
       "119  2019-02-27           93  \n",
       "126  2018-08-31           44  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualização da coluna cylinders com valores ausentes\n",
    "df.query('cylinders.isna()').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>cylinders</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>12.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</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>acura tl</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw x5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buick enclave</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cadillac escalade</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet camaro</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota sienna</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota tacoma</th>\n",
       "      <td>NaN</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota tundra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen jetta</th>\n",
       "      <td>NaN</td>\n",
       "      <td>339.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen passat</th>\n",
       "      <td>NaN</td>\n",
       "      <td>243.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "cylinders          3.0    4.0    5.0    6.0    8.0   10.0  12.0\n",
       "model                                                          \n",
       "acura tl            NaN    3.0    NaN  208.0    NaN   NaN   NaN\n",
       "bmw x5              NaN    NaN    NaN  200.0   51.0   NaN   NaN\n",
       "buick enclave       NaN    1.0    NaN  245.0    1.0   NaN   NaN\n",
       "cadillac escalade   NaN    NaN    NaN    3.0  290.0   1.0   NaN\n",
       "chevrolet camaro    NaN    9.0    NaN  223.0  106.0  34.0   NaN\n",
       "...                 ...    ...    ...    ...    ...   ...   ...\n",
       "toyota sienna       NaN    8.0    NaN  285.0    1.0   NaN   NaN\n",
       "toyota tacoma       NaN  240.0    2.0  486.0    2.0   NaN   NaN\n",
       "toyota tundra       NaN    1.0    NaN   33.0  500.0  13.0   NaN\n",
       "volkswagen jetta    NaN  339.0  123.0    4.0    NaN   NaN   NaN\n",
       "volkswagen passat   NaN  243.0   43.0   30.0    NaN   NaN   NaN\n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='model',values='price',columns='cylinders',aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modelos de carros podem possuir mais de uma quantidade de cilindros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Não foi encontrado uma causa para a existência dos valores ausentes. Para substituir os valores será usado a coluna **`model`**, pois a quantidade de cilindros depende do modelo do carro, mas um mesmo modelo pode possuir quantidades diferentes de cilindros, então será usada a moda para substituir. A mediana não pode ser usada, pois, se o modelo de carro possuir duas quantidades de cilindros a mediana será um número irreal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de valores ausentes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['cylinders'].fillna( df.groupby(['model'])['cylinders'].transform(lambda x : x.mode()[0]), inplace = True)\n",
    "\n",
    "print('\\nQuantidade de valores ausentes:')\n",
    "display(df['cylinders'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valores ausentes da coluna **`cylinders`** foram substituídos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigação da coluna **`odometer`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17990</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>ram 1500</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>red</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-05-15</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7500</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>jeep wrangler</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>red</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-05-17</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3950</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>chrysler 200</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-11</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11499</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>chevrolet malibu</td>\n",
       "      <td>like new</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>silver</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-07-26</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5495</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet impala</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>white</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-02-18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>7999</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>chrysler 200</td>\n",
       "      <td>good</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-03-08</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>11995</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>nissan rogue</td>\n",
       "      <td>like new</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>silver</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>34900</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>mercedes-benz benze sprinter 2500</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.0</td>\n",
       "      <td>diesel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>van</td>\n",
       "      <td>black</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4200</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>jeep grand cherokee</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  model_year                              model  condition  \\\n",
       "3    1500      2003.0                         ford f-150       fair   \n",
       "15  17990      2013.0                           ram 1500  excellent   \n",
       "23   7500      2004.0                      jeep wrangler       good   \n",
       "24   3950      2009.0                       chrysler 200  excellent   \n",
       "25  11499      2017.0                   chevrolet malibu   like new   \n",
       "29   5495      2010.0                   chevrolet impala  excellent   \n",
       "38   7999      2015.0                       chrysler 200       good   \n",
       "41  11995      2017.0                       nissan rogue   like new   \n",
       "42  34900      2013.0  mercedes-benz benze sprinter 2500  excellent   \n",
       "56   4200      2006.0                jeep grand cherokee  excellent   \n",
       "\n",
       "    cylinders    fuel  odometer transmission    type paint_color  is_4wd  \\\n",
       "3         8.0     gas       NaN    automatic  pickup       white     0.0   \n",
       "15        8.0     gas       NaN    automatic  pickup         red     1.0   \n",
       "23        6.0     gas       NaN    automatic     SUV         red     1.0   \n",
       "24        4.0     gas       NaN    automatic   sedan         red     0.0   \n",
       "25        4.0     gas       NaN    automatic   sedan      silver     0.0   \n",
       "29        6.0     gas       NaN    automatic   sedan       white     0.0   \n",
       "38        4.0     gas       NaN    automatic   sedan       black     0.0   \n",
       "41        4.0     gas       NaN    automatic     SUV      silver     1.0   \n",
       "42        6.0  diesel       NaN    automatic     van       black     0.0   \n",
       "56        6.0     gas       NaN    automatic     SUV       white     1.0   \n",
       "\n",
       "   date_posted  days_listed  \n",
       "3   2019-03-22            9  \n",
       "15  2018-05-15          111  \n",
       "23  2018-05-17           39  \n",
       "24  2018-06-11           40  \n",
       "25  2018-07-26           43  \n",
       "29  2019-02-18           20  \n",
       "38  2019-03-08           29  \n",
       "41  2018-09-20           21  \n",
       "42  2019-01-15           16  \n",
       "56  2018-07-01           79  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualização da coluna cylinders com valores ausentes\n",
    "df.query('odometer.isna()').head(10)"
   ]
  },
  {
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>odometer</th>\n",
       "      <th>odometer</th>\n",
       "      <th>odometer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>excellent</th>\n",
       "      <td>106687.682258</td>\n",
       "      <td>104230.0</td>\n",
       "      <td>20973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>183062.233577</td>\n",
       "      <td>181613.0</td>\n",
       "      <td>1370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>129919.930980</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>17053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like new</th>\n",
       "      <td>79426.738166</td>\n",
       "      <td>72982.5</td>\n",
       "      <td>4014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>43476.056000</td>\n",
       "      <td>8002.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salvage</th>\n",
       "      <td>140919.989796</td>\n",
       "      <td>142673.5</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    mean    median    count\n",
       "                odometer  odometer odometer\n",
       "condition                                  \n",
       "excellent  106687.682258  104230.0    20973\n",
       "fair       183062.233577  181613.0     1370\n",
       "good       129919.930980  129000.0    17053\n",
       "like new    79426.738166   72982.5     4014\n",
       "new         43476.056000    8002.0      125\n",
       "salvage    140919.989796  142673.5       98"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabela entre coluna condition e odometer\n",
    "df.pivot_table(index='condition',values='odometer',aggfunc=['mean','median','count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Será usada a mediana de **`odometer`** em relação as categorias da coluna **`condition`** para substituir os valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de valores ausentes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subst = df.groupby('condition')['odometer'].transform('median')\n",
    "df['odometer'].fillna(subst, inplace=True)\n",
    "\n",
    "print('\\nQuantidade de valores ausentes:')\n",
    "display(df['odometer'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valores ausentes da coluna **`paint_color`** foram substituídos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investigação da coluna **`model_year`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>88705.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chevrolet tahoe</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>147485.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>silver</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>12800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>108500.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>7800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>like new</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>97510.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>truck</td>\n",
       "      <td>white</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>subaru impreza</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>74000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>blue</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-08-07</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hyundai elantra</td>\n",
       "      <td>like new</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>151223.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>custom</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-09-15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>10800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chevrolet colorado</td>\n",
       "      <td>excellent</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>114922.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>truck</td>\n",
       "      <td>red</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>23700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nissan frontier crew cab sv</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>12901.0</td>\n",
       "      <td>other</td>\n",
       "      <td>pickup</td>\n",
       "      <td>black</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018-09-21</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>25300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chevrolet camaro lt coupe 2d</td>\n",
       "      <td>good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>3568.0</td>\n",
       "      <td>other</td>\n",
       "      <td>coupe</td>\n",
       "      <td>black</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-06-16</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>8995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chevrolet silverado 1500</td>\n",
       "      <td>excellent</td>\n",
       "      <td>8.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>119726.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>truck</td>\n",
       "      <td>grey</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-03-18</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     price  model_year                         model  condition  cylinders  \\\n",
       "1    25500         NaN                    ford f-150       good        6.0   \n",
       "20    6990         NaN               chevrolet tahoe  excellent        8.0   \n",
       "65   12800         NaN                    ford f-150  excellent        6.0   \n",
       "69    7800         NaN                    ford f-150   like new        8.0   \n",
       "72    3650         NaN                subaru impreza  excellent        4.0   \n",
       "84    4995         NaN               hyundai elantra   like new        4.0   \n",
       "102  10800         NaN            chevrolet colorado  excellent        5.0   \n",
       "114  23700         NaN   nissan frontier crew cab sv       good        6.0   \n",
       "116  25300         NaN  chevrolet camaro lt coupe 2d       good        6.0   \n",
       "144   8995         NaN      chevrolet silverado 1500  excellent        8.0   \n",
       "\n",
       "    fuel  odometer transmission    type paint_color  is_4wd date_posted  \\\n",
       "1    gas   88705.0    automatic  pickup       white     1.0  2018-10-19   \n",
       "20   gas  147485.0    automatic     SUV      silver     1.0  2018-08-05   \n",
       "65   gas  108500.0    automatic  pickup       white     0.0  2018-09-23   \n",
       "69   gas   97510.0    automatic   truck       white     1.0  2019-02-20   \n",
       "72   gas   74000.0    automatic   sedan        blue     1.0  2018-08-07   \n",
       "84   gas  151223.0    automatic   sedan      custom     0.0  2018-09-15   \n",
       "102  gas  114922.0    automatic   truck         red     1.0  2018-06-23   \n",
       "114  gas   12901.0        other  pickup       black     1.0  2018-09-21   \n",
       "116  gas    3568.0        other   coupe       black     0.0  2018-06-16   \n",
       "144  gas  119726.0    automatic   truck        grey     1.0  2019-03-18   \n",
       "\n",
       "     days_listed  \n",
       "1             50  \n",
       "20            28  \n",
       "65            15  \n",
       "69            39  \n",
       "72            60  \n",
       "84             1  \n",
       "102           35  \n",
       "114            8  \n",
       "116           34  \n",
       "144           27  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualização da coluna model_year com valores ausentes\n",
    "df.query('model_year.isna()').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>model_year</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acura tl</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw x5</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buick enclave</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cadillac escalade</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet camaro</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota sienna</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota tacoma</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota tundra</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen jetta</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen passat</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      median      count\n",
       "                  model_year model_year\n",
       "model                                  \n",
       "acura tl              2007.0        224\n",
       "bmw x5                2010.0        246\n",
       "buick enclave         2012.0        257\n",
       "cadillac escalade     2009.0        295\n",
       "chevrolet camaro      2013.0        392\n",
       "...                      ...        ...\n",
       "toyota sienna         2008.0        308\n",
       "toyota tacoma         2010.0        769\n",
       "toyota tundra         2009.0        568\n",
       "volkswagen jetta      2012.0        485\n",
       "volkswagen passat     2013.0        324\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tabela entre coluna model e model_year\n",
    "df.pivot_table(index='model',values='model_year',aggfunc=['median','count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Será usada a moda de **`model`** em relação a as categorias da coluna **`model_year`** para substituir os valores ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Quantidade de valores ausentes:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subst = df.groupby('model')['model_year'].transform(lambda x : x.mode()[0])\n",
    "df['model_year'].fillna(subst, inplace=True)\n",
    "\n",
    "print('\\nQuantidade de valores ausentes:')\n",
    "display(df['model_year'].isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Valores ausentes da coluna **`model_year`** foram substituídos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price           0\n",
       "model_year      0\n",
       "model           0\n",
       "condition       0\n",
       "cylinders       0\n",
       "fuel            0\n",
       "odometer        0\n",
       "transmission    0\n",
       "type            0\n",
       "paint_color     0\n",
       "is_4wd          0\n",
       "date_posted     0\n",
       "days_listed     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conferindo se ainda existem valores ausentes\n",
    "df.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Todos os valores ausentes foram corrigidos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correção das duplicatas implícitas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na coluna **`model`** existem duplicatas implícitas que precisam ser corrigidas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bmw x5', 'ford f-150', 'hyundai sonata', 'chrysler 200',\n",
       "       'chrysler 300', 'toyota camry', 'honda pilot', 'kia sorento',\n",
       "       'chevrolet silverado 1500', 'honda accord', 'ram 1500',\n",
       "       'gmc yukon', 'jeep cherokee', 'chevrolet traverse',\n",
       "       'hyundai elantra', 'chevrolet tahoe', 'toyota rav4',\n",
       "       'chevrolet silverado', 'jeep wrangler', 'chevrolet malibu',\n",
       "       'ford fusion se', 'chevrolet impala', 'chevrolet corvette',\n",
       "       'jeep liberty', 'toyota camry le', 'nissan altima',\n",
       "       'subaru outback', 'toyota highlander', 'dodge charger',\n",
       "       'toyota tacoma', 'chevrolet equinox', 'nissan rogue',\n",
       "       'mercedes-benz benze sprinter 2500', 'honda cr-v',\n",
       "       'jeep grand cherokee', 'toyota 4runner', 'ford focus',\n",
       "       'honda civic', 'kia soul', 'chevrolet colorado',\n",
       "       'ford f150 supercrew cab xlt', 'chevrolet camaro lt coupe 2d',\n",
       "       'chevrolet cruze', 'ford mustang', 'chevrolet silverado 3500hd',\n",
       "       'nissan frontier crew cab sv', 'subaru impreza',\n",
       "       'jeep grand cherokee laredo', 'nissan versa', 'ford f-250 sd',\n",
       "       'chevrolet silverado 1500 crew', 'ford f250 super duty',\n",
       "       'chevrolet camaro', 'ford mustang gt coupe 2d', 'subaru forester',\n",
       "       'ford explorer', 'ford f-350 sd', 'ford edge', 'nissan maxima',\n",
       "       'ford f-250', 'nissan sentra', 'ford f150', 'chevrolet suburban',\n",
       "       'ford expedition', 'dodge grand caravan', 'ford taurus',\n",
       "       'acura tl', 'ford f350 super duty', 'ford ranger', 'gmc sierra',\n",
       "       'hyundai santa fe', 'ford escape', 'gmc sierra 2500hd',\n",
       "       'honda civic lx', 'gmc sierra 1500', 'honda odyssey',\n",
       "       'cadillac escalade', 'volkswagen jetta', 'toyota corolla',\n",
       "       'chrysler town & country', 'volkswagen passat', 'toyota prius',\n",
       "       'buick enclave', 'ford fusion', 'toyota tundra', 'ram 3500',\n",
       "       'ram 2500', 'nissan frontier', 'chevrolet silverado 2500hd',\n",
       "       'jeep wrangler unlimited', 'ford f-250 super duty', 'gmc acadia',\n",
       "       'toyota sienna', 'ford focus se', 'ford f250', 'dodge dakota',\n",
       "       'ford f350', 'chevrolet trailblazer', 'ford econoline',\n",
       "       'nissan murano'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contando quantidade de valores unicos\n",
    "len(df['model'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna **`model`** apresenta duplicatas implícitas em carros da marca ford, modelos f-150, f-250 e f-350. As variações possuem sem traço, ou \"super duty\"( todos os modelos \"f\" são super duty) ou sd. Suas varifações são:\n",
    "- ford f-150- f150;\n",
    "- ford f-250- f250, f-250 sd, f-250 super duty, f250 super duty;\n",
    "- ford f-350- f350, f-350 sd, f350 super duty, f350 sd."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['model'] = df['model'].replace(['ford f250 super duty','ford f-250 super duty', 'ford f-250 sd','ford f250 sd','ford f250'], 'ford f-250')\n",
    "df['model'] = df['model'].replace(['ford f350 super duty','ford f-350 sd','ford f350 sd','ford f350'], 'ford f-350')\n",
    "df['model'] = df['model'].replace('ford f150', 'ford f-150')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bmw x5', 'ford f-150', 'hyundai sonata', 'chrysler 200',\n",
       "       'chrysler 300', 'toyota camry', 'honda pilot', 'kia sorento',\n",
       "       'chevrolet silverado 1500', 'honda accord', 'ram 1500',\n",
       "       'gmc yukon', 'jeep cherokee', 'chevrolet traverse',\n",
       "       'hyundai elantra', 'chevrolet tahoe', 'toyota rav4',\n",
       "       'chevrolet silverado', 'jeep wrangler', 'chevrolet malibu',\n",
       "       'ford fusion se', 'chevrolet impala', 'chevrolet corvette',\n",
       "       'jeep liberty', 'toyota camry le', 'nissan altima',\n",
       "       'subaru outback', 'toyota highlander', 'dodge charger',\n",
       "       'toyota tacoma', 'chevrolet equinox', 'nissan rogue',\n",
       "       'mercedes-benz benze sprinter 2500', 'honda cr-v',\n",
       "       'jeep grand cherokee', 'toyota 4runner', 'ford focus',\n",
       "       'honda civic', 'kia soul', 'chevrolet colorado',\n",
       "       'ford f150 supercrew cab xlt', 'chevrolet camaro lt coupe 2d',\n",
       "       'chevrolet cruze', 'ford mustang', 'chevrolet silverado 3500hd',\n",
       "       'nissan frontier crew cab sv', 'subaru impreza',\n",
       "       'jeep grand cherokee laredo', 'nissan versa', 'ford f-250',\n",
       "       'chevrolet silverado 1500 crew', 'chevrolet camaro',\n",
       "       'ford mustang gt coupe 2d', 'subaru forester', 'ford explorer',\n",
       "       'ford f-350', 'ford edge', 'nissan maxima', 'nissan sentra',\n",
       "       'chevrolet suburban', 'ford expedition', 'dodge grand caravan',\n",
       "       'ford taurus', 'acura tl', 'ford ranger', 'gmc sierra',\n",
       "       'hyundai santa fe', 'ford escape', 'gmc sierra 2500hd',\n",
       "       'honda civic lx', 'gmc sierra 1500', 'honda odyssey',\n",
       "       'cadillac escalade', 'volkswagen jetta', 'toyota corolla',\n",
       "       'chrysler town & country', 'volkswagen passat', 'toyota prius',\n",
       "       'buick enclave', 'ford fusion', 'toyota tundra', 'ram 3500',\n",
       "       'ram 2500', 'nissan frontier', 'chevrolet silverado 2500hd',\n",
       "       'jeep wrangler unlimited', 'gmc acadia', 'toyota sienna',\n",
       "       'ford focus se', 'dodge dakota', 'chevrolet trailblazer',\n",
       "       'ford econoline', 'nissan murano'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['model'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Contando quantidade de valores unicos\n",
    "len(df['model'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As duplicatas implícitas foram substituídas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correção dos tipos de dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os tipos de dados das colunas **`model_year`**, **`date_posted`**, **`is_4wd`**  e  **`cylinders`** serão corrigidos:\n",
    "- As colunas **`is_4wd`**, **`model_year`**  e **`cylinders`** são *float64*, devem ser *int64*.\n",
    "- A coluna **`date_posted`** é *object*, deve ser *datatime*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_4wd        int64\n",
      "cylinders     int64\n",
      "model_year    int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Transformando as colunas em int\n",
    "df[['is_4wd','cylinders','model_year']] = df[['is_4wd','cylinders','model_year']].astype(int)\n",
    "\n",
    "print(df[['is_4wd','cylinders','model_year']].dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2018-06-23'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualização da coluna date_posted\n",
    "df['date_posted'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "datetime64[ns]\n"
     ]
    }
   ],
   "source": [
    "# Transformando as colunas em datatime\n",
    "df['date_posted']= pd.to_datetime(df['date_posted'], format='%Y-%m-%d')\n",
    "\n",
    "print(df['date_posted'].dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enriqueça os dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para melhorar a análise serão adicionados quatro colunas, uma para o dia em que a propaganda foi postada, uma para a idade do veículo, uma para a quilometragem por ano, e uma para representar a coluna de condição por através de números."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9400</td>\n",
       "      <td>2011</td>\n",
       "      <td>bmw x5</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>black</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25500</td>\n",
       "      <td>2013</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>88705.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5500</td>\n",
       "      <td>2013</td>\n",
       "      <td>hyundai sonata</td>\n",
       "      <td>like new</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>2003</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>fair</td>\n",
       "      <td>8</td>\n",
       "      <td>gas</td>\n",
       "      <td>181613.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14900</td>\n",
       "      <td>2017</td>\n",
       "      <td>chrysler 200</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>80903.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  model_year           model  condition  cylinders fuel  odometer  \\\n",
       "0   9400        2011          bmw x5       good          6  gas  145000.0   \n",
       "1  25500        2013      ford f-150       good          6  gas   88705.0   \n",
       "2   5500        2013  hyundai sonata   like new          4  gas  110000.0   \n",
       "3   1500        2003      ford f-150       fair          8  gas  181613.0   \n",
       "4  14900        2017    chrysler 200  excellent          4  gas   80903.0   \n",
       "\n",
       "  transmission    type paint_color  is_4wd date_posted  days_listed  \n",
       "0    automatic     SUV       black       1  2018-06-23           19  \n",
       "1    automatic  pickup       white       1  2018-10-19           50  \n",
       "2    automatic   sedan         red       0  2019-02-07           79  \n",
       "3    automatic  pickup       white       0  2019-03-22            9  \n",
       "4    automatic   sedan       black       0  2019-04-02           28  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amostragem dos dados\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criação da coluna **`day_posted`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Faixas de day_posted dos veiculos:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.0, 3.0]       14809\n",
       "(3.0, 5.0]       14726\n",
       "(-0.001, 1.0]    14634\n",
       "(5.0, 6.0]        7356\n",
       "Name: day_posted, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Valores data/hora para quando a propaganda estiver colocada\n",
    "df['day_posted'] = df['date_posted'].dt.weekday\n",
    "\n",
    "# Faixas de day_posted dos veiculos\n",
    "print('Faixas de day_posted dos veiculos:')\n",
    "display(pd.qcut(df['day_posted'], q = 4).value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criação da coluna **`age`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Faixas de idade dos veiculos:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.11, 22.0]    50464\n",
       "(22.0, 44.0]       931\n",
       "(44.0, 66.0]       124\n",
       "(66.0, 88.0]         3\n",
       "(88.0, 110.0]        3\n",
       "Name: age, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Idade do veículo quando a propaganda for colocada\n",
    "df['age'] = pd.DatetimeIndex(df['date_posted']).year - df['model_year'] \n",
    "\n",
    "# Faixas de idade dos veiculos\n",
    "print('Faixas de idade dos veiculos:')\n",
    "display(pd.cut(df['age'], bins = 5).value_counts())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criação da coluna **`odo_year`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quilometragem média por ano\n",
    "df['odo_year'] = df['odometer'] / df['age']\n",
    "\n",
    "# Média de quilometragem média por ano\n",
    "df['odo_year'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2178"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('age == 0'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A coluna **`odo_year`** teve um problema, como a coluna **`age`** possui alguns valores zero, essas varíaveis não tiverem um número real como resultado. Será criada uma função para corrigir, os carros com menos de um ano terão a quilometragem por ano igual a quilometragem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Função para coorigir coluna odo_year\n",
    "def calc_odo_year(data):\n",
    "    if data['age'] == 0:\n",
    "        return data['odometer']\n",
    "    return data['odo_year']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['odo_year'] = df.apply(calc_odo_year,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19436.079495493916"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Média de quilometragem média por ano\n",
    "df['odo_year'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criação da coluna **`condition_index`**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "excellent    24773\n",
       "good         20145\n",
       "like new      4742\n",
       "fair          1607\n",
       "new            143\n",
       "salvage        115\n",
       "Name: condition, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def categ_condition(data):\n",
    "    if 'salvage' in data['condition']:\n",
    "        return 0\n",
    "    if 'fair' in data['condition']:\n",
    "        return 1\n",
    "    if 'good' in data['condition']:\n",
    "        return 2\n",
    "    if 'excellent' in data['condition']:\n",
    "        return 3\n",
    "    if 'like new' in data['condition']:\n",
    "        return 4\n",
    "    if 'new' in data['condition']:\n",
    "        return 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3    24773\n",
      "2    20145\n",
      "4     4742\n",
      "1     1607\n",
      "5      143\n",
      "0      115\n",
      "Name: condition_index, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df['condition_index'] = df.apply(categ_condition,axis=1)\n",
    "\n",
    "print(df['condition_index'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verifique os dados limpos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados estão prontos para análise. Uma última olhada para encontrar quais variáveis serão úteis para a análise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51525 entries, 0 to 51524\n",
      "Data columns (total 17 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   price            51525 non-null  int64         \n",
      " 1   model_year       51525 non-null  int64         \n",
      " 2   model            51525 non-null  object        \n",
      " 3   condition        51525 non-null  object        \n",
      " 4   cylinders        51525 non-null  int64         \n",
      " 5   fuel             51525 non-null  object        \n",
      " 6   odometer         51525 non-null  float64       \n",
      " 7   transmission     51525 non-null  object        \n",
      " 8   type             51525 non-null  object        \n",
      " 9   paint_color      51525 non-null  object        \n",
      " 10  is_4wd           51525 non-null  int64         \n",
      " 11  date_posted      51525 non-null  datetime64[ns]\n",
      " 12  days_listed      51525 non-null  int64         \n",
      " 13  day_posted       51525 non-null  int64         \n",
      " 14  age              51525 non-null  int64         \n",
      " 15  odo_year         51525 non-null  float64       \n",
      " 16  condition_index  51525 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(8), object(6)\n",
      "memory usage: 6.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>odometer</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>age</th>\n",
       "      <th>odo_year</th>\n",
       "      <th>condition_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.00000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "      <td>51525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12132.464920</td>\n",
       "      <td>2009.871033</td>\n",
       "      <td>6.121494</td>\n",
       "      <td>115199.014508</td>\n",
       "      <td>0.496303</td>\n",
       "      <td>39.55476</td>\n",
       "      <td>3.005434</td>\n",
       "      <td>8.436429</td>\n",
       "      <td>19436.079495</td>\n",
       "      <td>2.637535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10040.803015</td>\n",
       "      <td>6.145084</td>\n",
       "      <td>1.657296</td>\n",
       "      <td>60484.863376</td>\n",
       "      <td>0.499991</td>\n",
       "      <td>28.20427</td>\n",
       "      <td>1.997759</td>\n",
       "      <td>6.158509</td>\n",
       "      <td>17040.199641</td>\n",
       "      <td>0.712447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1908.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75668.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10426.071429</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9000.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>110908.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15126.545455</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16839.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>147821.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>53.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>22428.571429</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375000.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>990000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>271.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>373200.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               price    model_year     cylinders       odometer        is_4wd  \\\n",
       "count   51525.000000  51525.000000  51525.000000   51525.000000  51525.000000   \n",
       "mean    12132.464920   2009.871033      6.121494  115199.014508      0.496303   \n",
       "std     10040.803015      6.145084      1.657296   60484.863376      0.499991   \n",
       "min         1.000000   1908.000000      3.000000       0.000000      0.000000   \n",
       "25%      5000.000000   2007.000000      4.000000   75668.000000      0.000000   \n",
       "50%      9000.000000   2011.000000      6.000000  110908.000000      0.000000   \n",
       "75%     16839.000000   2014.000000      8.000000  147821.000000      1.000000   \n",
       "max    375000.000000   2019.000000     12.000000  990000.000000      1.000000   \n",
       "\n",
       "       days_listed    day_posted           age       odo_year  condition_index  \n",
       "count  51525.00000  51525.000000  51525.000000   51525.000000     51525.000000  \n",
       "mean      39.55476      3.005434      8.436429   19436.079495         2.637535  \n",
       "std       28.20427      1.997759      6.158509   17040.199641         0.712447  \n",
       "min        0.00000      0.000000      0.000000       0.000000         0.000000  \n",
       "25%       19.00000      1.000000      4.000000   10426.071429         2.000000  \n",
       "50%       33.00000      3.000000      7.000000   15126.545455         3.000000  \n",
       "75%       53.00000      5.000000     12.000000   22428.571429         3.000000  \n",
       "max      271.00000      6.000000    110.000000  373200.000000         5.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Informações gerais/resumidas sobre o DataFrame\n",
    "display(df.info())\n",
    "\n",
    "# Informações quantitativas sobre o DataFrame\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>age</th>\n",
       "      <th>odo_year</th>\n",
       "      <th>condition_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9400</td>\n",
       "      <td>2011</td>\n",
       "      <td>bmw x5</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>145000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>black</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-06-23</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>20714.285714</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25500</td>\n",
       "      <td>2013</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>good</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>88705.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>17741.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5500</td>\n",
       "      <td>2013</td>\n",
       "      <td>hyundai sonata</td>\n",
       "      <td>like new</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>red</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>18333.333333</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1500</td>\n",
       "      <td>2003</td>\n",
       "      <td>ford f-150</td>\n",
       "      <td>fair</td>\n",
       "      <td>8</td>\n",
       "      <td>gas</td>\n",
       "      <td>181613.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>pickup</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-03-22</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>11350.812500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14900</td>\n",
       "      <td>2017</td>\n",
       "      <td>chrysler 200</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>80903.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-04-02</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>40451.500000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14990</td>\n",
       "      <td>2014</td>\n",
       "      <td>chrysler 300</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>57954.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-06-20</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>14488.500000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12990</td>\n",
       "      <td>2015</td>\n",
       "      <td>toyota camry</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>79212.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>73</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26404.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15990</td>\n",
       "      <td>2013</td>\n",
       "      <td>honda pilot</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>109473.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>black</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>18245.500000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11500</td>\n",
       "      <td>2012</td>\n",
       "      <td>kia sorento</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>104174.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>silver</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-07-16</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>17362.333333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9200</td>\n",
       "      <td>2008</td>\n",
       "      <td>honda pilot</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>147191.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-02-15</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>13381.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  model_year           model  condition  cylinders fuel  odometer  \\\n",
       "0   9400        2011          bmw x5       good          6  gas  145000.0   \n",
       "1  25500        2013      ford f-150       good          6  gas   88705.0   \n",
       "2   5500        2013  hyundai sonata   like new          4  gas  110000.0   \n",
       "3   1500        2003      ford f-150       fair          8  gas  181613.0   \n",
       "4  14900        2017    chrysler 200  excellent          4  gas   80903.0   \n",
       "5  14990        2014    chrysler 300  excellent          6  gas   57954.0   \n",
       "6  12990        2015    toyota camry  excellent          4  gas   79212.0   \n",
       "7  15990        2013     honda pilot  excellent          6  gas  109473.0   \n",
       "8  11500        2012     kia sorento  excellent          4  gas  104174.0   \n",
       "9   9200        2008     honda pilot  excellent          6  gas  147191.0   \n",
       "\n",
       "  transmission    type paint_color  is_4wd date_posted  days_listed  \\\n",
       "0    automatic     SUV       black       1  2018-06-23           19   \n",
       "1    automatic  pickup       white       1  2018-10-19           50   \n",
       "2    automatic   sedan         red       0  2019-02-07           79   \n",
       "3    automatic  pickup       white       0  2019-03-22            9   \n",
       "4    automatic   sedan       black       0  2019-04-02           28   \n",
       "5    automatic   sedan       black       1  2018-06-20           15   \n",
       "6    automatic   sedan       white       0  2018-12-27           73   \n",
       "7    automatic     SUV       black       1  2019-01-07           68   \n",
       "8    automatic     SUV      silver       1  2018-07-16           19   \n",
       "9    automatic     SUV        blue       1  2019-02-15           17   \n",
       "\n",
       "   day_posted  age      odo_year  condition_index  \n",
       "0           5    7  20714.285714                2  \n",
       "1           4    5  17741.000000                2  \n",
       "2           3    6  18333.333333                4  \n",
       "3           4   16  11350.812500                1  \n",
       "4           1    2  40451.500000                3  \n",
       "5           2    4  14488.500000                3  \n",
       "6           3    3  26404.000000                3  \n",
       "7           0    6  18245.500000                3  \n",
       "8           0    6  17362.333333                3  \n",
       "9           4   11  13381.000000                3  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amostragem dos dados\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>transmission</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>date_posted</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>age</th>\n",
       "      <th>odo_year</th>\n",
       "      <th>condition_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51520</th>\n",
       "      <td>9249</td>\n",
       "      <td>2013</td>\n",
       "      <td>nissan maxima</td>\n",
       "      <td>like new</td>\n",
       "      <td>6</td>\n",
       "      <td>gas</td>\n",
       "      <td>88136.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>17627.200000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51521</th>\n",
       "      <td>2700</td>\n",
       "      <td>2002</td>\n",
       "      <td>honda civic</td>\n",
       "      <td>salvage</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>181500.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>white</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-11-14</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>11343.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51522</th>\n",
       "      <td>3950</td>\n",
       "      <td>2009</td>\n",
       "      <td>hyundai sonata</td>\n",
       "      <td>excellent</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>blue</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-11-15</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>14222.222222</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51523</th>\n",
       "      <td>7455</td>\n",
       "      <td>2013</td>\n",
       "      <td>toyota corolla</td>\n",
       "      <td>good</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>139573.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>black</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-07-02</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27914.600000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51524</th>\n",
       "      <td>6300</td>\n",
       "      <td>2014</td>\n",
       "      <td>nissan altima</td>\n",
       "      <td>good</td>\n",
       "      <td>4</td>\n",
       "      <td>gas</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>sedan</td>\n",
       "      <td>grey</td>\n",
       "      <td>0</td>\n",
       "      <td>2018-06-05</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>32250.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  model_year           model  condition  cylinders fuel  odometer  \\\n",
       "51520   9249        2013   nissan maxima   like new          6  gas   88136.0   \n",
       "51521   2700        2002     honda civic    salvage          4  gas  181500.0   \n",
       "51522   3950        2009  hyundai sonata  excellent          4  gas  128000.0   \n",
       "51523   7455        2013  toyota corolla       good          4  gas  139573.0   \n",
       "51524   6300        2014   nissan altima       good          4  gas  129000.0   \n",
       "\n",
       "      transmission   type paint_color  is_4wd date_posted  days_listed  \\\n",
       "51520    automatic  sedan       black       0  2018-10-03           37   \n",
       "51521    automatic  sedan       white       0  2018-11-14           22   \n",
       "51522    automatic  sedan        blue       0  2018-11-15           32   \n",
       "51523    automatic  sedan       black       0  2018-07-02           71   \n",
       "51524    automatic  sedan        grey       0  2018-06-05           10   \n",
       "\n",
       "       day_posted  age      odo_year  condition_index  \n",
       "51520           2    5  17627.200000                4  \n",
       "51521           2   16  11343.750000                0  \n",
       "51522           3    9  14222.222222                3  \n",
       "51523           0    5  27914.600000                2  \n",
       "51524           1    4  32250.000000                2  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Amostragem dos dados\n",
    "df.tail()"
   ]
  },
  {
   "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",
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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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>odometer</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>age</th>\n",
       "      <th>odo_year</th>\n",
       "      <th>condition_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.409826</td>\n",
       "      <td>0.300448</td>\n",
       "      <td>-0.393550</td>\n",
       "      <td>0.315145</td>\n",
       "      <td>-0.000682</td>\n",
       "      <td>-0.000343</td>\n",
       "      <td>-0.408579</td>\n",
       "      <td>0.217170</td>\n",
       "      <td>0.221518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.409826</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.145403</td>\n",
       "      <td>-0.431815</td>\n",
       "      <td>0.039764</td>\n",
       "      <td>-0.005229</td>\n",
       "      <td>-0.007149</td>\n",
       "      <td>-0.997189</td>\n",
       "      <td>0.471789</td>\n",
       "      <td>0.266694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>0.300448</td>\n",
       "      <td>-0.145403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085640</td>\n",
       "      <td>0.407433</td>\n",
       "      <td>0.003176</td>\n",
       "      <td>-0.003558</td>\n",
       "      <td>0.145058</td>\n",
       "      <td>-0.041614</td>\n",
       "      <td>-0.065569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>-0.393550</td>\n",
       "      <td>-0.431815</td>\n",
       "      <td>0.085640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033317</td>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.430329</td>\n",
       "      <td>0.115146</td>\n",
       "      <td>-0.322576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_4wd</th>\n",
       "      <td>0.315145</td>\n",
       "      <td>0.039764</td>\n",
       "      <td>0.407433</td>\n",
       "      <td>0.033317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>-0.039863</td>\n",
       "      <td>0.039754</td>\n",
       "      <td>0.012856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_listed</th>\n",
       "      <td>-0.000682</td>\n",
       "      <td>-0.005229</td>\n",
       "      <td>0.003176</td>\n",
       "      <td>0.003347</td>\n",
       "      <td>0.002003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>0.006062</td>\n",
       "      <td>-0.002091</td>\n",
       "      <td>-0.002404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_posted</th>\n",
       "      <td>-0.000343</td>\n",
       "      <td>-0.007149</td>\n",
       "      <td>-0.003558</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.003207</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006573</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>0.001152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.408579</td>\n",
       "      <td>-0.997189</td>\n",
       "      <td>0.145058</td>\n",
       "      <td>0.430329</td>\n",
       "      <td>-0.039863</td>\n",
       "      <td>0.006062</td>\n",
       "      <td>0.006573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.478726</td>\n",
       "      <td>-0.265829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odo_year</th>\n",
       "      <td>0.217170</td>\n",
       "      <td>0.471789</td>\n",
       "      <td>-0.041614</td>\n",
       "      <td>0.115146</td>\n",
       "      <td>0.039754</td>\n",
       "      <td>-0.002091</td>\n",
       "      <td>-0.002115</td>\n",
       "      <td>-0.478726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_index</th>\n",
       "      <td>0.221518</td>\n",
       "      <td>0.266694</td>\n",
       "      <td>-0.065569</td>\n",
       "      <td>-0.322576</td>\n",
       "      <td>0.012856</td>\n",
       "      <td>-0.002404</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>-0.265829</td>\n",
       "      <td>0.044397</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    price  model_year  cylinders  odometer    is_4wd  \\\n",
       "price            1.000000    0.409826   0.300448 -0.393550  0.315145   \n",
       "model_year       0.409826    1.000000  -0.145403 -0.431815  0.039764   \n",
       "cylinders        0.300448   -0.145403   1.000000  0.085640  0.407433   \n",
       "odometer        -0.393550   -0.431815   0.085640  1.000000  0.033317   \n",
       "is_4wd           0.315145    0.039764   0.407433  0.033317  1.000000   \n",
       "days_listed     -0.000682   -0.005229   0.003176  0.003347  0.002003   \n",
       "day_posted      -0.000343   -0.007149  -0.003558  0.001660  0.003207   \n",
       "age             -0.408579   -0.997189   0.145058  0.430329 -0.039863   \n",
       "odo_year         0.217170    0.471789  -0.041614  0.115146  0.039754   \n",
       "condition_index  0.221518    0.266694  -0.065569 -0.322576  0.012856   \n",
       "\n",
       "                 days_listed  day_posted       age  odo_year  condition_index  \n",
       "price              -0.000682   -0.000343 -0.408579  0.217170         0.221518  \n",
       "model_year         -0.005229   -0.007149 -0.997189  0.471789         0.266694  \n",
       "cylinders           0.003176   -0.003558  0.145058 -0.041614        -0.065569  \n",
       "odometer            0.003347    0.001660  0.430329  0.115146        -0.322576  \n",
       "is_4wd              0.002003    0.003207 -0.039863  0.039754         0.012856  \n",
       "days_listed         1.000000    0.002734  0.006062 -0.002091        -0.002404  \n",
       "day_posted          0.002734    1.000000  0.006573 -0.002115         0.001152  \n",
       "age                 0.006062    0.006573  1.000000 -0.478726        -0.265829  \n",
       "odo_year           -0.002091   -0.002115 -0.478726  1.000000         0.044397  \n",
       "condition_index    -0.002404    0.001152 -0.265829  0.044397         1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Amostragem da correlação entre variaveis quantitativas\n",
    "display(df.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estudando os parâmetros fundamentais\n",
    "\n",
    "Agora será feito uma análise univariada das colunas que foram consideradas relevantes para o preço. Será usado histogramas e boxplots. Os parâmetros são:\n",
    "- Preço\n",
    "- A idade do veículo quando a propaganda foi colocada\n",
    "- Quilometragem\n",
    "- Número de cilindros\n",
    "- Condição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hist_graph(data,x_bins, x_range, title, xlabel):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.hist(data, bins= x_bins, range = x_range)\n",
    "    plt.title(title,  fontsize = 16)\n",
    "    plt.ylabel('Quantidade de Veículos', fontsize = 15)\n",
    "    plt.xlabel(xlabel,  fontsize = 15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_graph(data, title, xlabel):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    data.value_counts().plot(kind='bar')\n",
    "    plt.title(title, fontsize = 16)\n",
    "    plt.ylabel('Quantidade de Veículos', fontsize = 15)\n",
    "    plt.xticks(rotation = 75)\n",
    "    plt.xlabel(xlabel,fontsize = 15);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_graph(dt, title, ylabel):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.boxplot(data = dt, orient=\"h\", showmeans=True);\n",
    "    plt.yticks(rotation = 90)\n",
    "    plt.title(title, fontsize = 16)\n",
    "    plt.ylabel(ylabel, fontsize = 15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma de Price\n",
    "display(hist_graph(df['price'], 100, (df['price'].min(),df['price'].max()) ,'Histograma de Price', 'Preço'))\n",
    "\n",
    "# Boxplot de Price\n",
    "display(box_graph(df['price'], 'Boxplot de Price', 'Preço'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma de Age\n",
    "display(hist_graph(df['age'], 35, (df['age'].min(),df['age'].max()) ,'Histograma de Age', 'Idade'))\n",
    "\n",
    "# Boxplot de Age\n",
    "display(box_graph(df['age'], 'Boxplot de Age', 'Idade'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma de Odometer\n",
    "display(hist_graph(df['odometer'], 40, (df['odometer'].min(),df['odometer'].max()) ,'Histograma de Odometer', 'Quilometragem'))\n",
    "\n",
    "# Boxplot de Odometer\n",
    "display(box_graph(df['odometer'], 'Boxplot de Odometer', 'Quilometragem'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico de barras de Cylinders\n",
    "bar_graph(df['cylinders'], 'Grafico de barras de Cylinders', 'Cilindros')\n",
    "\n",
    "# Boxplot de Cylinders\n",
    "display(box_graph(df['cylinders'], 'Boxplot de Cylinders', 'Cilindros'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico de barras de Condition\n",
    "bar_graph(df['condition'], 'Grafico de barras de Condition', 'Condição')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Foi percebido que existência de muitos valores ausentes nas colunas **`price`**, **`age`** e **`odometer`**, o que afeta a análise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cuidando dos valores atípicos\n",
    "\n",
    "Para que os gráficos demonstrem com mais clareza as distribuições, e com os valores atípicos afetando negativamente, foi decidido que será criado um novo dataframe sem os valores atípicos das colunas **`price`**, **`age`** e **`odometer`**. Os valores atípicos são definidos como os valores acima do intervalo interquartil. Como as varíveis dessas colunas começam em zero, será calculado apenas o limite superior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculando o intervalo interquartil \n",
    "price_IQR = df['price'].quantile(0.75) - df['price'].quantile(0.25)\n",
    "\n",
    "odometer_IQR = df['odometer'].quantile(0.75) - df['odometer'].quantile(0.25)\n",
    "\n",
    "age_IQR = df['age'].quantile(0.75) - df['age'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determinando o limite superior para valores atípicos de Price\n",
    "price_lim = df['price'].quantile(0.75) +  1.5 * price_IQR\n",
    "\n",
    "# Determinando o limite superior para valores atípicos de Odometer\n",
    "odometer_lim = df['odometer'].quantile(0.75) +  1.5 * odometer_IQR\n",
    "\n",
    "# Determinando o limite superior para valores atípicos de Age\n",
    "age_lim = df['age'].quantile(0.75) +  1.5 * age_IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "863"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('price <100'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Armazene os dados sem os valores atípicos em um DataFrame separado\n",
    "df_clean = df.query('(price < @price_lim) and (price > 100) and (odometer < @odometer_lim) and (age < @age_lim)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9178262979136341"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(len(df_clean) / len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O novo dataframe foi criado em **df_clean**, ele possui 93,44% do dataframe original."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estude os parâmetros fundamentais sem valores atípicos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A análise univariada será refeita com o novo dataframe. Será visto o novo histograma e o novo boxplot, o histograma será comparado com o antigo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma de Price Antigo\n",
    "display(hist_graph(df['price'], 100, (df['price'].min(),df['price'].max()) ,'Histograma de Price Antigo', 'Preço'))\n",
    "\n",
    "# Histograma de Price Novo\n",
    "display(hist_graph(df_clean['price'], 10, (df_clean['price'].min(),df_clean['price'].max()) ,'Histograma de Price Novo', 'Preço'))\n",
    "\n",
    "# Boxplot de Price\n",
    "display(box_graph(df_clean['price'], 'Boxplot de Price Novo', 'Preço'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZmVnFOPmbmZlVjJO/mZlZxTj5m5mZVYyTv5mZWcU4+ZuZmVWMk7+ZmVnFOPmbmZlVzMC+DsDMVn8jxl3V0vXNmbBPS9dnVjU+8jczM6sYJ38zM7OKcfI3MzOrGCd/MzOzinHyNzMzqxgnfzMzs4px8jczM6sYJ38zM7OKcfI3MzOrGCd/MzOzinHyNzMzqxgnfzMzs4px8jczM6sYJ38zM7OKcfI3MzOrGCd/MzOziun15C9pQ0mXSvqHpHsl7SxpsKSpkmbl50G5riSdJmm2pDslbVdYz5hcf5akMb39PszMzDpVXxz5/wi4JiLeALwNuBcYB0yLiK2BaXkaYC9g6/wYC5wJIGkwcDSwI7ADcHTtB4OZmZl1rVeTv6QNgPcA5wFExLMR8TgwGpiUq00C9s2vRwMXRnIjsKGkocCewNSIWBQRjwFTgVG99kbMzMw6WG8f+W8JLAQukHS7pHMlrQdsGhELcp0HgU3z682BuYXl5+WyZuXLkTRW0gxJMxYuXNjit2JmZtaZejv5DwS2A86MiLcDT/FyEz8AERFAtGJjEXFORIyMiJFDhgxpxSrNzMw6Xm8n/3nAvIi4KU9fSvox8FBuzic/P5znzweGF5YflsualZuZmVk3ejX5R8SDwFxJr89FuwP3AFcAtR77Y4DL8+srgINzr/+dgMX59MC1wB6SBuWOfnvkMjMzM+vGwD7Y5heAn0taE/gncCjpR8gUSYcBDwAH5LpXA3sDs4GluS4RsUjSccAtud6xEbGo996CmZlZ5+r15B8RdwAjG8zavUHdAA5vsp7zgfNbGpyZmVkFeIQ/MzOzinHyNzMzqxgnfzMzs4px8jczM6sYJ38zM7OKcfI3MzOrGCd/MzOzinHyNzMzqxgnfzMzs4px8jczM6sYJ38zM7OKKTW2v6SBwICIWFYo2wPYBvhjRNzWpvjMzMysxcre2OdiYDHwKQBJXwROBZYBAyR9KCKubEuEZmZm1lJlm/13It1et+ZrwA8iYh3gXOBbrQ7MzMzM2qNs8t8IeBBA0luAzYCz8rxLSM3/ZmZm1gHKJv+HgBH59SjggYi4P0+vA7zY4rjMzMysTcqe878EOEnS24BDgdML894OzGp1YGZmZtYeZZP/OOAJYHvgTOC7hXnvIHUINDMzsw5QKvlHxPPAsU3mfailEZmZmVlblT3yB0DSjsC7gMHAIuCGiLipHYGZmZlZe5Qd5Gc90nn/UcDzwKOkKwAGSLoG2D8ilrYtSjMzM2uZsr39TwZ2Bg4E1o6IocDawEG5/KT2hGdmZmatVjb5fxj4RkRcEhEvAkTEixFxCakz4P7tCtDMzMxaq2zy3wCY22TeXGD91oRjZmZm7VY2+f8N+JwkFQvz9OfyfDMzM+sAZXv7fxP4LfAPSZeRRvzbBNiPNPLfXm2JzszMzFqu7HX+10naDvg26fz+UGABcBPwoYi4p30hmpmZWSuVvs4/Iu4m9e43MzOzDlb2nL+ZmZmtJpoe+Uua0oP1REQc2IJ4zMzMrM26avYf0mtRmJmZWa9pmvwj4r29GYiZmZn1Dp/zNzMzq5iyN/Y5ubs6EfH1VQ/HzMzM2q3spX6Nxu4fRBrWdzHwGODkb2Zm1gFKNftHxJYNHhuS7uj3b+DjZTcoaY6kv0u6Q9KMXDZY0lRJs/LzoFwuSadJmi3pzjzQUG09Y3L9WZLG9ORNm5mZVdkqnfOPiJuA7wGn93DR90bEthExMk+PA6ZFxNbAtDwNadjgrfNjLHAmpB8LwNHAjsAOwNG1HwxmZmbWtVZ0+HsUeP0qrmM0MCm/ngTsWyi/MJIbgQ0lDQX2BKZGxKKIeAyYCoxaxRjMzMwqoWyHv3UbFK8JvBE4Fri7B9sM4HeSAjg7Is4BNo2IBXn+g8Cm+fXmLH8r4Xm5rFm5mZmZdaNsh78nSUm7noD5vHykXsa7ImK+pE2AqZL+UZwZEZF/GKwySWNJpwvYYostWrFKMzOzjlc2+X+KFZP/M6Qj7psj4rmyG4yI+fn54Xx74B2AhyQNjYgFuVn/4Vx9PjC8sPiwXDYf2LWufHqDbZ0DnAMwcuTIlvygMDMz63Rlb+k7sRUbk7QesEZELMmv9yCdNrgCGANMyM+X50WuAI6QNJnUuW9x/oFwLXBioZPfHsBRrYjRzMxsdVf2nP/uwPBGPwIkHQI8EBHXl1jVpsBlkmrb/kVEXCPpFmCKpMOAB4ADcv2rgb2B2cBS4FCAiFgk6Tjgllzv2IhYVOa9mJmZVV3ZZv8TgMuazNsY+Czpmv8uRcQ/gbc1KH8U2L1BeQCHN1nX+cD53W3TzMzMllf2Ur83ATOazLsd2KY14ZiZmVm7lU3+zwODm8zbqEWxmJmZWS8om/xvAL4mac1iYZ7+CvCnVgdmZmZm7VH2nP+3SD8AZku6GFgADCV1zNsAOKw94ZmZmVmrlb3U705J2wPjgU+SmvofJY3Df0xE3Ne2CM3MzKylGiZ/SSMiYk6xLCJmAh/tjaDMzMysfZqd858habdejcTMzMx6RbNm/88DkyWdGBGnSprSzXoiIg5scWxmZmbWBg2Tf0RMkfRX0u11TwU2ofGNfczMzKzDNO3wFxFz87C+RMSuvRaRmZmZtVWX1/nn4XWXo2QzSWUvEzQzM7N+pOwgP0jaW9JNpFv5zgXemst/KukTbYrPzMzMWqxU8pd0MOn2uv8AxgIqzL4PD/JjZmbWMcoe+X8L+F5EjAF+VjfvbnxjHzMzs45RNvm/BpjaZN4zwPqtCcfMzMzarWzynwu8vcm8kcDs1oRjZmZm7VY2+Z8HHJ079q2Ty5QvBfw68NN2BGdmZmatV/ZyvZOA4aRBf17IZX8BBgBnR8RpbYjNzMzM2qBp8pf0NeCXETEvX+9/uKRTgN1Jd/VbBFznO/qZmZl1lq6O/E8EvpuH+f0FcGlEzMbn983MzDpaV+f8NweOzK9PB+ZLukbSwZJe1fbIzMzMrC2aJv+IeDgiTo+Id5Mu9fsmqbl/IvCQpF9J+rCktXonVDMzM2uFUr3983n/70fE9sDWwAn5+RLgYUmT2hijmZmZtVDpsf1rIuL+iDgB2A34CfBKwGP7m5mZdYge3ZlP0gbAfsBBpOQv0sh/v2x9aGZmZtYO3SZ/SesCo0kJfw9gTdI1/kcCl0TEwnYGaGZmZq3V1XX+tSP8fYB1gTuA7wCTI2Jur0RnZmZmLdfVkf+vgFnA90mD/czsnZDMzMysnbpK/iMj4rZei8TMzMx6RVfX+Tvxm5mZrYZ6fKmfmZmZdTYnfzMzs4px8jczM6sYJ38zM7OKKZ38Jb1V0sWS7pe0TNJ2ufwESXu1L0QzMzNrpVLJPyf3W4FXAxcCryjMXgZ8ofWhmZmZWTuUPfL/LjAxInYh3dGv6A5g255sVNIASbdLujJPbynpJkmzc+vCmrl8rTw9O88fUVjHUbl8pqQ9e7J9MzOzKiub/N8AXJxfR928J4DBPdzul4B7C9MnAadExGuBx4DDcvlhwGO5/JRcD0nbkIYefhMwCjhD0oAexmBmZlZJZZP/w8BWTea9Cfh32Q1KGka6X8C5eVqkOwRemqtMAvbNr0fnafL83XP90aR7DCyLiH8Bs4EdysZgZmZWZWWT/2TgWEnvKpSFpNcB3wB+3oNtngp8HXgxT28EPB4Rz+fpecDm+fXmwFyAPH9xrv9SeYNlXiJprKQZkmYsXOibD5qZmUH55P9tYAbwB14+yr8cuAu4EzixzEokvR94OCJu7WGcKyUizomIkRExcsiQIb2xSTMzs36vqxv7vCQilgHvl7Q7sDuwMbAImBYRU3uwvXcCH5S0N7A2sD7wI2BDSQPz0f0wYH6uPx8YDsyTNBDYAHi0UF5TXMbMzMy6UCr510TENGDaym4sIo4CjgKQtCvw1Yj4uKRLgI+QTi+MIbUqAFyRp/+a518XESHpCuAXkn4IbAZsDdy8snGZmZlVSdPkL2mLnqwoIkp3+mvgG8BkSccDtwPn5fLzgIskzSa1NByUt3W3pCnAPcDzwOER8cIqbN/MzKwyujryn8OKl/V1pUeX2kXEdGB6fv1PGvTWj4hngP2bLH8CK445YGZmZt3oKvl/oPB6feBk0rX5vyZd+rcJ8GHSGABfa1eAZmZm1lpNk39EXFV7LWkicGVEfK6u2lmSziJdtz+5LRGamZlZS5W91O9DpCP+Rn4FfLA14ZiZmVm7lU3+TwPvajLv3cAzrQnHzMzM2q3spX5nAt+WtBHp8rvaOf/RwGdxxzszM7OOUXaQn/GSHiMNy/t50lUAAh4kXat/atsiNDMzs5YqPchPRPxI0o9JI+u9mpT450bEi10vaWZmZv1JT0f4exF4ID/MzMysA5Xt8GdmZmarCSd/MzOzinHyNzMzqxgnfzMzs4rpUYc/SYOAN5N6/P82Ih6TtDbwrHv9m5mZdYZSR/6SBkg6GZgH/AG4CNgyz/4VcHR7wjMzM7NWK9vsfyLwGeAIYCvSAD81l7P8HQDNzMysHyvb7H8wMC4iLpA0oG7e/aQfBGZmZtYByib/DUlJvpE1gfofBGbWxIhxV3Vfycysjco2+99FuolPI3sBt7UmHDMzM2u3skf+xwO/krQOcAnpxj7bStqPdFe/D7YpPjMzM2uxUkf+EXE58DHgfcBvSR3+zgUOAT4ZEde2K0AzMzNrrZ7c1W8KMEXS64CNgUXAzIiIdgVnZmZmrdejQX4AIuI+4L42xGJmZma9oGnyl/SdnqwoIo5d9XDMzMys3bo68v9C3fQ6wLr59ZPAK/Prpfnh5G+2GvCliGarv6Yd/iJiSO1B6s3/MPAJYL2IWB9YD/hkLm92GaCZmZn1M2XP+Z8GnBgRv6gVRMTTwM8lrQf8BNiuDfGZmZlZi5Ud5OfNwH+azJsPvLE14ZiZmVm7lU3+9wH/T9JaxcJ8O9//B8xsdWBmZmbWHmWb/b8AXA3MkzSVdJ5/E+B/SJ0A92pPeGZmZtZqZUf4+yOwNXABMBTYMz9fAGyd55uZmVkH6MkIfwuAr7cxFjMzM+sFZc/5m5mZ2Wqi9JG/pAOBzwCvA9aunx8Rm7QwLjMzM2uTUkf+kj4GTAJmA8OAK4Ar8/JPAKe3K0AzMzNrrbJH/l8DjgMmAGOBMyLiNkmvAqaShvc163OtHpp2zoR9Wro+M7P+oOw5/62BP0fEC8ALwPoAEbEEOAk4oj3hmZmZWauVTf5PALUBfupH9BOwUZmVSFpb0s2S/ibpbknH5PItJd0kabakiyWtmcvXytOz8/wRhXUdlctnStqz5PswMzOrvLLJ/xbgrfn1FcB3JH1G0hjge8CNJdezDNgtIt4GbAuMkrQTqfXglIh4LfAYcFiufxjwWC4/JddD0jbAQcCbgFHAGZIGlIzBzMys0som/+8C/86vvwPcDJxJGuTnEeCzZVYSyZN58hX5EcBuwKW5fBKwb349Ok+T5+8uSbl8ckQsi4h/kToi7lDyvZiZmVVaqQ5/EXEj+eg+Ih4HRudx/teKiCd6ssF8hH4r8FrS3QDvBx6PiOdzlXnA5vn15sDcvN3nJS0mnWLYnOVbG4rLFLc1ltRBkS222KInYZpZP+aOnWarZqUH+clH3T1K/Hm5FyJiW9IlgzsAb1jZGEps65yIGBkRI4cMGdKuzZiZmXWUpkf+ks7vyYoi4lM9rP+4pOuBnYENJQ3MR//DSJ0Kyc/DSTcUGghsADxaKK8pLmNmZmZd6KrZ/y1101sAQ0h39Kvd1W8TYCHwQJmNSRoCPJcT/zqkuwKeBFwPfASYDIwBLs+LXJGn/5rnXxcRIekK4BeSfghsRroU8eYyMZiZmVVd0+QfEdvXXkv6AHAqsF9E/KVQ/k5Sh7zjS25vKDApn/dfA5gSEVdKugeYLOl44HbgvFz/POAiSbOBRaQe/kTE3ZKmAPcAzwOH5zEIzMzMrBtlR/ibAPxfMfEDRMSfJX2HdPR+RXcriYg7gbc3KP8nDXrrR8QzwP5N1nUCcEKp6M3MzOwlZTv8bUXzIXyXAiNaEo2ZmZm1XdnkfxswXtLQYqGkzYDxpEv3zMzMrAOUbfYfC/wOmCPpVl7u8PcOUu/7T7QnPDMzM2u1Ukf+EXE38F/Al4GZpHH+Z+bp/4qIu9oWoZmZmbVU2SP/Wue7M9oYi5mZmfWClR7hz8zMzDpTVyP8PQzsGRG3S1pIugFPUxGxSauDM+trrR5D3sysP+iq2f8nwEOF110mfzMzM+sMXY3wd0zh9fheicbMzMzartQ5f0nXSWp49z1Jr5N0XWvDMjMzs3Yp2+FvV2D9JvPWB97TkmjMzMys7XrS23+Fc/6S1gR2Ax5sWURmZmbWVl319j8a+E6eDOBGSc2qf6/FcZmZmVmbdNXb/2rgEUDAacAPgDl1dZ4F/hERf2pLdGZmZtZyXfX2vwW4BUDSEuCqiHiktwIzMzOz9ig1vG9ETGp3IGZmZtY7SiV/Sa8AvgR8CBgGrF1fxyP8mZmZdYayN/Y5BfgscCVwPelcv5mZmXWgssl/f2BcRPygncGYmfWFVt/DYc6EfVq6PrNWK3udv4A72xmImZmZ9Y6yyf+nwEfbGYiZmZn1jrLN/g8BH5d0PTAVeLxufkTEma0MzMzMzNqjbPI/NT9vAezSYH4ATv5mZmYdoOx1/j25B4CZmZn1Y07qZmZmFVO22R8AScOA19F4kJ+rWxWUmZmZtU/ZEf5eBUwB9qgV5efibX4HtDAuMzMza5Oyzf7fJXX2ezcp8e8H7AqcB/wL2KkdwZmZmVnrlU3+ewMnADfl6f9ExB8jYixwOfC1dgRnZmZmrVc2+W8KzI2IF4CngMGFeVfz8ukAMzMz6+fKJv+5wMb59Szg/YV5OwLPtDIoMzMza5+yvf2nAu8DLiPd4W+SpHcAy4D3AL7hj5mZWYcom/y/AawLEBEXSXoS+AiwDnAEcHZ7wjMzM7NWKzvC31JgaWH6MlIrgJmZmXWYstf5r9tdnfwDwczMzPq5sh3+ngSWdPPolqThkq6XdI+kuyV9KZcPljRV0qz8PCiXS9JpkmZLulPSdoV1jcn1Z0kaU/odm5mZVVzZc/6fYvnR/AAGAXsC2wDHlVzP88BXIuK2PGrgrZKmAocA0yJigqRxwDhSP4O9gK3zY0fSnQN3lDQYOBoYmeO6VdIVEfFYyTjMzNpmxLirWr7OORP2afk6rbrKnvOf2GTWqZLOBN5Ucj0LgAX59RJJ9wKbA6NJIwYCTAKmk5L/aODCiAjgRkkbShqa606NiEUA+QfEKOCXZeIwMzOrslbc1e9XwME9XUjSCODtpFEDN80/DAAeJA0qBOmHwdzCYvNyWbNyMzMz60Yrkv/2pOv9S5P0StKPhiMj4onivHyUX3+KYaVIGitphqQZCxcubMUqzczMOl7Z3v4nNyheE3gjsDtwatkNSnoFKfH/PCJ+nYsfkjQ0IhbkZv2Hc/l8YHhh8WG5bD4vnyaolU+v31ZEnAOcAzBy5MiW/KAwMzPrdGU7/O3foOwZUnP7F8kJtjuSRLoT4L0R8cPCrCuAMcCE/Hx5ofwISZNJHf4W5x8I1wIn1q4KIN1b4KiS78X6kXZ0jDJbHbX6b8UdCKutbIe/LVu0vXcCnwT+LumOXPZNUtKfIukw4AHggDzvatIdBWeTBhk6NMezSNJxwC253rG1zn9mZmbWtbJH/i0RETcAajJ79wb1Azi8ybrOB85vXXRmZmbV0G2HP0lvlnSWpHslLZH0hKSZki6Q9O7eCNLMzMxap8vkn0fgux04ELgXOJd0zv5u0jX40yWdmuuuIenHbY3WzMzMVlnTZn9Je5Nu33sycGL9JXl5hL6jgG9Img/sAuwGfKF94ZqZmdmq6uqc/1eBSRExrtHMiFgCfDNfmjeBNDjPe1sfopmZmbVSV8n/HcBJJdYxmTTC38jCKH22GvJleWZmq4euzvmvQboRT3eeB5Y68ZuZmXWGrpL/3cD7S6zj/cBdrQnHzMzM2q2r5H8WcLikz+SR+VYg6dPA50m32jUzM7MO0PScf0RMlLQjcDbwVUn/H2n0PYDXAPsArwPOjogL2x6pmZmZtUSXI/xFxOfyOPpfIo20t1aetQz4CzAuIi5vtryZmZn1P90O7xsRvwF+I2kAsHEufiQiXmhnYLbq3DvfzMwaKT22f072D7UxFjMzM+sF3Y7tb2ZmZqsXJ38zM7OKcfI3MzOrGCd/MzOzinHyNzMzqxgnfzMzs4px8jczM6sYJ38zM7OKcfI3MzOrGCd/MzOzinHyNzMzq5jSY/vbilp945w5E/Zp6frMzMwa8ZG/mZlZxfjI38ysgtxyWW0+8jczM6sYJ38zM7OKcfI3MzOrGCd/MzOzinHyNzMzqxgnfzMzs4rxpX79SKsvvTEzM2vER/5mZmYV4+RvZmZWMU7+ZmZmFdOryV/S+ZIelnRXoWywpKmSZuXnQblckk6TNFvSnZK2KywzJtefJWlMb74HMzOzTtfbR/4TgVF1ZeOAaRGxNTAtTwPsBWydH2OBMyH9WACOBnYEdgCOrv1gMDMzs+71avKPiD8Ci+qKRwOT8utJwL6F8gsjuRHYUNJQYE9gakQsiojHgKms+IPCzMzMmugP5/w3jYgF+fWDwKb59ebA3EK9ebmsWfkKJI2VNEPSjIULF7Y2ajMzsw7VH5L/SyIigGjh+s6JiJERMXLIkCGtWq2ZmVlH6w/J/6HcnE9+fjiXzweGF+oNy2XNys3MzKyE/jDC3xXAGGBCfr68UH6EpMmkzn2LI2KBpGuBEwud/PYAjurlmM3MrKAdI5TOmbBPy9dpSa8mf0m/BHYFNpY0j9RrfwIwRdJhwAPAAbn61cDewGxgKXAoQEQsknQccEuud2xE1HciNDMzsyZ6NflHxEebzNq9Qd0ADm+ynvOB81sYmpmZWWX0h3P+ZmZm1ouc/M3MzCrGyd/MzKxinPzNzMwqxsnfzMysYpz8zczMKsbJ38zMrGKc/M3MzCrGyd/MzKxi+sPY/mZmZito9f0CfK+Al/nI38zMrGKc/M3MzCrGyd/MzKxinPzNzMwqxsnfzMysYpz8zczMKsbJ38zMrGJ8nb+ZmVWCxw14mY/8zczMKsbJ38zMrGKc/M3MzCrGyd/MzKxi3OHPzMxsJXRyB0If+ZuZmVWMk7+ZmVnFOPmbmZlVjJO/mZlZxTj5m5mZVYyTv5mZWcU4+ZuZmVWMk7+ZmVnFOPmbmZlVjJO/mZlZxTj5m5mZVYyTv5mZWcV0dPKXNErSTEmzJY3r63jMzMw6Qccmf0kDgJ8AewHbAB+VtE3fRmVmZtb/dWzyB3YAZkfEPyPiWWAyMLqPYzIzM+v3Ojn5bw7MLUzPy2VmZmbWhYF9HUA7SRoLjM2TT0qa2eJNbAw80uJ1VoX33crzvlt53nerxvtv5XW773RSW7b7mkaFnZz85wPDC9PDctlLIuIc4Jx2BSBpRkSMbNf6V2fedyvP+27led+tGu+/ldff9l0nN/vfAmwtaUtJawIHAVf0cUxmZmb9Xsce+UfE85KOAK4FBgDnR8TdfRyWmZlZv9exyR8gIq4Gru7DENp2SqECvO9WnvfdyvO+WzXefyuvX+07RURfx2BmZma9qJPP+ZuZmdlKcPJfCR5W+GWS5kj6u6Q7JM3IZYMlTZU0Kz8PyuWSdFreb3dK2q6wnjG5/ixJYwrl78jrn52XVe+/y9aQdL6khyXdVShr+75qto1O02T/jZc0P3//7pC0d2HeUXlfzJS0Z6G84d9v7jx8Uy6/OHckRtJaeXp2nj+il95yy0gaLul6SfdIulvSl3K5v3/d6GLfdfZ3LyL86MGD1LnwfmArYE3gb8A2fR1XH+6POcDGdWUnA+Py63HASfn13sBvAQE7ATfl8sHAP/PzoPx6UJ53c66rvOxeff2eV2FfvQfYDrirN/dVs2102qPJ/hsPfLVB3W3y3+ZawJb5b3ZAV3+/wBTgoPz6LOBz+fXngbPy64OAi/t6X6zEvhsKbJdfvwq4L+8jf/9Wft919HfPR/4952GFuzcamJRfTwL2LZRfGMmNwIaShgJ7AlMjYlFEPAZMBUbleetHxI2Rvv0XFtbVcSLij8CiuuLe2FfNttFRmuy/ZkYDkyNiWUT8C5hN+ttt+Pebj1J3Ay7Ny9d/FrX9dymwe6e1QEXEgoi4Lb9eAtxLGhHV379udLHvmumI756Tf895WOHlBfA7SbcqjagIsGlELMivHwQ2za+b7buuyuc1KF+d9Ma+araN1cURuWn6/EKTck/330bA4xHxfF35cuvK8xfn+h0pNx2/HbgJf/96pG7fQQd/95z8bVW9KyK2I91d8XBJ7ynOzEcBvqSkhN7YV6vh53Em8F/AtsAC4Ad9Gk0/J+mVwK+AIyPiieI8f/+61mDfdfR3z8m/57odVrhKImJ+fn4YuIzUtPVQbgYkPz+cqzfbd12VD2tQvjrpjX3VbBsdLyIeiogXIuJF4Kek7x/0fP89SmraHlhXvty68vwNcv2OIukVpOT184j4dS7296+ERvuu0797Tv4952GFM0nrSXpV7TWwB3AXaX/UegGPAS7Pr68ADs49iXcCFufmwGuBPSQNyk1newDX5nlPSNopn+c6uLCu1UVv7Ktm2+h4taSS7Uf6/kF6zwfl3tJbAluTOqQ1/PvNR6TXAx/Jy9d/FrX99xHguly/Y+TvxHnAvRHxw8Isf/+60Wzfdfx3r1U9B6v0IPWEvY/Uc/NbfR1PH+6HrUg9Vv8G3F3bF6RzUtOAWcDvgcG5XMBP8n77OzCysK5PkTrGzAYOLZSPJP1R3Q+cTh6YqhMfwC9JzYPPkc7rHdYb+6rZNjrt0WT/XZT3z52kf5RDC/W/lffFTApXiTT7+83f55vzfr0EWCuXr52nZ+f5W/X1vliJffcuUnP7ncAd+bG3v3+rtO86+rvnEf7MzMwqxs3+ZmZmFePkb2ZmVjFO/mZmZhXj5G9mZlYxTv5mZmYV4+Rv1sfytdSH5Lt2PSnpCaW7iO2zEuvaVVJIenOhLCQd0dqoV42kAyQd0tdxmFWVk79Z3zsDOJc0Xvh+wIHAv4ErJX25h+u6DdiZdB1xf3YAcEhfB2FWVQO7r2Jm7SJpX+B/SbfwPKsw67eSHgS+J2laRNxZZn2Rxhy/sfWR9o08rOqLEfFCX8ditjrxkb9Z3/oSafSunzaYdyLwJHAEgKQ5kr5frJBPF0S+6UjDZv9GJB0haZakZZJm17cwSBov6RFJO0qaIelpSTfkoUk3kfSbfIriXkm7NVj/pyXdndf/gKSvF+ZNBD4M7JJjDUnj87zpki6VNFbS/cAzwGaS3iBpsqS5kpbmdR8paY267b5V0l8kPZPr7J3jn1hX792S/pDX9aiknyoPVV23X7fLMS2VdEeeXk/SBZIWS/qnpI92ta/N+iMf+Zv1EaUbdewMnNHoyDYiFku6Htilxdv9DPBj4IeksdrfC/xA0loRMaFQdV3gHOBk4CngNNKQpsuA35JOV3wduETS8IhYmtf/NdIPl5OB6cA7gOMkLY2I04HjgC2ADYHP520Vbwf7TtLd0r4BLCXdxvR1pKFSfw4sId1J7RhgHeC7ebvr5vfzIPBR0tCopwCDeHncdSS9kzTM7G9I46VvBEzI9Wrjq9dMIg1Ve1KucylpmNX7c91PARdK+lNEzMOsQzj5m/WdjYG1gAe6qPMA6eYpLZGPlMcDEyPiK7n4d5I2AI6SdGpEPJPL1wG+GBF/yMtuRhrv/eiI+H4um0e6r8MupFMV6wNHA8dHxDF5PVNzYv4/SWdGxP2SFgFrRESjUxQbAttGxEOFsmn5UbvRyg2kHyefISd/4FBSIh8Z+W6TufXgJpY3AfhLRBxY2C/zgWmS3hwRdxXqfj8iJhW2exUwPSK+lctuJv0I+ADpFq9mHcHN/mb9XytvwDEM2Ix0s5Cii4H1gbcUyp4F/lSYnp2fr2tQtnl+3hlYj9QaMLD2yMtsyvK3fW3m1rrEj6S1JR0jaTap5eE54ARgS718K9Tt87Iv3fY5Im4GHiqsZ90c45S6+G7I63xHXSzTunr/EbEYWFh4/2YdwcnfrO88Qkpkr+mizmtId7JrldptSB+qK69NDy6ULYl0r/KaZ/Pz47WCiKiVrZ2fN87Pd5OSae1xfS4v3s+8mfrYIDW7f5V0GmJvUqI/vm7bryYl4nrFskHAANIpi2J8y4BXNIjv8cLrFd5/oXxtzDqIm/3N+khEPC/pL8A+kr5al2jJTei7km4XCqnz25p1qxnUw83WfkhsUle+aX5e1MP11ast/34aJ/GZJdbRqKVjf+DHEXFyrUArjoPwIPD6BssOKbx+PK9/PHB1g7r/KRGfWcdz8jfrW6cBlwGfJh3VFo0jnf++IE/PA95YV6en/QHmkRLc/qROezUHAE+Q7k++Kv4KPA1sFhFXdVGvp0fL65COzgGQNAA4qK7OLcDHJG1eOOe/Ay//sCEinpJ0I/D6iDi2B9s3W604+Zv1oYj4jaSzgJ9I2ga4kvR3eSBpEJzTI2J6rn4Z8GNJ3yQlug8Db+rh9l7Ml9WdLelRYCqps97ngG8WOvut7Pt5PK//R5JeA/yRdHrxdcB7I2K/XPUfwGilcQ7mAf+JiK6OuqcCh+dz/ouAw0mdJYsuAP6PNDhS7UqAY0jN/sVWla+TOve9SOq9v4R09cE+wLci4r6Vee9mncTJ36zvfZ7UI/1zpN7r6+bycaTL5WrOIV0C90VS4ruQdN777J5sLCJ+Kmlt0hgDXyIl369ExCmr8B6K6z9Z0n+ALwNfIZ2uuI/UqbDmDODtwPmkUxfHkJrim/kCcBbpaoOnSZfgXUahtSQilkoaRep1fzEwh5ToTya1atTq3SDpPXmbF5H6ADwAXEPjUxVmqx1FtLIjsZmtKkmbAzOA24EPeHS7lSdpS9IPj7ERcUF39c2qwsnfrB+StBNpgJzzIuLwPg6nY0g6itSn4QFSU/5RwAbAG/LQx2aGm/3N+qU8+I0vH+u5IA0ytBmpg+CfgK868Zstz0f+ZmZmFeNBfszMzCrGyd/MzKxinPzNzMwqxsnfzMysYpz8zczMKsbJ38zMrGL+fzSWyZbmA0bPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma de Odometer\n",
    "display(hist_graph(df['odometer'], 100, (df['odometer'].min(),df['odometer'].max()) ,'Histograma de Odometer Antigo', 'Quilometragem'))\n",
    "\n",
    "display(hist_graph(df_clean['odometer'], 20, (df_clean['odometer'].min(),df_clean['odometer'].max()) ,'Histograma de Odometer Novo', 'Quilometragem'))\n",
    "\n",
    "# Boxplot de Odometer\n",
    "display(box_graph(df_clean['odometer'], 'Boxplot de Odometer Novo', 'Quilometragem'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PBfYAVtDdGfASgKpaleQtwFltvYOratUchC9J0oI358m/qn4NbNpX9gu63v/96xZwwBT7OQo4ahQxSpK0NnOEP0mSxozJX5KkMWPylyRpzJj8JUkaMyZ/SZLGjMlfkqQxY/KXJGnMmPwlSRozJn9JksbMnI/wJ82XxctOmbNjXXrInnN2LEmaLa/8JUkaMyZ/SZLGjMlfkqQxY/KXJGnMmPwlSRozJn9JksaMyV+SpDFj8pckacyY/CVJGjMmf0mSxozJX5KkMWPylyRpzJj8JUkaMyZ/SZLGjMlfkqQxY/KXJGnMmPwlSRozJn9JksbMuoOslGRdYJ2quqWnbFfgocA3qurcEcUnSZKGbKDkD3wGuB54KUCSvwMOBW4B1knyzKr64kgilCRJQzVotf9OwKk9868D/q2q1gc+CvzTsAOTJEmjMWjy3xS4CiDJnwL3Bz7Ulp1AV/0vSZIWgEGT/9XA4ja9G3BZVf2kza8P/GHQAybZKMmJSX6U5IdJHpdkkyTLk1zc3jdu6ybJYUlWJDk/yWN69rO0rX9xkqWDHl+SpHE3aPI/AXhHkncBrweO7Vn2aODiWRzzfcCXq+qPgUcCPwSWAadX1fbA6W0eYHdg+/baHzgcIMkmwIHAjsAOwIETPxgkSdL0Bk3+y4APA39Ml4Df3rPsz+g6BM4oyX2BJwJHAlTVb6vql8BewDFttWOAvdv0XsCx1TkD2CjJlsBTgeVVtaqqrgOW09VISJKkGQzU27+qbgUOnmLZM2dxvO2Aa4GPJXkkcA7wamCLqrqyrXMVsEWb3gq4vGf7K1rZVOV3kGR/uhoDtt1221mEKUnS2mvQW/0ASLIj8HhgE2AV8M2qOnOWx3sM8KqqOjPJ+7i9ih+AqqokNZu4plJVRwBHACxZsmQo+5QkaaEbdJCfe9G1++8G3Ar8gu4OgHWSfBl4dlXdNMCurgCu6PnBcCJd8r86yZZVdWWr1r+mLV8JbNOz/datbCWwc1/51wb5LJIkjbtB2/zfCTwOeC6wXlVtCawH7NPK3zHITqrqKuDyJA9pRbsAFwInAxM99pcCJ7Xpk4F9W6//nYDrW/PAacCuSTZuHf12bWWSJGkGg1b7/xXw+qo6YaKgqv4AnNCS78HAqwbc16uATya5B/BT4CV0P0KOT7IfcBnwnLbuqcAewArgprYuVbUqyVuAs9p6B1fVqgGPL0nSWBs0+d+XO3aw63U5sOGgB6yq84AlkyzaZZJ1Czhgiv0cBRw16HGltdXiZafM2bEuPWTPOTuWpNEZtNr/e8DLk6S3sM2/vC2XJEkLwKBX/m8EvgT8KMnn6Ub82xx4Bt3If7uPJDpJkjR0g97n/5U2tO6/AM8GtgSuBM4EnllVF44uRIFVu5Kk4Rn4Pv+quoCud78kSVrABm3zlyRJa4kpr/yTHD+L/VRVPXcI8UiSpBGbrtp/0ZxFIUmS5syUyb+q/mIuA5EkSXPDNn9JksbMoA/2eedM61TVP65+OJIkadQGvdXv2ZOUbUw3rO/1wHWAyV+SpAVg0EF+tpusPMmOwBHA3w4zKEmSNDqr1eZfVWcC7wLeP5xwJEnSqA2jw98vgIcMYT+SJGkODNrhb4NJiu8B/AlwMHDBMIOSJEmjM2iHvxuBmqQ8wEpg72EFJEmSRmvQ5P9S7pz8bwauAL5TVb8balSSJGlkBu3tf/SI45AkSXNkoA5/SXZJ8uIplr04iUMBS5K0QAza2/+twBZTLNsMeNtwwpEkSaM2aPJ/GHD2FMu+Czx0OOFIkqRRGzT53wpsMsWyTYcUiyRJmgODJv9vAq9Lco/ewjb/D8B/DzswSZI0GoPe6vdPdD8AViT5DHAlsCXwHOC+wH6jCU+SJA3boLf6nZ/kscBBwIvoqvp/AZwOvLmqfjyyCCVJ0lBNmvyTLK6qS3vLquoi4HlzEZQkSRqdqdr8z07y5DmNRJIkzYmpqv1fARyX5G1VdWiS42fYT1XVc4ccmyRJGoFJk39VHZ/k28AxwKHA5kz+YB9JkrTATNnhr6ouT7JLm955ziKSJEkjNe19/lV1p6v9dO6fZNDbBCVJ0hpk0EF+SLJHkjPpHuV7OfCIVv6RJC+cxX4uTfL9JOclObuVbZJkeZKL2/vGrTxJDkuyIsn5SR7Ts5+lbf2Lkywd9PiSJI27QZ/qty9wMvAjYH8gPYt/zOwH+fmLqnpUVS1p88uA06tqe7qxA5a18t2B7dtrf+DwFs8mwIHAjsAOwIETPxgkSdL0Br3y/yfgXVW1FPhE37ILWP0H++xF17mQ9r53T/mx1TkD2CjJlsBTgeVVtaqqrgOWA7utZgySJI2FQZP/A+gS7GRuBjacxTEL+M8k5yTZv5VtUVVXtumruP3xwVvRNTFMuKKVTVUuSZJmMGinvcuBRwNfmWTZEmDFLI75+KpamWRzYHmSH/UurKpKMpTbCtuPi/0Btt1222HsUpKkBW/QK/8j6drVXwis38rSbgX8R+Ajgx6wqla292uAz9O12V/dqvNp79e01VcC2/RsvnUrm6q8/1hHVNWSqlqyaNGiQUOUJGmtNmjyfwfwcbr2+FWt7H+A04DPVNVhg+wkyb2S3GdiGtgV+AFdZ8KJHvtLgZPa9MnAvq3X/07A9a154DRg1yQbt45+u7YySZI0gymr/ZO8Dvh0VV3R7vc/IMl7gV3onuq3CvjKLJ/otwXw+SQTx/5UVX05yVnA8Un2Ay6je1QwwKnAHnTNCjcBLwGoqlVJ3gKc1dY7uKpWIUmSZjRdm//bgLe3YX4/BZxYVSuYXfv+HVTVT4FHTlL+C7ofFf3lBRwwxb6OAo66q7FIkjSupqv23wp4TZt+P7AyyZeT7DtRdS9JkhaeKZN/VV1TVe+vqifQ3er3Rrrq/qPpOuh9NslfJbnn3IQqSZKGYaAOf63d/91V9Vi60fbe2t5PAK5Jcsy0O5AkSWuMgcf2n1BVP6mqtwJPBj4A3BsYeGx/SZI0v2b1ZL4k9wWeAexDl/xDN/Lfp4cfmiRJGoUZk3+SDejG2N+H7n76e9Dd4/8a4ISqunaUAUqSpOGa7j7/iSv8PYENgPOANwHHVdXlU20nSZLWbNNd+X8WuBh4N91gPxfNTUiSJGmUpkv+S6rq3DmLRJIkzYnp7vM38UuStBaa9a1+kiRpYTP5S5I0Zkz+kiSNmVkN8iNpvC1edsqcHevSQ/acs2NJ42bgK/8kj0jymSQ/SXJLkse08rcm2X10IUqSpGEaKPm35H4OcD/gWODuPYtvAV41/NAkSdIoDHrl/3bg6Kp6Et0T/XqdBzxqiDFJkqQRGjT5/zHwmTZdfctuADYZWkSSJGmkBk3+1wAPnGLZw4CfDSccSZI0aoMm/+OAg5M8vqeskjwYeD3wyaFHJkmSRmLQW/3+BXgo8HXgqlZ2El0HwP8E3jb80CRJ0igMlPyr6hbgaUl2AXYBNgNWAadX1fIRxidJkoZsVoP8VNXpwOkjikWSJM2BKZN/km1ns6OqstOfJEkLwHRX/pdy59v6prPO6oUiSZLmwnTJ///2TG8IvBP4IfA5ulv/Ngf+im4MgNeNKkBJkjRcUyb/qrrtCR5Jjga+WFUv71vtQ0k+BOxJdzugJElaww16n/8z6a74J/NZ4OnDCUeSJI3aoMn/N8Djp1j2BODm4YQjSZJGbdBb/Q4H/iXJpsDJ3N7mvxfwMu78sB9JkrSGGnSQn4OSXAf8I/AKursAQjfa32ur6tCRRShJkoZq4EF+qup9Sf4d2IZuWN+rgMur6g+jCk6SJA3foG3+AFTVH6rqsqo6s73fpcSfZJ0k303yxTa/XZIzk6xI8pkk92jl92zzK9ryxT37eEMrvyjJU+9KHJIkjaNZJf8hejXdmAET3gG8t6oeBFwH7NfK9wOua+XvbeuR5KHAPnSPE94N+GASBxmSJGkAc578k2xNNy7AR9t8gCcDJ7ZVjgH2btN7tXna8l3a+nsBx1XVLVV1CbAC2GFOPoAkSQvcfFz5H0rXcXCiyWBT4JdVdWubvwLYqk1vBVwO0JZf39a/rXySbW6TZP8kZyc5+9prrx3yx5AkaWGa0+Sf5GnANVV1zlwcr6qOqKolVbVk0aJFc3FISZLWeLN6pG+SjYGH0/X4/1JVXZdkPeC3A3b++3Pg6Un2ANaje2bA+4CNkqzbru63Bla29Ve2Y12RZF3gvsAveson9G4jSZKmMdCVf+ud/0666vWvAx8HtmuLPwscOMh+quoNVbV1VS2m67D3lap6AfBV4FlttaXASW365DZPW/6VqqpWvk+7G2A7YHvgO4PEIEnSuBu02v9twN8ArwQeSDfAz4STuOMTAO+K1wN/n2QFXZv+ka38SGDTVv73wDKAqroAOB64EPgycEBV/X41Y5AkaSwMWu2/L7Csqj42yS11P6H7QTArVfU14Gtt+qdM0lu/qm4Gnj3F9m/FYYUlSZq1Qa/8N6JL8pO5B+A99pIkLRCDJv8f0N1bP5ndgXOHE44kSRq1Qav9/xX4bJL1gRPoHuzzqCTPoHuq39NHFJ8kSRqyga78q+ok4PnAU4Av0XX4+yjwYuBFVXXaqAKUJEnDNZun+h0PHJ/kwcBmwCrgonbrnSRJWiBmNcgPQFX9GPjxCGKRJElzYMrkn+RNs9lRVR28+uFIkqRRm+7K/1V98+sDG7TpG4F7t+mb2svkv5ZYvOyU+Q5BkjRCU3b4q6pFEy+63vzXAC8E7lVVGwL3Al7Uyqe6DVCSJK1hBm3zPwx4W1V9aqKgqn4DfDLJvYAPAI8ZQXySJGnIBh3k5+HA/06xbCXwJ8MJR5Ikjdqgyf/HdA/euWdvYXuc798DFw07MEmSNBqDVvu/CjgVuCLJcrp2/s2Bv6TrBLj7aMKTJEnDNugIf98Atgc+BmwJPLW9fwzYvi2XJEkLwGxG+LsS+McRxiJJkubAoG3+kiRpLTHwlX+S5wJ/AzwYWK9/eVVtPsS4JEnSiAx05Z/k+cAxwApga+Bk4Itt+xuA948qQEmSNFyDVvu/DngLcECb/2BVvRTYDvg53fC+kiRpARg0+W8PfKuqfg/8HtgQoKp+BbwDeOVowpMkScM2aPK/AZgY4Kd/RL8Amw4zKEmSNDqDdvg7C3gEcBpde/+bktwK/BZ4E3DGaMKTJEnDNmjyfzvwgDb9pjZ9OF3NwVnAy4YfmiRJGoWBkn9VnUG7uq+qXwJ7tXH+71lVN4wuPEmSNGwD3+ffr6puAW4ZYiySJGkOTJn8kxw1mx21W/8kSdIabror/z/tm98WWET3RL+Jp/ptDlwLXDaS6CRJ0tBNeatfVT124gUcDNwIPL6q7ldVj6iq+wFPAH4F/OvchCtJklbXoPf5HwL8c1X9T29hVX2Lrvf/O4YdmCRJGo1BO/w9kKmH8L0JWDyUaBaYxctOme8QJEmatUGv/M8FDkqyZW9hkvsDBwHnDDkuSZI0IoMm//3pOvddmuR/knwhyf8Al7Tyvx1kJ0nWS/KdJN9LckGSN7fy7ZKcmWRFks8kuUcrv2ebX9GWL+7Z1xta+UVJnjqLzyxJ0lgbKPlX1QXAHwH/D7iIbpz/i9r8H1XVDwY83i3Ak6vqkcCjgN2S7ETXZ+C9VfUg4Dpgv7b+fsB1rfy9bT2SPBTYB3gYsBvwwSTrDBiDJEljbeBBfqrqZuCDq3Owqiq6uwYA7t5eBTwZeH4rP4auKeFwYK82DXAi8P4kaeXHtYGGLkmyAtgB+PbqxCdJ0jgYtNp/aJKsk+Q8urEClgM/AX5ZVbe2Va4AtmrTWwGXA7Tl19M9QfC28km2kSRJ05huhL9rgKdW1XeTXEt3hT6lqtp8kANW1e+BRyXZCPg88MeDhzs7Sfan66/AtttuO6rDSJK0oExX7f8B4Oqe6WmT/2xV1S+TfBV4HLBRknXb1f3WwMq22kpgG+CKJOsC9wV+0VM+oXeb3mMcARwBsGTJkqHGL0nSQjVl8q+qN/dMHzSMgyVZBPyuJf71gb+k68T3VeBZwHHAUuCktsnJbf7bbflXqqqSnAx8Ksl7gPsD2wPfGUaMkiSt7QZq80/ylSSTVs8neXCSrwx4vC2BryY5HzgLWF5VXwReD/x967i3KXBkW/9IYNNW/vfAMrjt7oPjgQuBLwMHtOYESZI0g0F7++8MbDjFsg2BJw6yk6o6H3j0JOU/peut319+M/DsKfb1VuCtgxxXkiTdbja9/e/UZt4G43kycNXQIpIkSSM1XW//A+ke2gNd4j+ju8V+Uu8aclySJGlEpqv2PxX4ORDgMODfgEv71vkt8KOq+u+RRCdJkoZuut7+Z9F1yiPJr4BTqurncxWYJEkajYE6/FXVMaMORJIkzY2Bkn+SuwOvBp5JN6DOev3rDDrCnyRJml+D3ur3XuBlwBfpBuT57cgikiRJIzVo8n82sKyq/m2UwUiSpNEb9D7/AOePMhBJkjQ3Bk3+HwGeN8pAJEnS3Bi02v9q4AXtKXzLgV/2La+qOnyYgUmSpNEYNPkf2t63BZ40yfICTP6SJC0Ag97nP5tnAEiSpDXYoFf+krTWWrzslDk71qWH7Dlnx5KmMqvkn2Rr4MFMPsjPqcMKSpIkjc6gI/zdBzge2HWiqL33PuZ3nSHGJUmSRmTQtvy303X2ewJd4n8GsDNwJHAJsNMogpMkScM3aPLfA3grcGab/9+q+kZV7Q+cBLxuFMFJkqThGzT5bwFcXlW/B34NbNKz7FRubw6QJElruEE7/F0ObNamLwaeBpzW5ncEbh5yXNKCNpe9xyVptgZN/suBpwCfp3vC3zFJ/gy4BXgi4AN/JElaIAZN/q8HNgCoqo8nuRF4FrA+8Ergw6MJT5IkDdugI/zdBNzUM/95uloASZK0wAx6n/8GM63TfiBIkqQ13KDV/jdyxwF9JuMgP5IkLQCDJv+XcufkvzHwVOChwFuGGZQkSRqdQdv8j55i0aFJDgceNrSIJEnSSA3jUb2fBfYdwn4kSdIcGEbyfyzd/f6SJGkBGLS3/zsnKb4H8CfALsChQ4xJkiSN0KAd/p49SdnNwBXA3wFHDC0iSZI0UoN2+Ntu1IFIkqS5MYw2/4El2SbJV5NcmOSCJK9u5ZskWZ7k4va+cStPksOSrEhyfpLH9OxraVv/4iRL5/JzSJK0kM2Y/JM8PMmHkvwwya+S3JDkoiQfS/KEWR7vVuAfquqhwE7AAUkeCiwDTq+q7YHT2zzA7sD27bU/cHiLaRPgQLonCu4AHDjxg0GSJE1v2uTfrsy/CzwX+CHwUeBI4AJgL+BrSQ5t694tyb9Pt7+qurKqzm3Tv2r73Krt65i22jHA3m16L+DY6pwBbJRkS7rBhZZX1aqquo7uqYO7zeJzS5I0tqZs80+yB93je98JvK2qbuhbfh/gDcDrk6wEngQ8GXjVIAdOshh4NHAmsEVVXdkWXQVs0aa3Ai7v2eyKVjZVuSRJmsF0Hf5eCxxTVcsmW9iu3N/YrsQPoUvafzHIQZPcm25woNdU1Q1JevdbSWZ6jsBAkuxP11zAtttuO4xdSpK04E1X7f9nwHED7GNinSVVdeZMKye5O13i/2RVfa4VX91+RNDer2nlK4FtejbfupVNVX4HVXVEVS2pqiWLFi0a4KNIkrT2my75342ug95MbgVu6qm2n1K6S/wjgR9W1Xt6Fp0MTPTYXwqc1FO+b+v1vxNwfTvOacCuSTZuHf12bWWSJGkG01X7XwA8ja73/XSeBvxgwOP9OfAi4PtJzmtlb6RrNjg+yX7AZcBz2rJTgT2AFcBNwEsAqmpVkrcAZ7X1Dq6qVQPGIEnSWJsu+X8IOCLJhcBHq+pO7fBJ/hp4BfA3gxysqr4JZIrFu0yyfgEHTLGvo4CjBjmuJEm63ZTJv6qOTrIj8GHgtUn+g+6qHOABwJ7Ag4EPV9WxI49UkiQNxbTD+1bVy5OcBrya7gr8nm3RLcD/AMuq6qSptpckSWueGcf2r6ovAF9Isg6wWSv+eVX9fpSBSRpvi5edMt8hSGutQZ/qR0v2V48wFkmSNAfm9ME+kiRp/pn8JUkaMyZ/SZLGjMlfkqQxY/KXJGnMmPwlSRozJn9JksaMyV+SpDFj8pckacwMPMKfJGn1zeWwxZcesuecHUsLi1f+kiSNGZO/JEljxuQvSdKYMflLkjRmTP6SJI0Zk78kSWPG5C9J0pgx+UuSNGZM/pIkjRmTvyRJY8bkL0nSmDH5S5I0Zkz+kiSNGZO/JEljxuQvSdKYMflLkjRmTP6SJI0Zk78kSWNmTpN/kqOSXJPkBz1lmyRZnuTi9r5xK0+Sw5KsSHJ+ksf0bLO0rX9xkqVz+RkkSVro5vrK/2hgt76yZcDpVbU9cHqbB9gd2L699gcOh+7HAnAgsCOwA3DgxA8GSZI0szlN/lX1DWBVX/FewDFt+hhg757yY6tzBrBRki2BpwLLq2pVVV0HLOfOPygkSdIU1oQ2/y2q6so2fRWwRZveCri8Z70rWtlU5XeSZP8kZyc5+9prrx1u1JIkLVBrQvK/TVUVUEPc3xFVtaSqlixatGhYu5UkaUFbE5L/1a06n/Z+TStfCWzTs97WrWyqckmSNIA1IfmfDEz02F8KnNRTvm/r9b8TcH1rHjgN2DXJxq2j366tTJIkDWDduTxYkk8DOwObJbmCrtf+IcDxSfYDLgOe01Y/FdgDWAHcBLwEoKpWJXkLcFZb7+Cq6u9EKEmSpjCnyb+qnjfFol0mWbeAA6bYz1HAUUMMTZKksbEmVPtLkqQ5ZPKXJGnMmPwlSRozJn9JksaMyV+SpDFj8pckaczM6a1+kqS10+Jlp8zZsS49ZM85O9bayit/SZLGjMlfkqQxY/KXJGnMmPwlSRozJn9JksaMyV+SpDHjrX6StJaay9vvtLB45S9J0pgx+UuSNGZM/pIkjRmTvyRJY8bkL0nSmDH5S5I0ZrzVT5K0oPgEwdXnlb8kSWPG5C9J0pgx+UuSNGZM/pIkjRmTvyRJY8bkL0nSmDH5S5I0Zkz+kiSNGZO/JEljxuQvSdKYWdDJP8luSS5KsiLJsvmOR5KkhWDBJv8k6wAfAHYHHgo8L8lD5zcqSZLWfAs2+QM7ACuq6qdV9VvgOGCveY5JkqQ13kJO/lsBl/fMX9HKJEnSNNbqR/om2R/Yv83emOSiIR9iM+DnQ97nuPEcrj7P4erzHK6+tfIc5h1zerhRnMMHTFa4kJP/SmCbnvmtW9ltquoI4IhRBZDk7KpaMqr9jwPP4erzHK4+z+Hq8xyuvrk8hwu52v8sYPsk2yW5B7APcPI8xyRJ0hpvwV75V9WtSV4JnAasAxxVVRfMc1iSJK3xFmzyB6iqU4FT5zGEkTUpjBHP4erzHK4+z+Hq8xyuvjk7h6mquTqWJElaAyzkNn9JknQXmPzvAocVXn1JLk3y/STnJTl7vuNZKJIcleSaJD/oKdskyfIkF7f3jeczxjXdFOfwoCQr2/fxvCR7zGeMa7ok2yT5apILk1yQ5NWt3O/igKY5h3PyXbTaf5basMI/Bv6SbmChs4DnVdWF8xrYApPkUmBJVa119wWPUpInAjcCx1bVw1vZO4FVVXVI+zG6cVW9fj7jXJNNcQ4PAm6sqnfPZ2wLRZItgS2r6twk9wHOAfYGXozfxYFMcw6fwxx8F73ynz2HFda8qapvAKv6ivcCjmnTx9D9B6IpTHEONQtVdWVVndumfwX8kG6EVb+LA5rmHM4Jk//sOazwcBTwn0nOaSMx6q7boqqubNNXAVvMZzAL2CuTnN+aBayuHlCSxcCjgTPxu3iX9J1DmIPvoslf8+XxVfUYuqcyHtCqYrWaqmvHsy1v9g4H/gh4FHAl8G/zGs0CkeTewGeB11TVDb3L/C4OZpJzOCffRZP/7M04rLBmVlUr2/s1wOfpmlN011zd2g8n2hGvmed4Fpyqurqqfl9VfwA+gt/HGSW5O13S+mRVfa4V+12chcnO4Vx9F03+s+ewwqspyb1aBxeS3AvYFfjB9FtpGicDS9v0UuCkeYxlQZpIWM0z8Ps4rSQBjgR+WFXv6Vnkd3FAU53Dufou2tv/Lmi3XhzK7cMKv3V+I1pYkjyQ7mofulEmP+U5HEySTwM70z3962rgQOALwPHAtsBlwHOqyg5tU5jiHO5MV81awKXAy3rartUnyeOB/wa+D/yhFb+Rrs3a7+IApjmHz2MOvosmf0mSxozV/pIkjRmTvyRJY8bkL0nSmDH5S5I0Zkz+kiSNGZO/pDtpTxab9qFLSc5OcvSQjvfz9nAdSXPA5C9J0pgx+UuSNGZM/pJmlOThSb6V5OYkP0zy9EnWeVySk5NcmeTXSc5L8oJJ1ntiku+1fZ2T5P9Mccy9WtPCzUmuSvLONha6pNW07nwHIGnNlmR94DTg58DzgfXphre+N3ccd/wBwLeADwE3A38OfCzJH6rq021f9we+BHwHeBZwf+CTwAZ9x3wO8Gngw3RDnv4R8Ha6C5bXjuBjSmPF5C9pJi8BNgd2rKorAJJcCnyzd6WqOm5iuj205Bt0T738G7pEDvAauh8Ge1bVTW3dXwOf6Nv2XcCxVfWKnvJbgA8keXtV/WK4H1EaL1b7S5rJDsA5E4kfoKq+Rd/jWpNsnOSwJJcBv2uv/YEH9+1r+UTibz7PHT2Y7sEwxydZd+IFfAVYD3j4kD6XNLa88pc0k/sx+XPZ+8uOBnYC3gJcCNwAvBzYq29f5/duVFU3Jbmxp2iz9n7qFPFsM1DUkqZk8pc0k6uAP56kfPOJiSTrAU8DDqiqD/WU99cuXtW7XVtnA7r+AxMmHgG7P/DdSY57ycCRS5qUyV/STM4CXpBk6542/z/njkn8nnTNiLdMFCS5D/B0uueS9+7rpUk26Kn6f0bf8S4CVgKLq+ojQ/0kkgCTv6SZfQz4Z+CUNgrf+nRV+7eNAFhV1yc5C3hTkhuAPwDLgOuBDXv2dShwAPDFJO+h6+3/BuA3Pfv6Q5J/AD6eZEO6uwN+CzwQ2Bt4Vl+fAUmzZIc/SdNqifapwK+B44ADgX8ALutb9fnAT4FjgfcBn23TvftaCexB167/WeAVwAuBm/rW+wxdX4FHAScAn2vrnkv3Q0DSakhVzbyWJElaa3jlL0nSmDH5S5I0Zkz+kiSNGZO/JEljxuQvSdKYMflLkjRmTP6SJI0Zk78kSWPG5C9J0pj5/ymEq24dXLMhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograma de Age\n",
    "display(hist_graph(df['age'], 30, (df['age'].min(),df['age'].max()) ,'Histograma de Age Antigo', 'Idade'))\n",
    "\n",
    "display(hist_graph(df_clean['age'], 15, (0,25) ,'Histograma de Age Novo', 'Idade'))\n",
    "\n",
    "# Boxplot de Age\n",
    "display(box_graph(df_clean['age'], 'Boxplot de Age Novo', 'Idade'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A retirada dos valores atipícos permitiu uma análise mais clara. As conclusões sendem relação a preço, quilometragem, idade, cilindros e idade, sendo:\n",
    "\n",
    "- O preço varia em até 35.000 dólares, com a maioria dos dados entre 5.000 e 15.000 dólares. \n",
    "\n",
    "\n",
    "- A quilometragem começa entre 0 e 250.000, carros com 0 quilômetros têm idade igual a zero, por serem carros novos. A idade está entre 0 e 23 anos, a maioria entre 5 e 10 anos, a maioria dos carros está sendo vendido com um tempo de uso, sendo possível perceber uma relação entre idade e quilometragem.\n",
    "\n",
    "\n",
    "- Os cilindros mais comuns são de 8, 6 e 4. Os outros possuem uma quantidade desprezível em relação ao total.\n",
    "\n",
    "\n",
    "- As condições mais comuns são \"excelente\" e \"bom\". Ambos representam a grande maioria dos dados.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tempo de vida das propagandas\n",
    "\n",
    "Será visto por quantos dias as propagandas foram exibidas da coluna **`days_listed`**. Calcularei a média e a mediana. Descreverei o tempo de vida útil comum de uma propaganda. Determinando quando as propagandas foram removidas rapidamente, e quando elas foram listadas por um tempo anormalmente longo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histograma de days_listed\n",
    "display(hist_graph(df_clean['days_listed'], 40, (df_clean['days_listed'].min(),df_clean['days_listed'].max()) ,'Histograma de Days_listed', 'Quantidade de dias que ficou exibido'))\n",
    "\n",
    "# Boxplot de Age\n",
    "display(box_graph(df_clean['days_listed'], 'Boxplot de Days_listed', 'Quantidade de dias que ficou exibido'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média:\n",
      "39.55475982532751\n",
      "\n",
      "Mediana:\n",
      "33.0\n"
     ]
    }
   ],
   "source": [
    "print('Média:')\n",
    "print(df['days_listed'].mean())\n",
    "\n",
    "print('\\nMediana:')\n",
    "print(df['days_listed'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de propagandas retiradas em até 1 dia:\n",
      "214\n"
     ]
    }
   ],
   "source": [
    "print('Número de propagandas retiradas em até 1 dia:')\n",
    "print(len(df_clean.query('days_listed <2')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo considerado anormalmente longo:\n",
      "104.0 dias\n",
      "\n",
      "Quantidade de propagandas que ficaram acima de um tempo anormalmente longo:\n",
      "1560\n",
      "\n",
      "Em porcentagem:\n",
      "0.03298724915945952\n"
     ]
    }
   ],
   "source": [
    "days_listed_IQR = df_clean['days_listed'].quantile(0.75) - df_clean['days_listed'].quantile(0.25)\n",
    "days_listed_lim = df_clean['days_listed'].quantile(0.75) +  1.5 * days_listed_IQR\n",
    "\n",
    "print('Tempo considerado anormalmente longo:')\n",
    "print(f'{days_listed_lim} dias')\n",
    "\n",
    "print('\\nQuantidade de propagandas que ficaram acima de um tempo anormalmente longo:')\n",
    "print(len(df_clean.query('days_listed >= @days_listed_lim')))\n",
    "\n",
    "print('\\nEm porcentagem:')\n",
    "print(len(df_clean.query('days_listed >= @days_listed_lim')) / len(df_clean['days_listed']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de propagandas que ficaram em um tempo normal:\n",
      "45731\n",
      "\n",
      "50% das propagandas ficaram entre 19.0 e 53.0 dias\n"
     ]
    }
   ],
   "source": [
    "print('Quantidade de propagandas que ficaram em um tempo normal:')\n",
    "print(len(df_clean.query('days_listed < @days_listed_lim')))\n",
    "\n",
    "print(f'\\n50% das propagandas ficaram entre {df_clean[\"days_listed\"].quantile(0.25)} e {df_clean[\"days_listed\"].quantile(0.75)} dias')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Média de preços por cada tipo de veículo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisarei o número de propagandas e o preço médio para cada tipo de veículo. Construindo um gráfico mostrando a dependência do número de propagandas em relação ao tipo de veículo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SUV</th>\n",
       "      <td>10717.1</td>\n",
       "      <td>8980.0</td>\n",
       "      <td>11596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bus</th>\n",
       "      <td>18107.4</td>\n",
       "      <td>28990.0</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>convertible</th>\n",
       "      <td>13481.4</td>\n",
       "      <td>12997.5</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coupe</th>\n",
       "      <td>14073.9</td>\n",
       "      <td>13990.0</td>\n",
       "      <td>1935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>6918.0</td>\n",
       "      <td>5995.0</td>\n",
       "      <td>993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mini-van</th>\n",
       "      <td>8086.4</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>1139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offroad</th>\n",
       "      <td>14503.3</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>10587.6</td>\n",
       "      <td>8995.0</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup</th>\n",
       "      <td>15151.8</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>6230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>7085.4</td>\n",
       "      <td>5999.0</td>\n",
       "      <td>11828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>truck</th>\n",
       "      <td>15599.7</td>\n",
       "      <td>14995.0</td>\n",
       "      <td>10746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>van</th>\n",
       "      <td>9158.7</td>\n",
       "      <td>7990.0</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>8996.3</td>\n",
       "      <td>7925.0</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            <lambda>   median  count\n",
       "               price    price  price\n",
       "type                                \n",
       "SUV          10717.1   8980.0  11596\n",
       "bus          18107.4  28990.0     21\n",
       "convertible  13481.4  12997.5    330\n",
       "coupe        14073.9  13990.0   1935\n",
       "hatchback     6918.0   5995.0    993\n",
       "mini-van      8086.4   6500.0   1139\n",
       "offroad      14503.3  12500.0    180\n",
       "other        10587.6   8995.0    232\n",
       "pickup       15151.8  14000.0   6230\n",
       "sedan         7085.4   5999.0  11828\n",
       "truck        15599.7  14995.0  10746\n",
       "van           9158.7   7990.0    561\n",
       "wagon         8996.3   7925.0   1500"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.pivot_table(index='type',values='price',aggfunc=[lambda x: round(x.mean(), 1),'median','count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico de barras de Type\n",
    "bar_graph(df['type'], 'Grafico de barras de Type', 'Tipo de Veículo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48050460941290635"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.query('(type==\"SUV\") or (type==\"truck\")')) / len(df['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SUV e truck são os tipos mais comuns de veículo. Sepresentam 48% do dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fatores de preço"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisarei quais fatores impactam mais o preço. Os parâmetros idade, quilometragem, condição, tipo de transmissão e cor foram escolhidos para ver a dependencia, por possuirem correlação com o preço (visto na matriz de correlação). Para variáveis continuas serão usados gráficos de dispersão, para as variáveis categóricas (tipo de transmissão e cor) serão usados gráficos de extremos e quartis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scatter_graph(data, title, xlabel):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.scatter( data, df_clean['price'])\n",
    "    plt.title(title)\n",
    "    plt.ylabel('Preço')\n",
    "    plt.xlabel(xlabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def box_double_graph(dt, title, xlabel):\n",
    "    plt.figure(figsize = ((9,7)))\n",
    "    sns.boxplot(data=df_clean, x= dt, y='price')\n",
    "    plt.title(title, fontsize = 16)\n",
    "    plt.ylabel('Preço')\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>price</th>\n",
       "      <th>model_year</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>odometer</th>\n",
       "      <th>is_4wd</th>\n",
       "      <th>days_listed</th>\n",
       "      <th>day_posted</th>\n",
       "      <th>age</th>\n",
       "      <th>odo_year</th>\n",
       "      <th>condition_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.552851</td>\n",
       "      <td>0.350412</td>\n",
       "      <td>-0.453549</td>\n",
       "      <td>0.358747</td>\n",
       "      <td>-0.001784</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>-0.550781</td>\n",
       "      <td>0.256847</td>\n",
       "      <td>0.200342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0.552851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.171937</td>\n",
       "      <td>-0.513410</td>\n",
       "      <td>-0.031671</td>\n",
       "      <td>-0.002168</td>\n",
       "      <td>0.000921</td>\n",
       "      <td>-0.995749</td>\n",
       "      <td>0.528640</td>\n",
       "      <td>0.256245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cylinders</th>\n",
       "      <td>0.350412</td>\n",
       "      <td>-0.171937</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136509</td>\n",
       "      <td>0.407845</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>-0.006218</td>\n",
       "      <td>0.171321</td>\n",
       "      <td>-0.059272</td>\n",
       "      <td>-0.092462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odometer</th>\n",
       "      <td>-0.453549</td>\n",
       "      <td>-0.513410</td>\n",
       "      <td>0.136509</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.072498</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.511153</td>\n",
       "      <td>0.083124</td>\n",
       "      <td>-0.295197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_4wd</th>\n",
       "      <td>0.358747</td>\n",
       "      <td>-0.031671</td>\n",
       "      <td>0.407845</td>\n",
       "      <td>0.072498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.031369</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>-0.006689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_listed</th>\n",
       "      <td>-0.001784</td>\n",
       "      <td>-0.002168</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002323</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.001139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_posted</th>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000921</td>\n",
       "      <td>-0.006218</td>\n",
       "      <td>0.001276</td>\n",
       "      <td>0.002575</td>\n",
       "      <td>0.002323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.001892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.550781</td>\n",
       "      <td>-0.995749</td>\n",
       "      <td>0.171321</td>\n",
       "      <td>0.511153</td>\n",
       "      <td>0.031369</td>\n",
       "      <td>0.003097</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.537304</td>\n",
       "      <td>-0.254985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>odo_year</th>\n",
       "      <td>0.256847</td>\n",
       "      <td>0.528640</td>\n",
       "      <td>-0.059272</td>\n",
       "      <td>0.083124</td>\n",
       "      <td>0.012625</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>-0.537304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition_index</th>\n",
       "      <td>0.200342</td>\n",
       "      <td>0.256245</td>\n",
       "      <td>-0.092462</td>\n",
       "      <td>-0.295197</td>\n",
       "      <td>-0.006689</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>-0.254985</td>\n",
       "      <td>0.036083</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    price  model_year  cylinders  odometer    is_4wd  \\\n",
       "price            1.000000    0.552851   0.350412 -0.453549  0.358747   \n",
       "model_year       0.552851    1.000000  -0.171937 -0.513410 -0.031671   \n",
       "cylinders        0.350412   -0.171937   1.000000  0.136509  0.407845   \n",
       "odometer        -0.453549   -0.513410   0.136509  1.000000  0.072498   \n",
       "is_4wd           0.358747   -0.031671   0.407845  0.072498  1.000000   \n",
       "days_listed     -0.001784   -0.002168   0.003533  0.005377  0.002933   \n",
       "day_posted       0.000415    0.000921  -0.006218  0.001276  0.002575   \n",
       "age             -0.550781   -0.995749   0.171321  0.511153  0.031369   \n",
       "odo_year         0.256847    0.528640  -0.059272  0.083124  0.012625   \n",
       "condition_index  0.200342    0.256245  -0.092462 -0.295197 -0.006689   \n",
       "\n",
       "                 days_listed  day_posted       age  odo_year  condition_index  \n",
       "price              -0.001784    0.000415 -0.550781  0.256847         0.200342  \n",
       "model_year         -0.002168    0.000921 -0.995749  0.528640         0.256245  \n",
       "cylinders           0.003533   -0.006218  0.171321 -0.059272        -0.092462  \n",
       "odometer            0.005377    0.001276  0.511153  0.083124        -0.295197  \n",
       "is_4wd              0.002933    0.002575  0.031369  0.012625        -0.006689  \n",
       "days_listed         1.000000    0.002323  0.003097  0.000405         0.001139  \n",
       "day_posted          0.002323    1.000000 -0.001380  0.000628         0.001892  \n",
       "age                 0.003097   -0.001380  1.000000 -0.537304        -0.254985  \n",
       "odo_year            0.000405    0.000628 -0.537304  1.000000         0.036083  \n",
       "condition_index     0.001139    0.001892 -0.254985  0.036083         1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_clean.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_graph(df_clean['age'], 'Gráfico de Dispersão entre Price e Age', 'Idade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_graph(df_clean['odometer'], 'Gráfico de Dispersão entre Price e Odometer', 'Quilometragem')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nV2z01jBOAszMzArWta6Hxcu76d3ZB0DPjl4WL+8GyDUR8NgBZmZmBVu6csNAAtCvd2cfS1duyHW7TgLMzMwKdu+O3prijeIkwMzMrGCHtrfVFG8UJwFmZmYFWzR/Jq2TBg+E2zpJLJo/M9ftOgkwMzMbC7Sb6Rw4CTAzMyvY0pUb2Nk3uL/PnX3hioFmZmYTnSsGmpmZNSlXDDQzM2tSi+bPpK21ZVCsrbUl94qB7jHQzMysYP29Ao52t8G+EmBmZtakfCXAzMysYB47wMzMrEl57AAzM7Mm5SaCZmZmTcpNBM3MzJqUmwiamZk1qaKaCDoJMDMzGwM6Z3fkftIv59sBZmZmTcpJgJmZWZNyEmBmZtaknASYmZk1KScBZmZmTcpJgJmZWZNyEmBmZtaknASYmZk1KXcWZGZmNgZ0retxj4FmZmbNpmtdD4uXdw8MJ9yzo5fFy7sBck0EfDvAzMysYEtXbhhIAPr17uxj6coNuW7XSYCZmVnB7t3RW1O8UZwEmJmZFezQ9raa4o3iJMDMzKxgi+bPpK21ZVCsrbWFRfNn5rrd3JIASXtLulnSryWtl/TpFH++pDWSNkq6RtKeKb5Xmt6Y5h9esq7FKb5B0vyS+IIU2yjpnLz2xczMLE+dszs4YO/BScABe7fk3jogzysBTwInRMRLgaOBBZLmAV8AvhgRLwS2A+9Jy78H2J7iX0zLIelI4G3AUcAC4GuSWiS1AF8FTgaOBBamZc3MzMaVEy+4kQceeWpQ7IFHnuLEC27Mdbu5JQGReTRNtqZHACcA307xy4HO9PzUNE2a/xpJSvGrI+LJiPgDsBE4Nj02RsRdEfEUcHVa1szMbFy5c8tjNcUbJdc6AekX+23AFmAV8HtgR0Q8nRbZDPRf6+gANgGk+Q8Bzy2Nl72mWrxSOc6StFbS2q1btzZgz8zMzMa/XJOAiOiLiKOBqWS/3F+U5/aGKMdFETEnIuZMmTKliCKYmZmNOaPSOiAidgA3AK8A2iX191Q4FehJz3uAaQBp/nOAB0vjZa+pFjczM7NhyLN1wBRJ7el5G3AicAdZMvDmtNgZwPfS8xVpmjT/xxERKf621Hrg+cAM4GbgFmBGam2wJ1nlwRV57Y+ZmVleTp83vaZ4o+Q5dsAhwOWpFv8k4NqI+L6k3wJXSzoPWAdckpa/BPi/kjYC28hO6kTEeknXAr8FngbOjog+AEkfBFYCLcClEbE+x/0xMzPLxXmdswBYtmYTfRG0SCycO20gnpfckoCIuB2YXSF+F1n9gPL4E8BbqqzrfOD8CvHrgOtGXFgzM7OCndc5K/eTfjmPImhmZjYGeChhMzOzJuShhM3MzJqUhxI2MzNrUh5K2MzMrEl5KGEzM7MmVdRQwq4YaGZmVrD+yn9uHWBmZtaEOmd35H7SL+ckwMzMbAxY0tU9cXoMNDMzs+FZ0tXNlavvGZjuixiYzjMRcMVAMzOzgi1bs6mmeKM4CTAzMytYX0RN8UZxEmBmZlawFqmmeKM4CTAzMyvYwrnTaoo3iisGmpmZFay/8p9bB1jTKGLYTDOzseq8zlm5n/TLOQmwQhQ1bKaZme3iOgFWiKKGzTQzs12cBFgheqoMj1ktbmZmjeckwApRVHMYMzPbxUmAFaKojjHMzGwXJwFWiI72tpriZmbWeE4CrBCL5s+krbVlUKyttYVF82cWVCIzs+bjJoJWiP5mgO4nwMysOE4CrDCdszt80jczK5CTADMzszFgSVe3uw02MzNrNku6urly9T0D030RA9N5JgKuGGhmZlawZWs21RRvFCcBZmZmBSuq7xQnAWZmZgUrqhdVJwFmZmYFe8GUfWqKN4qTADMzs4LdtfXxmuKN4iTAzMysYK4TYGZm1qQmXJ0ASdMk3SDpt5LWS/pwip8rqUfSbenxupLXLJa0UdIGSfNL4gtSbKOkc0riz5e0JsWvkbRnXvtjZmaWl4Vzp9UUb5Q8rwQ8DXwsIo4E5gFnSzoyzftiRBydHtcBpHlvA44CFgBfk9QiqQX4KnAycCSwsGQ9X0jreiGwHXhPjvtjZmaWi/M6Z3H6vOkDv/xbJE6fN3389hgYEfcB96Xnj0i6Axiqo/hTgasj4kngD5I2AsemeRsj4i4ASVcDp6b1nQC8PS1zOXAucGGj98XMzCxv53XOyv2kX25U6gRIOhyYDaxJoQ9Kul3SpZIOTLEOoLRrpM0pVi3+XGBHRDxdFq+0/bMkrZW0duvWrY3YJTMzs3Ev9yRA0n7Ad4CPRMTDZL/UjwCOJrtS8L/zLkNEXBQRcyJizpQpU/LenJmZ2biQ6wBCklrJEoCrImI5QEQ8UDL/YuD7abIHKK0BMTXFqBJ/EGiXtEe6GlC6vJmZme1Gnq0DBFwC3BERF5TEDylZ7I3Ab9LzFcDbJO0l6fnADOBm4BZgRmoJsCdZ5cEVERHADcCb0+vPAL6X1/6YmZlNNHleCTgOeAfQLem2FPskWe3+o4EA/gi8DyAi1ku6FvgtWcuCsyOiD0DSB4GVQAtwaUSsT+v7BHC1pPOAdWRJh5mZmQ1Dnq0Dfg5U6uXguiFecz5wfoX4dZVel1oMHFseNzMzs93LtU6AmZmZDU/Xuh6WrtzAvTt6ObS9jUXzZ9I5e6iW9SPnJMDMzKxgXet6WLy8m96dfQD07Ohl8fJugFwTAY8dYGZmVrClKzcMJAD9enf2sXTlhly36yTAzMysYPfu6K0p3ihOAszMzAp2aHtbTfFGcRJgZmZWsEXzZ9LW2jIo1tbawqL5M3PdrisGmpmZFay/8p9bB5iZmTWhztkduZ/0yzkJMDMzGwOWdHWzbM0m+iJokVg4d1ruQws7CTAzMyvYkq5urlx9z8B0X8TAdJ6JgCsGmpmZFWzZmk01xRvFSYCZmVnB+iJqijeKkwAzM7OCtajSeHvV443iJMDMzKxgL5iyT03xRnESYGZmVrC7tj5eU7xRnASYmZkVzHUCzMzMbFS5n4A6da3rGfXuHScaH0Mzs2I5CahD17oeFi/vHhj7uWdHL4uXdwP4JDZMPoZmZsXz7YA6LF25YeDk1a93Zx9LV24oqETjj4+hmdku1RoC5ttA0ElAXe7d0VtT3J7Nx9DMbJdq1f/yrRboJKAuh7a31RS3Z/MxNDPbxZ0FjSOL5s+krbVlUKyttYVF82cWVKLxx8fQzGyXopoIumJgHforrrlme/18DM3Mdulob6Onwu3QjpyvjjoJqFPn7A6fsEbIx9DMLLNo/sxBLaZgdK6OOgkwMzMrWFFXR50EmJmZjQFFXB11ElCnJV3dLFuzib4IWiQWzp3GeZ2zii6WmZnZsDkJqMOSrm6uXH3PwHRfxMC0EwEzMxsv3ESwDsvWbKopbmZmNhb5SkAdimrPOdH4loqZWbF8JaAORfXsNJH031LpT5z6b6ks6eouuGRmZs3DSUAdFs6dVlPcns23VMzMipdbEiBpmqQbJP1W0npJH07xyZJWSboz/T0wxSXpy5I2Srpd0jEl6zojLX+npDNK4i+T1J1e82VpdH6Kn9c5i9PnTR/45d8icfq86b6UXQPfUjEzK16edQKeBj4WEb+StD9wq6RVwLuA6yPi85LOAc4BPgGcDMxIj7nAhcBcSZOBTwFzyAZUulXSiojYnpY5E1gDXAcsAH6Y4z4NOK9zlk/6I9AiVTzh+5aKmTWrIupJ5XYlICLui4hfpeePAHcAHcCpwOVpscuBzvT8VOCKyKwG2iUdAswHVkXEtnTiXwUsSPMOiIjVERHAFSXrsjHOt1TMzHYpqp7UqNQJkHQ4MJvsF/vBEXFfmnU/cHB63gGU3hDenGJDxTdXiNs48Ietj9YUNzObyIqqJ5V7EiBpP+A7wEci4uHSeekXfO43gSWdJWmtpLVbt27Ne3M2DL/4/baa4mZmE1lR9aRyTQIktZIlAFdFxPIUfiBdyif93ZLiPUDpteCpKTZUfGqF+LNExEURMSci5kyZMmVkO2VmZtZgRTU9z7N1gIBLgDsi4oKSWSuA/hr+ZwDfK4m/M7USmAc8lG4brAROknRgaklwErAyzXtY0ry0rXeWrMvMzGzcKKqeVJ6tA44D3gF0S7otxT4JfB64VtJ7gLuBv0nzrgNeB2wEHgfeDRAR2yR9FrglLfeZiOi/ZvwB4DKgjaxVwKi0DLCRO+6IyRUv/R93xOQCSmNmVqz+VgCj3TogtyQgIn4OVLuO8ZoKywdwdpV1XQpcWiG+FnjxCIppBbnqzFdw2sU3DUoEjjtiMled+YoCS2VmVpwimp577AArjE/4ZmbFchJgZmY2BhTRWZCTADMzs4L1dxbUr7+zICDXRMADCJmZmRXsW2vuqSneKE4CzMzMCvZMlT6BqsUbxbcD6tS1roelKzdw745eDm1vY9H8mXTOdq/FtfAxNDMrlpOAOnSt62Hx8m56d/YB0LOjl8XLs0EefBIbHh9DM7Nd2lon0bvzmYrxPA1r7ZJaJX1I0rfT4+9Tl8BNaenKDQMnr369O/tYunJDQSUaf3wMzcx2+dybXvKsE/KkFM/TcK8EXAi0Al9L0+9IsffmUaix7t4dvTXF7dl8DM3Mdum/Ajrat0iHe53h5RFxRkT8OD3eDbw8z4KNZYe2t9UUt2fzMTQzG2zt3du4/6EnCOD+h55g7d35j6o63CSgT9IR/ROSXgD0DbH8hLZo/kzaWlsGxdpaW1g0f2ZBJRp/nu6r/PGpFjczm8j6+wnoHzq4v5+AJV3duW53uEnAIuAGSTdK+gnwY+Af8ivW2NY5u4PPvWkWHe1tCOhob+Nzb5rlCm01eOCRp2qKm5lNZMvWbKop3ijDqhMQEddLmgH0/9TdEBFP5lessa9zdodP+mZm1hD9VwCGG2+U4bYO+BTQFhG3R8TtwD6S/meuJTMzM2sSLao86G61eKMM93ZAZ0Ts6J+IiO3A30r6hqTrcimZmZlZk5j3ggNrijfKcJOAPSTt2T8haW/gqYh4H3BILiWzCa2orNfMbCz644OVm0dXizfKcPsJuBL4T0mXpekzgEvS81c2ulA28RV1/8vMbCwqqu+U4VYM/IKkXwOvTaF/joiVad5jeRXOJq6O9jZ6Kny4O9xPgJk1oUOrfCfm3XdKLZ0S3wH8R0T8A/AzSfvnVCZrAu5rwcxsl0XzZ9I6afDt0NZJyv07cbitA84Evg18I4U6gK6cymRNwH0tmJmVKa8SNQpVpIZbJ+Bs4FhgDUBE3CnpebmVypqC+1owM8ssXbmBnX2D60Tt7AuWrtyQ6/fkcG8HPBkRA125SdoDcA0uMzOzBiiqYuBwk4CfSPok0CbpRODfgf+XX7HMzMyaR1GDqg03CfgEsBXoBt4HXAcsyatQZmZmzeTVL5pSU7xRdlsnQFILsD4iXgRcnGtpzMzMmtANv9taU7xRdnslICL6gA2SpudaEjMzsyY1pjsLAg4E1ku6GRjoHCgiTsmlVGZmZk2kfZ9Wtj++s2I8T8NNAjxioJmZWU6q9Zied0/qQyYBaaCgvwNeSFYp8JKIeDrfIpmZmTWXh3qffRVgqHij7K5OwOXAHLIE4GTgf+daGjMzsyY0VpsIHhkRp0fEN4A3A/8t19KYmZk1oaLGU9ldnYCB6xAR8bQ81rs1UNe6Hpau3MC9O3o5tL2NRfNnuhthM2tK/d99o/2duLsk4KWSHk7PRdZj4MPpeUTEAbmWziasrnU9LF7eTe/OPgB6dvSyeHk3gBMBM2tKRYynMuTtgIhoiYgD0mP/iNij5LkTAKvb0pUbBhKAfr07+1i6ckNBJTIzaz7D7Ta4ZpIulbRF0m9KYudK6pF0W3q8rmTeYkkbJW2QNL8kviDFNko6pyT+fElrUvwaSXvmtS/WeD1VOsCoFjczs8Ybbj8B9bgM+ApwRVn8ixHxL6UBSUcCbwOOAg4F/lPSn6fZXwVOBDYDt0haERG/Bb6Q1nW1pK8D7wEuzGtnyvl+9si0SPRVaADb4nonZtaklnR1s2zNJvoiaJFYOHca53XOynWbuV0JiIifAtuGufipwNUR8WRE/AHYCBybHhsj4q40lPHVwKnKaiieAHw7vf5yoLOR5R9K//3snh29BLvuZ3et6xmtIox7lRKAoeJmZhPZkq5urlx9z8B3YF8EV66+hyVd3bluN7ckYAgflHR7ul1wYIp1AJtKltmcYtXizwV2lHRc1B8fFb6fbWZmjbRszaaa4o0y2knAhcARwNHAfYxS50OSzpK0VtLarVtHPiJTUQM9mJnZxFTU1dFRTQIi4oGI6IuIZ8iGJT42zeoBppUsOjXFqsUfBNol7VEWr7bdiyJiTkTMmTJl5GMzF9Wzk5mZTUzVakPlXUtqVJMASYeUTL4R6G85sAJ4m6S9JD0fmAHcDNwCzEgtAfYkqzy4IiICuIGsF0OAM4DvjcY+ALz6RZUTiWpxMzOzobRMqny6rxZvlNxaB0haBhwPHCRpM/Ap4HhJRwMB/BF4H0BErJd0LfBb4Gng7IjoS+v5ILASaAEujYj1aROfAK6WdB6wDrgkr30pd8PvKt9SqBa3Z2trnUTvzmcqxs3Mms3Tz1S+7F8t3ii5JQERsbBCuOqJOiLOB86vEL8OuK5C/C523U4YVa4TMHJPVEgAhoqbmVnj+WdXHVwnYOR8DM3MdmmKOgETRVGjPU0ki+bPfNa9rpZJ8jE0s6Z02rzpNcUbxUlAHTpnd/C5N82io70NAR3tbXzuTbPcY2AN1t69jb6ye119zwRr7x5u/1JmZjZSeXYbPKEVMdrTRDJUxxh5d5NpZjbWFPWd6CsBVgh3G2xmtktR34m+ElAnDyA0Mh5AyMxsl6K+E30loA4eQGjk5r3gwJriZmYT2cK502qKN4qTgDp4AKGR++ODlftUqBY3M5vIzuucxenzpg/88m+ROH3e9NzrSPl2QB16qnQKVC1uz+YOl8zMBjuvc9aoV4x2ElAH388euUPb2yomTe4syMyaVRF1zZwE1ME120dunz0r34mqFjczm8j665r132rur2sG5JoI+Bu3DtUGdcp5sKcJ5c4tj9UUNzObyIqqa+YkoA7VBnXKebAnMzOboIqqJ+UkwMzMrGBFDarmJKAO7W2tNcXt2faocuukWtzMbCJ79Yum1BRvFCcBdTj3lKNoLasA0DpJnHvKUQWVaPx5usqtk2pxM7OJ7Ibfba0p3ihuHVCH/pqa7jbYzMwaoaj+Z5wE1MmjCJqZWaN47ABrKgfvv2dNcTOziayo/mecBFghTjzqz2qKm5lNZEVVOHcSYIX41pp7aoqbmU1k1a76590bvZMAK4Q7XDIz22XH4ztrijeKkwAzM7OCubMgMzOzJrVo/kzaWlsGxdpaW1g0f2au23UTQTMzs4IV1f+MkwAzM7MxoIj+Z5wE1KlrXY97DDQzs4ZZ0tXNsjWb6IugRWLh3Gmc1zkr1226TkAdutb1sHh5Nz07egmybh0XL++ma11P0UUbN9xZkJnZLku6urly9T0DnQP1RXDl6ntY0tWd63adBNRh6coN9O7sGxTr3dnH0pUbCirR+PPAI0/VFDczm8iWrdlUU7xRnATU4d4qAzpUi5uZmQ3F3QaPI8+p0o1jtbiZmdlQqg0U5AGExqCiunc0M7OJaeHcaTXFG8VJQB2K6t5xIjlgr5aa4mZmE9l5nbM4fd70gV/+LRKnz5uee+sANxGsw6HtbfRUuP+fd/eOE0lLyySgr0rczKz5nNc5K/eTfrncvnElXSppi6TflMQmS1ol6c7098AUl6QvS9oo6XZJx5S85oy0/J2SziiJv0xSd3rNl6XRuxhfVPeOE8n2KldNqsXNzKzx8vzZdRmwoCx2DnB9RMwArk/TACcDM9LjLOBCyJIG4FPAXOBY4FP9iUNa5syS15VvKzedszv465d1DLps89cvG/2enszMzEYityQgIn4KbCsLnwpcnp5fDnSWxK+IzGqgXdIhwHxgVURsi4jtwCpgQZp3QESsjogArihZV+661vVwzc2bBnXqcM3Nm9xZkJmZjSujXSfg4Ii4Lz2/Hzg4Pe8ASntE2JxiQ8U3V4iPinNXrGdn2cD3O58Jzl2x3lcDzMysLk3VbXD6BZ9vLwiJpLMkrZW0duvWrSNe347eKq0DqsTNzMyG0izdBj+QLuWT/m5J8R6gtDHk1BQbKj61QryiiLgoIuZExJwpU6aMeCfMzMwa6ao199QUb5TRTgJWAP01/M8AvlcSf2dqJTAPeCjdNlgJnCTpwFQh8CRgZZr3sKR5qVXAO0vWlbsD96ncM2C1uJmZ2VCq9Q6cc6/BuTYRXAbcBMyUtFnSe4DPAydKuhN4bZoGuA64C9gIXAx8ACAitgGfBW5Jj8+kGGmZb6bX/B74YV77Uu71LzmkpriZmdlYlFvFwIhYWGXWayosG8DZVdZzKXBphfha4MUjKWO9bvhd5XoF1eJmZmZD2ad1Eo/vfKZiPE/unq0OHkXQzMwa6U0vm1pTvFGcBNShWvfA7jbYzMzqUdQVZicBdXC3wWZm1kiVxqMZKt4oTgLq0Dm7g6kH7j0oNvXAvd1RkJmZ1aWlyvA31eKN4iSgDqddfBN3bnlsUOzOLY9x2sU3FVQiMzMbz/qqtAWsFm8UJwF1+MXvy4dEGDpuZmY2FF8JMDMza1K+EmBmZtakqv3ez/c6gJOAuhx3xOSa4mZmZkOp9ns/71H2nATU4aozX8GM5+07KDbjefty1ZmvKKhEZmZmtXMSUIeudT1s3v7EoNjm7U/Qta7qQIZmZmZjjpOAOixduYHenX2DYr07+1i6ckNBJTIzM6udk4A6eOwAMzNrpD1bKlcBrBZvFCcBdfDYAWZm1kg7+ypXAawWbxQnAXXw2AFmZtZI7fu01hRvlD1yXfsE1T9GwNKVG7h3Ry+HtrexaP5Mjx1gZmZ1qdYnUM59BTkJqFfn7A6f9M3MrCEe6t1ZU7xRfDvAzMysYEXVNfOVgDqddvFNgwYMOu6Iye4syMzM6vLqF03hytX3VIznyVcC6lCeAEA2gqCHEjYzs3pce8ummuKN4iSgDh5K2MzMGumpKk0Bq8UbxUmAmZlZk3ISYGZm1qScBJiZmRVsj0mVuweuFm8UJwF16KjSZKNa3MzMbCj77VW5sV61eKM4CaiDuw02M7NG2lGlU6Bq8UZxElCHztkdHDP9OYNix0x/jnsQNDOzulS76p/z3QAnAfVY0tVdsZ+AJV3dBZXIzMzGs2eqtASsFm8UJwF1WLamcucN1eJmZmZjkZOAOvRVGdapWtzMzGwo7W1VhhKuEm8UJwF1aFHlmzTV4mZmZkM595SjaC2rANA6SZx7ylG5btdJQB0Wzp1WU9zMzGwonbM7WPqWl9LR3obImpwvfctLc69w7lEEzczMxoDO2R2j3srMVwLq4IqBZmY2ERSSBEj6o6RuSbdJWptikyWtknRn+ntgikvSlyVtlHS7pGNK1nNGWv5OSWeMVvldMdDMzCaCIm8HvDoi/lQyfQ5wfUR8XtI5afoTwMnAjPSYC1wIzJU0GfgUMAcI4FZJKyJi+2juhJmZWSOcdvFNg/qgOe6IyVx15ity3eZYuh1wKnB5en450FkSvyIyq4F2SYcA84FVEbEtnfhXAQtGucxmZmYjVp4AQNYJ3WkX35TrdotKAgL4kaRbJZ2VYgdHxH3p+f3Awel5B1B6s31zilWLm5mZjSvlCcDu4o1S1O2AV0ZEj6TnAask/a50ZkSEpIbdYE+JxlkA06dPb9RqzczMxrVCrgRERE/6uwX4LnAs8EC6zE/6uyUt3gOUNsCfmmLV4pW2d1FEzImIOVOmTGnkrpiZmY1bo54ESNpX0v79z4GTgN8AK4D+Gv5nAN9Lz1cA70ytBOYBD6XbBiuBkyQdmFoSnJRiZmZmNgxF3A44GPiusi529wC+FRH/IekW4FpJ7wHuBv4mLX8d8DpgI/A48G6AiNgm6bPALWm5z0REvjdPzMzMJpBRTwIi4i7gpRXiDwKvqRAP4Owq67oUuLTRZTQzM2sGY6mJ4LjhAYTMzGwicBJQhxdM2aemuJmZ2VBmPG/fmuKN4iSgDndtfbymuJmZ2VCet/9eNcUbxUlAHTx2gJmZNVJRnQU5CTAzM2tSTgLMzMyalJMAMzOzgh13xOSa4o3iJMDMzKxgV535ime1BJjxvH2baihhMzOzptS1rofN258YFNu8/Qm61lUcEqdhnASYmZkVbOnKDfTu7BsU693Zx9KVG3LdrpOAOuzdUrlnwGpxMzOzody7o7emeKM4CajDE32V+wOoFjczMxtK+z6tNcUbxUmAmZlZwar1NZd3H3ROAszMzAr2UO/OmuKN4iTAzMysYM9pq3zZv1q8UZwEmJmZFWxn3zM1xRvFSYCZmVnBHnuqr6Z4ozgJMDMza1JOAszMzArWXuXef7V4ozgJMDMzK9i5pxxF66TBHc61ThLnnnJUrtvdI9e1m5mZ2W51zu4Asu6D793Ry6HtbSyaP3MgnhcnAWZmZmNA5+yO3E/65ZwEmJmZjQFd63p8JcDMzKzZdK3rYfHy7oGRBHt29LJ4eTdAromAKwaamZkVzEMJm5mZNSkPJWxmZtakDm1vqyneKE4CzMzMCrZo/kzaWlsGxdpaW1g0f2au23XFQDMzs4K5nwAzM7MmVkQ/Ab4dYGZm1qScBJiZmTUpJwFmZmZNynUCzMzMxoDTLr6JX/x+28D0cUdM5qozX5HrNsf9lQBJCyRtkLRR0jlFl8fMzKxW5QkAwC9+v43TLr4p1+2O6yRAUgvwVeBk4EhgoaQjiy2VmZlZbcoTgN3FG2VcJwHAscDGiLgrIp4CrgZOzXujB++/Z01xMzOzsWi8JwEdwKaS6c0plqs1/3jis074B++/J2v+8cS8N21mZtYwTVExUNJZwFkA06dPb8g6fcIfmeOOmFzxMtdxR0wuoDTjU3tbKzt6d1aMm9n4UtR34ni/EtADTCuZnppig0TERRExJyLmTJkyZdQKZ9VddeYrnvXhHo2asBPJuaccReskDYq1ThLnnnJUQSUaf2Y8b9+a4lbZv7716Jri9mxFfScqInLdQJ4k7QH8F/AaspP/LcDbI2J9tdfMmTMn1q5dO0olNMtX17qeUe9rfKI58YIbuXPLYwPTM563L6s+enxxBRqn/FkcuyTdGhFzKs4bz0kAgKTXAf8KtACXRsT5Qy3vJMDMzJrJUEnAuK8TEBHXAdcVXQ4zM7PxZrzXCTAzM7M6OQkwMzNrUk4CzMzMmpSTADMzsyblJMDMzKxJOQkwMzNrUk4CzMzMmpSTADMzsyblJMDMzKxJjftug2slaStwdwNXeRDwpwaurxn5GI6cj+HI+Rg2ho/jyDX6GB4WERVHz2u6JKDRJK2t1iezDY+P4cj5GI6cj2Fj+DiO3GgeQ98OMDMza1JOAszMzJqUk4CRu6joAkwAPoYj52M4cj6GjeHjOHKjdgxdJ8DMzKxJ+UqAmZlZk3ISUCdJCyRtkLRR0jlFl2c8knSppC2SflN0WcYrSdMk3SDpt5LWS/pw0WUabyTtLelmSb9Ox/DTRZdpvJLUImmdpO8XXZbxSNIfJXVLuk3S2lHZpm8H1E5SC/BfwInAZuAWYGFE/LbQgo0zkl4FPApcEREvLro845GkQ4BDIuJXkvYHbgU6/VkcPkkC9o2IRyW1Aj8HPhwRqwsu2rgj6aPAHOCAiHhD0eUZbyT9EZgTEaPWz4KvBNTnWGBjRNwVEU8BVwOnFlymcScifgpsK7oc41lE3BcRv0rPHwHuADqKLdX4EplH02RrevjXUY0kTQVeD3yz6LLY8DkJqE8HsKlkejP+4rWCSTocmA2sKbgo4066jH0bsAVYFRE+hrX7V+DjwDMFl2M8C+BHkm6VdNZobNBJgNkEIGk/4DvARyLi4aLLM95ERF9EHA1MBY6V5NtTNZD0BmBLRNxadFnGuVdGxDHAycDZ6ZZprpwE1KcHmFYyPTXFzEZduo/9HeCqiFhedHnGs4jYAdwALCi4KOPNccAp6Z721cAJkq4stkjjT0T0pL9bgO+S3XrOlZOA+twCzJD0fEl7Am8DVhRcJmtCqVLbJcAdEXFB0eUZjyRNkdSenreRVfj9XaGFGmciYnFETI2Iw8m+D38cEacXXKxxRdK+qXIvkvYFTgJybznlJKAOEfE08EFgJVlFrGsjYn2xpRp/JC0DbgJmStos6T1Fl2kcOg54B9kvr9vS43VFF2qcOQS4QdLtZAn+qohwEzcbbQcDP5f0a+Bm4AcR8R95b9RNBM3MzJqUrwSYmZk1KScBZmZmTcpJgJmZWZNyEmBmZtaknASYmZk1KScBZjaIpD+TdLWk36fuS6+T9OcjXOfx/SPLSTpldyNvSvqIpNWS/l3SrJFs28yqcxNBMxuQOh/6JXB5RHw9xV5KNircz0aw3uOBf/DIcmZji68EmFmpVwM7+xMAgIj4NVknJksl/SaNd/5WGPiFf6Okb0v6naSrUiKBpAUp9ivgTf3rk/QuSV9Jzw+W9F1Jv04dHc2RtJ+k6yX9Km3r1JLXfjSV4TeSPjI6h8Rs4tqj6AKY2ZjyYqDSIDBvAo4GXgocBNwi6adp3mzgKOBe4BfAcZLWAhcDJwAbgWuqbO/LZF3MvlHSHsA+wBPAGyPiYUkHAaslrQCOAd4NzAUErJH0k4hYN8J9NmtavhJgZsPxSmBZGm3vAeAnwMvTvJsjYnNEPAPcBhwOvAj4Q0TcGdk9x2qDyZwAfAOy7rjTCIgC/jl14/ufZMN0H5zK8N2IeCwiHgWWA/+t8btq1jycBJhZqfXAy2p8zZMlz/sY+RXG04ApwMvS8L4PAHuPcJ1mVoGTADMr9WNgL0ln9QckvQTYAbxVUoukKcCryAY5qeZ3wOGSjkjTC6ssdz3wvrSdPSQdADyHbGz6nZJeDRyWlv0Z0ClpnzTK2htTzMzq5CTAzAakS/dvBF6bmgiuBz4HfAu4Hfg1WaLw8Yi4f4j1PAGcBfwgVQzcUmXRDwMnSuoBfgXMAK4C5kjqBt5JGtY3In4FXEaWfKwBvun6AGYj4yaCZlY4SX8JzIyIfyu6LGbNxFcCzKxQkhYCVwD+RWI2ynwlwMzMrEn5SoCZmVmTchJgZmbWpJwEmJmZNSknAWZmZk3KSYCZmVmTchJgZmbWpP4/W4lZUYLhPjQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_graph(df_clean['condition_index'], 'Gráfico de Dispersão entre Price e Condition_Index', 'Condição')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_graph(df_clean['cylinders'], 'Gráfico de Dispersão entre Price e Cylinders', 'Cilindros')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     16306\n",
       "8     15360\n",
       "4     15079\n",
       "5       271\n",
       "10      240\n",
       "3        33\n",
       "12        2\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['cylinders'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_double_graph('is_4wd', 'Boxplot entre Price e Is_4wd','Carro é 4 por 4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_double_graph('transmission', 'Boxplot entre Price e Transmission','Tipo de Transmissão')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_double_graph('paint_color', 'Boxplot entre Price e Paint_color','Cor do Carro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A conclusão que chego sobre a relação entre as variáveis:\n",
    "\n",
    "- Os gráficos demonstram que a partir de 10 anos de idade os preços tendem a diminuir, com apartir 20 anos não a preços acima de 15000. Mostrando que o valor médio de venda do carro tende a cair com sua idade.\n",
    "\n",
    "\n",
    "- A partir de 150000 quilometros o preço médio tende a cair, em 250000 quilometros o preço não está acima de 10000 dolares. A quilometragem também tem uma relação negativa com o preço.\n",
    "\n",
    "\n",
    "- Carros em baixa condição tendem a não serem vendidos por um alto preço dolares, mas apartir da condição boa, se torna irrelevante. A relação seria positiva, então torna-se constante.\n",
    "\n",
    "\n",
    "- Em relação a quantidade de cilindros , os de \"3\" \"12\" não possuem um número suficiente para a analise. A quantidade de cilindros parece não afetar o preço.\n",
    "\n",
    "\n",
    "- O tipo de transmissão possue um preço mediano parecido entre transmissão manual e automatica, o tipo \"other\" possui uma mediana maior mas com alta variancia. A cor do carro tem uma mediana parecida entre si, não demonstra afetar o preço.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusão geral\n",
    "\n",
    "Pré-Processamento dos Dados\n",
    "\n",
    "A primeira fase da análise consistiu em fazer uma primeira visualização dos dados para procurar problemas. Foram encontrados valores ausentes, colunas com tipos de dados incorretos e duplicatas implícitas. Os valores ausentes foram os primeiros a serem tratados.\n",
    "\n",
    "As colunas model_year, cylinders, odometer, paint_color e is_4wd possuíam valores ausentes. Cada uma delas foi tratada de uma forma diferente:\n",
    "\n",
    "    is_4wd: é uma variável booleana, sendo 1 representando sim e 0 representando não. Os valores 0 estavam como nan. Foram substituídos os nan por 0.\n",
    "\n",
    "    paint_color: foi substituído usando a moda da cor do carro em relação ao modelo. Foi feito desta forma, pois intuitivamente, a cor do carro deve possuir relação com o modelo. Não foi encontrada uma causa para a existência dos valores ausentes.\n",
    "\n",
    "    cylinders: foi substituído usando a moda em relação ao modelo. Existe relação entre a quantidade de cilindros e o modelo de carro. Não foi encontrada uma causa para a existência dos valores ausentes.\n",
    "\n",
    "    odometer: foi substituído usando a mediana em relação à condição do carro. Não foi encontrada uma causa para a existência dos valores ausentes.\n",
    "\n",
    "    model_year: foi substituído usando a mediana em relação ao modelo. Não foi encontrada uma causa para a existência dos valores ausentes.\n",
    "\n",
    "Depois de resolvidos os valores ausentes, foram tratadas as duplicatas implícitas da coluna model. A correção do tipo de dados foi feita para as colunas is_4wd, model_year e cylinders, que foram transformadas em int64, e para a coluna date_posted, que foi transformada em datetime.\n",
    "\n",
    "Foram adicionadas quatro colunas para enriquecer a análise. Foi adicionada uma coluna para o dia em que a propaganda foi postada, uma para a idade do veículo, uma para a quilometragem por ano e uma para representar a coluna de condição por meio de números.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Análise de Dados\n",
    "\n",
    "Os primeiros parâmetros estudados para entender o que afeta o preço foram a idade do veículo, quilometragem, número de cilindros, condição do veículo e o próprio preço. Os valores atípicos afetaram a visualização, foi criado um novo dataframe sem os valores atípicos.\n",
    "\n",
    "Análises Univariadas:\n",
    "\n",
    "    O preço varia em até 35.000 dólares, com a maioria dos dados entre 5.000 e 15.000 dólares.\n",
    "    A quilometragem começa entre 0 e 250.000. Carros com 0 quilômetros têm idade igual a zero.\n",
    "    A idade está entre 0 e 23 anos, a maioria entre 5 e 10 anos.\n",
    "    Os cilindros mais comuns são de 8, 6 e 4.\n",
    "    As condições mais comuns são \"excelente\" e \"bom\".\n",
    "\n",
    "Análises Multivariadas:\n",
    "\n",
    "Os tipos mais comuns de carro são \"SUV\", \"truck\" e \"sedan\". \"SUV\" tem um preço mediano de 8980 dolares, \"truck\" de 14995 dolares e \"sedan\" de 5999 dolares.\n",
    "\n",
    "Os gráficos demonstram que a partir de 10 anos os preços tendem a diminuir, com apartir 20 anos não a preços acima de 15000. A partir de 150000 quilometros o preço médio tende a cair, em 250000 quilometros o preço não está acima de 10000 dolares. Carros em baixa condição tendem a não serem vendidos por um alto preço dolares, mas apartir da condição boa, se torna irrelevante. Em relação a quantidade de cilindros , os de \"3\" \"12\" não possuem um número suficiente para a analise. A quantidade de cilindros parece não afetar o preço. O tipo de transmissão possue um preço mediano parecido entre transmissão manual e automatica, mas o tipo \"other\" tende a possuir uma mediana maior mas com alta variancia. A cor do para possui uma mediana parecida entre as corre, não demonstra afetar o preço. Concluo que, a partir da minha análise, a idade e quilometragem do carro são as variáveis que mais afetam o preço"
   ]
  }
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